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        "full_name": "Steve Heffernan",
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        "title": "Mux Player: The <video> tag with Mux superpowers",
        "tagline": "Mux Player is free, open source, and the best way to build an amazing player experience on top of the Mux API.",
        "body": "Hello YC compatriots,\n\n⏱ **The quick and dirty**\n\nMux is video infrastructure for developers. We just launched the public beta for a web player, [Mux Player](https://mux.com/player), that allows you to instantly take full advantage of it, including things like adaptive streaming and preview thumbnails on the timeline, all while being fully responsive for mobile.\n\n🤕 **The problem**\n\nOnline video has come a _long_ way in the last 10+ years. You used to need to create 3 different versions of a file (mp4, webm, ogg) to support all the major browsers, and then you’d _still_ need a Flash fallback just in case that browser didn’t support HTML5 video.\n\nFast forward to 2022 and basically every browser supports one major codec, but now there are all sorts of ways to deliver streaming video. Instead of just 3 different codecs, now you need to create a bunch of different versions of a video to support people on different devices and connection speeds. It’s a **much** better experience for viewers everywhere, but it’s…a lot. On top of all of that, if you care about that experience, you need to quantify it, which means now you’ve got a bunch of data infrastructure and player instrumentation to build.\n\nThe good news is, Mux has been solving those infrastructure bits of the equation for years. We’ve got Mux Video that takes video in and then does all the magic stuff to have you streaming like Netflix. We’ve got Mux Data so you can both know how your video engagement is going, and how good the experience is while people are watching it.\n\nBut…what player do you use in your product? Do you pick a random open source player? Do you pick a random commercial player? One of the benefits of Mux is that you don’t need to be an expert to stream like one, but now you’ve got to know at least a bit about how we stream to make an informed decision.\n\n📼 **The solution**\n\nWe built `<mux-player>`. It’s a web component that understands Mux. Point it at a Mux video and you’ve immediately got a clean, responsive player with things like preview thumbnails on the timeline (you know, the cool hover scrubber thing), streaming that adapts to a viewer’s internet connection and device, instantly configurable poster images, and data built right in with no extra work required.\n\nYou can treat it like a futuristic `<video>` element. It works in all the major JavaScript frameworks, and we even built a special React version to make things nice and idiomatic.\n\n**💅 We’re particularly good at this**\n\nWe’re extremely proud to be experts and leaders in the online video industry in general, but we specifically have built the hell out of online players. The founding team created and helped maintain Video.js, the most popular HTML5 video player for over a decade, and hired a team that’s worked on some of the best commercial and open source players on the web.\n\nIf you’re interested, go [check out the docs](https://docs.mux.com/guides/video/mux-player). If there’s anything we can help with, reach out.\n\nCheers,\\\nMatt, Adam, Heff, and Jon",
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    "id": 1056,
    "name": "NetBeez",
    "slug": "netbeez",
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    "website": "https://netbeez.net",
    "all_locations": "Pittsburgh, PA, USA; Remote",
    "long_description": "NetBeez is the Digital Experience Monitoring platform enabling IT teams to easily support remote users by quickly troubleshooting communication and network performance issues from the user’s perspective.",
    "one_liner": "Network monitoring from the end-user perspective",
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        "answer": "We started NetBeez in 2012 because we realized that troubleshooting computer networks was a manual process that lacked meaningful data. The existing [network monitoring](https://netbeez.net/blog/what-is-network-monitoring/) tools were only reporting performance data from network devices (e.g. routers, switches, etc.) but lacked insights into the [digital experience](https://netbeez.net/digital-experience-monitoring/) of the end-users. We envisioned a tool that would monitor the network from the end-user perspective, helping IT support reduce time and resources when dealing with trouble tickets or network incidents."
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  {
    "id": 1910,
    "name": "Activeloop",
    "slug": "activeloop",
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      "Snark AI"
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c516ed5054847ecb1afb63f795f712b8d5c7f23d.png",
    "website": "https://activeloop.ai/",
    "all_locations": "San Francisco, CA, USA; Mountain View, CA, USA",
    "long_description": "We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that?\r\n\r\nGitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. \r\n\r\nOur founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. \r\n\r\nActiveloop is hiring, and we want you! Check out our open roles on our YC page and join the fun.\r\n\r\n10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2\r\nWhitepaper: https://www.deeplake.ai/whitepaper",
    "one_liner": "Database for AI",
    "team_size": 15,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1524690140,
    "tags": [
      "Computational Storage",
      "Deep Learning",
      "Generative AI",
      "Computer Vision",
      "Open Source"
    ],
    "tags_highlighted": [],
    "top_company": false,
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    "nonprofit": false,
    "batch": "Summer 2018",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/activeloop",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2018/activeloop.json",
    "year_founded": 2018,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/activeloop",
    "x_url": "https://twitter.com/activeloopai",
    "fb_url": "https://www.facebook.com/activeloopai",
    "cb_url": "https://www.crunchbase.com/organization/snark-ai",
    "github_url": "https://github.com/activeloopai",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c516ed5054847ecb1afb63f795f712b8d5c7f23d.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
    },
    "founders": [
      {
        "user_id": 78090,
        "full_name": "Davit Buniatyan",
        "title": "Founder",
        "founder_bio": "Founding CEO Activeloop, PhD on leave from Princeton, AI/ML, Data and Infra, Y Combinator S18, UCL 16’ Working on Data 2.0",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/davidbuniatyan",
        "x_url": "https://twitter.com/DBuniatyan",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/49efe4795d8a38cc955a0c873f7de567ef057a52.jpg"
      }
    ],
    "jobs": [
      {
        "id": 79215,
        "title": "Member of Technical Staff",
        "url": "https://www.ycombinator.com/companies/activeloop/jobs/zn1plZe-member-of-technical-staff",
        "location": "Mountain View",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $250K",
        "equity_range": "0.50% - 1.50%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": [
          "Amazon Web Services (AWS)",
          "Google Cloud",
          "Kubernetes",
          "MongoDB",
          "PostgreSQL",
          "Python",
          "Redis",
          "SQL",
          "Docker",
          "Microservices"
        ]
      }
    ],
    "news": [
      {
        "title": "Activeloop-L0: Agentic Reasoning on Your Multimodal Data",
        "url": "https://www.activeloop.ai/resources/activeloop-l-0-agentic-reasoning-on-your-multimodal-data/",
        "date": "May 04, 2025"
      },
      {
        "title": "Introducing Deep Research for Your Multi-Modal Data",
        "url": "https://www.activeloop.ai/resources/introducing-deep-research-for-your-multi-modal-data/",
        "date": "Feb 10, 2025"
      },
      {
        "title": "Releasing Deep Lake 4.0: The Fastest Multi-Modal AI Search on Data Lakes",
        "url": "https://www.activeloop.ai/resources/deep-lake-4-0-the-fastest-multi-modal-ai-search-on-data-lakes/",
        "date": "Oct 17, 2024"
      },
      {
        "title": "Activeloop Named 2024 Gartner Cool Vendor in Data Management",
        "url": "https://www.activeloop.ai/resources/gartner-cool-vendor/",
        "date": "May 31, 2024"
      },
      {
        "title": "Activeloop raises $11M to grow its specialized tensor database for AI training and inference - SiliconANGLE",
        "url": "https://siliconangle.com/2024/03/26/activeloop-raises-11m-grow-specialized-database-ai-training-inference/",
        "date": "Mar 26, 2024"
      }
    ],
    "launches": [
      {
        "id": 102722,
        "title": "Hivemind: Continual Learning for Coding Agents",
        "tagline": "Capture every coding agent interaction across your team, train it into reusable skills, and propagate those skills to every agent in your org. On your cloud storage.",
        "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will reshape how engineering teams work with coding agents.\n\n**Hivemind is a continual learning layer for coding agents. It works across Claude Code, Codex, Cursor, OpenClaw, Hermes, and pi, and it stores all your data on your cloud.**\n\n<https://youtu.be/rja1DfjUdww>\n\n### The Problem: Coding Agents Don't Learn\n\nEvery engineering team building with AI agents already knows the issues.\n\n* Your senior engineer's agent debugs a tricky bug Monday. Your junior engineer hits the same bug Tuesday and starts from zero.\n* Every session begins with re-explaining your codebase and your conventions.\n* Architectural decisions made in code review evaporate the moment the session ends.\n* Your team is solving the same problems over and over, paying for the same compute every time.\n\nCoding agents do not communicate with each other out of the box. Hivemind closes the gap. An agent figures something out, every agent inherits it. Your worst run starts from your best.\n\nThe tools tackling this treat it as a memory problem. They give each agent a personal notepad. Siloed. Invisible to the rest of the team. And most of them do it by shipping your code and context to their servers.\n\nBut memory without learning doesn't make agents better.\n\n**We decided to go beyond memory.**\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/51dc4451-7936-4e37-a0ad-e8139a646430)\n\n### Introducing Hivemind: Continual Learning for Coding Agents\n\nHivemind runs on a simple chain: **capture, codify, optimize, propagate.**\n\n**1. Trace capture.** Every agent interaction (prompts, tool calls, file reads, reasoning chains, outputs) is captured automatically as a structured trace.\n\n**2. Skill codification.** Repeated patterns across traces get codified into reusable skills. Mix of automatic detection and LLM-assisted extraction, with workspace-level scoping so skills don't leak between teams.\n\n**3. Skill optimization.** This is new today. Skills get trained and optimized. We've implemented SkillOpt (a text-space optimizer out of Microsoft, Shanghai Jiao Tong, and Fudan) directly into Hivemind. It improves each skill the way you'd tune a model, keeping only the edits that prove out on a held-out test.\n\nThat approach lifts agent accuracy by +19.1 points inside Claude Code and +24.8 inside Codex, and wins or ties on all 52 setups tested. It runs offline, so it adds zero cost at inference. Your skills get sharper over time instead of bloating.\n\n**4. Skill propagation.** Optimized skills flow into every agent's context at inference time, across every Hivemind-connected agent in your workspace.\n\nWorks across:\n\n* Claude Code\n* Codex\n* Cursor\n* OpenClaw\n* Hermes\n* pi\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/e1bddec6-5c2c-4409-aa2a-dd28fd8b2405)\n\n### On Your Cloud\n\nThis is the part our customers care about most. Your traces, your skills, your cloud storage. The other tools in this space treat your codebase as their training data. We don't. Hivemind store data on your cloud storage bucket, which is the only way a continual learning layer should ever touch your source.\n\n### **Codebase knowledge graph:**\n\n* Hivemind builds a graph of your codebase\n* Agents reason over how files, functions, patterns, and prior fixes connect\n* Structure beats keyword search: agents retrieve relevant context instead of grepping blindly\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/414a60d3-f2b1-4da0-8bec-236e44f16042)\n\n### Example\n\n> Your senior engineer's agent figures out a tricky migration pattern in your payment service on Monday.\n>\n> Tuesday, your junior engineer's agent hits the same migration shape in a related service. The skill has already crystallized, and SkillOpt has already sharpened it against the cases where the first version fell down. The agent executes it in one shot.\n\nRepeat work stops being repeat work.\n\n### The Results: 25% Lower Cost, 41% Fewer Tokens\n\nWe benchmarked Hivemind against memory tools using LoCoMo, the standard evaluation for long-horizon agent memory.\n\nHivemind matches Mem0 on accuracy.\n\n* **25% lower cost per 100 QA**\n* **41% fewer output tokens**\n* **31% fewer agent turns**\n\nSingle-agent benchmarks understate the real story. The compounding effect of org-wide skill propagation isn't captured in these numbers. In production, the gap widens every week as your trace library grows.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/3df657fa-e32b-4329-a667-d055d39d93be)\n\nFull methodology: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\n### Why This Matters\n\nMost developer tools deliver linear value. Hivemind compounds.\n\n* **Week 1:** your agents stop repeating mistakes.\n* **Month 1:** they're learning from each other.\n* **Quarter 1:** your whole engineering org is operating with capability that survives team changes, onboarding cycles, and turnover.\n\nYour team's hard-won knowledge accumulates.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/6dfbd341-418b-4bd6-bd63-0d191625378c)\n\n### What's Next: From Skills to Custom Models\n\nBecause we store trajectories in Deeplake's tensor format, traces are ready as PyTorch datasets. A handful of advanced customers are already fine-tuning their own open-source models on the trajectories their Claude and Codex agents generated last week.\n\nSame data layer. Two paths to continual learning.\n\nFull tutorial and public examples shipping in the coming weeks.\n\n### Get Started\n\nTry Hivemind: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\nGitHub: [github.com/activeloopai/hivemind](http://github.com/activeloopai/hivemind)\n\nSkillOpt paper: [arxiv.org/pdf/2605.23904](http://arxiv.org/pdf/2605.23904)\n\nThree commands and your team's agents share one brain by end of day.",
        "url": "https://www.ycombinator.com/launches/Qio-hivemind-continual-learning-for-coding-agents",
        "votes": 9,
        "created_at": "2026-06-08T23:07:52.598Z"
      },
      {
        "id": 95919,
        "title": "Activeloop Scientific Discover: The AI Analyst for Scientific Research",
        "tagline": "Search, reason, and discover across 450M+ pages and 175TB of scientific research papers. Not just text, but charts, molecules, formulas, and tables.",
        "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will fundamentally reshape how scientific discovery happens.\n\n**Activeloop’s Scientific Discover, an intelligence agent built on one of the largest datasets of indexed scientific research. Here are the details:**\n\nhttps://www.youtube.com/watch?v=x8Lv5-C9ntw\n\n## The Problem: Scientific Data Is Broken\n\nEvery scientist already knows the pain.\n\n* Critical insights are buried across PDFs, images, tables, and supplementary files.\n* Key figures live inside screenshots, not structured databases.\n* Twenty years of research is trapped in incompatible formats and outdated systems.\n* Every new project begins with weeks of manual data wrangling.\n\nThe White House recently launched the Genesis Mission, recognizing that fragmented scientific data is one of the biggest blockers to breakthroughs in drug discovery, materials science, climate modeling, and more.\n\nThe Genesis Mission aims to build an integrated AI platform that harnesses Federal scientific datasets. The goal is to train scientific foundation models and create AI agents that test hypotheses, automate research workflows, and accelerate scientific breakthroughs.\n\nThe country is trying to unify scientific data for AI.\n\n**We decided to show what that future looks like.**\n\n## Introducing L1: The Scientific Data Agent\n\nTo demonstrate what becomes possible when scientific data is indexed natively for AI, we built **Activeloop L1**, a multimodal scientific research agent running on top of:\n\n* 25M open access papers\n* 450M+ plus pages\n* 175TB of fully visual indexed scientific data\n\nUnlike traditional NLP or paper search, **L1 sees science**.\n\nIt understands:\n\n* charts\n* molecules\n* protein structures\n* equations\n* experimental tables\n* clinical graphs\n* and the text that surrounds them\n\nThis allows L1 to answer questions no text only model can touch.\n\n## The Results: A New State of the Art **48% SOTA on Humanity’s Last Exam with Tools**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/453774e7-7cc6-47de-a344-76a9f7ca2460)\n\nL1 (Gemini 3 Pro) outperforms top models including Grok Heavy with tools, and GPT 5 Pro with tools.\n\n### **Multimodal scientific reasoning**\n\nL1 synthesizes biochemical structures with clinical findings to answer end-to end research questions.\n\n### Example Query\n\n> Which compounds show novel synergy with metformin for treating type 2 diabetes?\n\nL1 scans molecular structures, clinical outcomes, and experimental tables at the same time and produces grounded, citation linked answers.\n\n## Why This Matters\n\nMultimodal scientific agents unlock capabilities that were not possible before.\n\nThis extends far beyond drug discovery. It applies to materials science, climate research, algorithmic innovation, and more.\n\nThe applications are enormous.\n\n**Try L1 Today with Our OpenAI Compatible API**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/f45683d6-c74b-4994-88e7-d54bda796ba0)\n\n## Get Started\n\nTry the Science Agent: https://chat.activeloop.ai/science\n\nDocs and API: https://docs.activeloop.ai\n\nBuild with the OpenAI compatible API today.",
        "url": "https://www.ycombinator.com/launches/Ox5-activeloop-scientific-discover-the-ai-analyst-for-scientific-research",
        "votes": 29,
        "created_at": "2025-12-02T15:44:42.819Z"
      },
      {
        "id": 94768,
        "title": "Activeloop - Unlock AI Data Analysis for GTM",
        "tagline": "Less Time in CRM, More Sales. Get instant insights across your entire stack from CRMs/ERPs to sales decks and product analytics. ",
        "body": "Hey 👋! Founder of Activeloop here. I am excited to introduce Activeloop AI Data Analysis for GTM operations. It lets you drill down from an executive dashboard to a single data point with just-in-time intelligence.\n\nWe didn’t built yet another dashboarding tool. It’s a solution to the single biggest bottleneck holding back your commercial strategy. The endless cycle of manual data preparation, integration, and reconciliation.\n\nhttps://www.youtube.com/watch?v=4Ua3Llox2HE\n\n### The Problem: You’re Drowning in Data Chaos\n\nThe story is always the same:\n\n* **Analysts as Data Janitors:** Your most valuable analysts are buried in manual work, stitching together data from multiple CRMs, reconciling Salesforce with SAP, and cleaning up messy, inconsistent fields.\n* **Reporting Backlogs:** A simple request for a new report kicks off a multi-week process. By the time it’s ready, the moment has passed and leadership doesn't trust the numbers anyway.\n* **Sales Reps Suffer:** Reps waste 4-6 hours every week just hunting for information and doing administrative work instead of selling.\n\nYou're tasked with driving strategy, but you're stuck managing data chaos.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/e282c1b1-d361-4f5a-a4ac-3a721b21d96d)\n\n### Our Solution: The AI Analyst that Vibes Intelligence\n\nActiveloop is an AI Data Analyst for your operations team. We automate the painful, time-consuming data work that currently consumes 70%+ of your team's time.\n\nInstead of fighting with brittle integrations, we connect your fragmented sources such as CRMs, ERPs, product analytics, databases, data warehouses, even unstructured contracts and call transcripts, into a unified, queryable resource.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/342debf4-c4fb-4a02-8480-ea5b128e26c9)\n\n### From Weeks to Minutes: Deliver “Just-in-Time” Intelligence\n\nActiveloop eliminates the wait, empowering your team with true self-service analytics.\n\n* **For Leadership:** In a forecast meeting, infinitely drill down into the data with simple questions like, \"Why did this region's deal cycle lengthen last quarter?\" Get immediate answers, without ever having to say, \"We'll get back to you.\"\n* **For Operations:** Free your analysts from being \"report builders\" and empower them to be strategists. They can finally focus on high-impact projects, confident that the underlying data is unified and trusted.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/f5a7e592-3655-494e-a620-f92e99dc74e9)\n\n### We Tackle Your Messy Data Head-On\n\nYou’re probably thinking: \"How can AI possibly navigate our messy data? We have 50 different fields for the same metric.\"\n\nYou're right. That's the biggest blocker for any data initiative. That’s why our platform includes an **AI-assisted data mapping and taxonomy management layer**. It helps you harmonize your complex environment to build a clean data foundation that generates insights everyone trusts.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/22c38e19-0d94-49cb-9485-e5de80ce8e3d)\n\n### Let's Solve Data Chaos Together\n\nIf you’re tired of seeing your team buried in manual data work, we want to talk to you.\n\n**Book a Call:** https://activeloop.ai/sales/\n\n\\\n**Get Started Now:** https://chat.activeloop.ai",
        "url": "https://www.ycombinator.com/launches/OeW-activeloop-unlock-ai-data-analysis-for-gtm",
        "votes": 107,
        "created_at": "2025-10-20T15:41:53.206Z"
      },
      {
        "id": 90294,
        "title": "Activeloop L0: State-of-the-Art RAG Accuracy on Your Data",
        "tagline": "Turn PDFs, images & tables into instant, cited answers. Agentic RAG that just works.",
        "body": "Hey! Davit here form Activeloop. While working on [Deep Lake](https://github.com/activeloopai/deeplake), I have seen many RAG systems collapse when exposed to production-scale corporate data. They often rely on predefined loops, custom logic and rigid agent scaffolds. Activeloop-L0 provides your agent with highly precise and answers grounded on your multimodal data.\n\nhttps://youtu.be/pnU9yd4KWy0\n\n**Why can’t we reliably analyze corporate documents?**\n\n* **Architectural hurdles**: messy data integrations, unexpected infra costs, and reliability/safety constraints.\n* **Commodity RAG** lacks depth for multimodal enterprise data (documents, images, audio).\n* **Infrastructure burden**: parsing, chunking, embeddings, indexing, vector DBs, and agent loops slow teams.\n\n**But wait, is RAG still relevant despite large context models?**\n\nLet’s consider four extensive NASA documents \\[[1](https://www.nasa.gov/wp-content/uploads/2022/03/sls-reference-guide-2022-v2-508-0.pdf), [2](https://www.nasa.gov/wp-content/uploads/2023/02/orion-reference-guide-111022.pdf), [3](https://www.lpi.usra.edu/lunar/artemis/Artemis-I-Reference-Guide_NP-2022-03-3045-HQ.pdf), [4](https://www.ulalaunch.com/docs/default-source/rockets/2023_vulcan_user_guide.pdf)\\], each between 80 to 100 pages, containing visual descriptions, and pose a highly complex question.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/c7a09db6-f7f7-46a4-a347-5b52154e6fc1)\n\nChatGPT with o3, despite having full PDFs in context, failed after 11 minutes of reasoning. Now, imagine you have thousands of corporate documents that can’t be contained in a context. In contrast, Activeloop-L0 provided the correct answer in 4 minutes and can scale to a million documents.\n\n**What is Activeloop-L0?**\n\n**Activeloop-L0** is a compound AI system that ingests your unstructured data and returns grounded answers. Behind the scenes, [Deep Lake](https://github.com/activeloopai/deeplake) indexes neural representations at scale, then fuses “thinking tokens” with high-precision retrieval for fast multi-hop reasoning.\n\nIt is available on [**chat.activeloop.ai**](http://chat.activeloop.ai) now.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/64161f83-f7ba-46eb-9445-5688f55506ab)\n\n**How is it different compared to a traditional RAG?**\n\n* **Multimodal:** Built-in support for images, PDFs, audio, and spreadsheets.\n* **Integrated Reasoning & Retrieval:** Eliminates the need for loops.\n* **Deep Indexing:** Cost-effective multi-layer indexing for richer context early on.\n* **Simple:** Focus on innovation, not maintaining infrastructure.\n* **Grounded and Accurate:** Clear citations for trustworthy insights.\n\n**How accurate is Activeloop-L0?**\n\nActiveloop-L0 achieves overall 85.6% state-of-the-art accuracy on 1,142 multimodal questions (292 PDFs, 5.5K pages). It outperforms text only RAG by +20%, visual RAG by +10%, and Alibaba’s ViDoRAG by +6% on their own ViDoSeek benchmark.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/8275782e-ffde-4351-bdc8-0d1c40918169)\n\n**Is there an OpenAI-compliant API?**\n\nYes, Activeloop-L0 is available with an OpenAI-compliant API. You can easily plug into your agents for providing high relevant context. You can get started here. https://docs.activeloop.ai/setup/quickstart\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/996c0193-4dab-49eb-ab85-6e78828b19ec)\n\n**Ready to Deploy on Your Data?**\n\nActiveloop is trusted by F500 including the likes of [**Bayer**](https://www.activeloop.ai/usecase/bayer/), [**Flagship Pioneering**](https://www.activeloop.ai/usecase/flagshippioneering/), [Matterport](https://www.activeloop.ai/usecase/matterport/) (W12 acquired by CoStar).\n\n* **Your Cloud**: Deploy on your cloud, ensuring data never leaves your infrastructure.\n* **Your Models**: Integrate your LLMs.\n* **Your Security**: SOC2 compliance, fine-grained access control, and SSO.\n\n[**Book a call**](https://www.activeloop.ai/contact/) to discuss enterprise deployment.",
        "url": "https://www.ycombinator.com/launches/NUM-activeloop-l0-state-of-the-art-rag-accuracy-on-your-data",
        "votes": 34,
        "created_at": "2025-05-12T16:26:50.543Z"
      }
    ]
  },
  {
    "id": 1917,
    "name": "Inscribe",
    "slug": "inscribe",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/56ca6c4bee93b67aa430b76fd900b6de1e467c6a.png",
    "website": "https://www.inscribe.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Inscribe is AI-powered document fraud detection software for banks, credit unions, and lenders. It detects forged, fabricated, and AI-generated financial documents (including deepfake bank statements, manipulated pay stubs, and synthetic tax forms) in seconds, giving risk teams the clarity they need to stop fraud without slowing approvals.\r\n\r\nInscribe created the document fraud detection category and has been purpose-built for financial institutions since 2017. Our agentic AI is trained on millions of real-world applications and strengthened by in-house data scientists and fraud experts. According to Inscribe's 2026 State of Document Fraud Report, 1 in 16 documents show signs of fraud — with AI-generated document fraud growing 5x from April to December 2025.\r\n\r\nCustomers include BCU ($5.6M in prevented fraud losses), Logix Federal Credit Union ($3M in losses prevented in 8 months), and Ramp (30-minute reduction in application processing). Inscribe is SOC 2 Type II and ISO 27001 certified, and is backed by Threshold Ventures, Uncork Capital, Y Combinator, Crosslink Capital, and Foundry.",
    "one_liner": "AI Document Fraud Detection for Banks, Credit Unions & Lenders",
    "team_size": 35,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1524711773,
    "tags": [
      "Fintech",
      "Fraud Detection",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2018",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
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        "body": "**TLDR -** [Porter](https://porter.run) is a Platform as a Service (PaaS) that runs in your own cloud. It brings the magical experience of deploying your applications with a few clicks into your own cloud account. After a year since we closed down our self-serve flow, we are opening Porter back up for self-serve today.\n\n* [Sign up here](https://dashboard.getporter.dev) and start deploying applications on your own infrastructure that scales with you.\n* [Claim $10,000 in credits](https://gcpjnf9adme.typeform.com/to/lHMQdJYb) if your company is enrolled in YC Startup School. Along with the AWS credits you can claim from Startup School, this deal effectively lets you deploy on a PaaS for free - it’s like using Heroku with your AWS credits!\n\n![uploaded image](/media/?type=post&id=71916&key=user_uploads/323071/cc2f3fac-340f-4f39-9e4d-533f7ab40053)\n\n## **What is Porter?**\n\nHi everyone,\n\nWe are Trevor and Justin from [Porter](https://porter.run/). 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        "body": "**tl;dr**: If you’re running microservices, you can now use Signadot to help developers ship features faster using lightweight dev environments. These environments spin up in seconds within a Kubernetes cluster **that you run** and scale well across hundreds of developers and microservices without incurring large infrastructure costs or maintenance burdens. These environments are built on top of “Sandboxes”, a new approach that is used in companies like [**Uber**](https://www.uber.com/blog/simplifying-developer-testing-through-slate/) and [**Lyft**](https://eng.lyft.com/scaling-productivity-on-microservices-at-lyft-part-3-extending-our-envoy-mesh-with-staging-fdaafafca82f) and we’re excited to make that available to everyone.\n\nWe launched about a year ago, with a focus on preview & test environments in Kubernetes. With our latest update, we’re adding the following new capabilities:\n\n* Developers can now run the **dev version of a microservice on their laptop** and seamlessly connect it to a shared Kubernetes cluster and start testing as though the whole application were running locally. They get 10x faster feedback by testing their changes in a live Kubernetes environment during active development. \n* Multiple such dev environments can be combined together to **test different microservices with each other** as they're being developed. This means developers can collaborate and get a feature spanning different microservices working end-to-end long before their code gets merged or hits a staging/production environment.\n\n### **How does it work?**\n\nJust as before, we use request routing (using a Service Mesh like [**Istio**](https://istio.io/) or a sidecar-based system that we built) to isolate these environments from one another in a shared Kubernetes cluster.\n\n![](https://ph-files.imgix.net/f5efe69b-ed3c-4306-af3e-c5d757877d7a.png?auto=compress&codec=mozjpeg&cs=strip&auto=format&fit=max)\n\nRead more about our launch [**here**](https://www.signadot.com/blog/bridging-the-gap-local-testing-in-shared-kubernetes-clusters), or watch a video of how teams use these dev environments here: [**https://www.youtube.com/watch?v=HXUmPJTdwm0**](https://www.youtube.com/watch?v=HXUmPJTdwm0\\*\\*)\n\n### **How do I get access**\n\nJust sign up using [**https://www.signadot.com/signup**](https://www.signadot.com/signup\\*\\*) and try one of our [**quickstart**](https://www.signadot.com/docs/quickstart) guides. Feedback is much appreciated!",
        "url": "https://www.ycombinator.com/launches/JIZ-developer-environments-built-for-kubernetes",
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        "id": 65752,
        "title": "Signadot: Ephemeral Test Environments built for Kubernetes",
        "tagline": "Spin up lightweight environments to enable pre-merge testing for microservices. Discover issues before merging code and ship new features faster.",
        "body": "### 😎 **TL;DR**\n\nWe’re building a Kubernetes-based solution that enables environments, called Sandboxes, to test your microservices early in the development lifecycle. Unlike traditional environments, Sandboxes are lightweight and spin up in seconds. This makes it cost-effective to create test environments for every pull request, branch, and commit.\n\n### 🤔 **The problem with traditional environments**\n\nMost engineering teams use pre-production environments, called staging environments, to test against real-world conditions. However, testing on staging introduces the following challenges:\n\n1. Staging environments are typically shared among engineering teams and regressions introduced here block other developers. This results in frequent roll backs to unblock others.\n2. Since commits from various teams are co-mingled on staging, breakages take a long time to troubleshoot and fix. As the engineering team grows, this problem only gets worse and development slows down.\n\n### **😅 Enter Signadot**\n\nWith Signadot, DevOps teams can set up lightweight environments, called Sandboxes, that can be used to test each pull request/commit. Teams run a suite of integration and end-to-end tests for each PR without impacting others. This helps find issues early that would otherwise be found in staging (or worse … in production!).\n\nOur CLI/YAML integrates with CI pipelines to automate the testing of Pull Requests. As shown in the image below, Sandboxes encapsulate “services-under-test” connected to other dependencies running in Kubernetes. Sandboxes are lightweight to spin up and cost-efficient at scale.\n\n![uploaded image](/media/?type=post&id=65752&key=user_uploads/47401/edb398f0-819b-43f3-9152-9ba015164069)\n\n### **🤓 How this helps your team:**\n\n* Minimize rollbacks on staging and production\n* Increase the frequency of production deployments\n* Save on infrastructure costs using lightweight Sandboxes\n\n### **🤩 Try us out!**\n\n1. [**Sign up for free**](https://www.signadot.com?utm_campaign=Launch%20YC&utm_source=yc&utm_medium=launchyc) **to start testing your microservices today**\n2. Join the discussion on testing strategies for microservices in our [Slack Community](https://signadotcommunity.slack.com/) or [GitHub Repo](https://github.com/signadot). You can also book a time to chat with one of our founders [here](https://calendly.com/signadot/demo).",
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        "body": "Hi Everyone! I’m Emmanuel, co-founder of [Termii](https://termii.com) (YC W20), and we’re launching [Sotel](https://sotel.com), a network-intelligent eSIM app that gives you seamless mobile connectivity in 180+ countries — no SIM cards, no hidden fees.\n\n📌 **TL;DR**\\\n[Sotel](https://sotel.com) is the easiest way to get mobile data globally. We intelligently and automatically analyze carrier coverage, switching you to the best local network in real-time based on your location, usage pattern, and performance needs.\n\n**The Problem:**\\\nMillions of people — especially remote teams, travellers, and startup nomads — suffer from:\n\n* Expensive roaming charges\n* Poor network performance when travelling\n* Fragmented eSIM platforms with no real-time optimization\n* No visibility into where their connection is weakest (until it fails)\n\n**Our Solution:**\\\n🔁 **Dynamic Network Optimization**: We evaluate local carriers in real-time and connect you to the one with the best speed, latency, and coverage — using network and signal intelligence.\\\n📱 **Global eSIM Access**: Download once, connect anywhere. We offer affordable plans across 180+ countries.\\\n📊 **Smart Usage Dashboard**: Track data usage, get alerts and optimize performance before a video call drops.\\\n🛡️ **Privacy-Focused**: No personal ID is needed for 95% of destinations.\n\n**Why We’re Building This**\\\nAt Termii, we power critical communication APIs used by 20,000+ businesses processing 2B+ transactions annually. As we expanded globally, we ran into the same issue our users faced — unpredictable and expensive connectivity. Sotel is our solution, built from the ground up for modern, mobile-first users.\n\n**Who It’s For**\n\n* International teams and remote workers\n* Frequent travellers, digital nomads\n* Businesses that want to keep their global staff always connected\n* Developers integrating global IoT/M2M connectivity\n\n**Traction So Far:**\n\n* Pilots running across 4 continents\n* Partnered with major telecom companies in Africa, US & Europe\n\n**Try it now**: [Google Play Store](https://play.google.com/store/apps/details?id=com.sotel.mobile&pcampaignid=web_share) and [Apple App Store](https://apps.apple.com/ng/app/sotel/id6740035152)\n\n**We're Looking For:**\n\n* Feedback from global users, remote workers, and telco nerds\n* Partnerships with travel, fintech, and remote work platforms\n* Early adopters to test our Intelligent switching engine\n* Investors interested in telco infrastructure + AI\n\nLet’s chat → [founders@termii.com](https://mailto:founders@termii.com)",
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        "title": "Slack Huddles API by Recall.ai",
        "tagline": "Programmatic access to Slack Huddles data",
        "body": "Hey everyone!\n\nWe just launched an API that lets developers programmatically access Slack Huddles data, making it possible to build apps that capture, analyze, and integrate Slack Huddles conversations.\n\n**Why we built this**\n\nSlack Huddles are great for quick discussions, but they don’t provide a way to retrieve transcripts, recordings, or speaker data. Once a huddle ends, the conversation is gone.\n\n**What developers can build with the Slack Huddle API**\n\nWith a single API call, you can:\n\n* Retrieve transcripts, recordings, and speaker timelines from Slack Huddles\n* Automate workflows for incident management, support escalations, and team handoffs\n* Integrate Slack Huddles data into CRMs, analytics platforms, and knowledge bases\n* Build AI-powered tools for summarization, insights, or search on top of huddle data\n\n**Get access**\n\nCheck out the [Slack Huddles API documentation](https://www.recall.ai/product/slack-huddles-api) and get started [here](https://www.recall.ai/get-started).\n\n![uploaded image](/media/?type=post&id=88317&key=user_uploads/316225/0c06a860-4f67-4669-98c0-3786623228ce)\n\n",
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        "title": "Recall.ai Desktop Recording SDK: Access real-time meeting data without bots 🚫🤖",
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        "body": "<https://youtu.be/stsH9jv715g>\n\nToday, we’re excited to introduce the [Recall.ai](https://www.recall.ai) **Desktop Recording SDK**. Built to simplify meeting data integration, this SDK allows you to get the recording, transcript and metadata directly within your desktop app — without the need for bots. Integrates with Electron applications in under 5 minutes to provide live data from platforms like Zoom, Google Meet, and Microsoft Teams.\n\n### **Why we built this**\n\nMany industries have strict compliance requirements that limit the use of bots, and sometimes bots may make people uncomfortable. \n\nTo address this, we developed an SDK that provides direct access to live meeting data, letting you build features that leverage this data, such as:\n\n* Lightweight note-taking\n* Speech coaching\n* Telehealth appointment recorders\n* Financial advisor calls\n* Real-time sales coaching\n\n### **Get started**\n\nCheck out the Desktop Recording SDK documentation [here](https://docs.recall.ai/docs/desktop-sdk-beta) for setup instructions and more details on how to get started.\n\nLet us know your thoughts — we’re eager to hear how the Desktop Recording SDK fits into your app and your use cases.\n\n![uploaded image](/media/?type=post&id=85723&key=user_uploads/316225/d873ac6f-9125-4204-9293-7dbd0cfcfb7b)\n\n",
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        "title": "Recall.ai: Output Media API - AI agents that talk in meetings 🤖",
        "tagline": "Use our Output Media API to build AI agents that join video conferences on Zoom, Meet, Teams, and Webex.",
        "body": "TLDR - [Recall.ai](https://www.recall.ai)’s Output Media API lets you build AI agents that join video conferences on Zoom, Meet, Teams, and Webex. These agents can output audio and video, listen, and respond like real participants. \n\n<https://youtu.be/YzzzqPpL47o>\n\nToday, we're excited to introduce the Output Media API. With this API, you can easily build live, interactive AI agents that can listen and react to meetings in real time. \n\n### 📈 The Problem and Solution\n\nFor developers, building AI agents have become much easier with APIs like the [OpenAI realtime API](https://openai.com/index/introducing-the-realtime-api/). The difficult part is actually getting your AI agent into a video conference. This is because there are no APIs to send and receive low-latency video streams from Zoom, MS Teams, Google Meet etc.\n\nThe solution is to use a service like [Recall.ai](https://www.recall.ai) which provides an API to create virtual participants that can join video conferences, receive the real-time data from the conference, and display the agent as a participant in the meeting. \n\n### 🤖 AI Agents as a Web App\n\nThe Output Media API works by rendering any web app into ultra-low-latency audio and video, and streaming that into your video conference through a bot.\n\nThis functionality makes it possible to develop AI agents that listen to the meeting audio and react in real-time, in just a few steps:\n\n1. Develop a webpage that listens to and reacts to audio coming from the microphone (e.g. via [getUserMedia](https://developer.mozilla.org/en-US/docs/Web/API/MediaDevices/getUserMedia))\n   * [Recall.ai](http://Recall.ai) sends the mixed audio of the meeting to the webpage microphone so your agent can listen to what’s being said\n2. Send a [Recall.ai](http://Recall.ai) bot to a meeting and configure it to show the webpage you developed\n\n```\n// POST /api/v1/bot/\n{\n  \"meeting_url\": \"https://us02web.zoom.us/j/1234567890\",\n  \"bot_name\": \"Recall.ai Notetaker\",\n  \"output_media\": {\n    \"camera\": {\n      \"kind\": \"webpage\",\n      \"config\": {\n        \"url\": \"https://{YOUR_WEBPAGE_URL}\"\n      }\n    }\n  }\n}\n```\n\n### 🚀 Getting Started:\n\n* Check out our [Output Media Guide](https://docs.recall.ai/docs/stream-media).\n* We’ve built 2 sample apps using Output Media, with walkthroughs of how they’re built.\n  * 💻 [Voice Agent Demo](https://github.com/recallai/voice-agent-demo) lets the bot have a conversation with meeting participants, using OpenAI’s [realtime API](https://openai.com/index/introducing-the-realtime-api/).\n  * 💻 [Real-time Translator](https://github.com/recallai/real-time-translator-demo) displays live meeting translations on screen for a spoken conversation.\n\n![uploaded image](/media/?type=post&id=85172&key=user_uploads/316225/39433a6c-c885-402b-922f-5d477117fde6)\n\n",
        "url": "https://www.ycombinator.com/launches/M9k-recall-ai-output-media-api-ai-agents-that-talk-in-meetings",
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        "created_at": "2024-10-31T03:20:30.529Z"
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        "id": 75617,
        "title": "Recall.ai — The universal API for meeting bots",
        "tagline": "A single API for meeting bots on Zoom, Google Meet, Microsoft Teams, Slack and more.",
        "body": "Hey all!\n\nWe’re building [Recall.ai](https://www.recall.ai), a single API for you to get meeting data from Zoom, Google Meet, Microsoft Teams, Slack, and more.\n\n![uploaded image](/media/?type=post&id=75617&key=user_uploads/316225/e4c4cc37-13fb-493d-900f-fb8124655197)\n\nThe data you receive includes:\n\n* Video recording\n* Audio recording\n* Transcription\n* Participant names\n* When people spoke in the meeting\n* When people joined/left the meeting\n* When people turned their cameras on/off in the meeting\n* What device someone is joining from\n* …and 10+ more pieces of meeting data.\n\n🎥 [**Click to watch our 2 min demo**](https://www.loom.com/share/60a7500d2a8c40c3b05a88dca376107e)\n\n## The Problem\n\n* Building robust meeting bots takes 1000s of engineering hours.\n* Running the infrastructure is a big operational burden, and complicates your architecture.\n* Companies like Gong have a team of 5 engineers dedicated to running this infrastructure to be stable and scalable.\n* People are building hacky workarounds like bots that screen-record Zoom in a web browser, but this breaks frequently.\n* Some meeting platforms (eg. Google Meet, Slack) don’t even have APIs.\n\n## The Solution\n\nWe provide 1 simple API that abstracts over differences between meeting platforms (even for platforms that don’t have an API). We run, monitor, and scale the complex real-time video infrastructure - so you can focus on building your core product.\n\nThis allows our customers to get into production in a week instead of a year.\n\n## Use Cases\n\nWe work with 150+ companies today that ingest meeting data, like Grain, Apollo.io, BrightHire, Fellow, and more. Some examples of products our customers are building with Recall:\n\n* Meeting productivity tools\n* Sales conversation intelligence tools\n* Interview intelligence tools\n* User research tools\n* Customer success tools\n* Financial advisor tools\n* Virtual deposition tools\n* Meeting translation tools\n* Video editing tools\n* Edtech tools\n\n## Can I try it?\n\nYes! We’d love to have you try [Recall.ai](https://www.recall.ai). Sign up and [get started for free](https://www.recall.ai/signup).",
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        "founder_bio": "Dan and I are working on data tools for SMBs, the types of modern data stacks that we've built at multiple high growth startups.  I've been running data and data-adjacent teams in the Valley for over a decade at startups from a variety of industries including gaming, social, HR/benefits, real estate, and cannabis.  Dan and I have been friends for 20 years and we previously co-founded a hot sauce company, Bacon Hot Sauce, 10 years ago selling hundreds of thousands of bottles.",
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        "date": "Apr 05, 2023"
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        "title": "Mozart Data raises $15M to help startups spin up a data stack – TechCrunch",
        "url": "https://techcrunch.com/2022/04/27/mozart-data-raises-15m-to-help-startups-spin-up-a-data-stack/",
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    "launches": [
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        "title": "Mozart Data for BigQuery",
        "tagline": "End-to-end data platform with BigQuery or Snowflake under the hood",
        "body": "**Mozart Data + Big Query:** \n\nDo you have GCP credits available? You can now get BigQuery, Looker, and use Mozart’s Sonata (free) offering to get your data to a central place and start your business intelligence reporting - ALL FOR FREE. <https://mozartdata.com/mozart-data-and-bigquery/>\n\n![](https://lh7-us.googleusercontent.com/VLxQv9WmV6PUd_VRSGK8Eycrlq_ZO1Ufsh86Omj4WGzYSvMSyio_aFAFFm50suDLOEAU-WxBaWkhLmaA3xUiPyt5R-ExWKpmzVaTg_UdguFvlDyPh485NNfvTdOeKZ6UN4hZhL7glPUNcbPJ6MGv3PQ)\n\nThis month, we’re launching **Mozart Data for BigQuery**. This is an extension of Mozart’s core offering: a **managed data platform** with best-in-class analyst support, so teams can reliably **centralize their data** and uncover actionable insights to drive their business more successfully. \n\nWe’ve helped hundreds of companies set up their data infrastructure without hiring a data engineer. With Mozart, you can set up BI reporting to have a business overview, answer questions about what customer segments are good from a usage or advertising perspective, and even gain insight into what customers are at risk of churn. \n\n**Best In Class Modern Data Stack:** \n\nSince YC’s S20 batch, we’ve had a Henry Ford-like stance on the Model T – any color the customer wants, as long as it’s black, or any data warehouse you want so long as it’s Snowflake.\n\nWe have always believed that we should offer a best-in-class gluing together of the modern data stack, so that startups can set up their data infrastructure without a data engineer, while also being able to set up their data infrastructure quickly without being slowed by the paradox of choice.\n\n![](https://lh7-us.googleusercontent.com/ZAqzbGQWvrFAJkhcQngoYlh_FFJlqvkino8OF5Y36mfGvVPj58G1TfAbZlfw8ubMm5-ic6Is__j-KdPbf_tJwGuIGNOIDUavikdjYH1RCfN0jh2hhACgHHKld1L37DWMEtbJ6755-BoelzMd04_yNOI)\n\n**Choose your Data Warehouse**: \n\nNow, we are changing – Mozart will help you get up and running with either Snowflake or BigQuery as your data warehouse. We want to help you use your GCP credits to get your data set up with an ETL and a data warehouse. You get first-class data infrastructure from the start without needing the data engineer to help make it happen – we’ll do it for you.\n\nAgain, the Mozart team will help you use your GCP credits and get your data infrastructure to a strong, self-sufficient state so that you can begin to use BI tools to optimize and report on your business. If using YC deals, this can be done for free.\n\n![](https://lh7-us.googleusercontent.com/o8z17dm77X-Atz10mA3vPdBSMR5J081iYxkqyO4LbNP_zCOOiFHuzTeCmaFtP3l6SRFI-Uel-zCpbezXCAAHaJ_WySxNnOYNt3JKNOD46LEBmMvV-0Yj7Xp4CxhYNuloJy5DmIKtrjqARG1gYxhM01I)\n\n**Learn more, and sign up:** \n\nWant to learn more about Mozart Data’s product and Google BigQuery integration, or just want to chat about data infrastructure or analytics, ping [support@mozartdata.com](mailto:support@mozartdata.com).",
        "url": "https://www.ycombinator.com/launches/L2t-mozart-data-for-bigquery",
        "votes": 21,
        "created_at": "2024-05-16T20:12:22.106Z"
      },
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        "id": 70031,
        "title": "Mozart Data Sonata - we set up and maintain world-class data infrastructure (ELT & Warehousing) for FREE",
        "tagline": "Every company should have the ability to use a Modern Data Stack from the start",
        "body": "**tl;dr:** We set up and manage your data warehouse (Snowflake), extract and load your data (Fivetran), and provide you a layer for transformation so that you can clean and organize your data before visualizing it (in your favorite tool). If you have startup data volumes and you want a free solution that sets up in less than an hour with no data engineering needed, you can do this with the Mozart Data Sonata package. [Signup for FREE](https://app.mozartdata.com/signup-sonata)!\n\n—\n\nWe’re Dan & Pete, co-founders of [Mozart Data](https://www.mozartdata.com/), and the other maestros on the Mozart team.\n\n![uploaded image](/media/?type=post&id=70031&key=user_uploads/490519/74d9ddad-1710-4156-96fb-a83c044bf708)\n\n🧠 Dan is the CTO and a product-oriented software engineer, with expertise in user experience, data engineering, and data science. He has a long history of founding and working at numerous startups across a variety of industries, including gaming (Gamador YCW10), fintech (GogoCoin 500S15), and healthcare (Clover Health CLOV). He and Pete also co-founded [Bacon Hot Sauce](https://baconhotsauce.com/) in 2010, which sold hundreds of thousands of bottles.\n\n🎯Pete aka “Fish” is the CEO and has over a decade of running data and data-adjacent teams at startups in a variety of industries. These include gaming (Playdom/Disney), social (Yammer/Microsoft), HR/benefits (Zenefits), real estate (Opendoor), cannabis (Eaze), B2B (GoDaddy), and sports (Philadelphia Eagles). He has a Ph.D. in economics and loves talking about correlation vs. causation.\n\n# What is the Modern Data Stack and why does it matter?\n\nWe jokingly say that the Modern Data Stack is about 500 logos on a VC pitch deck slide. Navigating through the noise is challenging because many of the modern tools aim to solve advanced problems that most companies never encounter. Instead, we’ve optimized the data infrastructure onboarding experience to be achievable without any data engineers or experts.  Mozart will set up your data pipelines – seamlessly gluing world-class ELT, data warehousing, data transformation, data monitoring & alerting, cost-optimization, and governance all together for you. This gives you the full power of a modern data stack (Snowflake + Fivetran) even if all you know is some basic SQL, so you can focus on your specific product and business challenges.\n\n![uploaded image](/media/?type=post&id=70031&key=user_uploads/490519/fe251d78-d894-4e17-bfb9-10622d4ab9cb)\n\nEarly examples of analysis teams do with a Modern Data Stack:\n\n* Answer basic questions (with auto-update) like: How many customers do we have? What does our revenue/cost look like over time?\n* Set up an Investor Dashboard with core metrics using Metabase to visualize\n* Set up Slack or email alerts when events happen in your application db\n* Get your Salesforce/Stripe/Shopify data syncing to GSheets\n* Reconcile how much each client is supposed to be paying and is actually paying\n* Set up a test and alert that we have a first and last name for every client (or any other necessary condition)\n* Push cleaned data about clients to a GSheet for customer service or operations teams to consume\n* View basic product funnels with Segment data through your favorite BI tool\n* Set up lead and churn (likelihood) scores for prospects and customers\n* SQL query collaborative editing with version control backed by GitHub\n* Save money on platforms that price based on seats with the same visibility into performance\n* Set up your long-term data infrastructure that can scale cost-effectively\n\nThere are many reasons to get your data infrastructure set up early.  There are many quick wins from reporting, alerting, effective operations, governance, and cost-saving.  With tools like Mozart Data, you can do this without data engineers.  With our Sonata package, you can see the value in the data for yourself for FREE.\n\n# Our ask 🙏\n\nGive Mozart Sonata a try, [sign up here](https://app.mozartdata.com/signup-sonata)\n\n* Bonus: self-serve\n* Double Bonus: load your first data source (just credentials needed), schedule a transformation, or graph/visualize a key metric\n* Triple Bonus: share your feedback with us at [founders@mozartdata.com](mailto:founders@mozartdata.com)",
        "url": "https://www.ycombinator.com/launches/IDX-mozart-data-sonata-we-set-up-and-maintain-world-class-data-infrastructure-elt-warehousing-for-free",
        "votes": 50,
        "created_at": "2023-03-08T00:34:43.016Z"
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        "founder_bio": "A tech enthusiast, always eager to learn how technology can create big businesses. \nCurrently co-founding SigNoz with a mission to give the best application performance tool to developers. Ex Citi, IIT Delhi alum.",
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        "url": "https://www.ycombinator.com/companies/signoz/jobs/ZCYWPvl-forward-deployed-engineer-remote-india-time-zones",
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        "experience": "1+ years",
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        "url": "https://www.ycombinator.com/companies/signoz/jobs/6O4Qa24-growth-marketing-remote-us-based",
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        "url": "https://www.ycombinator.com/companies/signoz/jobs/2kON1wN-don-t-see-your-role-apply-here",
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        "title": "Open source Datadog rival SigNoz lands on the cloud with $6.5M investment | TechCrunch",
        "url": "https://techcrunch.com/2023/09/28/open-source-datadog-rival-signoz-lands-on-the-cloud-with-6-5m-investment/",
        "date": "Sep 28, 2023"
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        "title": "SigNoz (YC W21) on LinkedIn: 10,000+ GitHub stars and counting!! ",
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      {
        "id": 68621,
        "title": "SigNoz - Open source alternative to DataDog, New Relic",
        "tagline": "Open Source Metrics, Traces and Logs in a single pane of glass",
        "body": "Hey folks 👋\n\nWe are thrilled to share SigNoz with the YC community today!\n\n[SigNoz](https://signoz.io?utm_source=launchYC&utm_medium=homepage) is an open source alternative to DataDog, New Relic which helps developers monitor their applications, trouble shoot problems and get proactive alerts\n\nWe have quite an active dev community with **11,000+** github stars, **1800+** members in our slack community and **80+** contributors across our repos.\n\n**😫 Problem**\n\n* Engineering teams are frustrated with using different tools for different telemetry signals like metrics, traces and logs. Correlating across different tools is super critical for teams to get value out of their telemetry data and solve issues faster.\n* Using proprietary vendor instrumentation code, locks you in to a vendor as it is difficult to change your instrumentation at a later stage.\n* Sending all data to vendor cloud may have privacy and security concerns for some users esp. who have customer PII data or have to maintain a tighter security posture\n\n**🚀 Why SigNoz?**\n\n1. Single pane of glass for metrics, traces and logs in a single application. Ability to correlate across different telemetry signals very easily.\n2. Natively based on [OpenTelemetry](https://opentelemetry.io) - and emerging open source standard for instrumentation and telemetry collection.\n3. Uses OLAP datastore (ClickHouse) which makes aggregation queries 10x faster compared to traditional DBs. \\\n   More than 50-60% of observability related queries are aggregation queries like group by, sum, count where users want to aggregate their data\n4. Can be self hosted easily. Keep all observability data in your own cloud.\n\n💻 **Demo**\n\nHere’s a quick demo of the product\n\n[Demo Video](https://youtu.be/Tl3Cz8tz4R8)\n\n💪 **Logs Performance Benchmark**\n\nWe also published a performance benchmark recently on how SigNoz logs product performs compared to other open source logs solutions like Elastic\n\n[https://signoz.io/blog/logs-performance-benchmark/](https://signoz.io/blog/logs-performance-benchmark/?utm_source=launchYC&utm_medium=logs-benchmark)\\\n\\\n**🙏 Asks**\n\nTry out SigNoz and share your feedback on how it can be improved. Here’s our github repo - <https://github.com/signoz/signoz>\n\nHave any questions? Join our [slack community](https://signoz.io/slack?utm_source=launchYC&utm_medium=slack-community) and drop a note\n\n😎 **What do we do for fun?**\n\nWe are a lean team constantly re-imagining the future of observability. Here’s a picture of our team from a recent workation at Goa\n\n![uploaded image](/media/?type=post&id=68621&key=user_uploads/95650/4a4d8bd7-7975-4e43-bc1e-394c7859d63d)\n\n![uploaded image](/media/?type=post&id=68621&key=user_uploads/95650/02c70ae9-d6a7-4fc3-97d5-5edd635dc5b9)\n\n",
        "url": "https://www.ycombinator.com/launches/Hqn-signoz-open-source-alternative-to-datadog-new-relic",
        "votes": 47,
        "created_at": "2023-01-23T13:19:49.300Z"
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    "name": "SlashAuth",
    "slug": "slashauth",
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    "long_description": "Slashauth builds a developer platform for web3 enabled apps. Slashauth’s platform provides login functionality, role based access control, data storage, and more to any web3 enabled application.",
    "one_liner": "The solution for fine-grained access control in web3",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "role_type": "Full stack",
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        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
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        "id": 96282,
        "title": "Deployment Strategist",
        "url": "https://www.ycombinator.com/companies/encord/jobs/O3v1WZP-deployment-strategist",
        "location": "New York",
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        "experience": "Any (new grads ok)",
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      {
        "id": 96283,
        "title": "Senior Software Engineer, Front-End Leaning",
        "url": "https://www.ycombinator.com/companies/encord/jobs/FXcMVmq-senior-software-engineer-front-end-leaning",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
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        "equity_range": "",
        "experience": "5+ years",
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        "id": 96284,
        "title": "Product Engineer, Physical AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/gQlAVmO-product-engineer-physical-ai",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
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        "equity_range": "",
        "experience": "Any (new grads ok)",
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        "id": 96285,
        "title": "Product Engineer",
        "url": "https://www.ycombinator.com/companies/encord/jobs/E9vvz7Y-product-engineer",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
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        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
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      {
        "id": 96286,
        "title": "Software Engineer, Full-Stack",
        "url": "https://www.ycombinator.com/companies/encord/jobs/elurIQG-software-engineer-full-stack",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
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      {
        "id": 96287,
        "title": "Human Data Solutions Engineer",
        "url": "https://www.ycombinator.com/companies/encord/jobs/3ORQrcy-human-data-solutions-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
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      {
        "id": 96288,
        "title": "Human Data Solutions Engineer",
        "url": "https://www.ycombinator.com/companies/encord/jobs/ochq0FK-human-data-solutions-engineer",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
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      {
        "id": 96289,
        "title": "Commercial Associate, Physical AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/qsDc1PU-commercial-associate-physical-ai",
        "location": "New York",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96290,
        "title": "Commercial Associate, Physical AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/FyzOIOY-commercial-associate-physical-ai",
        "location": "London",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96291,
        "title": "GTM Special Projects, Physical AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/zbhrvUY-gtm-special-projects-physical-ai",
        "location": "New York",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96292,
        "title": "Senior Product Manager",
        "url": "https://www.ycombinator.com/companies/encord/jobs/JDTYAXb-senior-product-manager",
        "location": "London",
        "type": "Full-time",
        "role": "Product",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96294,
        "title": "Senior Growth Manager",
        "url": "https://www.ycombinator.com/companies/encord/jobs/Iqy1rqR-senior-growth-manager",
        "location": "London / New York / San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96295,
        "title": "Senior Software Engineer, Infrastructure ",
        "url": "https://www.ycombinator.com/companies/encord/jobs/CtonqI4-senior-software-engineer-infrastructure",
        "location": "London",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96296,
        "title": "Software Engineer, Applied AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/ewUM3Z8-software-engineer-applied-ai",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96297,
        "title": "Software Engineer, Full-Stack",
        "url": "https://www.ycombinator.com/companies/encord/jobs/AuJykwC-software-engineer-full-stack",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96298,
        "title": "Senior Software Engineer, Full-Stack",
        "url": "https://www.ycombinator.com/companies/encord/jobs/AdAB4Zj-senior-software-engineer-full-stack",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96299,
        "title": "Software Engineer, Physical AI",
        "url": "https://www.ycombinator.com/companies/encord/jobs/CuyvF2n-software-engineer-physical-ai",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 96300,
        "title": "General Manager, Human Data Operations",
        "url": "https://www.ycombinator.com/companies/encord/jobs/WE79ZUs-general-manager-human-data-operations",
        "location": "Bengaluru",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
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      {
        "id": 100796,
        "title": "Finance Business Partner – GTM",
        "url": "https://www.ycombinator.com/companies/encord/jobs/A0pg6BM-finance-business-partner-gtm",
        "location": "London",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
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      {
        "id": 113039,
        "title": "Principal Engineer - Backend",
        "url": "https://www.ycombinator.com/companies/encord/jobs/7uH7QHx-principal-engineer-backend",
        "location": "London, England, GB",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "£150K - £200K GBP",
        "equity_range": "",
        "experience": "6+ years",
        "visa": "US citizenship/visa not required",
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    "news": [
      {
        "title": "The Data Layer for the Robot Economy",
        "url": "https://www.youtube.com/watch?v=cSBdukYWWxQ",
        "date": "Apr 30, 2026"
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      {
        "title": "Encord lands new cash to grow its data dev tools for AI | TechCrunch",
        "url": "https://techcrunch.com/2024/08/13/encord-lands-new-cash-to-grow-its-data-labeling-tools-for-ai/",
        "date": "Aug 13, 2024"
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      {
        "title": "10 Most Advanced Medical Imaging Solution Providers 2022 July2022 - Insights Care",
        "url": "https://insightscare.com/10-most-advanced-medical-imaging-solution-providers-2022-july2022/",
        "date": "Jul 29, 2022"
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      {
        "title": "Meet 23 Deeptech Startups Poised to Defy the Market Downturn, Per VCs",
        "url": "https://www.businessinsider.com/meet-23-deeptech-startups-poised-to-defy-market-downturn-vcs-2022-7",
        "date": "Jul 28, 2022"
      },
      {
        "title": "Encord: Stringing a Powerful Cord of Futuristic Data-Centric Computer Vision",
        "url": "https://insightscare.com/encord-stringing-a-powerful-cord-of-futuristic-data-centric-computer-vision/",
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  {
    "id": 22880,
    "name": "True3D",
    "slug": "true3d",
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      "True3D",
      "Hyperplane",
      "Hackpack",
      "LeetDesign",
      "QuickVid.ai",
      "TutorAI.me",
      "QuickVid.ai",
      "QuickVid",
      "True3D",
      "True3D Labs",
      "Outpaint.com"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a242a0c2ce6b1ca1518d1744187f98a3bf8eade9.png",
    "website": "https://www.true3d.com/",
    "all_locations": "New York City, NY, USA",
    "long_description": "We're a research lab building foundation models and products for what comes after flat video.",
    "one_liner": "Building the next generation of visual media",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1614739771,
    "tags": [
      "Video",
      "Media",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2021",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/true3d",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/true3d.json",
    "year_founded": 2020,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/outpaint/",
    "x_url": "https://x.com/outpaintcom",
    "fb_url": null,
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    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a242a0c2ce6b1ca1518d1744187f98a3bf8eade9.png",
    "app_video_url": null,
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    "company_photos": [],
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    "founders": [
      {
        "user_id": 579262,
        "full_name": "Daniel Habib",
        "title": "Founder",
        "founder_bio": "Ex-FB Engineer with expertise in live video, generative AI, virtual reality and scalable systems\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/daniel-habib-5b305767/",
        "x_url": "https://twitter.com/DannyHabibs",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/72749a3b280febc144aaf9f353d3bae566f8ae83.jpg"
      }
    ],
    "jobs": [],
    "news": [
      {
        "title": "QuickVid uses AI to generate short-form videos, complete with voiceovers | TechCrunch",
        "url": "https://techcrunch.com/2022/12/30/quickvid-uses-ai-to-generate-short-form-videos-complete-with-voiceovers/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAALptn1iOjHaR-DkOiiKW3VgHFJ-8yzAmJ1_pepYTTcFBxvZJKXVin2yxpZCf2HBw33Li8h5NOgrtFoBvEdqgIdE8kk-zXlWVKjVowvMpcsS9UyL_g5AYdLjlZZLCo9jjb4G5IghZ0RTWBJk3YaQMsIwXXObXlFNZA7bx2udYDk1b",
        "date": "Dec 30, 2022"
      },
      {
        "title": "ChatGPT Is on Fire, and a Cottage Industry of Bot Builders Is Exploding Alongside It — The Information",
        "url": "https://www.theinformation.com/articles/chatgpt-is-on-fire-and-a-cottage-industry-of-bot-builders-is-exploding-alongside-it",
        "date": "Dec 16, 2022"
      }
    ],
    "launches": [
      {
        "id": 103954,
        "title": "Outpaint.com, Ad Reframe - convert winning UGC ads to CTV",
        "tagline": "Send us your UGC ad and get back a TV & Desktop ready version within days. ",
        "body": "Today on [Outpaint](https://outpaint.com/ad-reframe) we are launching **Ad Reframe**, the easiest way for advertisers to convert their winning UGC (9:16) for connected TV (16:9). <https://outpaint.com/ad-reframe>\n\n![uploaded image](/media/?type=post&id=103954&key=user_uploads/579262/0d9469eb-a8fe-4035-82b5-8b8101014f76)\n\n**The problem**\n\nAdvertisers test endlessly to find a winning UGC video, then want to squeeze every bit of value from it, including running it on TV. But converting vertical to horizontal for the big screen today means black bars from pillarboxing or a smeared side blur.\n\n**The solution**\n\nInstead of adding black bars, we expand the content so it looks like it was shot natively for TV.\n\n![uploaded image](/media/?type=post&id=103954&key=user_uploads/579262/9c2c0fed-b0bb-4904-a4f4-55b28373782d)\n\nOutpainting wins on everything advertisers measure: engagement (skip rate falls to 17%, the lowest tested), quality (55% say it looks professionally produced vs. under 40% for the alternatives), and brand lift (consideration at 51%, favorability at 58%), because filling the screen with real content beats hiding the gap behind bars or blur. Report [link](https://outpaint.com/case-studies/ctv-format-test)\n\n![uploaded image](/media/?type=post&id=103954&key=user_uploads/579262/79d0f1d7-f541-4f01-9255-c811dde479f9)\n\n**How it works**\n\nThe conversion generates new content where the black bars used to be. We support any aspect ratio, resolutions up to 8K, any frame rate, and any color space.\n\n![uploaded image](/media/?type=post&id=103954&key=user_uploads/579262/b9f35bc8-49fc-4b12-ac11-e2cb85002956)\n\nHave an ad you want to convert? Grab 15 minutes on my calendar and we'll set you up. <https://cal.com/daniel-habib/15min>",
        "url": "https://www.ycombinator.com/launches/R2g-outpaint-com-ad-reframe-convert-winning-ugc-ads-to-ctv",
        "votes": 18,
        "created_at": "2026-06-25T14:53:02.827Z"
      },
      {
        "id": 102813,
        "title": "Outpaint.com - Expand any video to fit any screen",
        "tagline": "Cinema grade video outpainting models",
        "body": "I’m Daniel from [**Outpaint.com**](http://Outpaint.com) (W21) and we are putting an end to **black bars** around videos, from vertical ads to big theater screens.\n\n**Problem**\n\nPeople consume video on many different devices, but video can only be shot for a single screen at a time.\n\n**Solution**\n\nOur models expand any video to fit any screen by generating content to fill the extra space, while preserving every pixel of the original video.\n\n**Examples**\n\n9:16 →16:9 (Ads and Micro-dramas)\n\n![uploaded image](/media/?type=post&id=102813&key=user_uploads/579262/e014ce0f-23db-4ee6-a6ee-f2e4269bd9c5)\n\n16:9 → 1.43:1 (Premium big screens and IMAX full frame)\n\n![uploaded image](/media/?type=post&id=102813&key=user_uploads/579262/ab682ded-ee2d-4cb6-838b-b8f024ed41be)\n\n16:9 →180° Fisheye (Domes &amp; VR)\n\n![uploaded image](/media/?type=post&id=102813&key=user_uploads/579262/4e182eb6-7782-4047-bc53-6ed83af33ca1)\n\n**How [_outpaint.com_](http://outpaint.com) works**\n\nSimply send us a video link and your target aspect ratio, and we'll send back the expanded version. Our models handle any duration, resolution, frame rate, and color space.\n\nWe've expanded everything from vertical ads for horizontal CTV spots to full films for immersive screens like IMAX and dome venues. We even got a [**shoutout on Rogan**](https://youtu.be/D_ZGWj_38uA?si=hY74cps5IN5THreL&t=8415) for some of our VR conversions.\n\n**Have a video you want to expand?**\n\nSend me an email at [**hello@outpaint.com**](mailto:daniel@outpaint.com) with a link to your video, and I'll follow up within 24 hours with a quote.",
        "url": "https://www.ycombinator.com/launches/QkH-outpaint-com-expand-any-video-to-fit-any-screen",
        "votes": 7,
        "created_at": "2026-06-09T16:17:02.245Z"
      },
      {
        "id": 81306,
        "title": "🎥 QuickVid Autopilot - Post 1 viral clip per day to your socials",
        "tagline": "Autopilot curates, edits, and posts the best clips from your existing video content.",
        "body": "Hey everyone,\n\nI’m Dan, and I’m super excited to share that we are starting to roll out QuickVid [**Autopilot**](https://www.quickvid.ai/autopilot)!\n\n![uploaded image](/media/?type=post&id=81306&key=user_uploads/579262/891b19bb-7b43-492c-b234-ee213cd4a28b)\n\n### The Problem\n\nConsistently posting clips to your socials is just too time-consuming and expensive.\n\n🕗 Time Intensive: Curating and editing a 40-second clip from a 30-minute video can take up to 1-2 hours.\n\n💰 Expensive: A decent video editor can cost upwards of $200/clip\n\n🖥️ Difficult to get started: Editing requires specialized software like Adobe Premiere\n\n😰 You are too busy: Consistently posting to your socials is something that busy founders just don’t have time for!\n\n📊 Trends are always changing: What works on each social channel is always changing. We stay on top of trends, so you don’t have to.\n\n### The Solution\n\nConnect Autopilot to your long videos and desired posting locations, and you’re done! Autopilot will find the best clip from all your video sources and post it on your behalf every day.\n\n![uploaded image](/media/?type=post&id=81306&key=user_uploads/579262/a608e70e-d60d-4a25-98f6-97912f52d317)\n\n**Connect your video source**\n\nThese are the locations QuickVid will pull clips from. QuickVid checks for new content daily. (YouTube, Drive, Zoom, etc.)\n\n**Connect your video destinations**\n\nThis is where Autopilot will post your clips every day (YouTube, LinkedIn, Facebook, etc)\n\n**Choose your cadence**\n\nEither post once per day or let our AI decide the best time and cadence to post\n\n### About the Clips\n\nClips are:\n\n* Automatically cropped to a vertical 9:16 aspect ratio\n* Auto-Reframed to focus on the speaker\n* Baked with captions to help keep a viewer’s attention\n* Personalized with your branding\n\n![uploaded image](/media/?type=post&id=81306&key=user_uploads/579262/27645561-3a5a-4e38-af3a-0445c2ea712a)\n\n### Who is Autopilot for?\n\nIf you consistently create long **_talking_** videos (podcasts, interviews, zoom meetings, etc.) and are struggling to keep up with posting clips, sign up for Autopilot, and I will personally onboard you!\n\n[Sign Up Here](https://www.quickvid.ai/autopilot)",
        "url": "https://www.ycombinator.com/launches/L9O-quickvid-autopilot-post-1-viral-clip-per-day-to-your-socials",
        "votes": 9,
        "created_at": "2024-06-04T21:01:38.894Z"
      },
      {
        "id": 77477,
        "title": "QuickVid 🎥 Cut up long videos into viral clips",
        "tagline": "A generative AI video tool that repurposes long videos into shorts in one click. ",
        "body": "**Tl;dr:** QuickVid makes growing your Youtube channel effortless by creating ready-to-post shorts out of _talking_ videos (Video Podcasts, Interviews, etc.). For anyone looking for a hands-off solution, checkout our autopilot add-on!  \n\nHey everyone, I’m Daniel Habib.\n\nI just pivoted and am re-launching QuickVid (w21)\n\n![uploaded image](/media/?type=post&id=77477&key=user_uploads/579262/c54acda5-8765-4d5b-8272-36a515c77504)\n\n## **🧨The Problem**\n\nIf you’re looking to grow aggressively on LinkedIn, Youtube, or TikTok,  you should be posting **1-2 shorts per day**\n\nFor a busy founder, this can feel like an impossible task because:\n\n* Clipping shorts out of your videos takes too much time\n* Hiring a video editor is too expensive (sometimes over $300 per clip!)\n\n## **🎉The Solution**\n\nQuickVid is your automated video production studio. We take all of your source video content and repurpose it for different platforms at 1/1000th of the cost and time.\n\n![uploaded image](/media/?type=post&id=77477&key=user_uploads/579262/77ffd30d-f607-44e8-a4d5-6955d2233e1b)\n\nClips are:\n\n* Auto-reframed so the main subject is always in focus\n* Branded with subtitles and special effects\n* Spliced with B-Roll to keep engagement high – driving algorithm pickup\n* Supported in over 10 video languages\n* Editable, if you want to adjust a clip it’s easy to tweak\n* HD - 1080p supported\n* Graded with a Virality Score to help you find the best clips\n\n## **How it works**\n\nWe use our own proprietary models alongside multiple LLM and video processing chains to find and spice up the best clips.\n\nTo use, simply drop a link to your video and once processed, you’ll have dozens of clips ready for publishing.\n\n## **Full Autopilot (Beta)**\n\nFor those that are too busy to post their own shorts, we have an Autopilot add-on. Autopilot edits and posts one short a day on your behalf, it’s completely hands-off after initial setup. Every Friday you’ll get analytics on how your videos are performing.\n\n## **👋 Asks**\n\n* **Try out QuickVid!** (use promo code: _LAUNCHYC_ for a 20% discount for 2 years, only valid until next week)\n* **Share this post!** If you know any founders that regularly produce content I’d love to connect with them.\n* **Interested in Autopilot?** Shoot me an email with a link to your youtube channel: [daniel@quickvid.ai](mailto:daniel@quickvid.ai)",
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        "body": "![uploaded image](/media/?type=post&id=70447&key=user_uploads/234741/7662bcdc-8497-4a8c-a0c6-428ddc3bffe0)\n\nHey everyone! We’re Aditya and Karan! 👋\n\nWarrant is an open source **authorization service** that **helps developers implement and enforce secure, compliant access control in their applications.** It abstracts away the complexity of building application authorization and access control so developers can instead focus on building core product features.\n\nOur cloud-first, API-first approach has enabled us to build a highly-scalable and performant service that now processes millions of customer API calls per month. Today, we’re excited to announce that Warrant is officially [open source](https://github.com/warrant-dev/warrant)! ⭐️\n\n### The Problem\n\nDevelopers, we’ve all been there. What started as a simple _isAdmin_ flag to distinguish between internal and external users — just so you could release that _one_ internal-only feature — has grown into a nightmare to maintain. It’s now a hodgepodge of booleans, database flags, and a halfway built role based access control system that not only slows down your team’s day-to-day development velocity, but also opens up your company to a slew of [costly security vulnerabilities waiting to be exploited](https://www.hackerone.com/press-release/organizations-paid-hackers-235-million-these-10-vulnerabilities-one-year-4).\n\n![uploaded image](/media/?type=post&id=70447&key=user_uploads/234741/60544231-1dac-4013-8872-c97b83878da3)\n\nWhat’s more? Your customers are now requesting the ability to self-manage their team’s roles and permissions, so you’ll be spending another 1-2 months building the APIs, UIs, etc. for this risky, non-differentiating feature.\n\nIn short, application authorization and access control is difficult to build and even trickier to maintain as a company’s product evolves and its needs change.\n\n### The Solution\n\nTo help developers quickly build world-class access control, Warrant provides three things:\\\n(1) **A centralized datastore for storing your application’s authorization policies and related data.** Warrant’s management dashboard and APIs make it dead-simple to safely modify authorization policies globally or for a single user or tenant in real-time. With Warrant, updating policies for all users as your product changes becomes a breeze.\n\n(2) **A real-time, low latency access control service for enforcing authorization policies in your application at runtime.** With official Warrant SDKs available for the most popular server-side languages and frontend frameworks, checking if a user has access becomes as easy as a call to Warrant’s _check_ method.\n\n(3) **A library of thoughtfully designed, authorization-specific UI components** that help with building self-service permission management flows, granting/denying access to privileged pages/UIs, displaying audit logs to users, and more. These components are **customizable and _permission-aware_**, so developers can drop them into any application.\n\n### What’s Next?\n\nJoin us on our journey to solve authorization and access control for all developers:\n\n* Give us a 🌟 [on GitHub](https://github.com/warrant-dev/warrant) and tell us what you’d like us to build next\n* Give us a follow [on Twitter](https://twitter.com/warrant_dev) to keep up with the latest Warrant features\n* Come discuss everything authz/access control in our [Slack community](https://join.slack.com/t/warrantcommunity/shared_invite/zt-12g84updv-5l1pktJf2bI5WIKN4_\\~f4w)\n* Try out our managed cloud service, [Warrant Cloud](https://app.warrant.dev/signup)",
        "url": "https://www.ycombinator.com/launches/IKF-warrant-open-source-application-authorization-and-access-control",
        "votes": 25,
        "created_at": "2023-03-21T14:59:40.890Z"
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        "full_name": "Brandon Bayer",
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        "title": "Ravion – the easiest way to use AWS for agents and humans",
        "tagline": "Get a Vercel-like experience in your own AWS account. You and your agents get one place to manage and monitor Terraform resources, builds, and deploys.",
        "body": "**TL;DR:** Ravion is the easiest way to use AWS for agents and humans. Get a Vercel-like experience in your own AWS account, with one place to manage and monitor Terraform resources, builds, and deploys. Use your existing Terraform or start with our standard library of modules, then customize as your needs grow.\n\n![uploaded image](/media/?type=post&id=114496&key=user_uploads/280296/a5bdea0f-19b9-46f4-8553-35cd76b2c238)\n\n![uploaded image](/media/?type=post&id=114496&key=user_uploads/280296/841484c9-539d-4f3d-bb30-6770859a1a03)\n\n<https://www.youtube.com/watch?v=Fp5R5v8Uon4>\n\nWe launched Flightcontrol during W22 to make AWS as easy as using a PaaS. Over the past five years, Flightcontrol managed millions of AWS compute for thousands of users. Developers loved it, but devops teams needed something they could extend and govern.\n\nBefore AI, we were pursuing ease of use and good DX, but that’s less important now. Today when evaluating products, we want the most powerful product in the market that is also agent-friendly.\n\nRavion is what we built from scratch over the past 13 months for this new world.\n\n**WHAT IT IS**\n\nRavion is the agent-native cloud infrastructure platform that gives you one system to orchestrate everything in your AWS account.\n\n**Import or provision** new infrastructure, **build** and **deploy** any type of application, and **monitor** logs and metrics, all from one agent-friendly platform.\n\nUse your existing Terraform or start with our standard library of Terraform-backed modules.\n\n**Saves time and tokens** because AI agents get typed module schemas and connected context across infrastructure state, deployments, failures, logs and metrics.\n\n**Enhances safety** by gating infrastructure changes on preview plans and approval gates.\n\nThe result is a production-grade AWS DevOps system that you didn’t have to assemble from scratch and is fully customizable as your needs change.\n\n> I love Ravion's agent-friendly structure for getting great defaults on AWS while still being able to customize deployments just the way we need. And their support has been invaluable.” -Edgars Jekabsons, Engineering Manager at Ember\n\n**HOW YOU CAN USE IT**\n\n* **Start fresh:** use our production-ready module library to deploy any type of service in a single prompt\n* **Plug and play your existing Terraform:** start with Ravion as an Terraform Cloud or Spacelift alternative, then package existing stacks into governed modules over time.\n* **Have existing AWS infra from click-ops?** Import them into Ravion standard modules so they become tracked by infrastructure-as-code and exposed to agents through Ravion.\n\n**KEY FEATURES**\n\n* Deploy web servers, workers, static sites, Lambda function databases, caches, file storage, and more\n* Standard modules for ECS, EC2, VPC, load balancers, RDS, Aurora, CloudFront, Lambda, S3, Route53, IAM, ElastiCache, and more\n* GitHub action style pipelines that run in your AWS for builds and other CI\n\nLearn more and get started for free at [Ravion.com](https://www.ravion.com?ref=yc)\n\n---",
        "url": "https://www.ycombinator.com/launches/Tmi-ravion-the-easiest-way-to-use-aws-for-agents-and-humans",
        "votes": 8,
        "created_at": "2026-09-10T01:16:17.661Z"
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      {
        "id": 76674,
        "title": "Flightcontrol - Developer-first AWS infrastructure",
        "tagline": "Use the best AWS services without tedious infra work",
        "body": "AWS has been too intimidating and tedious, causing teams to settle for a simplistic black-box PaaS or hiring DevOps too early. But no more! 🙅🏼\n\nOur **Developer-first AWS infrastructure** enables you to use the best AWS services without manual infra work. In fact, you might even call it delightful 😍\n\n![](https://www.flightcontrol.dev/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F98758%2F1699399752-browser-min.png%3Ffit%3Dcrop%26q%3D90&w=3840&q=75)\n\n**Simple setup**\n\n1️⃣ Connect your GitHub & AWS accounts in 1-click\\\n2️⃣ Zero config or customize to your heart’s content\\\n3️⃣ Relax while we deploy to your AWS\\\n4️⃣ Never worry about hitting the technical or cost limits of black box PaaS\n\n**Deploy every language, globally**\n\n✅ Servers, background workers, static sites, databases, redis. Coming: cron, Lambda and more\\\n✅ Every language and framework\\\n✅ Use all 32 AWS regions. Multi-region deploys are in early access\n\n**A few years ago** I was building an app that should have been using native AWS services. I tried, but it was too hard. So I ended up on Vercel with a Frankenstein collection of third-party services. It worked ok until I accidentally racked up a $6,000 bill 😬 That was my fault, but the rickety setup of callbacks and lambda chains made it much easier to fail.\n\nSo I thought, why can’t I have a great developer experience on my own AWS account?! Then I met [**@Mina Abadir**](https://www.linkedin.com/in/mabadir/) who has been using AWS since S3 & EC2 were all they had. He had the same vision of a better cloud, so we joined forces and the rest is history!\n\n**You can now easily deploy and manage AWS services without having to use AWS console or CLI.** The all-new dashboard has runtime logs, metrics, and even live AWS cost reporting.\n\n![](https://bookface.ycombinator.com/media/?type=post&id=76674&key=user_uploads/280296/fb138ef1-3f3a-476b-8b35-ed751e7a35cd)\n\n2\\.0 barely scratches our vision. But it's now a mature product we're really proud of!\n\nCompanies of all sizes from startups to Series H are now using us to deploy thousands of times each month.\n\n**We’re known for providing an exceptional level of support.** So whether you are just starting or have a massive setup to migrate, we’re available to help and make sure everything goes smoothly.\n\n**If you are even a little bit frustrated** with your current infra setup, I invite you to take Flightcontrol for a test flight!\n\nStart a free trial: [**www.Flightcontrol.dev**](https://www.Flightcontrol.dev?ref=launchyc)",
        "url": "https://www.ycombinator.com/launches/Jwg-flightcontrol-developer-first-aws-infrastructure",
        "votes": 16,
        "created_at": "2023-12-05T16:23:03.821Z"
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    "one_liner": "Wingback provides tools and APIs for SaaS pricing and packaging.",
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        "full_name": "Torben Friehe",
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        "title": "Has Y Combinator’s new deal changed the early-stage investing game?",
        "url": "https://techcrunch.com/2022/01/13/has-y-combinators-new-deal-changed-the-early-stage-investing-game/",
        "date": "Jan 13, 2022"
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        "title": "How to hire without resumes from social media",
        "url": "https://www.fastcompany.com/90712236/we-skipped-resumes-and-sourced-candidates-directly-from-online-communities-like-discord",
        "date": "Jan 13, 2022"
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      {
        "title": "Y Combinator-Backed Wingback Raised $1.9 Million Using This Pitch Deck",
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        "full_name": "Bryce Cai",
        "title": "Founder",
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    "website": "https://www.cerebrium.ai/",
    "all_locations": "New York City, NY, USA",
    "long_description": "Cerebrium is a serverless infrastructure platform for AI applications. We make it easier for companies to build and deploy AI based applications. We offer Serverless GPU's with low cold start times, over 12 varieties of GPU chips, allow you to run large scale batch jobs, run realtime voice applications and much more. We are used by the teams at Tavus, CivitAI, Twilio and many more. \r\n\r\nCustomers typically experience 40% in cost savings when compared to using traditional cloud providers and can scale models to more than 10K requests per minute with minimal engineering overhead. ",
    "one_liner": "Serverless Infrastructure Platform for AI",
    "team_size": 4,
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        "full_name": "Michael Louis",
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        "title": "Cerebrium blog | Cerebrium Raises $8.5M led by Gradient to Scale the Leading High-Performance Serverless AI Platform",
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        "date": "Jul 08, 2025"
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      {
        "id": 69873,
        "title": "Cerebrium: An AWS Sagemaker alternative",
        "tagline": "A framework that makes it easier to train, deploy and monitor ML models across serverless GPU's",
        "body": "# TL;DR\n\n[Cerebrium](https://www.cerebrium.ai/) is a machine learning framework that makes it easy to train, deploy and monitor ML models. You can:\n\n* [⚙️](https://emojipedia.org/gear/)**Train** LLM’s such as FlanT5, GPT-NEOx-20b etc with a few lines of code (beta)\n* [🚀](https://emojipedia.org/rocket/)**Deploy** LLM’s or even custom models using just one line of code to serverless CPU’s/GPU’s.\n* [📈](https://emojipedia.org/chart-increasing/)**Monitor** your ML models with our built in integrations with Arize and Censius.\n\nHi Everyone! We’re Michael and Jonathan and we are so excited to introduce you to Cerebrium!\n\n# [❌](https://emojiguide.com/symbols/cross-mark/) **The Problem**\n\nImplementing machine learning based applications in a business remains challenging. You have multiple infrastructure concerns, such as expensive GPU’s, scaling issues, fragmented tooling while additionally also struggle to find machine learning talent and keep up with the latest research and techniques. These barriers to entry prohibit smaller companies from implementing machine learning into their business sooner.\n\n# [🚀](https://emojipedia.org/rocket/) **The Solution**\n\nCerebrium helps teams easily train, deploy and monitor machine learning models in production. Using our framework, you can train and deploy models (custom built or LLM’s) to serverless CPU’s/GPU’s using one line of code. You are only charged for inference time and we automatically handle the scaling and the versioning of models.\n\nWhen it comes to training and/or inference, we use the latest research techniques to reduce training and inference times by 20-40%. You just focus on adding value to your use case and we will take care of the rest!\n\n# [⚙️](https://emojipedia.org/gear/) **How it works**\n\n1. Create a free account [here](https://dashboard.cerebrium.ai). You get $10 in free serverless GPU credits.\n2. Install our Python library: **_pip install cerebrium_**\n3. Write one line of code to deploy your custom model or deploy a pre-built template\n4. Get an endpoint that automatically scales based on traffic\n5. Check out our [documentation](https://docs.cerebrium.ai) to see a greater list of examples and features.\n\n![Demo](https://cerebrium-assets.s3.eu-west-1.amazonaws.com/Cerebrium-Demo-1-March-2023.webp)\n\n### [🤔](https://emojipedia.org/thinking-face/) **Why we Built Cerebrium**\n\nAt our previous business, machine learning made a huge impact across various teams but it took months of building, testing and experimenting to get it there. We wanted to make the same sort of impact accessible to companies who don’t have the resources or know-how - and of course make it available in a fraction of the time\n\n### [🙏](https://emojidictionary.emojifoundation.com/folded_hands) **Our Asks**\n\n* Try out our framework! It takes the average user less than 4 minutes to get a model up and running (yes even if its custom) and give us your feedback - its free.\n* Share our framework with people you know who are building ML based applications or are struggling to fine-tune/deploy their ML models.\n* Join our [Slack](https://join.slack.com/t/cerebriumworkspace/shared_invite/zt-1lvbha401-lPsnCWYnVlGymGKS2E5fvA) and [Discord](https://discord.gg/ATj6USmeE2) communities\n* Upvote and share this post!",
        "url": "https://www.ycombinator.com/launches/IAz-cerebrium-an-aws-sagemaker-alternative",
        "votes": 21,
        "created_at": "2023-03-02T17:32:30.939Z"
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    "name": "LanceDB",
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        "body": "**Hey all!**\n\n**We’re [Eli](https://www.linkedin.com/in/mernit/) and [Luke](https://www.linkedin.com/in/luke-lombardi-2165968b/) – co-founders of [Beam](https://beam.cloud).** \n\n**TL;DR**\n\n[Beam](https://beam.cloud) is an open source alternative to Modal for running AI apps. Coca Cola, Magellan AI, Shippabo, and Stratum use Beam for serverless inference, sandboxes, and background jobs. We're 100% open source and self-hostable.\n\nhttps://www.youtube.com/watch?v=Itu_jCnyfJc\n\n**The Problem**\n\nIf you're building an AI product, you need a fast serverless platform to run your code, whether that's GPU inference, background jobs, or sandboxes for agents. \n\nExisting cloud platforms either lock you in (Modal) or aren't optimized for serverless AI workloads (Lambda, SageMaker, Kubernetes). Various sandbox providers exist (e2b, Daytona) but don’t support a full range of AI compute workloads.\n\n**Our Solution** \n\nBeam is your all-in-one serverless cloud for AI applications. You can run inference endpoints, background jobs, sandboxes, and more – all with a simple decorator in your Python or Typescript code. There’s no YAML and no configuration required. \n\nhttps://youtu.be/UxQ9O8pVVKk\n\nThis is possible thanks to a custom container runtime and distributed storage layer we built to run large container images with low cold start. We provide the security and isolation with gVisor, and build and launch new container images lightning fast.\n\nSome of the features we support include:\n\n* Fast image builds\n* GPU support\n* Memory/file system snapshotting\n* GPU checkpoint restore (stop and resume sandboxes while preserving running processes)\n* File system operations\n* Run long sessions that last over 24h\n* Open source and self-hostable\n\n **The Team**\n\nEli and Luke were college roommates and have been building products together pretty much since then.\n\nBefore starting Beam, Eli and Luke frequently attended hackathons and built a serverless framework to quickly ship web apps onto the cloud. When they started tinkering with AI, they realized that no serverless framework existed to make it easy to deploy AI models on the cloud. They decided to build it.\n\n![uploaded image](/media/?type=post&id=95756&key=user_uploads/313369/0bd4a323-556b-441a-ac00-7c61bf0c69c7)\n\n**Our Ask**\n\n* Try Beam here: [beam.cloud](http://beam.cloud)\n* Star us on Github: https://github.com/beam-cloud/beta9\n\nIf this post resonated with you, we’d love to connect: [founders@beam.cloud](mailto:founders@beam.cloud)",
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        "title": "Hydra: Serverless Realtime Analytics on Postgres",
        "tagline": "Unlock realtime analytics on live data in seconds",
        "body": "![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/fc2a33f3-1607-4213-bee9-7fb9693b0f5f)\n\n**Hydra is Serverless Realtime Analytics on Postgres.**\n\nBy separating compute from storage, Hydra enables compute-isolated analytics and bottomless storage. It is designed for low latency applications built on time series and event data.\n\n# [**Try Now! (free)**](https://start.hydra.so/get-started)\n\n<https://youtu.be/mib8ehnMmC8>\n\n# **Enable in seconds**\n\n![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/42f17c01-89e7-47ae-ba01-f76bc23c864e)\n\nSet up is simple. To unlock serverless realtime analytics on Postgres, run:\n\n```\npip install hydra-cli\n```\n\nFor setup\n\n```\nhydra\n```\n\n1. fetch your hydra token, paste in to enable Hydra\n2. create analytics schema / tables and insert data ([**quickstart docs**](https://docs.hydra.so/intro/quickstart))\n3. _voila_ - run queries, get insights in milliseconds\n\n# **Problem**\n\nFor decades, there’ve been two core problems with analytics on Postgres:\n\n**Slow** - aggregates and complex queries can take minutes to return results from large data sets.\n\n**Resource Contention** - Expensive analytics queries hog Postgres’ RAM / CPU resources and impair transactional performance. In other words, the entire app slows down, which makes users unhappy.\n\n# **Solution**\n\n### **Fast**\n\nHydra returns analytics queries 400X faster than standard Postgres. Hydra uses duckdb to perform isolated serverless processing on these tables in Postgres. In fact, Hydra is faster than most specialized analytics databases.\n\nIf you’re running AWS RDS, AWS Aurora, Heroku Postgres, Supabase, Fly Postgres, Render Postgres, GCP Cloud SQL, etc.. you can improve expensive analytics queries by 400X by using Hydra.\n\n![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/3fb7dc1e-622a-4303-ba7a-f729f18ab019)\n\nfun fact: the Snowflake instance (128x4XL) in the benchmark is $100k / month.\n\n![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/5db5d184-2a69-4811-b14e-5ad433f4038c)\n\nThese are results from clickbench which represents a typical workload in the following areas: clickstream and traffic analysis, web analytics, machine-generated data, structured logs, and events data. The table consists of exactly 99 997 497 records — rather small by modern standards but allows tests to be performed in a reasonable time.\n\n### **Isolated, serverless processing**\n\nUsing Hydra there is no impact on Postgres’ RAM / CPU resources when reading from or writing to an analytics tables.\n\nAs a result, here are more cool things Hydra can do: zero-copy clones for scaling read replicas, automatic caching, write isolation, bottomless storage with high data compression, and more.\n\n# **How it works**\n\n![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/8d8999aa-a370-4f0f-9d99-badfe4621c97)\n\nHydra integrates duckdb execution and features with Postgres using [**pg_duckdb**](https://github.com/duckdb/pg_duckdb), an open-source project we co-developed with the creators of DuckDB.\n\n**FAQ:**\n\nyes, you can join between an analytic table and a standard row-based table.\n\nyes, you can write (insert & update) to analytics tables.\n\nyes, Hydra is identical to using standard postgres.\n\nyes, data inserted into an analytics table is automatically converted into analytics-optimized columnar format.\n\ntell Hydra you are creating an analytics table by including the ‘using duckdb’ keyword - that’s it.\n\n# **Hydra is best for**\n\nHydra is both a row and columnstore, so you can use it for standard Postgres work, like multitenant apps, as well as analytical work, like monthly reporting. Here are several cool Hydra use cases that blend both transactional & analytics — more in our [**use case docs**](https://docs.hydra.so/guides/usecase).\n\n* time series, logs, events, traces\n* monitoring & telemetry\n* financial services\n* observability & metrics\n* cybersecurity & fraud detection\n* web & product analytics\n* iot\n* realtime ml / ai\n\n# **ok, but why use postgres instead of … \\[insert favorite tech\\]?**\n\nIt’s true, there are many specialized analytics databases. reviewing the benchmark above, the majority of analytics databases are actually _slower_ than Hydra. Regardless, isolated analytics databases produce their own set of challenges and costs.\n\n### **data pipelines (etl)**\n\nTraditionally, an analytics database is isolated, and in most cases, engineers must set up data pipelines for data movement and transformation between Postgres, S3, and the analytics db. Pipelines aren’t cheap. Also, there’s pipeline latency bottlenecking how stale the analytics are. And when pipelines break — because they do — you’re stuck with downtime and wrong results.\n\nHydra side-steps the latency and costs of data pipelines entirely with full support for inserts and updates on columnar files in analytics tables.\n\n### **overweight design**\n\nMany use cases don’t justify a heavy setup. From our time working at Heroku, we saw many transactional apps that only need a couple high level aggregates and a few complex analytical queries to return quickly. Hardly OLAP (online analytical processing) and not really HTAP (hybrid transactional and analytical processing) - just apps in need of a speed boost.\n\nThe most heavyweight option, data warehouses like BigQuery, are good at “big queries”, but not great at smaller, rapid analytics queries that’re more common with applications, rather than monthly reporting.\n\nThese traditional approaches are too heavy, can be brittle, and introduce extra costs and latency your startup doesn’t need.\n\n# **do we have a hosted cloud offering?**\n\nYes, and [it’s awesome](https://start.hydra.so/get-started).\n\n# [**Try now!**](https://start.hydra.so/get-started)\n\n## **useful links**\n\n[**website**](https://www.hydra.so/), [**docs**](https://docs.hydra.so/overview), [**local dev guide**](https://docs.hydra.so/guides/local_development), [**analytics guide**](https://docs.hydra.so/guides/analytics), [**architecture**](https://docs.hydra.so/intro/architecture), [**changelog**](https://docs.hydra.so/changelog/changelog)\n\nIf you’ve made it this far here’s a pic of me (joe) and my cofounder (jd) in hood river, oregon. Building Hydra has been a wild ride, but we found a way to have fun.\n\n![uploaded image](/media/?type=post&id=88443&key=user_uploads/54309/69da46c2-bfc2-48c6-8ed3-2364e71c34fd)\n\n",
        "url": "https://www.ycombinator.com/launches/N0V-hydra-serverless-realtime-analytics-on-postgres",
        "votes": 8,
        "created_at": "2025-03-11T12:27:09.916Z"
      }
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    "name": "Nullstone",
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    "website": "https://nullstone.io",
    "all_locations": "Atlanta, GA, USA; Remote",
    "long_description": "Nullstone is a suite of tools delivered as a single product that enables developer self-service while accommodating flexibility and control that platform engineers need to maintain secure, compliant, performant, and reliable infrastructure.\r\n\r\n\r\nNullstone provides a simple developer experience that guides developers through the architecture and release of cloud apps. By default, infrastructure launched through Nullstone is compliance-certified and vetted for security best practices. The software delivery process can be customized per app through external CI/CD tools or IaC tools like Terraform to meet the specific needs of customers or to optimize developer workflow.\r\n\r\n\r\nNullstone comes with many quality-of-life features that are hard to build with an in-house DevOps/Platform team: Auto-Build/Deploy, Preview Environments, GitOps, and Automatic Secrets Management.\r\n",
    "one_liner": "An easier way to deploy and manage cloud apps",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1642179452,
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        "full_name": "Brad Sickles",
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        "full_name": "Scott Sickles",
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        "founder_bio": "Scott is the co-founder and CTO of Nullstone. Early in his career as a developer, he fell in love with the ability of software to empower people. As CTO of IdentityStream, he helped banks across Asia and Africa serve people without forms of ID through the use of biometrics. As CTO of Listen360, he led successful cloud migrations, digital transformations, and help lead the company to 5x growth and a successful exit. He earned a degree in Computer Science from Georgia Tech.",
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      {
        "id": 73668,
        "title": "Nullstone: ☁️ Better dev experience for cloud providers",
        "tagline": "We improve developer productivity with a better dev experience, modern automation workflows, and composable tooling.",
        "body": "### tl;dr\n\nNullstone improves developer team productivity with quality-of-life features that are difficult and costly to build with an in-house DevOps/Platform team: Auto-Build/Deploy, Preview Environments, GitOps, and Automatic Secrets Management.\n\n### 🌩️ Problem\n\nLaunching and managing cloud apps is tedious and distracting for product teams. On average, building, maintaining, and orchestrating cloud infrastructure accounts for \\~25% of a software team’s budget. The current solutions are inflexible, require significant expertise, and/or are costly:\n\n* Platform-as-a-service (PaaS) are black boxes and don’t provide flexibility and control to achieve security, compliance, and reliability.\n* Hiring DevOps Engineers to build custom infrastructure and automation is costly and distracting from a team’s primary purpose.\n* Hiring Platform Engineers to build a custom, internal developer platform requires an enormous upfront build with a long-term maintenance cost of the platform that rarely provides an intuitive experience to developers.\n\nThe most precious resource for a software product are developers focused on building a fantastic product — a great developer experience is the [primary driver for productivity](https://queue.acm.org/detail.cfm?id=3595878).\n\n### ☀️ Solution\n\nNullstone is a suite of tools delivered as a single product that enables developer self-service while accommodating the flexibility and control that platform engineers need to maintain secure, compliant, performant, and reliable infrastructure.\n\n![uploaded image](/media/?type=post&id=73668&key=user_uploads/850501/1fe8a18b-3ecb-48ab-8023-983d9f2f6826)\n\nFor developers, Nullstone provides an easy-to-use developer experience to deploy and observe apps on major cloud providers using containers, serverless, static sites, or servers.\n\n![uploaded image](/media/?type=post&id=73668&key=user_uploads/850501/99e0b94f-0672-4777-aa43-484f94961d06)\n\nFor platform engineers and architects, Nullstone provides a simple mechanism to provide modern delivery workflows and infrastructure building blocks to developers. Using familiar tools like Terraform ensures company policies are followed without needing to build a developer experience.\n\n![uploaded image](https://bookface.ycombinator.com/media/?type=post&id=73668&key=user_uploads/850501/53d30583-a275-4694-bd33-25b505012695)\n\n### ⚙️ How it works\n\n* Developers pick from an extensive, curated catalog of open-source, compliance-certified infrastructure modules or from company-built infrastructure modules.\n* Developers choose to auto-build/deploy or use custom CI.\n* Developers launch preview (ephemeral) environments from a pull request, through the UI, or through the API.\n* Developers can use ClickOps or GitOps to control infrastructure.\n* Developers can observe and monitor applications without accessing a cloud console.\n* Platform Engineers build reusable infrastructure components for developers to self-serve through an easy-to-use UI.\n* Platform teams customize networks, clusters, and datastores while providing the right level of access for developer self-service.\n* Platform teams connect third-party log and metrics providers for developers.\n* Platform Engineers perform automated rollouts of infra upgrades and bug fixes.\n\nThe flexibility allows Nullstone to support monoliths, monorepos, microservices, and everything in between.\n\n### 🙏 Asks\n\n* [Launch an app](https://app.nullstone.io/) for free!\n* Want to learn more, [check us out](https://www.nullstone.io/) and [book a demo](https://www.nullstone.io/book-demo)!",
        "url": "https://www.ycombinator.com/launches/JAC-nullstone-better-dev-experience-for-cloud-providers",
        "votes": 31,
        "created_at": "2023-08-01T20:05:56.367Z"
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    "id": 25950,
    "name": "Decent",
    "slug": "decent",
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    "website": "https://www.decent.xyz/",
    "all_locations": "New York City, NY, USA",
    "long_description": "Any asset, every chain, in an instant.",
    "one_liner": "Cross-chain routing + trading APIs. One-click transactions with any…",
    "team_size": 6,
    "industry": "B2B",
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    "year_founded": 2021,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/decentxyz",
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        "full_name": "Charlie Durbin",
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        "founder_bio": "Co-Founder & CEO Decent (acq. MoonPay)\nPrinceton '19",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/charlie-durbin-b88544131/",
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        "full_name": "Alexander Carlson",
        "title": "Founder",
        "founder_bio": "Music Producer, Singer (Forester), Econ Head, Crypto Enthusiast ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/alexander-carlson-ab322b167",
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        "founder_bio": "Will Collier is a co-founder of Decent. He graduated from Brown University in 2020 with a double major in Economics and Architecture. Following graduation, Will co-founded The Farmlink Project, a food rescue organization that won a Congressional Medal of Honor Service Award in 2021.  Previously, Will worked as a consultant at Accenture, covering the firm's Media & Entertainment and Software & Platforms clients.",
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        "full_name": "Will Kantaros",
        "title": "Founder",
        "founder_bio": "Building decent.xyz, math cs econ @ Brown, previously SWE @ mongo.db, blockchain + poker enthusiast",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/willkantaros/",
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        "title": "Decent Raises $3.5M to Allow the Music Industry to Embrace Web3 Seamlessly – AlleyWatch",
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        "date": "Jan 13, 2023"
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      {
        "title": "The Block: NFT infrastructure protocol Decent raises $3.5 million from Y Combinator and others: Exclusive",
        "url": "https://www.theblock.co/post/195485/nft-infrastructure-protocol-decent-raises-3-5-million-from-y-combinator-and-others-exclusive",
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    "id": 25969,
    "name": "Shaped",
    "slug": "shaped",
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    "long_description": "Connect your data. Train your models. Query text, user or session context and retrieve relevant results in milliseconds. Explore our case studies to see how we’ve helped leading brands drive significant engagement and revenue: https://www.shaped.ai/case-study. ",
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    "team_size": 25,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1646119340,
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        "full_name": "Tullie Murrell",
        "title": "Founder/CEO",
        "founder_bio": "CEO of Shaped. \nPreviously an AI researcher at Facebook AI Research (FAIR).\nCreator of PyTorchVideo & original core contributor to PyTorch Lightning.\nPassionate about anything AI, machine-learning and statistics.",
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        "title": "Shaped Raises Series A, Launches Self-Serve Platform to Power the Future of AI-Driven Recommendations & Search | Shaped Blog",
        "url": "https://www.shaped.ai/blog/shaped-raises-series-a",
        "date": "Jul 17, 2024"
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      {
        "id": 97225,
        "title": "ShapedQL: The SQL engine for search, feeds, and AI agents",
        "tagline": "Connect your data. Train your models. Query text, user or session context and retrieve relevant results in milliseconds.",
        "body": "Hi everyone 👋\\\n\\\nI'm Tullie, the founder of [Shaped](https://www.shaped.ai/).\\\n\\\n**TL;DR**\\\n\\\nToday we’re launching ShapedQL, a new language purpose built for agents, feeds and search. We built ShapedQL because we realized that while retrieval has become easier (thanks to Vector DBs), ranking and relevance are still incredibly hard.\\\n\\\nLaunch video: <https://www.youtube.com/watch?v=Owj_uSUPNaU>\\\n\\\n**Problem ❌**\\\n\\\nMost engineering teams we talk to are stuck maintaining a \"Frankenstein\" stack. To build a \"For You\" feed or give an AI Agent personalized memory, they have to glue together a vector database, a feature store (like Redis), a reranking service, and thousands of lines of Python spaghetti code.\\\n\\\n**Solution ✅**\\\n\\\nWe built ShapedQL to turn that \"house of cards\" into a single interface.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/eed67b43-7c3b-4b0f-819a-5bbd31db2732)\n\nShapedQL is a domain-specific SQL dialect that compiles down to a high-performance, multi-stage ranking pipeline. With a single query, you can define the four stages of modern relevance:\\\n**1. Retrieve:** Fetch candidates from multiple sources (Hybrid Search, Collaborative Filtering, Trending).\\\n**2. Filter:** Apply hard constraints (e.g., \"in stock\" or \"within 50 miles\").\\\n**3. Score:** Rank results using real-time ML models (optimizing for clicks, purchases, or watch time).\\\n**4. Reorder:** Enforce diversity so your users (or Agents) don't see the same 5 items repeatedly.\n\nWe're seeing teams reduce 2,000+ lines of maintenance code down to \\~30 lines of ShapedQL, while shipping features like \"Cart Upsell\" or \"Conversational Recommendations\" in days instead of months.\n\nIf you're not a fan of SQL we also have Python or Typescript SDKs, perfect for a real-time production integration.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/d3cc12cc-3c74-448b-adf0-ae65d9359b79)\n\n**Ask 💬**\\\n\\\n**1.** We have a Product Hunt launch at this link: <https://www.producthunt.com/products/shaped>. Any support or and upvote would be greatly appreciated!\\\n\\\n**2.** Please try out our interactive demo at [playground.shaped.ai](https://playground.shaped.ai), we’d love to hear any feedback on the new language. Were you able to easily make a query? Do the docs make sense? What is it missing?\\\n\\\n**3.** If the problems above resonates and you’re struggling to get true relevance from your vector store or current retrieval system, please get in touch! Book on this calendar here: <https://www.shaped.ai/contact> and mention you’re from YC. I’ll make sure to jump on the call to help you out myself.\n\n![uploaded image](/media/?type=post&id=97225&key=user_uploads/812951/bb70b2b7-780a-4da6-a13f-511015e09776)\n\n",
        "url": "https://www.ycombinator.com/launches/PI9-shapedql-the-sql-engine-for-search-feeds-and-ai-agents",
        "votes": 15,
        "created_at": "2026-01-28T00:44:48.253Z"
      },
      {
        "id": 88281,
        "title": "Shaped — Value Modeling for Search and Recommendation Systems",
        "tagline": "A control panel for your business objectives",
        "body": "# **TL;DR**\n\nWe’ve built Shaped Value Modeling, a framework that enables real-time optimization of recommendation and search ranking models for multiple objectives. It allows teams to blend different objectives (e.g. relevance, conversions, diversity) and dynamically run AB tests that drive business outcomes (with built in analytics tooling). Our community has seen awesome results, so we’re excited to bring this framework to the masses!\n\nWatch our demo [here](https://youtu.be/gYLmtMR-N4I).\n\n**Key Features:**\n\n* **Multi-objective optimization:** Combine multiple models and signals (e.g., click prediction, purchase likelihood, content quality) into a single, customizable scoring function.\n* **Dynamic ranking logic:** Define and adjust ranking objectives as your business needs change using a simple python DSL.\n* **Real-time API:** Modify ranking scores on the fly with simple API calls, enabling rapid experimentation and A/B testing.\n* **Easy to integrate:** Connect and deploy rapidly with direct integration to your existing data sources.\n* **Explainability:** Gain insights into why certain items rank higher through interpretable scoring logic, and a built in analytics platform out of the box.\n\n![uploaded image](/media/?type=post&id=88281&key=user_uploads/857360/543b423e-521a-491d-808b-0bd914f06c23)\n\n**Why This Matters:**\n\n* Rapid iteration: Experiment with new ranking strategies and adapt to changing business needs without lengthy retraining cycles.\n* Improved control: Fine-tune your ranking algorithms with precision and transparency.\n* Better alignment: Optimize for multiple KPIs simultaneously, ensuring your recommendations drive both engagement and business value.\n\n**How it Works:**\n\n1.Train separate models for different objectives (e.g., LightGBM for click prediction, BERT4Rec for purchase likelihood).\n\n2\\. Define a \"value model\" that combines these models using mathematical expressions and custom logic. Let's say we have a lightgbm click-through-rate model called ctr_model and a transformerd based next purchase model called purchase_model, you can create an ensembled value model as follows:\n\n```\nvalue_model = 0.5 * ctr_model + 0.5 * purchase_model + log(item.quality_score)\n```\n\n3\\. Dynamically adjust the value_model via API calls to optimize for different goals or user segments:\n\n```\n{\n  \"user_id\": \"83NSLX\",\n  \"config\": {\n    \"value_model\": \"0.3 * ctr_model + 0.7 * purchase_model\"\n  }\n}\n```\n\n\\\nHow our customers are using value modeling:\n\n* **Marketplaces:** Dynamically mix personalized scores with a geo-location penalizer to connect users with nearby items.\n* **E-commerce:** Balance purchase intent, engagement, and product quality.\n* **Content platforms:** Implement time-decay, boost high-quality content, and personalize recommendations.\n* **Social media:** Dynamically switch between personalized and trending models.\n\n# **Our ask**\n\nIf you know decision-makers in any of these types of organisations, we’d be super grateful for an introduction at [daniel@shaped.ai](mailto:daniel@shaped.ai)\n\n1. Marketplaces\n2. E-commerce\n3. Content platforms\n4. Social Media\n\nFor more information see our website [here](https://www.shaped.ai/value-modelling) and developer docs [here](https://docs.shaped.ai/docs/model_creation_guides/value-modeling)\n\n[Get a demo](https://meetings.hubspot.com/matt2900/meet-with-shaped-?uuid=4076ed73-5725-4ee7-ac01-2e1869237a18) if you’re interested in testing value modeling!",
        "url": "https://www.ycombinator.com/launches/Mxt-shaped-value-modeling-for-search-and-recommendation-systems",
        "votes": 8,
        "created_at": "2025-03-05T15:46:26.414Z"
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    "all_locations": "Austin, TX, USA; Asheville, NC, USA",
    "long_description": "Netmaker is an open source virtual networking platform based on WireGuard. With Netmaker, you can bridge, clouds, the edge, IoT devices, and Kubernetes clusters. Netmaker provides the fastest possible virtual networks, with an average speed 10x faster than standard alternatives. Think of Netmaker like your VPC for distributed systems.",
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        "date": "Dec 02, 2022"
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        "title": "Probably Nothing Labs - Databricks for Web3",
        "tagline": "Real-time Indexer-as-a-Service to help Web3 teams power data backends for their apps in days",
        "body": "**🤔 The Problem**\n\nIndexing public blockchains is a major pain!\n\nEngineers need to build resilient poll loops around chain specific validators that request, parse, and track raw blocks, transactions and event logs as they arrive. This work requires in-depth knowledge of specific protocols, smart contract ABIs, and stateful stream processing (not to mention handling chain re-org or tracking the mempool). All of this work just to build a backend for your Uniswap position tracking dashboard or that Instagram for NFTs app.\n\nNevermind having to build systems to monitor individual positions for collateral drops in DeFi lending protocols. \n\n**🤌 Our Solution**\n\nWe built **Gigabrain**, a best-in-class blockchain indexer with support for Ethereum and Solana. Using advanced stateful streaming techniques, Gigabrain is able to parse, index, and store transactions within seconds of blocks being confirmed. In addition, we’ve created a versioned registry of all smart contracts on Ethereum as well its L2s.\n\nWith Gigabrain you can —\n\n* Run **complex stateful aggregations** for **on-chain events** as soon as - or before - they show up on the blockchain.\n* Never have to worry about keeping track of contract specific ABIs with our **Smart Contract Registry**.\n* **Run live SQL** against the **Ethereum mempool** (we use a globally distributed set of nodes that we manage ourselves).\n* Run **large scale analytics** against our datalake as well as **power robust data backends** for your own Web3 apps.\n\n[🎬](https://emojipedia.org/clapper-board/) **The Demo**\n\nCheck out our [short demo on Loom](https://www.loom.com/share/d4784d7e2a9f4503bd2beab5e070e840).\n\n**📞 Our Ask**\n\nIf you or anyone else in your network -\n\n* Need to work with on-chain data at scale or in real-time\n* Parse a wide array of smart contract specific on-chain events\n* Have the need to build sophisticated on-chain monitoring solutions for DeFi protocols or NFT Marketplaces\n\nPlease get in touch with us!\n\n**Amey**: [amey@probablynothinglabs.xyz](mailto:amey@probablynothinglabs.xyz) , **Telegram**: @ambrood, **Discord**: ambrood#9366\n\n**Matt**: [matt@probablynothinglabs.xyz](mailto:matt@probablynothinglabs.xyz), **Telegram**: @mgreenn, **Discord**: mattt#2076",
        "url": "https://www.ycombinator.com/launches/Gtx-probably-nothing-labs-databricks-for-web3",
        "votes": 18,
        "created_at": "2022-07-28T14:40:55.685Z"
      }
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    "id": 26666,
    "name": "Airtrain AI",
    "slug": "airtrain-ai",
    "former_names": [
      "Sematic AI",
      "Sematic"
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    "website": "https://airtrain.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Airtrain AI is a no-code data platform for Large Language Models.\r\n\r\nProprietary AI models such as GPT-4 are very powerful but also very costly, slow, unreliable, and unsecured.\r\n\r\nAs businesses look to scale their AI prototypes into production-grade products, they struggle with large AI bills, slow APIs and large failure rates.\r\nOn the other hand, smaller language models have been proven to be able to perform on-part with large ones with fine-tuned on high-quality datasets.\r\n\r\nAirtrain AI lets AI practitioners explore alternatives to proprietary models, build up training datasets, evaluate, fine-tune, and serve a large selection of open-source LLMs.",
    "one_liner": "No-code data curation for LLM fine-tuning and evaluation.",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1657048120,
    "tags": [
      "AIOps",
      "Developer Tools",
      "SaaS",
      "Cloud Computing",
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    "status": "Inactive",
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    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/airtrain-ai",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/airtrain-ai.json",
    "year_founded": 2022,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/airtrain-ai/",
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      "name": "Nicolas Dessaigne",
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    "founders": [
      {
        "user_id": 1014902,
        "full_name": "Emmanuel Turlay",
        "title": "Founder",
        "founder_bio": "Hi, I am Emmanuel.\n\nI spent my foundational years in academia (particle physics research at CERN) then branched out into tech. I built the order and payment infrastructure at Instacart, and lead ML platform at Cruise.\n\nI am the CEO and co-founder of Sematic (S22).\n\nReach out and say hello.",
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        "linkedin_url": "https://www.linkedin.com/in/emmanuelturlay/",
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      {
        "title": "Sematic Blog – Sematic Raises $3M to Build an Open-Source Continuous Machine Learning Platform",
        "url": "https://www.sematic.dev/blog/sematic-raises-3m-to-build-an-open-source-continuous-machine-learning-platform",
        "date": "Nov 17, 2022"
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    "launches": [
      {
        "id": 64701,
        "title": "Sematic – Prototype-to-production ML in days not weeks",
        "tagline": "Build and productionize arbitrary end-to-end ML/DS pipelines with only Python in a few hours.",
        "body": "**TL;DR –** [**Sematic**](https://sematic.dev) **is an** [**open-source**](https://github.com/sematic-ai/sematic) **framework to build and run arbitrary end-to-end ML/DS pipelines developed by two ex-Cruisers.**\n\nWhen COVID hit and restaurant parklets popped up around San Francisco, the Cruise robotaxis needed to update their ML models in a matter of days.\n\nThis fast turnaround in data processing > model training > evaluation > simulation > metrics generation > deployment was enabled by talented ML Engineers using **state-of-the-art pipelining tools**.\n\n## Your problem\n\nIf you are an **ML/DS developer** 🧑‍💻, you likely spend a lot of time battling obscure infrastructure instead of leveraging your core competency: extracting insights and predictions from data.\n\nIf you are a **business leader** 🧑‍💼, you likely want more models to grow and optimize your business; and retrain them frequently to adapt to ever-changing world conditions (hello COVID!).\n\nExisting solutions (e.g. Kubeflow, Argo, Airflow, Prefect) are still too low-level and not usable by ML/DS developers.\n\nTools such as Ruby on Rails and Heroku enabled generations of web developers to build prototypes into unicorns. **Where is that experience for ML/DS development?**\n\n## Our solution\n\n[Sematic Beta Launch Demo](https://www.youtube.com/watch?v=G0Hj0T-bbiY)\n\n[Sematic](https://sematic.dev/) is **a lightweight** [**open-source**](https://github.com/sematic-ai/sematic) **ML/DS pipeline development and execution framework based on learnings from working at Cruise**.\n\nWith easy-as-pie onboarding, simply **use native Python** to develop and run arbitrary end-to-end pipelines that track and version all your assets and artifacts (models, datasets, plots, metrics, etc.), and visualize them in a slick UI.\n\nRun locally or **leverage your cloud resources** (GPUs!!) seamlessly without infrastructure work.\n\n**Collaborate with your team** in Sematic to keep the conversation close to the data context.\n\n**Sematic aims to facilitate the journey from Notebook prototypes to automated production-grade pipelines.**\n\n## Our ask\n\n⭐ [Star our Github repo](https://github.com/sematic-ai/sematic)\n\nTry us out:\n\n```\n$ pip install sematic\n$ sematic start\n```\n\nJoin our [Discord](https://discord.gg/4KZJ6kYVax) **and give us your feedback to make the product more useful to you and your company**.\n\n## Early Bird offer\n\nWe offer a white-glove onboarding service and dedicated support to build out your end-to-end pipelines.\n\n[Try us out](https://sematic.dev/), reach out on [Discord](https://discord.gg/4KZJ6kYVax), or email us at [emmanuel@sematic.dev](mailto:emmanuel@sematic.dev).",
        "url": "https://www.ycombinator.com/launches/GpZ-sematic-prototype-to-production-ml-in-days-not-weeks",
        "votes": 27,
        "created_at": "2022-07-18T17:06:21.384Z"
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    "id": 26732,
    "name": "BricksAI",
    "slug": "bricksai",
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      "Bricks",
      "Bricks Cloud Technologies",
      "Bricks",
      "BricksLLM",
      "Bricks"
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "BricksAI is an open-source AI gateway that helps you manage how each of your services is calling different LLM providers.\r\n\r\nYou can create API keys with rate-limits, spend-limits, and expiration dates, then use them to make requests to different LLMs. With our SSO dashboard, you also get (1) role-based access control, and (2) fine-grained metrics (e.g. cost, number of requests, tokens) over each API key.",
    "one_liner": "Streamlining LLM application management for enterprises",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "city": "San Francisco",
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        "user_id": 991808,
        "full_name": "Spike Lu",
        "title": "Founder",
        "founder_bio": "Emory University - Class of 2019\nSenior Software Engineer at Unity Technologies - 2019 - 2022",
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        "user_id": 1109574,
        "full_name": "Donovan So",
        "title": "Founder",
        "founder_bio": "hi",
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      {
        "id": 76778,
        "title": "🧱 BricksAI Cloud - Simple cost control & monitoring for LLMs",
        "tagline": "We help engineers protect and track their GenAI expenses",
        "body": "**Tl;dr**: Are you finding it difficult to track your LLM costs per project/user? Do you want to set a custom rate/spend-limit for each project/user?\n\n**Create a **[**free account**](https://www.trybricks.ai/sign-in)** and try us out today 😄**\n\nHey everyone, this is [**@Spike Lu**](http://linkedin.com/in/spike-lu-38227b105) and [**@Donovan So**](https://www.linkedin.com/in/donovanso/)! We're excited to share that we've launched [BricksAI Cloud](https://www.trybricks.ai/).\n\n# **The Problem**\n\nA lot of enterprise features are still missing from today's LLM providers, particularly:\n\n1. there isn't a good way of controlling LLM spendings in a granular way (e.g. per user/project/env/feature etc.)\n2. there isn't a good way of monitoring LLMs in a granular way\n\nBut building an LLM wrapper in-house takes away valuable engineering time from shipping features that actually drive business value.\n\n# **The solution**\n\nBricksAI Cloud is an [open-core](https://github.com/bricks-cloud/BricksLLM) SaaS solution that protects and monitors your LLM usage. We're a proxy, so instead of using API keys from your providers, you'd create and use Bricks API keys that come with custom limits (e.g. expiry date, rate-limit, or spend-limit).\n\n[**Demo video**](https://www.loom.com/share/fe0553312e8a4378b3467c12f22e8714?sid=39bbbec1-da60-4054-899a-9ce991b068a9) ⭐\n\n![uploaded image](/media/?type=post&id=76778&key=user_uploads/1109574/8811f428-392d-4533-973d-516979be917d)\n\nWe currently have official support OpenAI and Anthropic, meaning you’ll be able to accurately track your token usage and cost without any configuration. We also support integrations with custom models.\n\nOver the past few months, we have streamlined LLM operations for a wide range of clients, including a late-stage sustainability tech startup, a major leadership training nonprofit, a top European travel agency, and multiple AI startups.\n\nNow, we're opening up our tool to the world with the launch of BricksAI Cloud. We'd love to see how it can transform your GenAI stack.\n\n# **The ask**\n\n1. Star us on [**Github**](https://github.com/bricks-cloud/BricksLLM) ⭐\n2. Create a [free account](https://www.trybricks.ai/sign-in) and try us out today. _Heads up: we require a 15-min account activation call, this helps us get to know your use case better. Don’t worry, it’s super quick and casual!_\n3. Questions? Feedback? Feature requests? [Book a call with us](https://calendly.com/spike-at-bricks/coffee-chat-with-spike) or email [founders@bricks-tech.com](mailto:founders@bricks-tech.com).",
        "url": "https://www.ycombinator.com/launches/JyM-bricksai-cloud-simple-cost-control-monitoring-for-llms",
        "votes": 9,
        "created_at": "2023-12-11T21:19:06.212Z"
      },
      {
        "id": 74841,
        "title": "BricksAI🔐: A simple OpenAI access manager for enterprises",
        "tagline": "BricksAI helps enterprises develop LLM apps more securely",
        "body": "**Tl;dr:** [BricksAI](https://www.trybricks.ai/) helps enterprises develop LLM apps more securely. We do this through an access manager that can set a spend limit, rate limit and expiration date on individual API keys. \n\n**The Problem: OpenAI does not provide enterprise level security features**\n\n> _“You wouldn’t believe how many organizations are building OpenAI wrappers.”_\n>\n> _\\- A machine learning director at a unicorn startup._\n\nOpenAI does not have enterprise-level security features. A lot of companies need to build enhanced security features on top of what OpenAI offers before using them.\n\n> “We simply assign each team an OpenAI key”\n>\n> \\- A senior engineer working at a public fintech company.\n\nDeveloping OpenAI applications often means that developers have to share OpenAI key credentials with each other to speed up development.\n\nThere are several risks and drawbacks associated with this approach:\n\n* **Increased likelihood of security breaches**: more than 50% of breaches happen due to shared secrets\n* **No cost visibility**: no way of knowing what constitutes a $20k OpenAI bill\n* **No access control**: no source of truth for OpenAI access\n* **Degraded production performance**: OpenAI has an organization-level rate limit. One team’s experimentation with OpenAI could impact the performance of production OpenAI applications if not properly rate limited.\n\nAzure OpenAI service helps fill some of the gaps but this product is simply not available to users of GCP and AWS for competitive reasons.\n\n**The Solution: A self-hosted OpenAI access manager**\n\n[BricksAI](https://www.trybricks.ai/) checks all requests between your applications and different LLMs. We ensure every call is authorized, and does not exceed any traffic and cost limits.\n\nYou can create a custom API key with a **rate-limit**, **spend-limit**, and **expiration date** either programmatically or through our UI:\n\n![](https://lh6.googleusercontent.com/2PBxx_RzNu9dsiySB2IXD4ySeziMayS-afN25SnUBofJEuRla9tBclCHbLg1OGdj_87mHcr3NSI4SMgTkEUtSRHIQr0yLgK1Ty0ZZWnvKSAi4tCEcktVTexY0RkVakz6krMl7mU2m2CO0tvGxJ3W_7g)\n\nThen use the API key to access an LLM like you normally would. When a key reaches its limit (e.g. has expired), our gateway blocks off your requests:\n\n![](https://lh3.googleusercontent.com/nJ4yb431KR5fhWm-5SHhrxEddPFtRWb5kUD4z7Gw2i3gnn3IO4_p7a2aeIqPQhLyW2l1XOx_0J-nv_rVmPv8KHR7adRa3uVd6bzgBbYPPmlfCOhjUoIZe9qIOqrEX3Kd4A8kOmcH9zqcC9yonuZich8)\n\nIn addition, our enterprise offering includes \n\n* **SSO**: SSO integration\n* **Dashboard**: Dashboard view of the keys created\n* **Analytics**: Analytics on usage and cost by team and service\n* **Access Control**: Access control over who can view and create keys\n\n![](https://lh6.googleusercontent.com/B1ecI3KdyzeqrdPU6JRoYflPXpt-y3YiRGuf3wE0_n6gJZxKZX6bxnHCFXFvSh4Rf5MsCTXNWpZEEIsXnsSiEyBqCJ9_GIVaRyWb8BoT6xjITG2pN1SMtiuDLJCryMENyfbPY_Y-2vAfW0fgUjMRuZg)\n\n**Who are we?**\n\n![](https://lh3.googleusercontent.com/QVEctbu3DQ5cZPDkK_P_-9KGBcMg64t7qzcFlEpbO_EpUefLvVcu5ApqHH6oZYx0x8hBDxZdX7PdngRjIkCXp_6ALUFsAn9ZCrB636TWAQ2o5pr1J7z-GVEoYgplvK__JBvVpVnXdnv7Fq5KQXqQJdI)\n\n[Spike](https://www.linkedin.com/in/spike-lu-38227b105/) (on the right) was a senior software engineer at Unity for three years. He worked on an internal API gateway used by hundreds of developers.\n\n[Donovan](https://www.linkedin.com/in/donovanso/) (on the left) was a software engineer at Credit Suisse, building internal tools dealing with financial instruments used by institutional investors.\n\nWhile working on our previous [AI powered Figma to code idea](https://www.figma.com/community/plugin/1178847414663679049/Bricks---AI-Powered-Design-to-TailwindCSS-Code), we realized that developing applications using OpenAI credentials is easy to get started but poses huge security risks. After constantly hearing news about [leaked OpenAI keys](https://www.darkreading.com/application-security/cybercrooks-scrape-openai-keys-pirate-gpt-4), we are inspired to create a solution that helps make OpenAI development safer at enterprises.  \\\n\\\n**Interested in learning more?**\n\n* 📅 [Book a call](https://calendly.com/spike-at-bricks/quick-chat?month=2023-09)\n* ✉️ Email us at [spike@bricks-tech.com](mailto:spike@bricks-tech.com)\n* 🤝 Connect with us on [LinkedIn](https://www.linkedin.com/in/spike-lu-38227b105/)",
        "url": "https://www.ycombinator.com/launches/JT7-bricksai-a-simple-openai-access-manager-for-enterprises",
        "votes": 50,
        "created_at": "2023-09-13T21:28:53.172Z"
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    "id": 26793,
    "name": "Windmill",
    "slug": "windmill",
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    "website": "https://windmill.dev",
    "all_locations": "Paris, Île-de-France, France",
    "long_description": "Windmill is an open-source developer platform that turns scripts into workflows, internal tools, and full-stack apps.\r\n\r\nWrite scripts in Python, TypeScript, Go, Bash, Rust, SQL - Windmill auto-generates UIs from their parameters, handles dependencies, credentials, permissions, and scheduling so you focus on business logic, not infra.\r\n                                                                                     \r\nOpen-source alternative to Airflow, Temporal, Retool and n8n. Chain scripts into flows with branching, parallelism, retries. Build dashboards with the app builder. Trigger via cron, webhook, or UI. All-in-one runtime, editor, secret manager, and OAuth platform - enterprise-ready out of the box.\r\n                                                                                                                            \r\nStack: Rust / TypeScript + Svelte / PostgreSQL. Self-hostable, easy to deploy, built for performance and DX.",
    "one_liner": "Open-source platform to turn scripts into internal apps & workflows",
    "team_size": 7,
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    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1651798285,
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    "year_founded": 2022,
    "city": "Paris",
    "country": "FR",
    "linkedin_url": "https://www.linkedin.com/company/windmill-dev",
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        "full_name": "Ruben Fiszel",
        "title": "Founder",
        "founder_bio": "Ruben was top of his class MSc in CS at EPFL, did research on compilers from Scala to Hardware at Stanford, built the first open-source library for reinforcement learning for Java, , built distributed systems at scale at Palantir and ran the engineering team at a Multi-Party Computation startup. He is now building Windmill, an  open-source alternative to Retool, Pipedream, Airplane that contains the infra to run it and that you can deploy at scale.",
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        "id": 93198,
        "title": "Founding Growth & Developer Marketing",
        "url": "https://www.ycombinator.com/companies/windmill/jobs/t7ToQBK-founding-growth-developer-marketing",
        "location": "Paris, Île-de-France, FR / Remote (FR; DE; GB; ES; IT)",
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        "id": 93199,
        "title": "Product Software Engineer",
        "url": "https://www.ycombinator.com/companies/windmill/jobs/aXpL7BC-product-software-engineer",
        "location": "Paris, Île-de-France, FR / Geneva, GE, CH / Remote (FR; CH)",
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      {
        "id": 65264,
        "title": "Windmill: Turn scripts into internal apps and workflows",
        "tagline": "Fully open-source developer platform to turn any python, typescript, SQL into internal apps, integrations and workflows",
        "body": "## Developer platform for APIs, background jobs, workflows and UIs\n\nWindmill is an open-source developer platform to quickly build production-grade internal apps, workflows and integrations from scripts in Python, Typescript, Go, Bash, SQL.\n\nOpen-source alternative to Airplane and Pipedream. Simplified Temporal with automatic app generation. Usable as a self-hosted AWS Lambda.\n\nSync your scripts from your GitHub repo or write them directly on Windmill, have them be converted to apps and no-code modules automatically. Use those apps as standalone (triggered on demand, by webhook or schedule) or build powerful flows using those scripts. The experience is similar to no-code tools except that at any point, any module can have its code inspected and tweaked. Leveraging the open-source community, we will build an exhaustive library of scripts for all APIs and generic tasks on [hub.windmill.dev](https://hub.windmill.dev/).\n\nYou may also wrap Windmill with your own frontend to provide automation as a feature on top of your own SaaS.\n\nFocus on your business logic for all kind of flows, Windmill is the platform to build, share and run them.\n\n## Active Founders\n\n**Ruben Fiszel**\n\nRuben was top of his class MSc in CS at EPFL, did research on compilers from Scala to Hardware at Stanford, built the first open-source library for reinforcement learning for Java, built distributed systems at scale at Palantir and ran the engineering team at a Multi-Party Computation startup. He is now building Windmill, an open-source alternative to Retool, Pipedream, Airplane that contains the infra to run it and that you can deploy at scale.\n\n### The sweet spot between low-code and code from scratch\n\n### Open-source and self-hostable alternative to Pipedream and Airplane. Simplified Temporal\n\nTo build internal apps for ops, integrations between services that cannot talk to each other directly, or to run background jobs that run your business logic and analytics, the two main options today are no-code solutions and old-fashioned, roll-your-own scripting. Both have problems, and our goal with Windmill is to find a new sweet spot between the two. No-code solutions are productive _if_ your problem matches the tool exactly - but if not, they are rigid, hard to extend and quickly become tech debt, annihilating their initial time advantage. Indeed, no-code is just code but made by an opinionated someone else and hidden as a blackbox with an UI.\n\nThe alternative is to do it the old-fashioned way, writing everything from scratch, both backend and frontend, perhaps deploying it on the latest flavor of serverless, and pray to never have to touch it again because that took way too much time and it has now became a burden that the ops and business team might poke you about regularly.\n\nFurthermore, the landscape of SaaS is specialized tools for everything—alerting, data analytics, administration panels, support management, integration between services—when it feels like a few scripts and some SQL would have been as good or even better and spared you the need of depending on one yet another tool. This could be even further facilitated if there was a way to import the right bunch of scripts from a fellow community of engineers, tweak it and deploy it like you can do in communities where automation can be shared as simple JSON files, for instance in the node-red or home assistant community… That’s the idea of Windmill: to bring back the power of scripting in an easy but high-scale production-grade way.\n\n## What are good use cases for Windmill\n\n1\\. The biggest pain we solve is to turn scripts that you would run on your own laptop as internal apps that you can share with your team.\n\n2\\. Avoid SQL queries to production in favor of templatized SQL made into apps instead (automatically!). Making live SQL queries is common in DevOps, support and ops in general. It can be very error-prone, stressful and inconvenient.\n\n3\\. Integrations between tools you already use but that are unable to talk to each other.\n\n4\\. Workflows, background jobs: code that runs very frequently to react to new events, transform data and run your business logic. Many companies are a frontend on top of a database, and that database is updated in the background. We make it possible to build those workflows from simple scripts so that you can build it faster, more reliable and easier to maintain.\n\n5\\. The last one only apply if you are a SaaS that wants to provide automation as a feature of your product, or a no-code tools yourself. Because we focused very much on the hard-engineering of orchestration and specs for workflows, you might simply want to wrap Windmill to offer it to your own clients.\n\n### Where is the catch, how do you make money\n\nWe make money from support, dual licensing of commercial licenses (for SaaS wrapping Windmill or for clients requiring it) and the hosted solution. On self-hosted, our Enterprise plan embedds:\n\n* SLA & Priority Support 24/7\n* Design partners for roadmap (priority on feature requests)\n* Enterprise-specific features (Audit logs, Distributed dependency cache backed by S3, SAML support etc.)\n\nWe are workflows experts and would be happy to support you to transition to clean code working on open-source software.\n\nYou can however self-host it for free forever as long as you do not go against the AGPL license (keep open-source or do not provide it as your service).\n\n![uploaded image](/media/?type=post&id=65264&key=user_uploads/229730/0fe6dab5-5378-4e2e-ad66-930378bbe8ac)\n\n![uploaded image](/media/?type=post&id=65264&key=user_uploads/229730/5d176de7-2303-49de-adb4-9bb98c559506)\n\n![uploaded image](/media/?type=post&id=65264&key=user_uploads/229730/e1d39016-bd36-4241-b0df-a5f7799ef4f5)\n\n**Docs:** [https://windmill.dev](https://windmill.dev/)\n\n**Hosted app:** [https://app.windmill.dev](https://app.windmill.dev/)\n\n**Self-host (star us!):** <https://github.com/windmill-labs/windmill#how-to-self-host>\n\n**Discord to chat with the team/community:** <https://discord.com/invite/V7PM2YHsPB>\n\n**Schedule a demo:** <https://www.windmill.dev/book-demo>\n\n**Windmill on HackerNews:**\n\n* [Aug 9 2022, HN Launch](https://news.ycombinator.com/item?id=32400849)\n* [May 12 2023, Before v2](https://news.ycombinator.com/item?id=35920082)\n* [Nov 22 2023, Why Windmill is the fastest self-hostable workflow engine?](https://news.ycombinator.com/item?id=38383138)",
        "url": "https://www.ycombinator.com/launches/Gye-windmill-turn-scripts-into-internal-apps-and-workflows",
        "votes": 17,
        "created_at": "2022-08-10T13:00:24.836Z"
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        "founder_bio": "TLV. Ex-8200. 1st CTO at ProctorExam (EdTech), acquired by TurnItIn. Chief Architect at SignifAI (AIOps), acquired by New Relic.",
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        "full_name": "Luigi La Corte",
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        "title": "Account Executive (Mid-Market, Toronto)",
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        "location": "Toronto, ON, CA",
        "type": "Full-time",
        "role": "Sales",
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        "salary_range": "$140K - $170K CAD",
        "equity_range": "0.20% - 0.65%",
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        "url": "https://www.ycombinator.com/companies/provision/jobs/dseNXSQ-senior-machine-learning-engineer",
        "location": "Toronto, ON, CA / Toronto, Ontario, CA / Remote (CA)",
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        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$140K - $300K CAD",
        "equity_range": "0.10% - 0.50%",
        "experience": "6+ years",
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        "title": "Provision 🏗️📃 - Ironclad for construction",
        "tagline": "Provision helps owners and constructors understand their commercial documents, collaborate with team members, and reduce risk, increasing profits by 5-10%. ",
        "body": "We’re Brendan and Luigi and we’re the co-founders of Provision.\n\n**Problem**\\\nThe construction industry spends $500B annually (\\~4% of market value) on issues stemming from document complexity. Documents are becoming longer, harder to read, and contain obligations that go unchecked eroding profitability. Currently, people use CTRL+F to navigate thousands of pages and Excel to extract information, often resulting in missed obligations, and unexpected costs.\n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/8f4cc15a-b090-4ac2-a0d5-a9eee38cc0eb)\n\n\n**Need**\\\nConstruction professionals need to understand and manage all of the requirements held within these documents, because missing them means delays, budget overruns and even failed projects.\n\n**Enter Provision**\\\nProvision ingests contracts, specs and drawings, and gives users tools to manage risks, obligations and compliance.\n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/edbcd4d6-2298-48c2-9025-edcd7959b3d4)\n\n\n**Backstory**\\\nBoth Luigi and Brendan worked in the construction industry for years before starting Provision. Brendan left the industry, and built two startups over a couple of years but always had aspirations to go back to construction. Luigi worked as a Director at an infrastructure developer for 6 years, where he saw even the most sophisticated constructors fail to understand their documents. We came together in December of 2021 through YC to build something impactful. \n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/dc0bd0eb-5339-4efa-9159-cd50050156cb)\n\n\\\n**Ask**\\\nUpvote this post! :)\n\nAnd if anyone has any warm connections to GCs or subcontractors in North America who builds big projects (50M+), we would love an intro. [founders@getprovision.co](mailto:founders@getprovision.co)\n\nAlso, we’re hiring for Data Engineer and SWE roles. [careers@getprovision.co](mailto:careers@getprovision.co) \n\nFollow us on [LinkedIn](https://www.linkedin.com/company/86033411), and drop us a line if you want to chat.\n\nThanks!",
        "url": "https://www.ycombinator.com/launches/H0A-provision-ironclad-for-construction",
        "votes": 16,
        "created_at": "2022-08-14T15:53:59.802Z"
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    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/neosync",
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        "full_name": "Evis Drenova",
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        "founder_bio": "Evis is the CEO/Co-founder of Nucleus. Before Nucleus, he was one of the first Product hires at Skyflow, a data privacy and security company, and led Product and GTM for the Fintech market from Seed -> Series B. Prior to Skyflow, he was Head of Product at Truedata, a Managing Consultant at IBM and started his career in Enterprise Sales at Experian and Oracle. He was born in Albania, grew up in Boston and currently resides in San Francisco.",
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        "title": "Nucleus aims to simplify the process of managing microservices • TechCrunch",
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        "title": "Neosync - PII Detection and Anonymization in Free-form Text",
        "tagline": "Real time PII detection and redaction in structured and unstructured data.",
        "body": "We’re an open source data anonymization and synthetic data platform that companies like Intel, Siemens, C2FO, Alasco and others use to anonymize their sensitive production data and sync it to lower-level environments.\n\nToday, we’re launching a new product designed to detect and anonymize PII data in free-form text.\n\nThese are two main use cases:\n\n1. **Detecting and redacting PII data before sending it to an LLM for inference**. If you’re building agentic systems or working with LLMs and sensitive data, you shouldn’t be sending your sensitive data to those LLMs. You can now use our API to first detect and anonymize that data and then send it to your LLM provider.\n2. **Detecting and redacting PII data before training.** You generally want to avoid training a model on PII (especially if others will be using it). You can use our API to detect and redact free-form text in training data so that you’re not training it on PII.\n\nFor example:\n\nThe text:\n\n```\n{ text: \"Dear Mr. John Chang, your physical therapy for your rotator cuff injury is approved for 12 sessions. Your first appointment with therapist Jake is on 8/1/2024 at 11 AM. Please bring a photo ID. We have your SSN on file as 246-80-1357. Is this correct?\"}\n```\n\nWould be transformed to:\n\n```\nAnonymization result: '{\"text\":\"Dear Mr. \\u003cREDACTED\\u003e, your physical therapy for your rotator cuff injury is approved for 12 sessions. Your first appointment with therapist \\u003cREDACTED\\u003e is on \\u003cREDACTED\\u003e at \\u003cREDACTED\\u003e. Please bring a photo ID. We have your SSN on file as \\u003cREDACTED\\u003e. Is this correct?\"}' \n```\n\nYou can also customize this with custom allow/deny lists and even custom recognizers.\n\nWe’re already working with companies in Healthtech and Fintech on this and would love to open it up to more companies. If you’re interested in trying it out, shoot me a note at [**evis@neosync.dev**](mailto:evis@neosync.dev), and I can get you a sandbox and free credits to trial it.\n\n![uploaded image](/media/?type=post&id=85312&key=user_uploads/89898/ea3d02aa-68e7-4a21-8bd0-c35f9786490e)\n\n",
        "url": "https://www.ycombinator.com/launches/MC0-neosync-pii-detection-and-anonymization-in-free-form-text",
        "votes": 10,
        "created_at": "2024-11-06T01:45:44.699Z"
      },
      {
        "id": 64124,
        "title": "Nucleus - The Modern Backend Platform for Microservices",
        "tagline": "Nucleus automates security, observability, secrets management and more for microservices in a single platform.",
        "body": "**Nucleus is a platform that helps developers build, deploy and manage a production-grade microservices architecture in minutes.**\n\n### **Problem**\n\nBuilding a microservices architecture is complex and can easily take 6+ months. From our experience at our own companies and from talking with many engineering teams, we see that most engineering teams try to build this platform from scratch by piecing together AWS or GCP building blocks. The problem is that this is difficult, incredibly time consuming and if not done right, can be a massive security risk. You shouldn’t have to be an infrastructure and security expert to have a secure, scalable architecture. That’s where Nucleus comes in. \n\n**Who uses Nucleus?**\n\nCompanies that want to use microservices but don’t want to spend months building out their infrastructure. These can be early stage companies that are just starting out, to mid-market companies that are architecting for growth.\n\n**How it works**\n\nNucleus is delivered through an interactive CLI that helps you declaratively define your service. Here is an example of the declarative file:\n\n![uploaded image](/media/?type=post&id=64124&key=user_uploads/980900/fb0612aa-3ab3-4e0f-8a48-aa76dd40fb8b)\n\nHere is how to deploy a microservice:\n\n1. Install the CLI through homebrew and sign up for an account.\n2. Write your service’s code or use one of our example repos to quickly get started.\n3. Declaratively define your service using the interactive Nucleus CLI. All services are automatically authenticated and secured. Manage things like environment variables, secrets, and logging, all from the CLI.\n4. Deploy your service, and it will be available in less than 2 minutes.\n\n**Can I try it?**\n\nYes! We just kicked off our private beta and would love to have you test the system to the limits. To get started, just shoot us your email so we can add you to the allowlist. Easy as that!\n\n**The Ask**\n\n1. Try us out and give us feedback. We obsess over with the developer experience, and want to make it as intuitive and magical as possible.\n2. Talk to us about your backend :) We’re focused on microservices and want to learn more about what you like, don’t like or don’t know about them. Collectively, we have 25 years of experience in engineering & product. We can be a sounding board or help you design your architecture.\n\nHave questions? Shoot me an email at [evis@usenucleus.cloud](mailto:evis@usenucleus.cloud) or feel free to book some time directly here: <https://calendly.com/evis1/30min>",
        "url": "https://www.ycombinator.com/launches/GgG-nucleus-the-modern-backend-platform-for-microservices",
        "votes": 39,
        "created_at": "2022-06-28T01:18:30.749Z"
      }
    ]
  },
  {
    "id": 26882,
    "name": "Coris",
    "slug": "coris",
    "former_names": [
      "Coris Inc."
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8d68f694a6b7f9bb0fb7015ac9814c591f45f5d.png",
    "website": "https://coris.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Coris is the AI platform for payments risk and operations. We work with the most ambitious companies around the globe such as GoFundMe, Kajabi, Clio and Cherry to automate merchant onboarding, monitoring, and payment operations. What risk teams only imagined two years ago, Coris is delivering today with our risk platform and AI agents.\r\n\r\nWe’re backed by Y Combinator, Lux Capital, Pathlight Ventures, and other world-class investors — and we’re just getting started !",
    "one_liner": "AI risk and ops platform for Payment Processors, Banks, SaaS…",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1659411610,
    "tags": [
      "Fintech",
      "B2B",
      "Compliance",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2022",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/coris",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/coris.json",
    "year_founded": 2022,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/corisinc/",
    "x_url": "https://twitter.com/CorisInc",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8d68f694a6b7f9bb0fb7015ac9814c591f45f5d.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 808733,
        "full_name": "Vinodh Poyyapakkam",
        "title": "Founder",
        "founder_bio": "Payments Fraud, SMB, Risk & Compliance. Prev: Google, WePay, Paysafe, PayPal, Accenture",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/vinodhpo/",
        "x_url": "https://twitter.com/psvinodh",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/92775bbd1f7783cd2ef5767994ce17309a74124e.jpg"
      },
      {
        "user_id": 982526,
        "full_name": "Shyam Maddali",
        "title": "Founder",
        "founder_bio": "ex-WePay, ex-eBay",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/shyammaddali/",
        "x_url": "https://twitter.com/mlshyam",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4716428289cef5c1212db1017901c9efa78c039f.jpg"
      }
    ],
    "jobs": [
      {
        "id": 100286,
        "title": "Chief of Staff",
        "url": "https://www.ycombinator.com/companies/coris/jobs/I26T5Uv-chief-of-staff",
        "location": "Palo Alto, CA, US",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$120K - $150K",
        "equity_range": "0.40% - 0.70%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "AI Agents"
        ]
      },
      {
        "id": 116108,
        "title": "Backend Engineer (AI-Native) — Risk Infrastructure & Integrations",
        "url": "https://www.ycombinator.com/companies/coris/jobs/h0nDly8-backend-engineer-ai-native-risk-infrastructure-integrations",
        "location": "Palo Alto, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$160 - $175",
        "equity_range": "0.25% - 0.50%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Django",
          "JavaScript",
          "PostgreSQL",
          "Python",
          "TypeScript"
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      }
    ],
    "news": [
      {
        "title": "Coris raises $3.7M, sets sights on AI-driven paradigm shift in SMB risk management | VentureBeat",
        "url": "https://venturebeat.com/ai/coris-raises-3-7m-sets-sights-on-ai-driven-paradigm-shift-in-smb-risk-management/",
        "date": "Feb 01, 2024"
      }
    ],
    "launches": [
      {
        "id": 65063,
        "title": "Coris - Manage Small Business Risk Thoughtfully",
        "tagline": "Our data and software helps Fintechs manage SMB Risk with no stress !",
        "body": "**Coris is a Risk API for Fintechs that underwrite SMBs**\n\nHi everyone !\n\nThis is [Shyam](https://www.linkedin.com/in/shyammaddali/) and [Vinodh](https://www.linkedin.com/in/vinodhpo/) from [Coris](https://www.coris.ai/). We are super stoked to share with y’all what we are building.\n\n### **Background**\n\nShyam and I have spent last 20+ years at companies such as eBay, PayPal, WePay/JPMC (_YC S09_) and Google managing small business risk for payments and lending products. Two of the biggest problems we faced were in gaining a solid understanding of what a SMB does at the time of underwriting and continuing to keep a tab externally on how they operate.\n\n### **Problem**\n\n**Traditional bureaus hardly help** with small business credit risk assessment given their abysmal coverage of small businesses, lack of predictive data, painful integrations, and expensive products. Fintechs especially struggle to get the right data on quality of a small business to accurately assess credit exposure\n\nWhile payments data and bank-level financial data have helped somewhat in recent times to fill the void in SMB underwriting, they still miss some huge signals on how a business operates. Biggest problem is lot of **valuable data on a business remain unstructured** in the open internet for models/rules to consume\n\nAlso once a small business is approved for payments, lending or another credit product, **most companies are in dark about them** until they see chargebacks come in or see missed payments\n\n### **Solution**\n\nWe believe there is tremendous potential for alternative credit-relevant data in SMB risk management. With that said, we have two products to start with:\n\n1. **Alternative data on every SMB** in the US through one developer-first API - Licensing details, Website Professionalism signals, Online Reviews based signals, Business Incorporation details, and Local Inspection data\n2. **Monitoring of small businesses** across various online/offline surfaces and raise an alert for action when a business deterioration/failure is suspected, much before chargebacks or defaults start happening\n\n### **Our Asks**\n\n1. If you are running a Fintech/Platform and serve SMBs in the US, we would love to get your direct feedback on what we are building. Even better, **sign up to our** [**beta here**](https://www.coris.ai/#contact_form) !\n2. **Connect us to platforms/companies** that might benefit from what we are building or for us to learn from\n3. **Feel free to say hi at** [founders@coris.ai](mailto:founders@coris.ai) ! We love riffing on SMB underwriting/fraud/KYB and talk shop on where the industry is heading\n4. Last but not the least, **please share this post** ! Please help spread the word on how we are looking to make life easier for Fintechs to serve their SMB customers !!",
        "url": "https://www.ycombinator.com/launches/GvP-coris-manage-small-business-risk-thoughtfully",
        "votes": 71,
        "created_at": "2022-08-02T07:55:11.922Z"
      }
    ]
  },
  {
    "id": 26900,
    "name": "Pluralith",
    "slug": "pluralith",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e7957008ca3bafd61f38b146c1fef179229d84a4.png",
    "website": "https://www.pluralith.com",
    "all_locations": "Vienna, Vienna, Austria; New York, NY, USA",
    "long_description": "Pluralith automates compliance by documenting cloud infrastructure directly from the codebase. Companies like Jefferson Health pay us to automate their infrastructure compliance and save 5h a week per infrastructure engineer.\r\n\r\nMore simply put: We generate infrastructure diagrams directly from Terraform state.",
    "one_liner": "Automated infrastructure compliance and documentation",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1657804203,
    "tags": [
      "Developer Tools",
      "DevSecOps",
      "SaaS"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2022",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "Austria",
      "United States of America",
      "Europe",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/pluralith",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/pluralith.json",
    "year_founded": 2022,
    "city": "Vienna",
    "country": "AT",
    "linkedin_url": "https://www.linkedin.com/company/pluralith",
    "x_url": "https://twitter.com/PluralithApp",
    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/pluralith",
    "github_url": "https://github.com/Pluralith",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e7957008ca3bafd61f38b146c1fef179229d84a4.png",
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      "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/603a5608ccc4a80b5b89729d00106eb942d1574f.png"
    ],
    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 690046,
        "full_name": "Daniel Putzer",
        "title": "Founder",
        "founder_bio": "dev",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/danielputzer/",
        "x_url": "https://twitter.com/danceladus",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4ac50a0c8e065d2f3de7f02a6d7c63fe0ab1e9b8.jpg"
      },
      {
        "user_id": 1012880,
        "full_name": "Philipp Weber",
        "title": "Founder",
        "founder_bio": "Building Pluralith",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/philipp-weber-a8517b231/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9c679d454ea367a6d7598db186b1f76063d6e968.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 65103,
        "title": "Pluralith - Automated Terraform Infrastructure Visualization & Documentation",
        "tagline": "Never spend a second drawing infra diagrams again. Completely automate your infra docs and compliance materials.",
        "body": "Hi we are Dan and Phi…\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/00d3f03c-7be2-4245-be6a-b1ac5931d882)\n\n… and we are building Pluralith!\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/f7d9a731-4769-423f-9dfd-7bdf920137e6)\n\n### **TL;DR**\n\nTitle says it all. Pluralith lets you generate instant infrastructure diagrams from your Terraform codebase, locally (for development) and in CI (for documentation). [**Try it out here!**](https://docs.pluralith.com)\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/9884a243-92d0-46ea-92f7-7dcd0cedb91c)\n\n### **Watching > Reading**\n\nSo here’s a [freshly baked demo](https://youtu.be/Jbbil9kA-EE) just for you!\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/42257960-a912-40f1-acb6-f089d4da1153)\n\n### **The Problem(s)**\n\n**Problem 1**: Infrastructure documentation is super important and often even required for proper compliance, but can be really time intensive and nobody wants to do it. So you’ve got two options:\n\n→ Stay compliant and have good docs **BUT** loads of dev hours spent on them\n\n→ No/few dev hours spent on docs **BUT** break compliance and no docs when you need them\n\n**Problem 2**: Terraform is awesome, but can become quite messy and intransparent at scale. Understanding what is actually going on requires digging through loads of code and terminal output. Again, lots of dev hours lost here.\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/1fc7af56-e700-4227-bcb9-0fc872b49446)\n\n### **The Solution: Enter Pluralith**\n\n**_Pluralith completely automates problem 1 away._**\n\nRun it in CI, automate parts of your compliance process and always have up-to-date infra diagrams whenever you need them. We throw them into PR comments and have a dedicated markdown integration so you can plug it straight into your existing docs (ReadMes, Confluence etc.). We do change highlighting, drift detection, cost calculations and compliance checks (coming soon) and keep a history of all this stuff in your Dashboard for you so you have a perfect record on how your infra changes over time.\n\n**_Pluralith alleviates problem 2._**\n\nReplace miles of terminal output on a terraform plan with a comprehensive diagram that shows you what your infrastructure looks like, how it’ll change on apply, how much it’ll cost and wether it breaks compliance (coming soon). All you have to do is run `pluralith plan` instead.\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/079e25e0-225e-443b-b052-3eaab564aa50)\n\n### **How It Works**\n\n**Locally**: Download our desktop app and follow it’s setup guide. In a minute you should be able to run `pluralith plan` in any of your Terraform project directories and get a diagram instantly.\n\n**In CI**: The illustration below says it all. Set up Terraform in your pipeline, run Pluralith and there you go. Right now we have dedicated integrations for _GitHub Actions_ and _Gitlab CI._ (Many more to come)\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/42ca4f68-1bff-48c5-a0c0-e9b1c3786746)\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/0f6a5cbc-52d6-4a6c-bfdf-bb4672190d94)\n\n### **Help Us Out?**\n\n→ [**Give it a run**](https://docs.pluralith.com) (either in CI or locally) and let us know all the ways our stuff broke through the emails below!\n\n→ Our open source CLI is looking for some stars. [**Give it a star!**](https://github.com/Pluralith/pluralith-cli)\n\n→ If you are a DevOps professional → [**Pick any slot on here!**](https://calendly.com/dantheputzer/meet) We’d be stoked to chat about the infra landscape!\n\nIf there are any questions or anything else we can assist with, shoot us an email at [dan@pluralith.com](mailto:dan@pluralith.com) or [phi@pluralith.com](mailto:phi@pluralith.com)!\n\nCheers,\n\nDan and Phi\n\n![uploaded image](/media/?type=post&id=65103&key=user_uploads/690046/952c66c1-40c8-41b8-a31c-50dbbacb5c7b)\n\n",
        "url": "https://www.ycombinator.com/launches/Gw3-pluralith-automated-terraform-infrastructure-visualization-documentation",
        "votes": 48,
        "created_at": "2022-08-03T16:42:45.345Z"
      }
    ]
  },
  {
    "id": 27040,
    "name": "Grai",
    "slug": "grai",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/217dbf57c9649c7822bbee101a62b2d34a750a85.png",
    "website": "https://www.grai.io",
    "all_locations": "San Francisco, CA, USA; London, England, United Kingdom; St. Louis, MO, USA",
    "long_description": "Grai is open source version control for metadata. We can determine how database changes will affect deployed machine learning models, apis, and dashboards because we understand how data relates across systems which don’t otherwise talk to each other.",
    "one_liner": "Open source version control for metadata",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1660014966,
    "tags": [
      "Developer Tools",
      "SaaS",
      "Analytics",
      "Open Source",
      "Data Engineering"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2022",
    "status": "Inactive",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "United Kingdom",
      "America / Canada",
      "Europe",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/grai",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/grai.json",
    "year_founded": 2021,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/grai-io/",
    "x_url": "",
    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/grai",
    "github_url": "https://github.com/grai-io",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/217dbf57c9649c7822bbee101a62b2d34a750a85.png",
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    "founders": [
      {
        "user_id": 696649,
        "full_name": "Ian Eaves",
        "title": "Founder",
        "founder_bio": "Ian is a recovering ex-physicist turned machine learning engineer with experience building MLE teams everywhere from startups (Bellhops) to fortune 50 companies (Centene). An active open source contributor he's written software with over 11M downloads (Visions). Grai was born from the pain of working on distributed data particularly within enterprise settings.",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/ieaves/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a0e6ffe7f84a454640193fd1395f3940c644a5db.jpg"
      }
    ],
    "jobs": [],
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    "launches": [
      {
        "id": 65887,
        "title": "✨ Grai - Open-source version control for metadata",
        "tagline": "Grai knows how a database migration will impact deployed ML models, APIs, and more because it understands how data relates across systems.",
        "body": "![uploaded image](/media/?type=post&id=65887&key=user_uploads/696649/b51bcf0a-5353-4db3-b9c4-8836f2ea423a)\n\n**TL;DR Grai makes developers smarter by bringing metadata from across their stack into their development tools. We make testing data flows between applications painless.**\n\nGo to 👉 [grai.io](http://grai.io)\n\n### 😭 **Problem**\n\nTracking (let alone testing) data once it leaves a production environment is challenging. Whether in a data engineers transformation pipeline, an ML model, or the CFO’s metrics dashboard, that data _will_ be used elsewhere. Without visibility on those use cases data changes remain risky and outage prone.\n\n### 🚀 **Solution**\n\nGrai is an open-source data management platform designed to help you better use your data.\n\n* **Automated data lineage** - Pre-built connectors to keep metadata fresh.\n* **Integrated with git** - Changing a column in your DB? Run data integration tests for all downstream users as part of your standard CI/CD process.\n* **One-stop-shop** - A unified model of your entire data stack.\n* **Your data belongs to you** - Grai is open-source & self-hosted with a cloud option coming soon.\n\n### 🎉 **Closing thoughts (and asks!)**\n\nMy ask: give it a shot!\n\n* Browse the [code](https://github.com/grai-io/grai-core)\n* Check out the [demo](https://github.com/grai-io/grai-core/tree/master/examples/quick_start_postgres)\n* Reach out! [ian@grai.io](mailto:ian@grai.io)\n\n\\~ Ian\n\n![uploaded image](/media/?type=post&id=65887&key=user_uploads/696649/7ecc72b3-40f5-4473-ae0f-157159a139eb)\n\n",
        "url": "https://www.ycombinator.com/launches/H8h-grai-open-source-version-control-for-metadata",
        "votes": 51,
        "created_at": "2022-09-07T00:00:00.601Z"
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    "all_locations": "Santiago, Santiago Metropolitan Region, Chile",
    "long_description": "Plutto is a supply-chain risk management and compliance platform for Latin America. We automate due diligence, compliance checks, and conflict-of-interest reviews—helping corporates across LatAm work with vendors and contractors safely.",
    "one_liner": "Supply-chain risk management and compliance platform for Latin…",
    "team_size": 20,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1657201173,
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    "nonprofit": false,
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    "country": "CL",
    "linkedin_url": "https://www.linkedin.com/company/plutto",
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        "user_id": 890662,
        "full_name": "Antonia San Martin",
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        "founder_bio": "Worked at Heineken and Nike.\nCo-founded a travel startup for 1 year.\nNow I’m CEO @ Plutto, building verification service for businesses in Latin America.",
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        "full_name": "Felipe Domínguez",
        "title": "Founder",
        "founder_bio": "Fullstack Dev for some years, now co-founder @ Plutto. \nI spend a lot of time in the mountains. \n",
        "is_active": true,
        "linkedin_url": "http://linkedin.com/in/fdominguezc",
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        "user_id": 547013,
        "full_name": "Ignacio Márquez",
        "title": "Founder",
        "founder_bio": "Graduated as Software Engineer. Worked a couple years as a full-stack developer and scrum master in Platanus, Chile. Co-founded a travel startup for 1 year.\n\nNow I'm CTO @ Plutto, building KYB solutions for Latin America.",
        "is_active": true,
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      {
        "id": 64411,
        "title": "Plutto, business background checks for LatAm",
        "tagline": "Meet the business verification tool that increases your on-boarding speed from weeks to minutes",
        "body": "“I’m sorry but we’re not going to accept your investment. At the moment we’re not taking business clients”. This was the answer we got from a fintech that got us to start Plutto right away.\n\n### **Hey YC!**\n\nWe’re Antonia, Ignacio, Felipe and Sebastián and we’re the co-founders of Plutto! **We help financial services to verify and validate their business customer in minutes for LatAm.**\n\n### **So, what’s the problem?**\n\nBusiness information is all over the place in LatAm.\n\nIf you’re a fintech, bank, payment company, or basically anyone who needs to onboard B2B clients, chances are you spend a lot of time gathering information to know who you are dealing with, and sometimes, to be compliant with the law. This process is called Know Your Business (KYB).\n\nThis a **time-consuming and expensive** process that usually involve lawyers and other people to be completed. **A business can wait up to 3 months to open a bank account in Chile** and other countries in LatAm.\n\nWe allow our clients to solve this problem in a fast and automated way.\n\n### **How does it work?**\n\nThe only thing we need is your client’s company registration number (TIN equivalent) and we **gather the information for you**. This can be done using our dashboard or API. We also help our customers to request private documents that can’t be obtained from any source providing a white label on-boarding form. We also process and review the information of this documents so they don’t have to be manually reviewed.\n\nThe outcome is a company profile with all the information, documents and on-boarding steps our clients need **to make a solid decision.**\n\n### **This is something that had to be done!**\n\nIn previous experiences we struggled with this problem, paying up to $100 per customer on-boarding and waiting weeks until they were done. Some of our clients take less than 10 minutes to validate their users and we charge between 6-20 bucks for it.\n\nAs we hinted at the start, we once tried to invest our company’s money in a Chilean fintech and get rejected because they were not accepting business clients due to this issue. Absolute madness. We started on this idea the following day.\n\n# How can you help us?\n\nAt the moment we’re working with our first 6 clients in Chile and we’re looking to expand to other LatAm countries. Specially Perú, Colombia and Mexico so any of the following **introductions or meetings** will be much appreciated:\n\n* People who **work in the on-boarding or legal team** in a B2B LatAm fintech.\n* Someone who is **an expert in law and regulation for fintech**, banks or any other business that requires KYB (Know Your Business) in 🇵🇪, 🇨🇴, 🇨🇱 or 🇲🇽.\n* Any fintech or company that might need our services in the region.",
        "url": "https://www.ycombinator.com/launches/Gkt-plutto-business-background-checks-for-latam",
        "votes": 31,
        "created_at": "2022-07-07T14:34:17.524Z"
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    "long_description": "We make it easy to query, trace, and govern R&D data in the age of AI, serving big pharma enterprises, biotech, and academia.\r\n\r\nOur core product is an open-source, lineage-native, format-agnostic lakehouse for agents and teams.",
    "one_liner": "Open data platform for traceable, multimodal AI",
    "team_size": 12,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1655969538,
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      "Open Source",
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    "top_company": false,
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    "nonprofit": false,
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        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/falexwolf/",
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        "user_id": 1014442,
        "full_name": "Sunny Sun",
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        "linkedin_url": "http://linkedin.com/in/sunnyosun",
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    "id": 27146,
    "name": "Sunpia",
    "slug": "sunpia",
    "former_names": [
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      "Sunpia",
      "Cloudless"
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    "long_description": "Sunpia lets developers easily experience the cost and speed benefits of serverless infrastructure, without having to rewrite their code. Developers annotate their code and Sunpia automatically designs a microservice version of it they can deploy on their own cloud.",
    "one_liner": "Deploy serverless without rewriting code",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1662145175,
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      "Data Engineering"
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    "country": "US",
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        "user_id": 642913,
        "full_name": "Alan Pham",
        "title": "Founder",
        "founder_bio": "I graduated from UC Berkeley with Bachelors and Masters in EECS, conducting speech, systems, and machine learning research in image and natural language processing.",
        "is_active": true,
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        "user_id": 1018399,
        "full_name": "Ivy Pham",
        "title": "Founder",
        "founder_bio": "I graduated from Harvard with my B.A. in Economics and have a professional background in product management and business analytics. I'm interested in building solutions that can streamline workflows and improve efficiency. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ivylp/",
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        "user_id": 1018726,
        "full_name": "Vikranth Srivatsa",
        "title": "Founder",
        "founder_bio": "I graduated from UC Berkeley EECS doing systems and machine learning research. I do research as part of the UC Berkeley SKY Lab. I am interested in cloud/edge computing.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/vikranth-srivatsa-921067126",
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    "id": 27167,
    "name": "Blockscope",
    "slug": "blockscope",
    "former_names": [
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    "all_locations": "New York City, NY, USA",
    "long_description": "Blockscope is a Web3 data platform that enables anyone interested in on-chain data to quickly spin up live or historical data pipelines (for blocks, event, transaction, wallets, token, NFTs, etc). Similar to AWS, users have access to variety of managed software services to analyze, interact with or inject captured data into their apps.\r\n\r\nOur platform is built around the core idea of off-loading the on-chain data that our customers need for their Web3 apps, and reliably storing them for quick access/use.\r\n\r\nWe offer products on top of the core data we index for our customers. Some of these services are contract analytics, wallet profiling, events & transactions database as a service, push notifications, webhooks, and developer tools.\r\n\r\nOur mission is to enable everyone to easily access and consume on-chain data!",
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    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1657226714,
    "tags": [
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        "user_id": 245828,
        "full_name": "Adit Patel",
        "title": "Founder",
        "founder_bio": "Adit Patel is a cofounder & CEO at Blockscope. After building multiple projects in the Web3 space, it was abundantly clear to us that interacting with on-chain data is an unnecessarily complex process. We are building out a Web3 Data Platform that enables all types of individuals, developers, portfolio managers, institutions or even crypto newcomers to capture, analyze and understand any on-chain data! Easy to use crypto tools for all.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/pateladit/",
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        "user_id": 1018612,
        "full_name": "Maharshi Patel",
        "title": "Founder",
        "founder_bio": "Maharshi is a software engineer at Blockscope.\n\n\n\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/maharshipatel1997/",
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      {
        "id": 72077,
        "title": "Blockscope - Easy to use crypto tools for all!",
        "tagline": "Blockscope offers a suite of tools to understand web3 data in a useful way.",
        "body": "# **What is Blockscope?**\n\nBlockscope is a data platform with easy to use tools for the most common use cases in web3 like monitoring for on-chain events, crypto forensics, data indexing, investigations, building dapps & more! Blockscope is used by crypto users, trading firms, web3 startups and institutions.\n\n### **❌ Problem:**\n\nWeb3 has a usability problem, currently any interaction is complex and fragmented. For simple things like getting alerted when money moves to your wallet or understanding what happened in a transaction, it’s like you need a degree in cryptography. As developers, traders, and researchers who faced these issues, we realized that without better tools, web3 adoption will not take off. This is precisely the problem we’re solving.\n\n### **✅ Solution:**\n\nAt Blockscope we’ve built a suite of tools that work together to help you understand on-chain data in useful ways and take action. All of our tools help do one of the following:\n\n* Save engineering hours for developers\n* Make on-chain data readable and understandable\n* Enable traders to take informed actions\n* Help monitor & fight against scams, fraud and hacks\n* Empower new comers and non-technical individuals\n\nTools currently available on Blockscope:\n\n![uploaded image](/media/?type=post&id=72077&key=user_uploads/245828/ec3ccf9e-9e8c-4279-b891-ed0051eb168f)\n\n---\n\n### 💪 **Some of our products:**\n\n✔️ **Data Delivery Pipeline Tool** - Index blockchain data and deliver anywhere in real-time!\n\n<https://youtu.be/7vFxONaGwdI>\n\n✔️ **Watchtower Tool** - No-code tool to monitor, capture and send alerts based on specific on-chain activity set by users\n\n![uploaded image](/media/?type=post&id=72077&key=user_uploads/245828/4c906f2f-f9b5-4ad8-bba0-466ddbad65b9)\n\n✔️ **Tracer Tool** - Track funds moving into and out of different wallets\n\n![uploaded image](/media/?type=post&id=72077&key=user_uploads/245828/2f872b79-0b0a-48ae-8546-cb0bb831efe9)\n\n✔️ **Transaction Decoder Tool** - Decode all data in any transaction and understand what is going on! ex: a Flash Loan attack and addresses involved\n\n![uploaded image](/media/?type=post&id=72077&key=user_uploads/245828/cc32d118-d093-4879-b159-035090f3d652)\n\n### **🚀 Use Cases:**\n\n* Devs have built discord bots to notify when specific NFTs in certain collections are listed for sale using [Data Delivery Pipelines Tool](https://www.blockscope.co/)\n* Traders are monitoring for new Uniswap pool creation with [Watchtower Tool](https://www.blockscope.co/)\n* Flash loan attacks can be investigated with [Transaction Decoder Tool](https://www.blockscope.co/)\n* Forensics teams can analyze sanctioned addresses with the [Tracer Tool](https://www.blockscope.co/)\n* Developers can measure DAUs & MAUs and other metrics with [Contract Usage Tool](https://www.blockscope.co/)\n* Understand holdings and activity for any address with [Address Profiler Tool](https://www.blockscope.co/)\n* Devs can trigger state changes for their protocols based on on-chain events with the [Webhooks Tool](https://www.blockscope.co/)\n* … many more!\n\n# **👋 Our Ask:**\n\n* Sign up for access at <https://www.blockscope.co/signup>\n* Tell us about any on-chain data issues you’ve had at <https://www.blockscope.co/contact-us>\n* Follow us on [Twitter](https://twitter.com/BlockscopeCo) & [LinkedIn](https://www.linkedin.com/company/blockscopeco/)\n* Reach out at [hello@blockscope.tech](mailto:hello@blockscope.tech) for any inquires!",
        "url": "https://www.ycombinator.com/launches/IkX-blockscope-easy-to-use-crypto-tools-for-all",
        "votes": 82,
        "created_at": "2023-06-06T14:31:08.441Z"
      }
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    "id": 27258,
    "name": "AccessOwl",
    "slug": "accessowl",
    "former_names": [],
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    "website": "https://www.accessowl.io/",
    "all_locations": "Berlin, Berlin, Germany; Remote",
    "long_description": "AccessOwl automates employee SaaS account provisioning and permissioning for any tool. IT teams from companies like Zeplin and Sary save 30 minutes on every request.\r\n\r\nAccess management used to be an enterprise problem. With work from home, exploding SaaS usage and the need for SOC2 certifications startups have the same problems as enterprises. \r\n\r\nMathias and Philip have built several startups and teams before and experienced the same problems around access management over and over again. Philip was the CEO and Co-Founder of blik, an award winning IoT solution that was used by Volkswagen and BMW. Mathias built the leading private payment infrastructure provider in Germany as well as the first digital car insurance as Co-Founder and CTO. Based on these experiences we decided to build a solution we wish we could have used when building our former companies.\r\n\r\nAccessOwl can be deployed within minutes instead of weeks by simply installing a Slack application. We save companies that use AccessOwl 30 minutes for every access request, on- and offboarding. ",
    "one_liner": "Managing your Employees' Access to SaaS",
    "team_size": 15,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1655853975,
    "tags": [
      "SaaS",
      "B2B",
      "Compliance",
      "Security"
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2022",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
      "Germany",
      "Europe",
      "Remote",
      "Fully Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/accessowl",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/accessowl.json",
    "year_founded": 2022,
    "city": "Berlin",
    "country": "DE",
    "linkedin_url": "https://www.linkedin.com/company/accessowl",
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    "founders": [
      {
        "user_id": 165231,
        "full_name": "Philip Eller",
        "title": "Founder",
        "founder_bio": "As CEO and Co-Founder of blik I sold thousands of IoT devices to BMW and Volkswagen before building out new business units for Compado (Ad-Tech). Scaling from 0-1.8M in profits selling within a single year. AccessOwl is solving the problems we had ourselves when scaling business in the past. ",
        "is_active": true,
        "linkedin_url": "https://de.linkedin.com/in/philip-eller-7667238b",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d03166921ce998207b30302568cfd90ca8fde25c.jpg"
      },
      {
        "user_id": 824799,
        "full_name": "Mathias Nestler",
        "title": "Founder",
        "founder_bio": "Mathias Nestler is the co-founder and CTO of AccessOwl. He is a Forbes 30 under 30 Entrepreneur and CTO with 10+ years of experience in Technology. Former CTO & Co-Founder of FRIDAY.de (InsurTech). Former CTO of viafintech.com (FinTech). Loves to work on great products.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/mathias-nestler-a8436044/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4975f24c7b47682decfc9ecdd3ac8fb7eb7aaf8d.jpg"
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    "launches": [
      {
        "id": 85154,
        "title": "AccessOwl - SaaS Spend Management",
        "tagline": "Track, manage and reduce your SaaS spend in a single place.",
        "body": "**tl;dr** AccessOwl SaaS Spend management is your single pane of glass across all your SaaS apps, allowing you to keep track, manage, and reduce your SaaS costs.\n\n![uploaded image](/media/?type=post&id=85154&key=user_uploads/165231/fc45f909-ef8a-4412-bf4f-35dbf1c1b8a5)\n\n**The Problem**\n\nWith SaaS apps multiplying across teams, budgets spiral due to overlooked renewals and unused licenses. Manual tracking via spreadsheets is inefficient, leading to hidden costs and wasted resources for finance and IT teams.\n\n**The Solution**\n\n**AccessOwl SaaS Spend Management** offers real-time cost visibility and centralized tracking to streamline SaaS management. By automating renewal alerts and usage insights, it empowers companies to cut waste and control SaaS spend efficiently.\n\n![uploaded image](/media/?type=post&id=85154&key=user_uploads/165231/594c73b0-0c58-4484-874e-c06f9d5f5f81)\n\n**Key Features**\n\n• **Cost Transparency on Access Requests**: Employees can now see the price tag on every seat and license before adding new software, keeping budgets on track from the start.\n\n• **Centralized Cost Tracking**: Filter SaaS costs by department, team, or individual employee. Get clear insights without the usual hassle of piecing together spreadsheets.\n\n• **Renewal Calendar**: Never miss a renewal or an opportunity to negotiate. The renewal calendar and usage insights help teams optimize spend, avoiding the waste of unused or forgotten subscriptions.\n\n• **Automatic Usage Tracking**: AccessOwl automatically tracks usage and flags unused accounts so you can cut out the fat and make every dollar count.\n\n![uploaded image](/media/?type=post&id=85154&key=user_uploads/165231/5203a6b2-3e37-4441-9a66-726230261a02)\n\n**Uncover all of your SaaS apps first**\n\nThe [free Shadow IT scan](https://www.accessowl.com/scan) gives you a quick and easy overview of every SaaS app that’s used within your organization.\n\n**🚀 Getting started with tracking, managing, and reducing SaaS spend**\n\nVisit [AccessOwl.com](http://AccessOwl.com) to learn more, or book a demo [here](https://calendly.com/d/dyj-q7w-qmb/accessowl-demo).",
        "url": "https://www.ycombinator.com/launches/M9S-accessowl-saas-spend-management",
        "votes": 13,
        "created_at": "2024-10-30T18:56:16.804Z"
      },
      {
        "id": 81875,
        "title": "AccessOwl: Discover every hidden SaaS app and their users",
        "tagline": "🔍 Losing track of your SaaS apps? Uncover your Shadow IT with a single-click",
        "body": "Losing track of who has access to which apps is a nightmare. In [**Mathias Nestler**](https://www.linkedin.com/in/mathias-nestler-a8436044/) and my previous roles as CTO, COO, and CEO, the problem usually came up in one of three ways:\n\n1. **Missed Employee Offboardings:** If I don’t know which apps the employee used, it’s impossible to fully offboard them. There’s nothing worse than finding out an employee who left for your competition still has access to company data.\n2. **Failing Compliance:** The first question an auditor will ask is what systems you use. Knowing where your company data lives is at the core of any audit.\n3. **Unwanted Costs:** It sucks to find out that you pay for multiple apps that essentially do the same. You can only keep your team accountable if you know what they use.\n\nIn the world of IT administration this challenge is called ‘Shadow IT' - apps you are not aware being used by your team.\n\nHistorically, the only options have been to repeatedly ask employees to fill out a Gsheet with any apps they remember using or to manually search through Google Workspace OAuth logs.\n\n**Here’s a better option:** [**Uncover all SaaS apps and their users in a single click!**](https://www.accessowl.io/scan)\n\n![](https://bookface.ycombinator.com/media/?type=post&id=81875&key=user_uploads/165231/04941cdd-2f76-4b0c-b7f4-e44d0b1a41d1)\n\n[**Scan now!**](https://www.accessowl.io/scan)\n\n_The Shadow IT scanner will find any SaaS app your employees use in combination with Google or Microsoft sign-in._\n\n### FAQ\n\n> **Why do you offer this for free?**\n\nKnowing who has access to what should be the standard for all of us. We want to bring attention to the need to manage your access proactively, which is why we decided to offer a part of our product for free.\n\n> **How does it work?**\n\nAccessOwl reads your organization’s OAuth logs and matches them with our SaaS database. As a result, you’ll see which apps have been used and by whom.\n\n> **What if a user signed up with email/password and didn’t use ‘Sign in with Google/Microsoft’?**\n\nThis feature is not currently part of the free scan. However, our core product offers advanced Shadow IT detection, which includes checking for invitation emails.\n\n> **Is this safe to use?**\n\nYes, reading OAuth logs is common practice for SaaS management vendors. The data is only extracted when you actively request a scan. AccessOwl is SOC 2 Type II audited and GDPR compliant.\n\n[**Start your Shadow IT scan now!**](https://www.accessowl.io/scan)\n\nSurprised by the results? [_Book a free 1:1 consultation_](https://calendly.com/accessowl/30m) _with one of the AccessOwl founders to learn about best practices for keeping track of user access!_",
        "url": "https://www.ycombinator.com/launches/LIZ-accessowl-discover-every-hidden-saas-app-and-their-users",
        "votes": 19,
        "created_at": "2024-07-03T15:20:28.896Z"
      },
      {
        "id": 64758,
        "title": "AccessOwl: Automate SaaS access for your employees",
        "tagline": "SaaS account provisioning and permissioning for your workforce. Save 30 minutes on every access request.",
        "body": "**tl;dr AccessOwl automates how your employees receive access to SaaS tools. Provision any tool, no matter what API is available. IT teams from companies like Zeplin save 30 minutes on every access request.**\n\n## **📜 The Story**\n\nManually creating software accounts, documenting it in Gsheets and rushing through dozens of tools with every offboarding. Those are struggles we faced time over time when building prior startups. Available solutions only cover a fraction of the tools and require expensive upgrades to enterprise-subscriptions ([SSO.Tax](http://SSO.Tax)). That’s why we built an easy-to-use tool with a Slack-UI that every employee knows how to work with and bundled it with unmatched provisioning capabilities (hundreds of apps!).\n\n![uploaded image](/media/?type=post&id=64758&key=user_uploads/165231/b1769bda-60e3-4cf9-9085-59489077abd7)\n\n## **❌ The Problem**\n\n1. **30 minutes wasted for every Access Request**: Your workforce needs to request, approve, provision, communicate & document for every single access request\n2. **Enterprise subscriptions needed**: Other solutions only work with specific APIs (SCIM/SAML) for which the highest enterprise subscription is needed (”SSO Tax”).\n3. **Compliance & Security**: Documenting who has access to what is a manual and tedious process that never is 100% accurate\n\n## **✨ The Solution**\n\n1. **Automated workflows in Slack**: On- & Offboardings, Access Requests and Approvals all handled directly in Slack. Stop the JIRA ticket-flood - saving your workforce time and nerves.\n2. **Provisioning & Permissioning**: AccessOwl is the future of Access Management. It handles all account changes (creation, deletion, updates) for all SaaS applications. The best: It works no matter what API is available.\n3. **Stay SOX, SOC2, HiPAA, ISO27001 compliant:** Full Audit-trail about who has access to what and ability to schedule recurring Access Reviews.\n\n![uploaded image](/media/?type=post&id=64758&key=user_uploads/165231/22da2853-f0c9-4beb-afd3-0d940596e675)\n\n## ⚡ **How it Works**\n\n1. **Install it in Slack:** Give your employees access in one single click\n2. **App Discovery & Sync**: Let AccessOwl discover all applications used by your team by connecting to your Google Workspace\n3. **Start (De)Provisioning**: Have AccessOwl automate all of your provisioning and deprovisioning tasks\n\n## 🌐 **Integrations**\n\nAccessOwl already works with over 100+ applications. Let us know which are the most relevant to you!\n\n![uploaded image](/media/?type=post&id=64758&key=user_uploads/165231/f9b7f249-5d53-42db-b35e-672c9f09eacc)\n\n### **Try out AccessOwl 7-days for free** or [book a demo with us](https://calendly.com/accessowl/accessowl-demo)",
        "url": "https://www.ycombinator.com/launches/GqU-accessowl-automate-saas-access-for-your-employees",
        "votes": 38,
        "created_at": "2022-07-20T13:07:22.390Z"
      }
    ]
  },
  {
    "id": 27666,
    "name": "Middleware",
    "slug": "middleware",
    "former_names": [
      "Middleware Labs Inc"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dc8d28e84cb985c9cc0b597aae845c99c32f9a23.png",
    "website": "https://www.middleware.io",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Middleware is a full-stack cloud observability platform. We allow the dev and ops teams to debug the issue faster by bringing all the metrics, logs, traces, and events to a single unified timeline and train data to AI so it can give better insight into infrastructure and application.  \r\n\r\n- Single command installation to collect all the metrics, logs, traces, and events.\r\n- AI-based anomaly and error detection \r\n- GPT 4-based error resolution\r\n- Support Infrastructure monitoring, Logs, APM, Kubernetes Monitoring, Synthetic Monitoring,  RUM, Custom dashboard, and Alerts. \r\n- Full-stack observability, observe any stack at any scale.\r\n\r\nWe are at Middleware and wanted to make cloud observability faster, cheaper, and better. \r\n",
    "one_liner": "AI-based full stack observability platform",
    "team_size": 40,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1674636018,
    "tags": [
      "AIOps",
      "SaaS",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2023",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/middleware",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/middleware.json",
    "year_founded": 2022,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/middleware-labs/",
    "x_url": "https://twitter.com/middleware_labs/",
    "fb_url": "https://www.facebook.com/middlewarelabs/",
    "cb_url": "https://www.crunchbase.com/organization/middleware-4460",
    "github_url": "https://github.com/middleware-labs/",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/dc8d28e84cb985c9cc0b597aae845c99c32f9a23.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 1149383,
        "full_name": "Laduram Vishnoi",
        "title": "Founder",
        "founder_bio": "Founder & CEO at Middleware. Ex-founder of Acquire.io, a customer engagement platform, I took the company from just myself to 200 people and raised $57m in venture capital until the Series B round. Side angel investor and advisor. Born in a small town in India called Jodhpur, grew up in London and living in San Francisco since 2016. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/laduramvishnoi/",
        "x_url": "https://twitter.com/laduramvishnoi",
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      }
    ],
    "jobs": [
      {
        "id": 61083,
        "title": "Founding AI Engineer",
        "url": "https://www.ycombinator.com/companies/middleware/jobs/8l5TKYq-founding-ai-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$200K - $400K",
        "equity_range": "1.00% - 2.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Python",
          "TypeScript"
        ]
      },
      {
        "id": 87983,
        "title": "Founding Account Executive ",
        "url": "https://www.ycombinator.com/companies/middleware/jobs/8i09BX9-founding-account-executive",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$250K - $400K",
        "equity_range": "0.50% - 1.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      }
    ],
    "news": [
      {
        "title": "Middleware raises $6.5M in seed funding to transform cloud observability with AI | VentureBeat",
        "url": "https://venturebeat.com/business/middleware-raises-6-5m-to-simplify-cloud-monitoring-with-ai/",
        "date": "Aug 10, 2023"
      },
      {
        "title": "How observability has changed in recent years, and what's coming next | VentureBeat",
        "url": "https://venturebeat.com/data-infrastructure/how-observability-has-changed-in-recent-years-and-whats-coming-next/",
        "date": "Apr 03, 2023"
      },
      {
        "title": "Middleware Launches AI-powered observability platform for cloud native.",
        "url": "https://www.prnewswire.com/news-releases/middleware-launches-ai-powered-observability-platform-for-cloud-native-301740450.html",
        "date": "Feb 08, 2023"
      }
    ],
    "launches": [
      {
        "id": 91625,
        "title": "Ops AI by Middleware",
        "tagline": "Ops AI is an observability co-pilot that detects production issues across various sources, including APM traces, RUM, and logs. It then fixes these issues with a single click and generates a pull request in GitHub. ",
        "body": "I’m [Laduram](https://www.linkedin.com/in/laduramvishnoi/), founder and CEO of Middleware. \\\n**TL;DR** \\\nVery excited to introduce our latest launch: [Ops AI](https://middleware.io/product/ops-ai/) — your observability co-pilot that _not only detects_ issues in production, but actually _fixes_ them.\n\n![uploaded image](/media/?type=post&id=91625&key=user_uploads/1149383/7d2659aa-933b-4206-856a-f6107e930704)\n\nWith Ops AI, resolving production problems becomes as simple as clicking a button. When something breaks, you’ll get an alert. From there, just ask Ops AI for a fix — it analyzes the issue, identifies the affected code using GitHub’s MCP server, and automatically creates a pull request with the fix. No more digging through logs or guessing root causes.\n\nhttps://www.youtube.com/watch?v=29pzH-1_Dpc\n\n### Why Ops AI?\n\nMiddleware is a full-stack observability platform — we do infra monitoring, APM, Logs, RUM, Synthetic monitoring, and browser testing. That means we have complete context when something goes wrong and can resolve issues faster and smarter than traditional tools.\n\n# **🙏 Our Ask**\n\nTo celebrate our launch, we’re offering an exclusive **2 million free tokens** and [YC deal](https://bookface.ycombinator.com/deals/2598) to all the YC companies — just [sign up](https://app.middleware.io/auth/register?ga=enabled) and ask for the deal! Or [book a quick demo with me](https://cal.com/laduram/30min?user=laduram).\n\nWould love to hear what you think 💙",
        "url": "https://www.ycombinator.com/launches/Npp-ops-ai-by-middleware",
        "votes": 73,
        "created_at": "2025-06-25T15:14:51.809Z"
      },
      {
        "id": 68705,
        "title": "🚀 Middleware 📊 : A unified observability platform for cloud native, microservices and distributed apps",
        "tagline": "A real-time cloud-native observability platform to bring all metrics, logs, and traces in one unified timeline to debug issues faster.",
        "body": "Hey YC! 👋 \\\n\\\nI am super excited to launch [Middleware](https://middleware.io)—a **real-time observability platform** that brings all the **metrics, logs, traces, and events** into a single timeline so you can monitor your distributed apps and cloud native platforms in real time.\n\n![uploaded image](/media/?type=post&id=68705&key=user_uploads/1149383/7c01ff47-1f21-4647-9254-b2951dc8d1b8)\n\n### **Problem with existing legacy products**\n\nCurrent observability solutions are built to monitor monolith architectures, not distributed and microservices. This results in the following problems: \n\n* Surface-level information makes it **extremely hard to debug** issues. 🧐\n* Costs 💵 to monitor everything in the infrastructure and applications are **incredibly high**.\n* All the data live in separate apps like logs, metrics, traces, and events and can’t **correlate to causation**.\n\nBecause cloud native produces more data than ever, it’s become more challenging to understand what’s happening inside applications and infrastructure. Unfortunately, the result often **wastes engineering time** tracking incidents, creates alert fatigue, and, yes, costs a fortune.\n\n### **Why we founded Middleware**\n\nI faced this problem when building my last startup. While building out the backend, it was **challenging to monitor microservices and Kubernetes**, as there was no all-inclusive cloud monitoring tool available.\n\nMy few early engineers from **Splunk** and **Newrelic** faced this problem as well. After speaking to engineering and IT leaders at several other companies—and they were facing the same problem—we started to work on Middleware full-time.\n\n### **Our solution to cloud native observability**\n\nMiddleware is a **real-time observability platform** for cloud-native, microservices, and distributed apps that brings all the metrics, logs, traces, and events to a **single timeline**. This empowers **dev and ops teams** to find and debug issues faster. \n\n![uploaded image](/media/?type=post&id=68705&key=user_uploads/1149383/84b686ed-f3f2-4a40-9ad4-b4fec457d860)\n\nAdditionally, our **AI-powered algorithms** collect and analyze data from across your entire infrastructure to spot signs of potential problems before they even occur. \n\nMiddleware offers a wide range of observability from **infrastructure** monitoring, **logs** monitoring, application performance monitoring (**APM**), **synthetic** monitoring, **database** monitoring, and real-time **alerting** solution with cost-effective prices.\n\n### **What our customers say about us!**\n\nCliff from **Puresend** - \"_We migrated from Datadog to Middleware because we absolutely love their unified view. It's such a simple but incredibly useful tool. This, among other features, made it a no-brainer for us to migrate. We monitor our logs, metrics, and traces at a very affordable cost. I highly recommend Middleware as the best in class for cloud-native monitoring._\"\n\nEduardo from **Berliner Philharmoniker** - “The Digital Concert Hall only uses cutting-edge technology, and Middleware was able to provide that with its all-in-one monitoring solution and near real-time metrics.”\n\n### **Reinventing observability for the future 🔮**\n\nOur mission is to change how companies **observe infrastructure and applications** cost-effectively. Our tools provide everything you need to **remediate issues** faster while getting observability data growth under control. It provides real-time insight into your cloud-native infrastructure, applications, and business.\n\n* Vendor agnostic by using and contributing to **Open Telemetry**.\n* Store only the data that you need to save on **costs.** 💰\n* Correlate the traces from the **browser to the front end and back end** with logs and metrics.\n* Using **AI to predict** any anomaly and possible downtime.\n\n### **Our ask/ how to reach us with contact detail 🗓️**\n\n1. Try Middleware and give us feedback. We love to work with **YC companies**. [TRY AT LEAST FREE FOREVER PLAN!](https://app.middleware.io/auth/register)\n2. Tell us your use case; we love building solutions quickly. **Schedule 15 mins** to call me [here](https://calendly.com/middleware_io/15-min-quick-call).\n3. If you are paying too much for your Observability, we love to **cut costs by 2x to 3x.**",
        "url": "https://www.ycombinator.com/launches/Hs9-middleware-a-unified-observability-platform-for-cloud-native-microservices-and-distributed-apps",
        "votes": 217,
        "created_at": "2023-01-25T16:49:47.084Z"
      }
    ]
  },
  {
    "id": 27673,
    "name": "Reflex",
    "slug": "reflex",
    "former_names": [
      "Pynecone"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/677513c7f80f22199de3136c5024581799a126f2.png",
    "website": "https://reflex.dev",
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        "url": "https://www.ycombinator.com/companies/reflex/jobs/KpOaIpx-growth-lead",
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        "id": 94833,
        "title": "Reflex: AI app builder for creating production-grade web apps entirely in Python",
        "tagline": "Just connect your data and enter a prompt. Reflex generates production-ready apps that integrate any data source, API, or Python package.",
        "body": "[**Reflex**](https://www.linkedin.com/company/reflex-dev/) is an AI app builder for creating production-grade web apps entirely in Python, powered by its own open-source framework.\\\n\\\nPython is one of the most popular programming languages in the world, and web development is one of the most popular applications of programming. So why can’t we make full-stack web apps using just Python?\\\n\\\nWith Reflex, you just connect your data and enter a prompt. It generates production-ready apps that integrate any data source, API, or Python package.\\\n\\\nFounded by [**Nikhil Rao**](https://www.linkedin.com/in/nrao95/) and [**Alek Petuskey**](https://www.linkedin.com/in/aleksanderpetuskey/), Reflex has over 1 million apps created, 27k GitHub stars, and has been used by 30% of Fortune 500 companies for internal tools and data-driven applications.\\\n\\\nTry it at [**https://build.reflex.dev**](https://build.reflex.dev).\\\n\\\nhttps://www.youtube.com/watch?v=Hy3uhBVRdtk&feature=youtu.be",
        "url": "https://www.ycombinator.com/launches/OfZ-reflex-ai-app-builder-for-creating-production-grade-web-apps-entirely-in-python",
        "votes": 10,
        "created_at": "2025-10-22T18:11:26.021Z"
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        "id": 68556,
        "title": "Pynecone: Web apps in pure Python 🐍",
        "tagline": "Build web apps in minutes. Deploy with a single command.",
        "body": "Hello! We’re Alek and Nikhil and we’re excited to launch [Pynecone](https://pynecone.io)!\n\n## **TL;DR:**\n\n**Pynecone is an open-source framework to build web apps in pure Python and deploy with a single command. This can be anything from a small data science/internal app to a large multi page web app.**\n\n![uploaded image](/media/?type=post&id=68556&key=user_uploads/1164088/65dd0f6c-0edd-4c0c-9f18-e4b78b06a443)\n\n[Here is an example](https://dalle.pynecone.app/) of a Dalle Pynecone App created in \\~50 lines of Python.\n\nYou can see how to install Pynecone and start building in minutes [here](https://pynecone.io/docs/getting-started/introduction).\n\n### **The Problem**\n\nPython is one of the most popular programming languages in the world. Webdev is one of the most popular applications of programming. So why can’t we make full-stack web apps using just Python?\n\nWe found that even skilled engineers who wanted to make a web app but didn’t know traditional frontend tools like Javascript / React found it too overwhelming and time consuming. And even after they made their app, deploying it was a nightmare. We wanted to provide them with a framework where they can leverage their existing Python skills to create and deploy apps, without compromising on flexibility or customization.\n\nRecent no code and low code alternatives have issues. No code solutions that save time in the development process lack the flexibility and robustness of traditional web development. Low code frameworks are often confusing and only solve part of the problem forcing you to use multiple tools to get the job done.\n\n# **Solution**\n\n## **1️⃣ Language For Your Web App**\n\nWith Pynecone we wanted to enable software engineers to build performant customizable web apps without the overhead of learning traditional webdev. You can make anything from a small data science project to a full-scale, multi page web app in Pynecone. We have over 60+ built-in components and are adding more.\n\n## **🔋Batteries-Included Hosting**\n\nOnce you have your app built, the next big challenge is deploying it. Learning to host on a cloud service can be confusing and time-consuming. On top of hosting your frontend/backend you need to learn how to configure and maintain SSL, CDNs, and monitoring/analytics.\n\nSince we specialize in hosting a single type of app, we can provide all of this out of the box with zero configuration. With a single `pc deploy` command we will automatically set up your infrastructure so you can get your ideas out faster. You can also host with your own cloud provider.\n\n## **🥞Empower Your Team**\n\nNow instead of having separate dedicated engineers for frontend, backend, and infra, every engineer can understand and work across the whole stack. This reduces communication overhead and lets your team go from idea to working product as efficiently as possible.\n\n## **💬 We’d love to talk to you:**\n\n* Do you or your team use Python and have any internal tools/customer facing web apps. We would love to chat to see if Pynecone can simplify your tech stack and provide value.\n* ⭐️ us on [GitHub](https://github.com/pynecone-io/pynecone) to keep up with releases and news.\n* Join [our Discord](https://discord.gg/T5WSbC2YtQ)!\n\n(A short video of a user reviewing Pynecone)\n\n<https://www.youtube.com/watch?v=47BL6WLZJ1g>",
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        "created_at": "2023-01-19T21:01:27.254Z"
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        "full_name": "Sean Brown",
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        "founder_bio": "Previously Founder and CEO at Mercarto (eCommerce/marketplace platform - acquired) and Hatch (Influencer Platform Acquired by Social Chain AG), Sean is fanatical about building technology to provide critical business infrastructure. \n\nHis strengths lie in Product and Business Development, by merging the two results in great product that delivers best in class products that provide high value solutions to clients. ",
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        "full_name": "Dan Jones",
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        "founder_bio": "Dan is a CTO/Founder who loves getting stuck into code and is an advocate for all things cloud-native. With experiences in both public and private sectors, spanning industries such as Finance, Telecoms, Healthcare and Commerce, Dan has grounded himself as a versatile software engineer capable of solving complex problems in simple ways.",
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        "title": "Manchester start up Versori wins $500k Y-Combinator funding Prolific North",
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        "id": 91368,
        "title": "Versori: Integration & workflow automation magic",
        "tagline": "Go live with integrations built with Versori AI in hours, not weeks",
        "body": "![uploaded image](/media/?type=post&id=91368&key=user_uploads/1164100/21b1f014-0560-4373-9b82-52041e7d69b8)\n\nHey YC 👋\n\nWe just rebuilt the way integrations are done.\n\nLike many of you, we’ve wrestled with the limitations of traditional iPaaS tools: rigid templates, slow development cycles, poor customisation, and too many disconnected tools. \n\nSo we decided to take a different approach.\n\n**Versori AI** is a generative AI-powered integration and automation platform that enables businesses to build, deploy, and manage custom integrations and workflows with unprecedented speed and ease. \n\nWith Versori AI, businesses can connect APIs, automate processes, and deploy powerful integrations, across legacy systems, modern SaaS tools, and everything in between. Without the heavy lift of coding or cobbling together multiple platforms.\n\n[Here’s an intro to the new platform.](https://youtu.be/BF5_oVSQnKQ?feature=shared)\n\n**Key Features:**\n\n* **Prompt to Build:** Describe your integration needs in natural language, and Versori's AI will generate a complete workflow, including connector creation, data mapping, and deployment. ​[versori.com](http://versori.com)\n* **Flexible Modes**: Use a visual low-code editor, go full-code, or let the AI take the wheel. You’re in control.\n* **Transformer Mapping**: Powered by Agentic AI, we handle complex data transformations and mappings instantly, no manual field-matching required.\n* **Enterprise-Grade Security:** Benefit from SOC 2 compliance, data encryption, isolated environments per organization, and 24/7 monitoring and support.\n\nWe’re here to redefine what’s possible with integrations. Faster builds, deeper automation, more control, with the power of generative AI behind every step.",
        "url": "https://www.ycombinator.com/launches/Nlg-versori-integration-workflow-automation-magic",
        "votes": 10,
        "created_at": "2025-06-18T04:47:50.968Z"
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        "id": 73415,
        "title": "Versori Sparkboard: AI Integrations",
        "tagline": "Create integrations and workflows with just a prompt",
        "body": "[**Sparkboard**](https://sparkboard.versori.io/) is a prompt-based data integration and workflow automation tool. Just tell Sparkboard what you want to connect and automate and it will use generative AI agents to create a task list and get to work with those tasks.\\\n\\\n**Sparkboard will:**\\\n\\- Create a task list for agents to fulfill \\\n\\- Scrape the internet for the appropriate documentation\\\n\\- Build an OpenAPI spec with the correct auth methods and operations\\\n\\- Use [Versori](https://www.versori.com/) transformation tools to transform and map data\\\n\\- Offer full editing of the workflow with additional tools\\\n\\- Review your finished integration and publish!\n\n[Super Cool Marketing Video (please watch)](https://youtu.be/oIPXrf2088c)\n\n![uploaded image](/media/?type=post&id=73415&key=user_uploads/920068/b7876f5d-4802-444f-b1db-56d8e631c262)\n\n![uploaded image](/media/?type=post&id=73415&key=user_uploads/920068/59d61e64-5a49-4597-8446-225a5bcde39a)\n\n![uploaded image](/media/?type=post&id=73415&key=user_uploads/920068/1289df89-e45c-4da3-a7c3-8cccdfc29470)\n\n**Try it for free:** [**https://sparkboard.versori.io/**](https://sparkboard.versori.io/)",
        "url": "https://www.ycombinator.com/launches/J67-versori-sparkboard-ai-integrations",
        "votes": 12,
        "created_at": "2023-07-25T16:54:38.432Z"
      },
      {
        "id": 69359,
        "title": "Versori - System & data integration platform",
        "tagline": "We help enterprises connect any internal system to any external system without hiring expensive consultants.",
        "body": "**Hello YC!** 👋\n\nAwesome to be here and proud to be part of this fantastic community.\n\nWe’re Sean and Dan, the Founders of [**Versori.**](https://www.versori.com/)\n\n![uploaded image](/media/?type=post&id=69359&key=user_uploads/920068/f5ba23d6-ef01-4adc-ac46-181a9a2236d9)\n\n### **The Problem 😫**\n\nThe enterprise integration market is controlled by traditional system integrators (Accenture, Cognizant, Capgemini etc), who charge for professional services and manual development.\n\nTime, money and resource is wasted on manual system and data integrations. Complex iPaaS solutions aren’t easy, and easy iPaaS solutions aren’t complex.\n\n### **The Solution 😊**\n\nVersori “Switchboard” is a no-code system and data integration platform, primarily for major retailers that lets them connect any internal system to any external system without hiring expensive consultants. \n\nFor example, Sephora used Versori to build an integration that imports 3rd party brand products into its own ecommerce site in a few clicks. Reducing integration time from 9 weeks to 2 hours.\n\n[Here’s an intro video](https://www.youtube.com/watch?v=d7T1z3nxoD0&t=3s&ab_channel=Versori)\n\n**Create data sources (connectors) in seconds, not weeks.**\n\n* Drag and drop or paste the URL to your API specs\n* Switchboard strips and stores the auth and server types, end points, operations and schemas.\n\n**Build complex and advanced integrations with no-code tools**\n\n* **_Transformer_** provides functionality to take data from multiple sources into a format suited for your target, with all the features you’d expect such as filtering, converting and mapping.\n* **_Selectors_** gives you access to any data from all previous steps in your board, allowing you to perform complex tasks such as merging or flow-control.\n* **_Data Mapping_** allows you to define mappings where some value in one system is equivalent to another value in another system. These can be defined up front in our dashboard, or interactively updated within your board.\n* **_Filter_** allows you to stop or continue execution of a workflow based on a set of conditions. These conditions can be as simple or as complex as your data requires, and can be combined with Selectors to compare data from multiple workflow steps.\n* **_Delay_** halts execution of the current message until a predefined duration has elapsed.\n* **_Batch_** aggregates all data received over a specified batch period before releasing the result to subsequent steps and is useful when integrating real-time APIs with bulk import/export APIs.\n* **_Trigger_** allows a board to be triggered manually or via a schedule instead of via an external API call\n* **_CSV Processing_** allows you to truncate, join and deduplicate CSV files\n* **_Splitter_** takes in an array and outputs multiple new messages for each element in that array\n\n**Deploy integrations and workflows your way**\n\n* Public Cloud\n* Private Cloud\n* On Prem\n* SDK\n\n### **We can integrate your systems, that Zapier can’t.. ⚡️**\n\nYou need the break free from the static guard rails of existing integration platforms!\n\nIf your company has bespoke system or data integration requirements, that can’t be done with an existing IPaaS provider, please reach out to us at [sales@versori.io](mailto:sales@versori.io). We would love to give you a demo!\n\n### **The Deal 🤑**\n\nFor all YC Alum, the first 10,000 data messages are on the house!",
        "url": "https://www.ycombinator.com/launches/I2h-versori-system-data-integration-platform",
        "votes": 132,
        "created_at": "2023-02-16T17:19:59.571Z"
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        "user_id": 888634,
        "full_name": "Shanelle Roman",
        "title": "Founder",
        "founder_bio": "Shanelle is the cofounder and CEO of Nebula. Shanelle was previously a PM at Google, working on the buying experience for a new $XB line of Google ads. Originally from Minnesota, she graduated from Yale with a degree in Computer Science.  ",
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        "user_id": 595906,
        "full_name": "Vishnu Menon",
        "title": "Founder",
        "founder_bio": "Vishnu Menon is the Co-founder & CTO of Nebula, where he builds tools that help engineers be productive in microservice architectures. Previously, he was co-founder at an ML startup (Toucan AI) and lead data & platform projects at a MarTech software provider (AddShoppers). He earned a degree in Computer Science from Duke University.",
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      {
        "id": 69881,
        "title": "Nebula 🌌 - Understand & debug enterprise-scale codebases",
        "tagline": "Simplifying microservice development ",
        "body": "### TL;DR\n\n[**Nebula**](https://www.trynebula.com/) **is a code navigation tool that helps developers quickly understand & debug distributed systems. We make it easy to follow service dependencies & the flow of data across your microservice architecture, saving developers tens of hours per week. Request early access** [**here**](https://mzgq3tvfxmu.typeform.com/to/DD8HB5A5)\n\nHey everyone, we're the founders of Nebula!\n\n![uploaded image](/media/?type=post&id=69881&key=user_uploads/888634/dd916b21-6237-4b46-ab5b-91a02851586a)\n\n**Shanelle** was previously a PM at Google, where she saw firsthand the productivity loss that came with complex microservice architectures.\n\n**Vishnu** is a second-time founder who has previously built developer platforms & ML-based document retrieval systems.\n\nWe've experienced the frustration of seemingly simple features taking months to launch because of the complexity of unwieldy microservice architectures. Building on open-source tooling designed to handle Google-scale codebases, Nebula aims to make it simple and painless to develop microservices.\n\n### Problem 😔\n\n[85% of enterprise companies](https://www.statista.com/statistics/1236823/microservices-usage-per-organization-size/) use a microservice architecture. However, as these codebases scale in size & complexity, developer velocity often slows drastically:\n\n1\\. **trivial changes take weeks to implement** because they impact multiple services or teams across the org\n\n2\\. it takes **longer to debug** issues because it's hard to track the flow of data across your system\n\n3\\. **onboarding** a new developer onto your codebase can take weeks, if not months\n\nCompanies that switch to microservice architecture often see a dramatic increase in the cognitive overhead required for their engineers to be productive.\n\nWe've done deep dives with best-in-class companies like Netflix and Linkedin to learn how they dealt with the growing complexity of their codebases. We're combining their most effective strategies with insights from Shanelle's experience at Google to build Nebula.\n\n### How it works [🥳](https://emojipedia.org/partying-face/)\n\nNebula deploys in your cloud & continuously analyzes your codebase to generate an index of inter-service communication, then exposes this info in three ways:\n\n1\\. An **IDE extension** that shows how a line of code fits into the overall system (e.g. service dependencies, cross-references, recent traces, etc.)\n\n2\\. An **auto-generated service catalog** that includes service interfaces, usage examples, and service contributors (no maintenance required)\n\n3\\. A **GraphQL endpoint** that exposes all this data for integration with your existing DevTools\n\n![uploaded image](/media/?type=post&id=69881&key=user_uploads/888634/69cc9b1f-5205-402a-9317-48b163d68832)\n\n![uploaded image](/media/?type=post&id=69881&key=user_uploads/888634/a714fb97-d518-478a-9952-aa58ed4d1dd9)\n\n### Our Ask [🙏](https://emojipedia.org/folded-hands/)\n\n1\\. If you're a company with >100 microservices or engineers that is looking to boost its development velocity, you can [email me](mailto:shanelle@trynebula.com?subject=YC%20Launch%20-%20interested%20in%20Nebula) or schedule a [quick chat](https://book.vimcal.com/p/z5RvBJx51onH1zTj).\n\n2\\. Know any teams that are struggling to wrangle their microservice architecture? [Intro us](mailto:shanelle@trynebula.com?subject=YC%20Launch%20-%20Nebula%20customer%20intro)! **If your referral becomes a paying customer, we'll send you a $500 Amazon gift card**",
        "url": "https://www.ycombinator.com/launches/IB7-nebula-understand-debug-enterprise-scale-codebases",
        "votes": 130,
        "created_at": "2023-03-02T19:58:46.522Z"
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    "long_description": "Update: ZeroDev has been acquired by Offchain Labs.\r\n\r\n-----------------------------------------------\r\n\r\nUse ZeroDev to create self-custody wallets for your users, powered by account abstraction.  These wallets are truly non-custodial, while being easier to use and way more powerful than even custodial wallets, enabling features such as:\r\n\r\n- Gas-free transactions\r\n- Transaction bundling\r\n- Programmable security policies\r\n\r\nZeroDev is perfect for anyone who wants to create wallets for users, including Web 2.5 apps, wallet apps, onramps, marketplaces, and more!",
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        "full_name": "Eric Chen",
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        "full_name": "Jessie Young",
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        "title": "Cakework - Custom user code in any developer product",
        "tagline": "Build your own serverless platform in days instead of months",
        "body": "**tldr**\n\n_We make it easy to run custom user code using APIs instead of infrastructure. Build extensibility directly into your developer product, or launch your own serverless cloud solution._\n\nHi friends, we're Jessie and Eric from [Cakework](https://www.cakework.com). We're ex-AWS engineers that got tired of managing infra and wanted our tools to do it for us. We started Cakework to bake serverless into every developer product and never have to host our own code again.\n\n![uploaded image](/media/?type=post&id=70142&key=user_uploads/461027/608b19e2-7cf0-4402-8173-aa280ba89b58)\n\n[Jessie](https://www.linkedin.com/in/jyoung73/) - Senior Engineer that led development of AWS call center AI analytics and NLP services.\n\n[Eric](https://www.linkedin.com/in/ericchen90/) - Engineering Manager that led a team of engineers, scientists, and clinicians in building AWS services for healthcare.\n\n**Problem**\n\nModern developer experiences let users run custom code natively, so users can build extensions and applications without any devops. This can be anything from in-browser scripting to webhook handlers or web services that deploy from Github.\n\nBuilding a platform to do this is hard because cloud services like AWS are designed by default to run your code, not your users'. You need to package your users code, handling dependencies and multiple languages. You need to provide logging, metrics, and metering. Serverless options like Lambda help with security and cost, but have restrictions on timeout and prevent you from using protocols like gRPC.\n\nYou’re basically building your own Heroku.\n\nOne of our early customers had given up on building their own serverless cloud solution. With us, they were able to launch in a week.\n\n**How did we do it?**\n\nCakework is a set of simple APIs for building serverless directly into your developer product. You describe how you want to package and run your users' code, and use Cakework to build images from Github, CLI, or even a browser. You use our APIs to run that code securely, and show logs and metrics back to your users.\n\nHere's a gif of Cakework in action with a developer SaaS tool that wanted their users to be able to run custom code on webhooks in their system.\n\n![uploaded image](/media/?type=post&id=70142&key=user_uploads/461027/72dd917a-6381-4859-8341-6d278385f1d0)\n\nYou can build your own in-browser scripting with multiple languages!\n\n![uploaded image](/media/?type=post&id=70142&key=user_uploads/461027/7a15ec40-7de5-4641-8a73-87b612c1ba7c)\n\n**Asks**\n\nWe're onboarding customers in early access now. If you or someone you know is working on a developer product and want to run your user's code, reach out at [eric@cakework.com](mailto:eric@cakework.com)!",
        "url": "https://www.ycombinator.com/launches/IFK-cakework-custom-user-code-in-any-developer-product",
        "votes": 57,
        "created_at": "2023-03-10T17:19:23.787Z"
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        "full_name": "Peter Marton",
        "title": "Founder",
        "founder_bio": "Hi, I'm Peter. I worked on Stripe's usage and cost platform and built Netflix's serverless infrastructure. Earlier, I co-founded RisingStack, a Node.js performance monitoring company. I recently co-founded OpenMeter.io to help AI & API companies monetize their usage.",
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        "full_name": "Andras Toth",
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        "founder_bio": "Andras is coming from an enterprise cloud and Kubernetes platform company Banzai Cloud (acquired by Cisco) where he mainly worked on the company's service mesh product, Backyards (renamed to Calisti). Previously, he worked at DataCamp and RisingStack & participated in Google Summer of Code two times.",
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        "title": "Kong Acquires OpenMeter to Bring API and AI Monetization to the Agentic Era  | Kong Inc.",
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        "date": "Sep 03, 2025"
      },
      {
        "title": "OpenMeter makes it easier for companies to track usage-based billing | TechCrunch",
        "url": "https://techcrunch.com/2024/03/12/openmeter-makes-it-easier-for-companies-to-track-usage-based-billing/",
        "date": "Mar 12, 2024"
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        "id": 72230,
        "title": "OpenMeter.io: Real-time and scalable OSS metering",
        "tagline": "Usage Metering for AI, Billing, Infrastructure, and IoT use-cases.",
        "body": "[OpenMeter.io](http://OpenMeter.io) is an open-source real-time usage metering solution to power SaaS, Billing, and IoT use cases. OpenMeter can collect and meter millions of events per second. It integrates seamlessly with cloud infrastructure components, making it an ideal solution for DevTool, AI, and IoT applications with Usage-Based-Pricing (UBP) or event-driven analytics requirements.\n\nCheck out [OpenMeter on GitHub](https://github.com/openmeterio/openmeter)\n\n![uploaded image](/media/?type=post&id=72230&key=user_uploads/1163072/fb0d1720-b48f-4e25-bf33-03306006f9c7)\n\n### **Why does it matter?**\n\nAs the PLG (Product-Led Growth) motion gains momentum and the macroeconomic landscape limits growth potential with seat-based pricing, many companies are adopting usage-based pricing models, requiring accurate metering. Looking ahead, it is expected that the majority of SaaS products will offer AI capabilities. To effectively cover costs and generate revenue, these companies must meter AI usage and attribute it to their customers.\n\n[See common OpenMeter Use Cases](https://openmeter.io/use-cases?utm_source=newsletter&utm_medium=email&utm_campaign=yc)\n\n### **The Metering Challenge**\n\nUsage metering requires accurately processing **large volumes** of events in **real-time** to power billing use cases and modern data-intensive applications like DevTool, AI, and IoT.\n\nIt’s challenging for engineers to balance out scale, accuracy, latency, and cost as:\n\n* [🎈](https://emojipedia.org/balloon/) Monitoring systems fall short in terms of accuracy and consistency necessary for billing\n* 💸 Scaling databases to handle large volumes of writes and real-time queries can be expensive\n* [⏳](https://emojipedia.org/hourglass-not-done/) Warehouses processing leads to stale usage data and longer feedback cycles\n* [💼](https://emojipedia.org/briefcase/) Building out streaming aggregation requires experience in handling idempotency and failures\n\nCompanies need to extract usage data from various cloud infrastructure components (Kubernetes, AWS, etc.), vendors (OpenAI, Twilio, etc.), and hardware components to attribute metered usage to their customers.\n\n### **The Solution: OpenMeter**\n\n![uploaded image](/media/?type=post&id=72230&key=user_uploads/1163072/c04f742b-747e-4490-b2b4-57f162d55f4b)\n\n[Watch the demo video](https://www.loom.com/share/bc1cfa1b7ed94e65bd3a82f9f0334d04)\n\nA real-time, open-source, and infrastructure-first metering solution that you can start quickly and grow with rapidly:\n\n* **Real-time Metering**: Instant aggregations and queries\n* **Scalable Ingestion**: Handles millions of usage events per second\n* **Accurate**: Precise metering for billing and industry use-cases\n* **Fault Tolerant**: Built-in idempotency, event backfills, and meter resets\n* **Infrastructure plugins**: Usage ingestion plugins for Kubernetes, AWS, etc.\n* **RevOps plugins**: Integrations for Billing (Stripe), CRM, and data warehouses\n\nWe aim to make OpenMeter the standard to collect and share usage across many solutions and providers.\n\n[Check out OpenMeter on GitHub](https://github.com/openmeterio/openmeter)\n\n### **About the Tech**\n\nUnder the hood, OpenMeter harnesses the power of Kafka to deliver billing-grade and scalable metering. Its idempotent event ingestion uses the CloudEvents specification managed under CNCF. The usage metering aggregations are configurable, which allows tracking specific event properties on various time windows. OpenMeter is available on GitHub (published under the Apache 2.0 Open Source license).\n\n[Check out the OpenMeter Examples](https://github.com/openmeterio/openmeter/tree/main/examples)\n\n### **Join the OSS Community**\n\n* [Follow OpenMeter on GitHub](https://github.com/openmeterio/openmeter)\n* [Check out the Roadmap](https://openmeter.io/?utm_source=newsletter&utm_medium=email&utm_campaign=yc#roadmap)\n* [Contact Us](https://us10.list-manage.com/contact-form?u=c7d6a96403a0e5e19032ee885&form_id=fe04a7fc4851f8547cfee56763850e95)\n\nOpenMeter is built with ❤️ by Tailfin and experience from Netflix, Banzai Cloud, and Stripe.",
        "url": "https://www.ycombinator.com/launches/In0-openmeter-io-real-time-and-scalable-oss-metering",
        "votes": 77,
        "created_at": "2023-06-13T14:39:57.994Z"
      },
      {
        "id": 69167,
        "title": "Tailfin - Maximize revenue with usage-based pricing",
        "tagline": "Tailfin unlocks revenue by identifying and pricing sales opportunities.",
        "body": "B2B SaaS companies see up to 25% revenue increase by adopting usage-based or hybrid pricing models, but realizing this potential requires the right tools and insights. This is where [Tailfin](https://tailfin.cloud?utm_source=bookface&utm_medium=post&utm_campaign=launch1) has you covered.\n\nThis is Andras and Peter - coming from Stripe, Netflix, Banzai Cloud, and Cisco; we understand that unlocking the full revenue potential requires to:\n\n* Identify revenue opportunities and forecast over-usage to start the sales cycle early\n* Find customers ready to be converted to enterprise plans\n* Forecast usage and revenue to price deals accordingly\n\n![uploaded image](/media/?type=post&id=69167&key=user_uploads/112934/0aa156bf-ec1b-48b3-a713-639a51217424)\n\n## 💸 The Challenge\n\nWith the rise of Product-Led-Growth (PLG), pay-as-you-go is becoming the dominant pricing model, but unfortunately, many SaaS companies miss out on revenue opportunities. For example, enterprise customers often underestimate their usage and pre-purchase too little credit, leaving you with the dilemma of offering your service for free or renegotiating the deal. Moreover, self-serve customers may outgrow their plans without you knowing, and customer success and support may not prioritize users according to their spending and future potential.\n\n## 🐬 Our Solution\n\nTailfin is a monetization platform explicitly designed for usage-based pricing. We help businesses to maximize revenue by providing insights about customers and highlighting opportunities so your sales and customer success teams can be data-driven.\n\n![uploaded image](/media/?type=post&id=69167&key=user_uploads/112934/92610c86-d525-4e40-9e52-cf924668dcac)\n\nTailfin provides:\n\n* Subscription predictions like detecting overage before it happens so you can outreach and start the sales process in time.\n* Price simulations to calculate historical and future revenue based on usage and different pricing models.\n* Accurate metering with high-frequency event aggregation; can include events such as API calls, disk storage usage, or other product-related actions that could result in charges.\n* Real-time dashboard and APIs to extract usage and revenue data\n\n## [🐋](https://emojipedia.org/whale/) Sign up\n\nDon’t leave revenue on the table. Ask for a demo today:\n\n<https://calendly.com/tailfin-cloud/founders>  or email me at [peter@tailfin.cloud](mailto:peter@tailfin.cloud)\n\n[https://tailfin.cloud](https://tailfin.cloud/?utm_source=bookface&utm_medium=post&utm_campaign=launch1) \n\nBest,\n\nTailfin Founders, Andras & Peter",
        "url": "https://www.ycombinator.com/launches/Hzb-tailfin-maximize-revenue-with-usage-based-pricing",
        "votes": 99,
        "created_at": "2023-02-09T18:55:55.742Z"
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    "id": 27902,
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    "long_description": "Browser based remote access: Think SSH, but from your browser",
    "one_liner": "Remote Shell Access in the Browser",
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    "launched_at": 1678082739,
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        "full_name": "Yarden Shem Tov",
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        "full_name": "Yotam Hamiel",
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        "is_active": false,
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        "id": 70432,
        "title": "Magicflow - Webflow for AI development",
        "tagline": "A no-code tool to design and run AI workflows, optimized for cost and speed",
        "body": "Hey! We are [Yotam Hamiel](https://www.linkedin.com/in/hyotam/) and [Yarden Shem Tov](https://www.linkedin.com/in/yardenst/), the founders of [Magicflow](https://magicflow.ai/)!\n\nMagicflow is a no-code drag-and-drop tool to design and run AI workflows that easily integrate into your app.\n\nComplex workflows tend to be expensive and slow. We can optimize them for cost and speed so you don’t have to deal with the painful infrastructure work.\n\n![uploaded image](/media/?type=post&id=70432&key=user_uploads/1006285/cf3b8f4e-4f92-40a4-b9f3-bb02603a99f8)\n\n### The Problem:\n\nAI capabilities such as ChatGPT and Stable Diffusion are more accessible than ever. However, they still require a fair amount of technical know-how to deploy and orchestrate, particularly when combining multiple models to create optimized and cost-efficient workflows.\n\n### Our Solution:\n\nMagicflow — a no-code AI model orchestration solution.\n\nWe provide a user-friendly playground that integrates models from top services like [HuggingFace](https://huggingface.co/), [OpenAI](https://openai.com), and [Replicate](https://replicate.com).\n\nWith Magicflow anyone can easily stitch together various models without requiring any DevOps or code.\n\nOnce ready to deploy, workflows are cost/speed optimized and made ready for production.\n\n![uploaded image](/media/?type=post&id=70432&key=user_uploads/1006285/e84b74b9-a806-4ad6-9421-5995b02caa08)\n\nAn example of a workflow built with [Magicflow](https://magicflow.ai).\n\nWe came up with Magicflow by talking to our pre-pivot customers (who were mostly using us as a proxy for AI services). They told us that they needed a way to quickly iterate on their workflows, debug them and deploy them to production in a scalable and cost-efficient way.\n\nOur goal is to provide an accessible solution, for both engineers and non-engineers, to utilize the vast AI capabilities available to enhance their own products.\n\nSuhail (Mixpanel founder) [described it best](https://twitter.com/Suhail/status/1632064689466376194?lang=en):\n\n![uploaded image](/media/?type=post&id=70432&key=user_uploads/1006285/f8ef6c1a-849e-40cb-bf30-2a13db968340)\n\n### How you can help:\n\nWe’re looking to work with companies who rely on AI workflows, and are looking to make them cheaper and faster. If you know someone please contact us at [founders@magicflow.ai](mailto:founders@magicflow.ai)",
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        "votes": 71,
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    "long_description": "Coming soon",
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        "full_name": "Felipe Aragão",
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        "founder_bio": ":)",
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        "linkedin_url": "https://linkedin.com/in/felipearp",
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      {
        "id": 70073,
        "title": "Fiber: The API data you need, straight from your database",
        "tagline": "We keep your database in sync with your customers' third-party data",
        "body": "_TLDR: Fiber syncs customers’ data from popular APIs straight into your database to power your SaaS integrations. We help your team skip months of development (and years of maintenance), and focus on what really matters: your core product and features._\n\nHey, [Felipe here](https://www.linkedin.com/in/felipearp/). I’m a software engineer who spent the last 3+ years building apps and plugins for e-commerce companies. Project after project, I was shocked at the amount of boilerplate required just to reliably sync from 3rd-party APIs. I decided to start Fiber to solve this problem and help SaaS companies ship faster.\n\nLet me explain: 👇\n\n## **The Problem**\n\nIf you’re building a SaaS tool, you’re probably hoping to ship some integrations next quarter. To do so, your team will have to write a ton of code to authenticate, paginate, backfill, and listen for changes on each API you’re reading from. Once all this work is done, you will need to worry about hosting, maintaining and scaling this integration pipeline… _forever_. 😱\n\nIf your API vendor is in a fragmented vertical such as finance or HRIS, you are probably better off using a unified API to skip most of this integration trouble. Otherwise, if you need deep integrations with unique tools, you’re out of luck. You had to do all this work from scratch.\n\n**_But now…_** you can use Fiber! 🌀\n\n## **The Solution**\n\nFiber is an ETL service that is laser-focused on the needs of _developers_ (no offense to data people). We help you skip the work of pulling users’ data from 3rd-party APIs (eg. their Quickbooks reports, Salesforce accounts, Github issues etc), and just consume it straight from your main database:\n\n![uploaded image](/media/?type=post&id=70073&key=user_uploads/203888/dfc9f660-4146-4ece-9146-b1b83d0a928f)\n\n_(Yes, we have a great dashboard too, but you don’t even need to use it!)_\n\nHere are all the required steps:\n\n1. Register your database with us. (We will walk you through creating a database user that can’t access any of your existing, sensitive information.)\n2. Choose which third-party API to pull data from, and which of their resources you need.\n3. Hit the Fiber API whenever a client connects your SaaS to this third-party tool.\n\nThat’s it. You can now focus on your core products, knowing the user data you need will be kept up-to-date inside your database. 🤘\n\n## **The Ask**\n\nAre you or someone you know building a SaaS? Let’s chat! You can [pick a time here](https://calendly.com/portalform/25min) or email me directly at felipe@portalform.com.",
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    "name": "Odigos Technologies Inc.",
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        "full_name": "Ari Recht",
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        "full_name": "Eden Federman",
        "title": "Founder",
        "founder_bio": "I love eBPF and OpenTelemetry. Working on helping developers fix issues faster.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/edenfed/",
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        "id": 67880,
        "title": "🔍 Keyval - Distributed traces for every application without code changes",
        "tagline": "We help developers fix production issues faster",
        "body": "Hi YC!\n\nWe're Ari and Eden, the founders of [Keyval](https://keyval.dev/).\\\nOdigos, our open-source project, boosts your existing monitoring tools by automatically generating distributed traces for all your applications within minutes.\n\n![uploaded image](/media/?type=post&id=67880&key=user_uploads/171492/8220e796-3f4d-456f-9040-8e69c20591f0)\n\nCheck out our [video](https://www.youtube.com/watch?v=9d36AmVtuGU)\n\n### **The Problem**\n\nDistributed traces are the most effective signal to monitor distributed applications, but they are also the most difficult to implement.  Unlike metrics or logs, the real value of distributed tracing occurs only after implementing it across multiple applications successfully.  If just one of your applications does not produce distributed tracing, the entire context propagation is broken and the value of the traces drops significantly.\n\n### **The Solution**\n\nOdigos, our open-source project, helps developers fix production issues faster by boosting existing monitoring tools, and automatically providing them with distributed traces, within minutes.\n\nA quick installation scans and recognizes all your applications, auto-instruments them, and deploys the required collectors. This is all done automatically - all you need to do is:\n\n1. Select your target applications\n2. Select a backend you want to send the information to\n\nThat’s it!\n\n### **Why developers choose to work with Odigos:**\n\n✨ **Immediately and automatically** get distributed traces for newly-deployed application\\\n🏆 **Eliminate blind spots**! Gain confidence and understand what is (and what isn’t) covered by your existing monitoring tools\\\n🚀 **Ease of use** – one-step installation completed within minutes\\\n**⚖️ Auto-scaled collectors/agents** - deployed and scaled according to the amount of traffic generated by your applications\n\n### **Try it yourself**\n\nCheck out our [GitHub repository](https://github.com/keyval-dev/odigos), and our [getting started guide](https://odigos.io/docs/getting-started/).\n\nWe are also maintainers of OpenTelemetry auto-instrumentation for Go applications. Feel free to contact us at [founders@keyval.dev](mailto:founders@keyval.dev) with any questions regarding monitoring / eBPF / OpenTelemetry.\n\nWe’d love to invite you to our office in San Francisco for lunch and a demo, book a time with us [here](https://calendly.com/edenfed) 😃",
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    "website": "https://www.arroyo.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Arroyo is a serverless stream processing platform built on top of the open-source Arroyo streaming engine. We allow companies to transform, filter, aggregate, and join their Kafka streams in real-time just by writing SQL. We charge purely based on usage, with no fixed costs or clusters to manage.\r\n\r\nIn 2025, Arroyo was acquired by Cloudflare and now powers Cloudflare Pipelines",
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    "team_size": 2,
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    "launched_at": 1674589009,
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        "full_name": "Micah Wylde",
        "title": "Founder",
        "founder_bio": "Micah was previously tech lead for streaming compute at Splunk and Lyft, where he built real-time data infra powering Lyft's dynamic pricing, ETA, and safety features. He spends his time rock climbing, playing music, and bringing real-time data to companies that can't hire a streaming infra team.",
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        "tagline": "Build real-time data pipelines in SQL without a real-time platform team",
        "body": "Hey everyone, we’re Micah and Jackson, and we’re building Arroyo. Arroyo is the easiest way to build powerful streaming ⚡️ data pipelines. Just connect your Kafka, then you can filter, aggregate, window, and join your event data in real time with sub-second results. We fully manage the infrastructure, autoscale with your data, and charge based on usage with no fixed costs.\n\n![uploaded image](/media/?type=post&id=69175&key=user_uploads/61370/f468f5b0-8088-4982-abd8-a45e143f02a8)\n\n## **💥 The problem: real-time data processing is too hard.**\n\nMany companies have adopted Kafka to collect and route their event data. But if they want to actually _do_ anything complex with that data, their options today are to use a complex solution like Flink or dump it in a data warehouse for batch processing.\n\n## **⚡️ The solution**\n\nAfter leading streaming teams at Lyft and Splunk, we’ve seen how critical real-time data can be. At Lyft this infrastructure was vital to the operation of the product, powering features like dynamic pricing, ETAs, safety, and fraud. We’re working to bring it to everyone by solving the hard infrastructure and UX problems of existing solutions.\n\nIt’s three steps to get started writing real-time queries:\n\n1. Tell us how to connect and authenticate against your Kafka cluster\n2. Configure the source and sink topics\n3. Write SQL\n\nFrom there, our system will compile your query into an efficient dataflow program that responds to events within milliseconds and pushes results to Kafka. We charge purely on usage (based on data in, query complexity, and window sizes) with no minimums. Our system automatically scales with your data volume, and there’s no need to manage clusters or provision fixed infrastructure.\n\n![uploaded image](/media/?type=post&id=69175&key=user_uploads/61370/8789103e-9032-4cf3-bc1e-3f9bcf03a026)\n\n## **What can you use this for?**\n\n* Compute real-time ML features\n* Detect fraud and abuse as it happens\n* Turn your daily analytics jobs into real-time queries\n* Filter/redact/aggregate your logs before indexing, or turn them into metrics\n* Reduce costs in your data warehouse by pre-aggregating before ingestion\n\n## **The backstory**\n\nMicah spent four years at Lyft and Splunk building streaming platforms on top of Apache Flink and Kubernetes. Teams shipped impactful features backed by that infrastructure, but it still wasn’t easy enough for non-experts to adopt. Users wanted the same UX they had for batch: just write SQL, don’t worry about operations, scale, or the nuances of the infra. But the underlying tech wasn’t good enough.\n\nWe started Arroyo because we believe this goal is now achievable, but it requires looking beyond existing technology like Flink. That’s why we’re building a new, cloud-native stream processing engine in Rust. We’ve designed it from the ground up for near-instant rescaling and recovery even with very large windows (something that can take hours with Flink!) by separating storage and compute. And by leveraging WASM for user-defined functions, we can run user code efficiently and securely.\n\n## **Get in touch!**\n\nIf you use a stream processing system like Flink or Spark Streaming, or would like to make use of your event data in Kafka, we’d love to talk! We’re excited help you solve your real-time data problems or turn an existing batch workflow into a streaming pipeline. Reach us at [founders@arroyo.systems](mailto:founders@arroyo.systems).",
        "url": "https://www.ycombinator.com/launches/Hzj-arroyo-serverless-stream-processing",
        "votes": 29,
        "created_at": "2023-02-09T20:11:44.194Z"
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        "title": "Our favorite startups from YC's Winter 2023 Demo Day — Part 1 | TechCrunch",
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        "id": 70240,
        "title": "👾 Rivet - Heroku for multiplayer games",
        "tagline": "Run multiplayer games with managed game servers, matchmaking, and DDoS mitigation.",
        "body": "**TL;DR:** [_Rivet_](https://rivet.gg) makes it easy to run multiplayer games with managed game servers, matchmaking, and DDoS mitigation. Contact us at [_yc@rivet.gg_](mailto:yc@rivet.gg) to get access to the closed beta!\n\n![uploaded image](/media/?type=post&id=70240&key=user_uploads/1203698/e8281ea7-b796-4b7d-83e1-91367747b058)\n\n[**Rivet**](https://rivet.gg) **is trusted by publicly traded companies to indie studios to serve millions of monthly players on our infrastructure.**\n\n**The Team**\n\nIn high school, we procrastinated our homework by building and launching multiplayer games together for fun. We always wanted to make building multiplayer games more accessible, so we started Rivet to do exactly that.\n\n![uploaded image](/media/?type=post&id=70240&key=user_uploads/1203698/b4463d5c-b8d6-4fe2-9e89-268db904ebfa)\n\n**Together, we built a small game studio and launched 4 games with one acquisition.**\n\n[Nathan](https://www.linkedin.com/in/nathanflurry/) (right) independently built infrastructure serving over 15M MAU & 20k concurrent players while still in high school on games like [Krunker.io](http://Krunker.io) (acquired by FRVR). He was an Apple WWDC scholar from 2016-2018 and was featured in TechCrunch for his visual programming language.\n\n[Nicholas](https://www.linkedin.com/in/nicholaskissel/) (left) was a PM at the [Paradox.ai](http://Paradox.ai) R&D department to help built AI HR solutions for companies like McDonalds. He continued to work at Addicting Games as a PM on multiple multiplayer games and helped launch [Apes.io](http://Apes.io), a cutting-edge browser-based battle royale.\n\n**The Problem**\n\nModern games need game servers to support large lobbies, anti-cheat, low latency, and mobile platforms. **The need for game servers is growing, but** **studios have to build their own game server infrastructure from scratch.**\n\nTherefore, **studios spend more time building in-house game server infrastructure than adding content** when trying to launch games with game servers. This makes it unreasonably difficult for small to medium-sized studios to launch and maintain these types of games.\n\n**Our Solution**\n\n![uploaded image](/media/?type=post&id=70240&key=user_uploads/1203698/5918c6af-05a5-404b-943a-66c4ccc9c276)\n\n**We make it easy for game developers to run their game servers on **[**Rivet**](https://rivet.gg)**.** We take care of auto-scaling servers, matchmaking, and DDoS mitigation so the developer can focus on adding content. Rivet works with all major game engines and custom server code.\n\n**Integrating Rivet into a game takes 4 lines of code in any language.** We provide no-downtime deploys with easy rollbacks.\n\n![uploaded image](/media/?type=post&id=70240&key=user_uploads/1203698/49c025ec-b2b3-4148-8f08-55625dca99a6)\n\nOnce a game is up and running, Rivet provides a sleek dashboard for operating the game and streamlining DevOps. Developers can manage their lobbies, view analytics, rollback versions, diagnose crashes, and manage namespaces.\n\n![uploaded image](/media/?type=post&id=70240&key=user_uploads/1203698/3fe4f55a-dc32-42b0-a444-4de15f618120)\n\nWant to see Rivet in action? Watch our [3-minute crash course](https://www.youtube.com/watch?v=pBE7s-PK0j0)!\n\n**Our Ask**\n\nIf you or a friend is building a multiplayer game – even if it’s a hobby project – we’d love to chat! Contact us at [yc@rivet.gg](mailto:yc@rivet.gg).",
        "url": "https://www.ycombinator.com/launches/IGu-rivet-heroku-for-multiplayer-games",
        "votes": 57,
        "created_at": "2023-03-13T20:31:18.735Z"
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    "name": "SuperAPI",
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    "all_locations": "Bengaluru, KA, India",
    "long_description": "SuperAPI is a programmable API gateway that makes all your fetch APIs cacheable, reducing the response time 10x. \r\nOur customers use SuperAPI to cache their critical APIs without spending 6-9 months rewriting their code. ",
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    "team_size": 2,
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        "title": "SuperAPI - Make all your fetch APIs cacheable",
        "tagline": "We help make your APIs much faster by making them cacheable ",
        "body": "### **✨ TL;DR**\n\nSuperAPI is a programmable API gateway that makes all your fetch APIs cacheable, and makes sure your cached API responses never goes stale.\n\nHey everyone - it’s Aditya and Adithya here introducing **SuperAPI**.\\\n\\\n—\n\n### **🤯 Problem**\n\nA lot of application APIs are considered non-cacheable either because:\n\n1. the underlying data changes frequently\n2. they use GraphQL making it difficult to cache\n3. they’re REST APIs that fetch using POST because they have their search parameters within the body\n\nMost applications in this situation do not cache their API responses, and end up serving APIs with a high latency, or setup multiple clusters close to their users to give them low latencies.\n\n# **💡 Solution**\n\nSuperAPI makes such APIs cacheable by monitoring your incoming **POST / PUT / PATCH** API calls, and your database updates to invalidate stale data and keep your API cache fresh.\n\nBecause we listen to your database changes, SuperAPI can also update your cache in-place. This means, any updates in your database will only modify the relevant portions of your cached API responses.\n\nThis reduces the amount of computations on your database & compute instances, helping reduce your cloud spend.\n\n# **🚀 How does SuperAPI work?**\n\nSuperAPI can either be deployed as a separate API gateway, or can run on top of other caching providers like **Cloudflare & Cloudfront.**\n\nThis API gateway listens to all incoming update API calls (POST / PUT / PATCH), and invalidates any API cache that could go stale as a result of this incoming update API call.\n\nWe also have a tiny docker container running within your network that listens to your database updates (using the replication stream or database triggers), which helps us catch any invalidations coming from internal cron jobs & batch processes.\n\nRead more about how SuperAPI works [here](https://trysuperapi.com/blog/how-does-superapi-work-clet0rvf1446071ko73x4q6qtr).\n\n![uploaded image](/media/?type=post&id=70198&key=user_uploads/11606/c7fdb60e-043e-49d6-b23b-e6123de076e6)\n\n# **📜 How did we get here?**\n\nBoth of us founders worked together at a Tiger Global backed Indian tech company before starting on SuperAPI.\n\nI was the first engineer there, and built out majority of the product & platform for the first couple of years. For the next 2 years, I was heading the engineering team, growing the team from 6 engineers to \\~55 engineers, designers & PMs.\n\nAditya was heading the platform engineering team, where he built a majority of the infrastructure to scale the platform and bring down the API latencies to less than 50ms.\n\nWe’ve been obsessed with API latencies since our time working together, and are using a lot of learnings from back then to build out SuperAPI.\n\n# ❓ **Asks**\n\nIf you are looking to **reduce your API latencies**, let’s talk. Drop us a note at: [founders@trysuperapi.com](mailto:founders@trysuperapi.com).",
        "url": "https://www.ycombinator.com/launches/IGE-superapi-make-all-your-fetch-apis-cacheable",
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        "full_name": "Rahul Sheth",
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        "founder_bio": "Rahul is co-founder and CEO of Texel.ai. He worked on Bitmoji at Snapchat for almost 8 years after completing a Computer Science PhD at Stanford in character animation and physics simulation. He is interested in scalable systems for computer graphics and artificial intelligence.",
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        "full_name": "Eli Semory",
        "title": "Founder",
        "founder_bio": "Eli is the Co-founder and CTO of Texel.ai. He worked at Snapchat for the last 5 years, building one of the worlds largest graphics GPU deployments. At Texel.ai he's solving the next big problems in Generative AI enabling new and exciting large scale applications.",
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      {
        "id": 69917,
        "title": "Texel: Optimizing AI and video pipelines",
        "tagline": "Helping businesses make their GPU-based AI infrastructure 10x more efficient",
        "body": "**tl;dr: Texel makes your AI infrastructure 10x more efficient. We do this by providing tools and APIs that allow you to quickly encode or decode media and optimize models for inference.**\\\n\\\n💸 **The problem:**\\\nMany companies run inference on media, to do things like generate images or detect events in scenes. **Running inference is expensive both in compute and cost, and video decoding and encoding is too.**\n\nMost developers use the best tools available to optimize inference and media processing in isolation, but shuffling the data between these pipelines is slow. As an example, four different inference pipelines means first decoding a video frame and then sending it to each of four GPUs running different models and finally compositing all the frames before re-encoding.\n\n**😎 The solution:**\n\n![uploaded image](/media/?type=post&id=69917&key=user_uploads/1188792/4bdc96f4-6c64-44a8-bc65-c07bd88e8437)\n\nVanilla PyTorch running Stable Diffusion (left), Texel-accelerated version (right)\\\n\\\nWe optimize the entire pipeline for you, from input to inference to output, by providing APIs that allow you to control the features you want to activate and taking care of the data flow and scaling behind the scenes. You can bring your own custom models that we optimize, cutting down on inference time and reducing GPU memory size. If you want to just optimize inference, training on media, or encode a video, that’s totally fine and we can do that for you! \n\nIn the example mentioned above, the video decode, all four models, and the video encode would be streamlined to run on as few GPUs as possible. **With these optimizations, editing images and videos with AI can become real-time.** **You’ll also save money by using less GPUs.**\n\n**📖 The story:**\n\n![uploaded image](/media/?type=post&id=69917&key=user_uploads/1188792/97257823-69f1-4c89-a8b4-af5bae7b0b92)\n\nRahul (left) and Eli (right) spent years working together building one of the world’s largest GPU-based services at scale while at Snapchat and discovered many optimization techniques. **We built out and ran a fleet of thousands of GPUs serving hundreds of millions of daily active users generating over one billion images per day in real-time.** **Our optimizations saved the company over $20 million a year in operational costs.** AI is taking the world by storm and mainly runs on GPUs, so we saw an opportunity to use our expertise and build the best product in the space.\n\n**🔧 How it works:**\n\nWe deploy your models and provide an API for you to control them and run your inference faster. You can also add rendering, video encoding, or any other GPU-based workflow and it will be accelerated. The deployment can be hosted or on-premise, depending on your needs.\n\n**🙏 Our asks:**\n\n* Please reach out to us at [founders@texel.ai](mailto:founders@texel.ai) if:\n  * You are running inference and/or processing video/image data and are interested in optimization\n  * You want to try our API out and want to request an API key\n  * Would like to introduce us to others that want to optimize their MLOps and/or VideoOps",
        "url": "https://www.ycombinator.com/launches/IBh-texel-optimizing-ai-and-video-pipelines",
        "votes": 19,
        "created_at": "2023-03-03T17:03:24.107Z"
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Artie is software that streams data from databases to data warehouses in real-time. Today, most companies run their ETL process every few hours or overnight, so their data warehouse is always out of date; with Artie, the warehouse always has live production data.",
    "one_liner": "Software that streams data from databases to warehouses in real-time",
    "team_size": 20,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1684375922,
    "tags": [
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    "top_company": false,
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    "batch": "Summer 2023",
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    "industries": [
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      "Infrastructure"
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    "regions": [
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    "stage": "Growth",
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    "url": "https://www.ycombinator.com/companies/artie",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/artie.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/artie-technologies/",
    "x_url": "https://x.com/artie_labs",
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    "cb_url": "https://www.crunchbase.com/organization/artie-a438",
    "github_url": "https://github.com/artie-labs/transfer",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/973d2f0c6c4d07505113e974a08d18864eb95d54.png",
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    "primary_partner": {
      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
    },
    "founders": [
      {
        "user_id": 1276059,
        "full_name": "Jacqueline Cheong",
        "title": "Founder",
        "founder_bio": "Co-founder and CEO at Artie. Building the real-time data streaming platform for AI. \n\nWe give companies the kind of streaming pipelines and deep observability that Netflix, DoorDash, and Instacart built in-house - without hiring 10+ engineers and spending 1-2 years on platform work. Artie automates the entire data ingestion lifecycle, from capturing changes to merges, backfills, and observability, and scales to billions of change events per day. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jacqueline-cheong/",
        "x_url": "https://x.com/JacquelineSYC19",
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      },
      {
        "user_id": 674147,
        "full_name": "Robin Tang",
        "title": "Founder",
        "founder_bio": "CTO @ Artie. Software engineer with a background in distributed systems.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/tang8330/",
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    ],
    "jobs": [
      {
        "id": 85441,
        "title": "Enterprise Account Executive",
        "url": "https://www.ycombinator.com/companies/artie/jobs/HyaHWUs-enterprise-account-executive",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$240K - $320K",
        "equity_range": "",
        "experience": "6+ years",
        "visa": "Will sponsor",
        "skills": []
      },
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        "id": 94613,
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        "url": "https://www.ycombinator.com/companies/artie/jobs/APdtYeH-solutions-engineer",
        "location": "San Francisco, CA, US",
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        "role_type": null,
        "salary_range": "$175K - $200K",
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      {
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        "title": "Product Engineer",
        "url": "https://www.ycombinator.com/companies/artie/jobs/A5udo6A-product-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $250K",
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        "title": "Senior Software Engineer ",
        "url": "https://www.ycombinator.com/companies/artie/jobs/7KTFRRy-senior-software-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
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        "location": "San Francisco, CA, US",
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    "news": [
      {
        "title": "Artie: Real Time Data Streaming For The AI Age",
        "url": "https://www.youtube.com/watch?v=M57MeOY-n3g",
        "date": "Jan 26, 2026"
      },
      {
        "title": "Artie helps companies put data to work faster with real time syncing | TechCrunch",
        "url": "https://techcrunch.com/2024/02/14/artie-helps-companies-put-data-to-work-faster-with-real-time-syncing/",
        "date": "Feb 14, 2024"
      }
    ],
    "launches": [
      {
        "id": 111491,
        "title": "Artie Free Plan: real-time data replication for $0/mo",
        "tagline": "Stream data to your warehouse or another database, free forever – up to 1M rows a month.",
        "body": "### TL;DR\n\nArtie streams production data into your warehouse or another database in real time, so your downstream systems and workflows never run on stale data.\n\nWe launched Artie’s Free plan so startups and solo builders can get real-time database replication without a card, sales call, or CDC infrastructure project.\n\n[Check out our launch video:](https://www.youtube.com/watch?v=jXPNzOyPVH0)\n\n### The problem\n\nA solo developer wiring Supabase or Postgres into a warehouse doesn’t want to take on a CDC project. A startup’s first data hire needs fresher data for a product workflow, payments, or support, but doesn’t have a data platform team to build it. A larger company wants to test a narrow use case before pulling in procurement and security.\n\nAll three have the same initial question: will this work with our database, our destination, and our data? Answering that should take less than an afternoon, without needing to put down a credit card or join a sales call.\n\nFor Keep, a fintech startup, setting up a connector on Artie took around 10 minutes. Their team wanted Postgres and MongoDB data in Snowflake, but didn’t want to spend months building and maintaining pipelines. Their CTO’s reason for that was straightforward: he wanted the team differentiating on product, not babysitting data infrastructure. [Read Keep’s story](https://www.artie.com/customer-stories/keep-enables-real-time-data-stack-and-audit-logs?utm_source=ycombinator&utm_medium=organic&utm_campaign=free_plan).\n\n### Why now\n\nStartups are building products and operating workflows around production data with smaller teams than ever. They want that data in their warehouse (or another database) in real time, but need a way to test a real workload without building and running Kafka, Debezium, connectors, and monitoring.\n\n### What's included\n\nArtie Free includes:\n\n* Two active pipelines\n* 1 million billable rows per month\n* Backfills, not counted toward the monthly row limit\n* Sources: Postgres, MySQL, MongoDB, DynamoDB, CockroachDB, DocumentDB, Amazon Keyspaces, and PlanetScale\n* Destinations: Snowflake, BigQuery, Redshift, Databricks, MotherDuck, Postgres, S3, GCS, and ClickHouse\n\n[See the full plan details](https://www.artie.com/pricing?utm_source=ycombinator&utm_medium=organic&utm_campaign=free_plan).\n\n### Artie’s MCP Server\n\nArtie also has [an MCP server,](http://mcp.artie.com/?utm_source=ycombinator&utm_medium=organic&utm_campaign=free_plan) so you can manage pipelines from Claude or Codex instead of switching to the dashboard. You can list and create pipelines, inspect schema changes, manage backfills and connectors, and search the Artie data catalog, all from a conversation.\n\nThe MCP server is now a [ChatGPT Plugin](https://chatgpt.com/plugins/plugin_asdk_app_6a62a153d4088191a5e551ec94e025da?q=artie) and listed in [Claude's connector directory](https://claude.ai/directory/artie). You can also use another MCP-compatible client, such as Cursor, by pointing it at `https://mcp.artie.com/mcp` with an API key from your Artie dashboard. [Full setup docs here](https://www.artie.com/docs/api/mcp?utm_source=ycombinator&utm_medium=organic&utm_campaign=free_plan).\n\n### Our ask\n\nIf you are a founder, solo developer, or first data hire, [try Artie’s Free plan](https://app.artie.com/signup?utm_source=ycombinator&utm_medium=organic&utm_campaign=free_plan) with one real workload. Tell us what gets in the way: permissions, configuration, schemas, or getting the first row through.",
        "url": "https://www.ycombinator.com/launches/T0F-artie-free-plan-real-time-data-replication-for-0-mo",
        "votes": 33,
        "created_at": "2026-08-20T16:50:12.222Z"
      },
      {
        "id": 102815,
        "title": "Artie - Real-time data replication to your data warehouse",
        "tagline": "Give your AI agents fresh data they can actually act on",
        "body": "**TL;DR:** Artie is a fully managed, real-time data replication tool that streams every row-level change from your production database to your warehouse in under 60 seconds. You can now sign up and start replicating today, no call required. Most teams run their first sync in under an hour.\n\n<https://www.youtube.com/watch?v=BYgz9NaIBLg>\n\n**The Problem**\n\nGetting data from your production database to your warehouse in real time has always meant picking your poison.\n\nYou build it yourself: Kafka, Debezium, custom merge logic, months of engineering, and years of maintenance as schemas evolve and the engineers who built it move on.\n\nYou use an existing CDC tool: usually something inherited, unreliable at scale, and not built for the warehouse destinations you're actually running.\n\nOr you accept stale data: dashboards refreshing every 3-24 hours, AI pipelines reasoning over yesterday's snapshot, analytics teams filing 9 am tickets asking why the numbers don't add up.\n\nMost teams are somewhere on this spectrum – either maintaining a pipeline they didn't want to build, or working around data they can't trust. That was a manageable problem when stale dashboards just annoyed your analytics team, but it becomes a bigger beast when AI agents are making pricing decisions, routing support tickets, and scoring risk off the same lagged data.\n\nA warehouse that's 12 hours behind isn't a minor inconvenience anymore – it's a liability.\n\n**The Solution**\n\nSee it work: <https://www.youtube.com/watch?v=xp0Yp77_Z9w>\n\nArtie handles the entire replication layer so you don't have to. No self-built Kafka stack to maintain, no unreliable CDC tool to babysit, no stale data to apologize for. Connect your source, choose your destination, and your warehouse stays in sync with production continuously.\n\nAI agents are only as good as the data they act on. If your warehouse is hours behind, your agents are too. Artie gives your AI layer what it actually needs: a warehouse that reflects production in real time, without engineering overhead.\n\n**How It Works**\n\nArtie uses change data capture (CDC) to read directly from your database's replication logs – capturing every insert, update, and delete as it happens. Those changes stream continuously to your warehouse, landing in under 60 seconds. There's no bulk export, no scheduled job, no polling. Just a live, accurate reflection of your production data.\n\nUnder the hood, Artie handles the operational complexity: schema changes propagate automatically, delivery is exactly-once even through failures, and your destination tables stay in sync without any pipeline code on your end. The things that take most teams a year or two to get right are already solved.\n\n**Sources:** PostgreSQL, Amazon Aurora/RDS, GCP CloudSQL, Supabase, MySQL, Microsoft SQL Server, MongoDB, DocumentDB, DynamoDB, Oracle, CockroachDB, Amazon Keyspaces (Cassandra), and an Events API\n\n**Destinations:** Snowflake, BigQuery, Redshift, Databricks, ClickHouse, PostgreSQL, MySQL, Microsoft SQL Server, MotherDuck, S3, GCS, Iceberg REST Catalog, and Amazon S3 Tables\n\n**The Team**\n\nRobin (co-founder and CTO) has been building large-scale data pipelines his entire career – from processing 14 billion daily events at [Outbound.io](http://Outbound.io), to filing patents on event streaming at Zendesk, to running data infrastructure at Opendoor. He started Artie because he kept hitting the same ceiling: pipelines that worked until they didn't, and teams spending years maintaining infrastructure that should have been solved.\n\nBefore co-founding Artie, I covered enterprise software at Bank of America and Balyasny Asset Management and kept running into the same gap: companies burning engineering cycles on data pipelines that shouldn't require a team to maintain. We've been building Artie together for three years, went through YC S23, and closed a $12M Series A led by Dalton Caldwell at Standard Capital.\n\n**Our Ask**\n\nIf you're a data engineer or engineering manager tired of stale warehouse data, [try it yourself](https://go.artie.com/launch) today. See real-time replication running in your own environment – not a demo, not a sandbox, but against your actual data.\n\nIf you need private networking, dedicated infrastructure, or compliance support, [book a demo](https://go.artie.com/2822ee) and we'll walk you through what an enterprise setup looks like.\n\n[Try Artie today](https://go.artie.com/launch)",
        "url": "https://www.ycombinator.com/launches/QkJ-artie-real-time-data-replication-to-your-data-warehouse",
        "votes": 43,
        "created_at": "2026-06-09T17:12:18.002Z"
      },
      {
        "id": 88730,
        "title": "🐘 Artie for Enterprise - Data Replication at Petabyte Scale",
        "tagline": "High-volume, low-latency data replication that scales effortlessly",
        "body": "# TL;DR\n\n[Artie](https://www.artie.com/?utm_campaign=launch-week-q125&utm_source=yc-launch) streams data from databases to data warehouses in real-time and more reliably than traditional ETL solutions. Many companies are still running their ETL process every few hours, so their data warehouse is constantly out of date; with Artie, the data warehouse always has live production data.\n\nArtie was founded to solve an enterprise data movement problem. Today, we’re launching Artie for Enterprise, for teams requiring high-volume, low-latency data replication that scales effortlessly. Whether dealing with massive volumes, strict security requirements, or mission-critical workloads, Artie ensures data is always live, accurate, and easy to manage. 🚀\n\n![uploaded image](/media/?type=post&id=88730&key=user_uploads/89898/675b1398-6b54-40a0-888f-a3f4f487ffc3)\n\n# Problem\n\nEnterprises need real-time, reliable, and secure data replication—but existing solutions make this far more painful than it should be.\n\nTraditional ETL pipelines often run on fixed schedules, updating data warehouses hours behind reality and leaving teams with outdated insights. Many replication tools try to brute-force data movement by running expensive, inefficient queries that **increase load on production databases**, slowing down applications and, in extreme cases, even **bringing them down entirely**. Scaling these systems across thousands of tables typically requires **manual, error-prone configurations**, forcing teams to spend countless hours debugging pipelines just to keep data flowing.\n\nOn top of that, many enterprises operate in highly regulated environments where data can’t be fully offloaded to the cloud. Traditional replication solutions struggle to support hybrid deployments, forcing teams to choose between security and ease-of-use.\n\n# Artie for Enterprise provides reliable, secure, and high-volume data replication\n\n### Terraform support\n\n_“Um, we have thousands of tables to configure. Do I really need to click thousands of times on your dashboard to get connectors set up?”_ We hear this question a lot and our answer is “**Absolutely not”**, which is where our Terraform provider comes in. \n\nJust because your data volume is high does not mean management needs to be equally complex. No more endless clicks—just code it, deploy it, and go.\n\n![uploaded image](/media/?type=post&id=88730&key=user_uploads/1276059/ed2daad8-2763-4dfc-aaad-ea336ba4daf0)\n\n### Multi-step merge\n\nMerging large, frequently updated tables can be expensive and slow. We wanted to solve this problem without forcing customers to increase compute costs or scale up their virtual data warehouses.\n\nWith multi-step merge, Artie now loads data into a staging table in multiple bursts, allowing updates to accumulate before merging into the target table. This reduces latency and improves efficiency. Customers can now control how often data lands in the staging table before triggering a final merge—giving them real-time syncs with more flexibility and cost savings.\n\n![uploaded image](/media/?type=post&id=88730&key=user_uploads/1276059/0dcc6696-60c9-4ab8-a301-9764abbb5756)\n\n### Hybrid deployment\n\nEnterprise-grade replication means deployment flexibility is non-negotiable. There are many organizations out there that handle data containing sensitive information and are required to operate under strict regulations.\n\nOur hybrid deployment model ensures the security of on-premise data processing with the ease of use of cloud services. What’s more? Artie’s fully-managed service allows for zero-maintenance, and removes the need to install and manage client-side agents.\n\n![uploaded image](/media/?type=post&id=88730&key=user_uploads/1276059/5464e25f-5c07-4bbc-8926-ea10fc6c48e1)\n\n### MySQL Connector upgrade\n\nWe’ve made major improvements to our MySQL connector, making it enterprise-grade with improved performance, efficiency, and automation. These updates streamline database operations, reduce disk usage and I/O load, and automates data synchronization without complex configuration requirements and management overhead. \n\nKey improvements include full DDL and gh-ost migration support for seamless, non-disruptive schema changes, automatic fan-in for partitioned tables, and GTID support for reliable transaction replication.\n\n### PostgreSQL CTID scanning\n\nBackfills can be disruptive and resource-intensive. That’s why Artie is built to seamlessly recover from errors and avoid backfills unless absolutely necessary. \n\nHowever, in the event that backfills are required, how do we minimize disruption? Enter PostgreSQL CTID; an alternative backfill method that is **10-20x faster** than traditional methods.\n\n### Datadog integration\n\nAt Artie, we’ve always been big on observability. What good are your data pipelines if nobody has visibility into them? We prioritize accurately monitoring your pipeline performance and making sure you know what’s up. \n\nWith our Datadog integration, you can now track all Artie-exposed metrics directly from your Datadog dashboard. This allows you to **proactively monitor pipeline performance, set up custom alerts**, and build dashboards for deeper insights.\n\nStay on top of your data pipeline health—effortlessly.\n\n![uploaded image](/media/?type=post&id=88730&key=user_uploads/1276059/38889e0e-30ca-495f-b777-4af640e9aa3b)\n\n# Curious to learn more?\n\nFollow our journey on [LinkedIn](https://www.linkedin.com/company/artie-technologies), [X](https://x.com/artie_tech) and reach out to us [here](https://www.artie.com/contact?utm_campaign=launch-week-q125&utm_source=yc-launch) to see how you can benefit from enterprise-grade replication!",
        "url": "https://www.ycombinator.com/launches/N58-artie-for-enterprise-data-replication-at-petabyte-scale",
        "votes": 17,
        "created_at": "2025-03-18T14:06:08.364Z"
      },
      {
        "id": 76461,
        "title": "Artie’s Analytics Portal - Real-time data pipeline observability",
        "tagline": "Real-time observability into database pipelines and peripheral infrastructure",
        "body": "# TL;DR\n\n[Artie](http://www.artie.so) is a real-time database replication solution. We leverage change data capture (CDC) and stream processing to perform data syncs in a more efficient way, which enables sub-minute latency and helps optimize compute costs. Today, we’re launching our Analytics Portal to provide visibility into our streaming pipelines and offer production-level monitoring for related system infrastructure out-of-the-box.\n\nWith the Analytics Portal, we hope to help alleviate some of the challenges that data teams face when running their data stack. By offering real-time observability into database pipelines and peripheral infrastructure, we hope companies can increase their understanding of how systems impact one another, reduce downtime/debug issues faster, and generate proactive alerts to maintain robust infrastructure. \n\n# **Visibility related to database replication is lacking**\n\nThe core of database replication is transferring data in a timely, accurate, and reliable manner. This is table stakes. In addition, there is a lot more happening in the peripheral, such as database monitoring, data pipeline visibility, data latency monitoring, and others. Data engineers need visibility to answer questions like: \n\n* How many rows have been synced in the past hour? How does that compare to the average/median/last month? \n* What is the data latency for our organization’s three most important tables? What are the factors that impact data latency and how are those metrics trending? \n* How are my systems performing?\n  * Are there any database permission errors that may cause my data pipeline to go down? \n  * Are my databases sized correctly? How is CPU, memory, and storage utilization? \n  * Is my replication slot growing? By how much?\n\n \n\nSetting up these metrics and monitors is important to help with debugging and maintaining a robust database replication solution. However, this requires expertise and domain knowledge that may not be accessible at every company. There is also no standardization of which metrics to track and what benchmarks to follow. To make matters worse, when it comes to adopting cloud solutions, database/pipeline visibility is severely limited. When pipelines break down, customers are often left in the dark, not knowing what broke, why it broke, and how to fix it. \n\n# **Artie’s Analytics Portal unlocks visibility & reduces MTTD**\n\nWe are extremely excited to announce our Analytics Portal to increase the visibility and observability of our streaming pipelines. This will provide insights into system-level infrastructure and help with monitoring database and pipeline health. When identifying and resolving issues, one of the most important metrics is to reduce MTTD (mean time to detection). With Artie’s streaming pipelines and periodic jobs like [Postgres Watcher](https://docs.artie.so/real-time-sources/postgresql#postgresql-watcher), metrics are being sent to our Analytics Portal in flight, as the underlying data is still being processed. \n\n![uploaded image](/media/?type=post&id=76461&key=user_uploads/1276059/f4fe85ba-5ced-4892-9653-ed030638826d)\n\nWith the first iteration of our Analytics Portal, we are providing industry-standard telemetry to streaming pipelines and related infrastructure. Data teams will be able to observe the following: \n\n* Data ingestion latency by table and database/deployment\n* Operation distribution by table and database/deployment\n* Rows synced by table and database/deployment\n* Database monitors that are related to and can impact pipeline performance: \n  * Permission errors that may interfere with data replication\n  * Replication slot size\n  * Free disk space\\*\n  * CPU utilization\\*\n  * Memory and Disk I/O (input/output)\\*\n  * Average transaction time\\*\n  * Existence of long-running queries\\*\n\n\\*coming soon\n\n# Production level monitoring out-of-the-box\n\nThe Analytics Portal initially comes with a set of pre-built charts and monitors. Customers are able to drill down to get deployment, database, and table-level statistics.\n\n![uploaded image](/media/?type=post&id=76461&key=user_uploads/1276059/bcbbd68a-9808-48c5-92e5-1f61b53f9d86)\n\n![uploaded image](/media/?type=post&id=76461&key=user_uploads/1276059/b5ec3d58-4f94-4af0-89d3-14ab920b2003)\n\nThe pre-built monitors that we are launching with include alerts for database permission errors and replication slot growth (for Postgres users). Over time, we will add alerting for the other monitors we mentioned above and more. This enables customers to have production-level monitoring set up out-of-the-box for their business-critical data. \n\nFor example, an e-commerce company might be watching its `online transactions` table closely during the holidays. Let’s say they observe data ingestion latency going up for `online transactions`. They zoom out and realize it’s not just the `online transactions` table that is experiencing higher latency, but all tables under their Postgres connector are impacted and very few rows have been synced in the past 5 minutes. To troubleshoot this, they look over to the database monitors and realize their database’s replication slot has been growing and the culprit is a long-running query that has locked the table and is preventing Postgres from advancing the replication slot. After a quick Slack message to their internal DevOps team, the query is killed and the issue is resolved. \n\n# Making the Analytics Portal more actionable & customizable over time\n\nOver time, we will make the data more actionable and customizable. In the near future, we plan to enable row-based monitoring such that customers can monitor custom business logic. In addition to the pre-built charts and monitors that we provide out-of-the-box, we want to allow customers to create custom charts and configure views based on metrics that matter to their business. \n\nFor example, say you are working at a Fintech company that wants to monitor live transactions to detect fraud and abuse on your platform. You have a `transactions` table and this table is being synced to your Snowflake instance. You should be able to generate a chart to plot the average, median, p95, and max transaction sizes across various lookback periods (30 minutes, 1 hour, 24 hours, 7 days, etc). Then you can set up business logic monitors such as:\n\n* Flag transactions that are 1.5 standard deviations above average coming from a merchant who signed up on the platform less than one week ago\n* Flag key accounts where transaction volume has increased above or fallen below a certain threshold in the past hour\n\n![uploaded image](/media/?type=post&id=76461&key=user_uploads/1276059/1a22097c-f925-43c4-9fac-c5bf5035b55d)\n\nDepending on how you’d like to be notified, Artie plans to support the following escalation channels:\n\n* Email\n* Slack\n* Webhooks\n\nIn this example, the escalation channel is a webhook to your API server so you can then run a more rigorous machine-learning fraud model against a particular merchant account or transaction.\n\n# **Contact us at **[**hi@artie.so**](http://www.artie.so)** to learn more!**",
        "url": "https://www.ycombinator.com/launches/JtF-artie-s-analytics-portal-real-time-data-pipeline-observability",
        "votes": 24,
        "created_at": "2023-11-27T17:42:21.200Z"
      },
      {
        "id": 72536,
        "title": "Artie - real-time data streaming for databases ⚡",
        "tagline": "We transfer data from databases to data warehouses in real-time with CDC streaming",
        "body": "# _TL;DR_\n\n_Artie is an open-source, streaming version of Fivetran - we transfer data from databases to data warehouses in real-time. Setting up a connector takes minutes and Artie leverages change data capture (CDC) to help companies reduce their data warehouse costs by 50%! We enable organizations to unlock real-time insights for better decision making. _[_Star us on GitHub_](https://github.com/artie-labs/transfer)_!_\n\n![uploaded image](/media/?type=post&id=72536&key=user_uploads/1276059/60958f68-f519-4d78-bb98-c2c33974cb39)\n\nHi everyone, we are Jacqueline and Robin and we’re the team behind [Artie](https://www.artie.so/?utm_source=YC&utm_medium=newsletter&utm_campaign=launchYC&utm_id=launchYC). We’re on a mission to eliminate data latency and make it easy for every company to enable real-time data streaming!\n\nData is typically synced from production databases to the data warehouse once every X hour(s)/day(s) - this is a constraint that companies have lived with for decades. Robin personally felt the pain of not having access to production data in real-time and there were no easy to use out-of-the-box solutions, so we decided to build one! \n\n# **❌ The Problem**\n\nDoes your company sync data to the data warehouse every 6 hours, or worse, once a day? Are your analytics always lagged and filled with stale insights? **Why settle for a data platform that’s barely good enough when you can have real time data AND reduce your data warehouse costs?!** Not to mention you can have Artie set up in minutes!\n\nTraditional ETLs are based on **batched processes** that operate on a cron schedule (DAGs, Airflow) and **cannot achieve real-time data syncs**.\n\nBuilding and managing streaming data pipelines is hard. Most companies have a small team of data engineers and they often spend all day maintaining their data pipelines, which is not productive.\n\nFactors companies should consider if they want to self-manage pipelines:\n\n* Can the solution scale to multiple different data sources?\n  * How easy is it to add new data sources?\n  * How easy is it to manage across all the data sources?\n* Can the solution scale to handle 1m+ queries per second?\n  * Is the solution horizontally scalable?\n  * Do workers require coordination? Or are they stateless and distributed?\n* How do you ensure there are no out of order or missing events (even when the system crashes)?\n* Can the solution handle schema evolution without creating breaking changes downstream?\n\n# **🎉 Solution**\n\nArtie leverages change data capture (CDC) and stream processing to achieve **sub-minute data latency (\\~typically 10-20 seconds)**. Since we **only transfer changed data**, Artie is more efficient than traditional ETLs and can help you **cut down on your data warehouse cost by 50%!**\n\nSetting up a connector requires no programming. Just follow the setup guide and deploy in minutes! After the initial snapshot, any changes in your database will be reflected in your data warehouse in real-time.\n\n![uploaded image](/media/?type=post&id=72536&key=user_uploads/1276059/879c552d-7c8a-402e-9378-56f55380308a)\n\n# **🎯 Who Needs Artie?**\n\n**Engineers** that are exhausted stitching together Airflow + AWS Glue + Apache Spark + AWS Kinesis/Kafka + Apache Flink 😵‍💫\n\n**Companies that are using traditional ETLs or batched processes**. Once you enable real-time, there is no going back (your data engineers/BI analysts won’t let you)! Think of all the previously unattainable use cases that you can now implement without data latency.\n\n**Companies that have a cost cutting initiative.** Adopt Artie’s CDC streaming capabilities to reduce your data warehouse costs!\n\n# **🙏 Ask**\n\nEmail [hi@artie.so](mailto:hi@artie.so) or sign up [here](https://artie.so/contact?utm_source=YC&utm_medium=newsletter&utm_campaign=launchYC&utm_id=launchYC) to try Artie today! \n\nWant to use open source? Install Artie at <https://github.com/artie-labs/transfer> (give us a ⭐!) and ping us on [Slack](http://artie.so/slack?utm_source=YC&utm_medium=newsletter&utm_campaign=launchYC&utm_id=launchYC), we’re happy to help you get started.",
        "url": "https://www.ycombinator.com/launches/Irw-artie-real-time-data-streaming-for-databases",
        "votes": 108,
        "created_at": "2023-06-26T15:13:53.399Z"
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    "id": 28804,
    "name": "Bland AI",
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        "user_id": 1314105,
        "full_name": "Isaiah Granet",
        "title": "Founder",
        "founder_bio": "building things that talk back. \n\nno more waiting on hold, no more phone trees. \n\nthe future is bland.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/isaiah-n-granet",
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        "full_name": "Sobhan Nejad",
        "title": "Founder",
        "founder_bio": "Co-founder of Bland AI. Building the enterprise platform for AI phone calling.",
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        "title": "Support Engineer",
        "url": "https://www.ycombinator.com/companies/bland-ai/jobs/AInt422-support-engineer",
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        "role_type": null,
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        "id": 68954,
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        "url": "https://www.ycombinator.com/companies/bland-ai/jobs/sKOsJDQ-ai-ml-engineer",
        "location": "San Francisco, CA, US",
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        "id": 83033,
        "title": "Full Stack Engineer",
        "url": "https://www.ycombinator.com/companies/bland-ai/jobs/gnjM27L-full-stack-engineer",
        "location": "San Francisco, CA, US",
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    "news": [
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        "title": "Exclusive: Voice AI startup Bland raises $50 million after being rejected by 180 investors | Fortune",
        "url": "https://fortune.com/2026/06/16/voice-ai-bland-50-million-after-being-rejected-by-180-investors/",
        "date": "Jun 16, 2026"
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      {
        "id": 83557,
        "title": "Bland AI: The enterprise platform for AI phone calls",
        "tagline": "Build, test, and scale AI phone agents, and integrate with our API.",
        "body": "### **TL;DR:**\n\n* 13\\.5 billion phone calls are made every single day. Hundreds of millions of those occur in call centers.\n* The call center economy is a >$30 billion industry that is growing >10% year over year.\n* Our customers use Bland to build AI phone agents that handle the bulk of their call volume. Whether these calls are for customer support, sales, or appointment setting, Bland makes these calls cheap and efficient.\n\n### **The Problem**\n\nMore than half of the population prefers talking on the phone with businesses instead of email and chat. The average cost per call for US-based companies is somewhere between $3.00 and $5.00. Enterprises need call centers– but they are impractical to run and difficult to affordably staff. \n\nCall center jobs have some of the highest turnover in the world. They are grueling, don’t pay well, and highly repetitive. This means:\n\n* High cost for training, onboarding, and recruiting\n* Agents that don’t have the context or education to handle moderately complex calls\n\n**These calls are the backbone of how enterprises interact with their customers. We’ve all been stuck on hold for hours, transferred through dozens of different organizations to get to the right supervisor, all to waste the bulk of a day fixing a simple problem.**  Customers hate this, get exasperated and angry, and the call center worker, two weeks into the job, has to deal with a rightfully frustrated customer. The business loses trust with the customer and has no real way to build a better process.\n\n### **Solution**\n\nUsing Bland, companies build AI phone agents that are hyper-educated, have strict guardrails, and integrate into every part of their tech stack. **By building a true end-to-end infrastructure, these agents have Enterprise-grade stability, sub-1 second latency, and the ability to infinitely scale. Companies build the agent, integrate it into their CRMs and databases, and expand the use case whenever they need to**.\n\n**To negate hallucinations** we’ve built out our own programming language called _Conversational Pathways_, which allows you to split prompts into unique nodes.\n\n**For best-in-class latency and security,** we built and hosted our own TTS, Inference, and Transcription models.\n\n**For observability,** we provide full transparency into what’s happening during every call, from real-time logs to post-call analysis.\n\nWe’re trusted by Sears, [Better.com](http://Better.com), and backed by investors like Scale Partners and Jeff Lawson to change the way enterprises interact with their customers forever.\n\n<https://youtu.be/kev5w-6WUyA>",
        "url": "https://www.ycombinator.com/launches/Ljh-bland-ai-the-enterprise-platform-for-ai-phone-calls",
        "votes": 25,
        "created_at": "2024-08-28T20:35:23.871Z"
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      {
        "id": 72612,
        "title": "Intelliga Health - Automating insurance workflows for medical practices",
        "tagline": "Helping patients get the treatments they need.",
        "body": "**TLDR**; At Intelliga Health, we use AI to streamline insurance workflows for medical practices. We integrate seamlessly with fax machines, digitize and organize inbound insurance communication, and write appeals back to insurance.\n\n### **Problem**\n\nManual workflows associated with running medical practices - like organizing faxes, scanning files, and dealing with insurance - distract doctors from the most important part of their days, caring for their patients.\n\nPrior Authorization, for example, costs an estimated 16 hours each week, while causing delays and sometimes denials to patients’ treatments.\n\n### **Solution**\n\nWe’re developing an AI-powered system to automate back-office operations for medical practices. Our first product, Appeal Suite, helps practices organize inbound faxes and win more prior authorization appeals. Try our public demo [here](https://appeals.intelligahealth.com/)!\n\n### **Product Snapshot**\n\nAfter receiving a fax from insurance, our system can:\n\nCategorize the fax by type, patient, and authorization case within an electronic inbox\n\n![uploaded image](/media/?type=post&id=72612&key=user_uploads/1312013/c11b5cae-8a02-4f20-b21a-5214b9d01701)\n\nGenerate a persuasive and evidence-based appeal letter to inbound denials\n\n![uploaded image](/media/?type=post&id=72612&key=user_uploads/1312013/7ad28ccd-c881-4df3-bc62-bc300e201c36)\n\n### **Why are we building it?**\n\nOne month ago my aunt passed away from pancreatic cancer. During her hospitalization insurance repeatedly denied treatment, preventing her from accessing chemotherapy because it wasn’t “medically necessary”.\n\nWe’re fighting every day to help other patients like my aunt get the life-saving treatments they need.\n\n### **Asks**\n\n* Share our [Appeal Portal](http://appeals.intelligahealth.com/) with the healthcare providers you know!\n* If you have experience selling to medical practices and/or hospitals, we’d love to meet with you.",
        "url": "https://www.ycombinator.com/launches/ItA-intelliga-health-automating-insurance-workflows-for-medical-practices",
        "votes": 21,
        "created_at": "2023-06-28T15:06:58.075Z"
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    "id": 28812,
    "name": "Cedana",
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    "long_description": "Cedana (YC S23) brings hyperscaler and frontier-lab orchestration capabilities for AI workflows. Our core capability is live migration for CPUs and GPUs workloads. This increases cost savings up to 80%, accelerates time to first token 2-10x, and enables stateful reliability of training jobs even through catastrophic GPU failures. We've integrated our solution into K8s, and support Kueue and Slurm for training distributed jobs, and Kserve for serving inference.    \r\n\r\nOpenAI, Meta and Microsoft have flavors of these capabilities internally and we’re bringing them to everyone. \r\n\r\nOur vision is to transform cloud compute into a real-time, arbitraged commodity. \r\n\r\nhttps://www.cedana.ai",
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        "full_name": "Neel Master",
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        "founder_bio": "CEO of Cedana. Previously  CEO/co-founder of Engooden, AI-powered chronic disease management  proven to improves outcomes and lower costs for patients (Series B).  VP of Corp Development for Petra Systems  (predictive smart grid/solar company) scaled from $0-$70M ARR.  At TL Ventures ($1.6B VC fund) investing across semi, software and systems.  Built a system for large-scale, automated ML and computer vision at MIT CSAIL.  Patents and publications in AI, computer vision.",
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        "full_name": "Niranjan Ravichandra",
        "title": "Founder",
        "founder_bio": "Co-founder/CTO at Cedana. Previously Robotics at Shopify. Previously previously Mech/Aero, published in control systems, neuromorphic computing and satellite formation flight. ",
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        "title": "Forward Deployed Engineer: AI + HPC",
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        "salary_range": "$140K - $180K",
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        "title": "Systems Engineer: Linux Kernel Test and Validation",
        "url": "https://www.ycombinator.com/companies/cedana/jobs/BntWOIg-systems-engineer-linux-kernel-test-and-validation",
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        "type": "Full-time",
        "role": "Engineering",
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      {
        "id": 73681,
        "title": "Cedana - Real-time save, move and resume for compute",
        "tagline": "Solving critical GPU performance, reliability, capacity and cost problems",
        "body": "Hey, we’re Neel and Niranjan from [Cedana](https://cedana.ai/?utm_source=cedana&utm_medium=yc&utm_campaign=launch).\n\n![uploaded image](/media/?type=post&id=73681&key=user_uploads/77597/b1097aa9-66f2-4253-8243-63e63d5c23e9)\n\n### **The Problem**\n\nLosing work because of infra problems is painful. Imagine you have a long-running compute job and the instance fails. Your 20-hour job finished but because your pipeline was misconfigured you have to restart it from scratch.  \n\nBurning cash is stressful.  Poor utilization results in your inference jobs costing more.  If you’re managing a cluster of 1,000s of GPUs, poor utilization leaves money on the table even while demand is skyrocketing.    \n\nCold start times impact your customer satisfaction and their reliance on your solution.  \n\nLimited GPU access makes it difficult for you to innovate, and finding GPUs can be a full-time job of constantly identifying, evaluating, and adapting different vendors.    \n\n### **Our Solution**\n\nCedana is real-time migration for compute.  We automatically schedule and move workloads across instances and vendors without interrupting progress or breaking anything.\n\nThere are several critical benefits:\n\n* Utilization is maximized to save costs and eliminate idle resources.  \n* Job-level SLAs dynamically allocate compute between inference and training, prioritizing costs, latency, and performance based on preferences.  \n* Avoid re-running jobs from scratch due to infra, pipeline, or memory failures. Jobs continue their progress in the event of infra failure or spot revocation.\n* Access planet-scale compute through vendor aggregation\n* Solve the cold start problem by enabling fast auto-suspend-resume \n* Spot management that migrates and provisions new instances automatically upon revocation or failure.\n\n  \n\nOpenAI, Meta, Microsoft, and Databricks employ some of these methods internally and we’re bringing them to everyone.\n\n### **How it works**\n\n[Cedana](https://cedana.ai/?utm_source=cedana&utm_medium=yc&utm_campaign=launch) is available as an [open-source package](https://github.com/cedana/cedana-cli) and as a [managed service](https://www.ycombinator.com/launches/JAP-cedana-real-time-save-move-and-resume-for-compute). \n\n* Cedana needs no code modification and works with Linux processes or containers\n* Current use cases and customers range from AI Training and Inference, High-Performance Compute, DevTools, ML Ops platforms, and Computational Biology.\n* It automatically provisions and manages infra with your existing credentials. Our managed service can leverage our vendor relationships if preferred.\n\nHere’s a 1m30s [demo video](https://www.youtube.com/watch?v=KC4STzSQ_DU)\n\n### **Our Team**\n\nOur team has built real-world robotics and large-scale computer vision systems across a number of places including 6 River Systems/Shopify and MIT.  We’ve led the development, commercialization, and scale of NLP for clinical workflows used in the delivery of patient care.   Our team’s publications span computer vision, computer graphics, robotics optimization, and spacecraft/aerospace controls, with patents in AI use cases for grid energy management, optimal battery control, and healthcare. \n\n### **We kindly ask**\n\n* Give Cedana a try! [Check out our Github Repo](https://github.com/cedana/cedana-cli)\n* Please support us with a Github star",
        "url": "https://www.ycombinator.com/launches/JAP-cedana-real-time-save-move-and-resume-for-compute",
        "votes": 26,
        "created_at": "2023-08-01T22:03:04.520Z"
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    "id": 28865,
    "name": "Hegel AI",
    "slug": "hegel-ai",
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    "website": "https://hegel-ai.com",
    "all_locations": "New York City, NY, USA",
    "long_description": "We build tools for developing applications with generative AI models. Whether you are using LLMs, vector databases, and text-to-image models like Stable Diffusion, our tools help you find the right model, prompt, configuration and consistently monitor their behaviors in production.\r\n\r\n---\r\n\r\n**PromptTools**: the first open-source, developer-focused SDK for experimenting with and evaluating prompts, models, and vector databases. You can try the repo here: https://github.com/hegelai/prompttools. If you're building with LLMs and struggling with evaluation, reach out and we can help.",
    "one_liner": "Open source tools for developing generative AI applications",
    "team_size": 2,
    "industry": "B2B",
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        "user_id": 1424101,
        "full_name": "Kevin Tse",
        "title": "Founder",
        "founder_bio": "Building open-source developer tools for large language models at Hegel AI.\n\nHappy to connect and chat about how your company is using AI and LLMs!",
        "is_active": true,
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      {
        "id": 73501,
        "title": "Hegel AI: Open source tools for language model evaluation",
        "tagline": "Experiment with models, prompts, and vector databases in just a few lines of code",
        "body": "**TL;DR** [Hegel AI](https://hegel-ai.com/) is building developer tools for LLM and prompt evaluation and continuous testing, with over a thousand downloads per week since launch. We’re a team of ex-PyTorch engineers from Meta and Google.\n\n# The Problem\n\n_Is Llama 2 really better? Is GPT-4 getting worse?_ Developers building with language models are constantly facing challenges with (1) model drift (2) new models and (3) insufficient evaluation systems. Besides academic metrics and “eyeballing it”, there’s no existing solution for continuous LLM regression testing or experimentation.\n\n![uploaded image](/media/?type=post&id=73501&key=user_uploads/1324508/f9481110-9296-4fb1-b5ab-68ce35d6bdfc)\n\nWhat’s more, the complexity of LLMs and related tools like vector databases make it hard for developers to set up honest apples-to-apples comparisons for their use case.\n\n![uploaded image](/media/?type=post&id=73501&key=user_uploads/1324508/e7dc639a-27c3-4849-8245-9bda29648954)\n\n# Our Mission\n\nWe want to make high quality evaluation and testing systems available to everyone, which is why we’re building an open source platform for language model evaluation. Developers can use our product to [monitor GPT-4 regressions](https://github.com/hegelai/prompttools/blob/main/examples/notebooks/GPT4RegressionTesting.ipynb), audit models for safety, and [measure performance of vector databases](https://github.com/hegelai/prompttools/blob/main/examples/notebooks/ChromaDBExperiment.ipynb). \n\n# How it works\n\nDevelopers come to our product with some test cases, and an idea of the models or vector databases they want to evaluate. We handle all of the testing infrastructure to help them (1) set up experiments across models/DBs in notebooks and (2) scale those experiments into reusable test suites. \n\n![uploaded image](/media/?type=post&id=73501&key=user_uploads/1324508/da633110-74ba-449b-b4f1-017ad8ec6ffd)\n\n\\\nWe support multiple strategies for evaluation: auto-eval by another model, systematic evaluations like structured output validation or semantic similarity to an expected output, or even through gathering human feedback with our notebook UI.\n\n![uploaded image](/media/?type=post&id=73501&key=user_uploads/1324508/13b7aa5e-e9d8-43de-b191-6d5c5fc1f04a)\n\n# The Team\n\n![uploaded image](/media/?type=post&id=73501&key=user_uploads/1324508/6dbe38e4-9070-464e-b06b-55ac3d21be7f)\n\n[Kevin Tse](https://www.linkedin.com/in/kevin-tse-35051367/) and [Steven Krawczyk](https://www.linkedin.com/in/steventkrawczyk/) met 9 years ago when we started our undergraduate studies at the University of Chicago. We crossed paths again years after graduation when we were both working on [PyTorch](https://github.com/pytorch/pytorch), an open-source deep learning library, at Meta and Google. We worked on the core library, data loading, and the XLA compiler library. Prior to that, we worked at Amazon and Bridgewater Associates.\n\n# Testimonials\n\n_It’s hard to convince senior management that investing in LLMs is worth it, without doing expensive experimentation on my own._\n\n* _Soma Szabo, Data Science Manager at McMaster-Carr_\n\n_If you are doing anything remotely related to prompting, you'd know how frustrating & inefficient it is to compare between prompts. I, for one, am using it to make my life easier in building Watto AI._\n\n* _Rishabh Panwar, Co-Founder & CEO, Watto AI_\n\n# Asks\n\n* Install the [Python library](https://pypi.org/project/prompttools/) and try it yourself!\n* Give us a star on [GitHub](https://github.com/hegelai/prompttools) and [join our Discord community](https://discord.gg/72a9xh5Z7P).\n* [Contact us](mailto:team@hegel-ai.com) if you want enterprise support for experimentation or early access to our hosted UI and observability tools",
        "url": "https://www.ycombinator.com/launches/J7V-hegel-ai-open-source-tools-for-language-model-evaluation",
        "votes": 88,
        "created_at": "2023-07-27T15:26:58.756Z"
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    "id": 28887,
    "name": "Skyvern",
    "slug": "skyvern",
    "former_names": [
      "Wyvern AI (Previously Ikonomos)",
      "Wyvern AI"
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    "website": "https://www.skyvern.com/",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "Skyvern helps companies automate browser based workflows using LLMs and Computer Vision, fully automating manual workflows and replacing brittle or unreliable scripts.\r\n\r\nExamples include:\r\n1. Automating materials procurement from commerce websites\r\n2. Completing complex multi-step workflows (ie getting an insurance quote from Geico.com)\r\n3. Automatically logging into portals and downloading invoices\r\n4. Navigating legacy content systems to do data extraction or data entry",
    "one_liner": "Open Source AI Agent to automate browser workflows via an API",
    "team_size": 11,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1687648021,
    "tags": [
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    "batch": "Summer 2023",
    "status": "Active",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/skyvern",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/skyvern.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/95726232",
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      "name": "Nicolas Dessaigne",
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        "full_name": "Suchintan Singh",
        "title": "Founder",
        "founder_bio": "I'm the founder of Skyvern. We help companies automate complex workflows using AI agents and computer vision.\n\nIn the past, I've built the ML Platforms at both Faire and Gopuff, which generated over $100M of GMV across their two businesses, \n\nI also love bad jokes and puns, so if you have any or are in the need of any feel free to hit me up!",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/suchintansingh/",
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      {
        "user_id": 1157498,
        "full_name": "Shuchang Zheng",
        "title": "Founder",
        "founder_bio": "I am the CTO and cofounder of Skyvern. For the past 5 years, I've been building developer and platform tools.\n\nAt Lyft, I built testing tools (simulation platform, load test framework, and end to end test framework) used by 1000+ engineers to boost dev productivity, ensure Lyft doesn't go down during peak events, and reduce infra cost.\n\nAt Patreon, I helped scale the payment and db infra to process 20M+ transactions per month and free 100+ engineers from the 3-day monthly code freeze ",
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        "title": "AI Agent Builder / Workflow Wizard",
        "url": "https://www.ycombinator.com/companies/skyvern/jobs/YFDIaHd-ai-agent-builder-workflow-wizard",
        "location": "CA / Remote (CA)",
        "type": "Full-time",
        "role": "Operations",
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        "salary_range": "$60K - $85K CAD",
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        "experience": "Any (new grads ok)",
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      {
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        "title": "Founding SDR, AI-Powered Outbound",
        "url": "https://www.ycombinator.com/companies/skyvern/jobs/3P7vhT3-founding-sdr-ai-powered-outbound",
        "location": "US / CA / Remote (US; CA)",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
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        "experience": "Any (new grads ok)",
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        "id": 95749,
        "title": "Founding Account Executive - AI Infrastructure",
        "url": "https://www.ycombinator.com/companies/skyvern/jobs/9eucL4u-founding-account-executive-ai-infrastructure",
        "location": "US / CA / Remote (US; CA)",
        "type": "Full-time",
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        "title": "Founding Developer Marketing – Open Source AI",
        "url": "https://www.ycombinator.com/companies/skyvern/jobs/1qRTlVx-founding-developer-marketing-open-source-ai",
        "location": "US / CA / Remote (US; CA)",
        "type": "Full-time",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "$100K - $150K",
        "equity_range": "0.10% - 0.30%",
        "experience": "3+ years",
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      {
        "id": 105883,
        "title": "Rustwright: Rewrite of Playwright in rust that uses 70% less memory (and is 2.55x faster)",
        "tagline": "Remove the subprocess and save so much memory",
        "body": "Hey everyone, Suchintan from Skyvern (YC S23). We run browser automation for AI agents at scale, and we're launching [Rustwright](https://github.com/Skyvern-AI/rustwright): [Playwright on an in-process Rust CDP engine.](https://github.com/Skyvern-AI/rustwright) The engine consumes 70% less memory than Playwright, and is 2.55x faster to boot.\n\n**We built this to improve Skyvern’s memory footprint**, and thought this would be independently valuable to the broader community\n\nThe whole idea fits in two lines:\n\n```\nplaywright-python:  your code ──pipe──► Node driver (separate process) ──CDP──► Chromium\nrustwright:         your code ────────────────── raw CDP ─────────────────────► Chromium\n```\n\nRemoving the driver changes three things:\n\n1. **Less abstraction.** The driver subprocess and its pipe speaks CDP straight to Chromium. In our runs, the new engine is 2.55× and **consumes 70% less memory** than the equivalent playwright process\n2. **No Playwright driver fingerprint.** The driver never loads, so its signatures never appear: no `__playwright__binding__` globals, no `Runtime.enable` on the default path (the well-known console-serialization leak). The claim is deliberately narrow: no Playwright-specific automation fingerprint, not \"undetectable\"\n3. **A single rust core**: a single Rust core backs both language bindings (Python with sync and async; Typescript) with plans to expand to many popular languages (Ruby / Go / Java to name a few). Within the supported surface, migration is often a one-line import change — `from rustwright.sync_api import sync_playwright.`Clicks and typing go through real CDP input events, not synthetic DOM calls, and cross-origin iframes auto-attach with `frame_locator()` routing across origins.\n\n[playwright-python](https://github.com/microsoft/playwright-python) pipes every call through a bundled Node driver process. [Rustwright](https://github.com/Skyvern-AI/rustwright) drives Chromium over raw Chrome DevTools Protocol from a Rust core, an async CDP client on Tokio (WebSocket, plus opt-in Unix-pipe transport), exposed in-process through thin PyO3 bindings for Python and napi-rs bindings for Node.\n\nStatus: alpha, Chromium-only\n\n**Want to give it a try? Change one line of code!**\n\n```jsx\npip install rustwright\n\n- from playwright.sync_api import sync_playwright\n+ from rustwright.sync_api import sync_playwright\n```\n\n⭐ Give it a try here and let us know what you think: <https://github.com/Skyvern-AI/rustwright>",
        "url": "https://www.ycombinator.com/launches/RXn-rustwright-rewrite-of-playwright-in-rust-that-uses-70-less-memory-and-is-2-55x-faster",
        "votes": 8,
        "created_at": "2026-07-15T17:10:22.738Z"
      },
      {
        "id": 94724,
        "title": "Skyvern: Asking AI to build scrapers should be easy right?",
        "tagline": "Let AI maintain those pesky scrapers",
        "body": "![uploaded image](/media/?type=post&id=94724&key=user_uploads/1120458/6473b24c-3925-48bf-80c8-00430f81e5ef)\n\n**TL;DR - We just gave Skyvern the ability to write and maintain its own code, making it 2.7x cheaper and 2.3x faster. Give it a prompt (or a series of prompts), and the AI will generate and maintain playwright code while it runs. Try out the via [Open Source](https://github.com/Skyvern-AI/Skyvern?ref=skyvern.com) or [Cloud](http://app.skyvern.com/?ref=skyvern.com)**\n\n💡 **Recap: What is Skyvern?** It’s [an open source tool](https://github.com/Skyvern-AI/Skyvern?ref=skyvern.com) that helps companies automate things in the browser with AI. We use computer vision + LLMs to turn prompts into automations that run. We serve both technical and non-technical customers, and have helped them automate things like applying to jobs, fetching invoices or utility bills, filling out government forms, and purchase products from hundreds of different websites.\n\n# **Asking AI to build scrapers should be easy right? Right??**\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132106.418.png)\n\nSome of you may remember our [Hackernews launch](https://news.ycombinator.com/item?id=39706004&ref=skyvern.com) from last year. All of the discussion circled around the same idea: “Building the automation is the hard part… we just want Skyvern to write the code”.\n\nWe agreed. Keeping the agent in the loop means invoking an expensive and non-deterministic LLM call on every run. If Skyvern could compile its reasoning into code and run that instead of keeping an LLM in the loop, automations would become **faster, cheaper, and more reliable.**\n\nSo we tried to teach Skyvern to do exactly that… but it turns out, asking AI to write code the same way you and I would wasn’t easy. We ran into two big problems:\n\n1. Requirements for automations are ambiguous at best, and misleading at worst — even humans struggle to define them clearly\n2. The internet is messy: drop-downs masquerade as textboxes, checkboxes that are always checked, and search bars that are secretly buttons.\n\nGetting an agent to navigate that chaos, understand intent, and still produce maintainable code came through one major breakthrough: **reasoning models**.\n\nReasoning models unlock two important capabilities:\n\n1. They [boosted the agent accuracy](https://www.skyvern.com/blog/web-bench-a-new-way-to-compare-ai-browser-agents/) enough for production use\n2. They let the agent leverage its trajectory to write a script resembling something an engineer would write\n\n# **This is too abstract. When does this matter?**\n\nBefore we dive into the solution, let’s look at a real-world example: Registering new companies for payroll with [Delaware.gov](http://Delaware.gov)\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132100.688.png)\n\nHere’s a simple prompt that reliably powers the workflow:\n\n> _Your goal is to fill out the EIN registration form. Fill out the form until you're at the form confirmation page with a summary of all information. Your goal is complete once you see a summary of all of the information._\n\nein_info: {{ein_info}}\n\nWriting deterministic code should be easy right?\n\nHere’s what a naive AI Generated implementation looks like:\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image-1.png)\n\nAnd here’s where it falls apart almost immediately.\n\n1. Coupled interactions. Choices on this form aren’t independent. Sometimes radio buttons are linked together, but aren’t represented as such in the DOM. Other times, different buttons trigger different follow-up questions, so a static script breaks as soon as you pick something unexpected.\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132055.697.png)\n\n**Random failures.** Government websites love to go down at night, change field layouts between sessions, or throw you a “try again later” page mid-run.\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132051.371.png)\n\n### **Coupled interactions**\n\nConsider the radio button example above. Any seasoned developer would know that these legal structures are linked together. You’d instinctively model them as a finite set of entity types, and create a tree-like script that branches into different paths based on the input.\n\nBut that abstraction doesn’t exist for an agent. To agents like Skyvern, this is just a list of buttons. The relationship between the users’ input and the available set of legal structures doesn’t exist ahead of time — it must be discovered at runtime.\n\nThe agent has to infer, from the DOM and the page transitions, which choices lead where.\n\n### **Random non-deterministic failures**\n\nAgents shine when things don’t go as planned. We don’t want to hard code every single edge case when compiling an agent into a deterministic script.. because we’re back to writing brittle scripts. Instead, we want to leverage agents for these situations.\n\nTake this government form: Delaware’s portal is unavailable at night or over the weekend. Or sometimes, it’ll require you to call the IRS or sending them a fax / mail to proceed with the form. You want some intelligence in the loop to handle these scenarios gracefully.\n\n# **So.. how can you codify this in an agent?**\n\nAfter a few runs like the ones above, we realized “have the agent write code” wasn’t enough. We needed to copy how developers actually work: figure out the flow, add logic where it breaks, and bake that behavior into Skyvern.\n\nSo we split its job into two:\n\n1. Explore mode, where the agent learns how to navigate a website for a given flow, generating any metadata necessary for it to operate in subsequent runs\n\nReplay mode, it compiles those learnings into deterministic Playwright and runs fast and cheap, only falling back to the agent when something new or weird happens\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132046.901.png)\n\n### **Explore once: get the agents’ trajectory**\n\nLet’s start with a plain prompt for Skyvern. The `ein_info` field is just a json blob with all of a company’s metadata (entity type, responsible party, etc). The goal of this explore run isn’t to finish fast, it’s to learn the flow and record a trajectory we can compile later.\n\n> _Go to [https://sa.www4.irs.gov/modiein/individual/index.jsp](https://sa.www4.irs.gov/modiein/individual/index.jsp)\\\n> and generate an SS4 with the following information: {{ ein_info }}_\n\n### **Step 1: generate a naive script**\n\nFrom that trajectory, the agent can spit out a basic Playwright script. It runs, but it’s brittle—no context, no fallbacks:\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image-2.png)\n\n**What’s missing:** it doesn’t know _why_ it’s clicking “Corporation,” what should appear next, or how to recover if the DOM shifts (or the portal is down). That’s the gap we close in the next section (intent metadata + deterministic replay with targeted fallbacks).\n\n### **Step 2: Ask the agent to write down its intention so we can re-use it later**\n\nExploration gives us a working script, but it’s brittle because it only knows _what_ to click, not _why_. The fix was to capture **intents** to every action so the run can recover when the page shifts.\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image---2025-10-17T132041.611.png)\n\nTo get to this intention, we generate 2 additional parameters at runtime: `user_detail_query` and `user_detail_answer` to capture the **essence** of the action (beyond the interaction itself)\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image-3.png)\n\nThen, we pass it through another LLM call to reverse engineer the action into the following:\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image-4.png)\n\nIf this fails at replay time, we don’t blindly guess a new selector—we reuse the **intention** to recover:\n\n1. Try an alternate selector for the same intention (looser match, nearby label, aria text)\n2. If the flow changed, ask the model **once**: “How do I ‘Select legal structure: Corporation’ on this page?”\n3. If we hit a dead end (downtime/error), fallback to the original prompt to decide what to do next\n\nAt this point, we have generated code that looks like this:\n\n![](https://www.skyvern.com/blog/content/images/2025/10/image-5.png)\n\n### **Step 3 - Run it on the cheap**\n\nNow that we have a plan and a fallback in place, subsequent runs are all using plain playwright — no LLM in the loop.\n\nWe benchmarked this across our customers and saw:\n\n* Average automation run time goes from 278.95s → 119.92s (2.3x faster)\n* Average run cost goes from $0.11 → $0.04 (2.7x cheaper)\n* And maybe more important than either: **runs are now deterministic.**\n\n![](https://www.skyvern.com/blog/content/images/2025/10/Screenshot-2025-10-16-at-14.18.17.png)\n\n![](https://www.skyvern.com/blog/content/images/2025/10/Screenshot-2025-10-16-at-14.18.07-1.png)\n\n# **How is this being used in the wild?**\n\nSkyvern’s “explore → replay” pattern is already running quietly inside a bunch of workflows that used to require brittle, human-maintained scripts. A few examples:\n\n1. **Invoice Downloading**\n\nAgents log into vendor or utility portals with 1000s of different accounts, navigate to the right billing period, and pull invoices. When layouts or date filters change, the intent metadata lets them recover automatically instead of failing.\n\n2. **Purchasing**\n\nTeams use it to automate repeat purchases — think renewing software licenses or buying supplies through the same vendor dashboard each month. The first run learns the checkout path, the fallbacks handle the variety of products, and the replays run deterministically, flagging if a price or SKU changes.\n\n3. **Data Extraction from Legacy Systems**\n\nSkyvern navigates authenticated dashboards, scrapes tables or PDFs, and pushes the structured output into into a database via Webhooks. If the DOM shifts, Skyvern reuses the same intention (“extract transaction rows”) to remap selectors.\n\n4. **Government Form Filling**\n\nFrom payroll registration to business license renewals, Skyvern handles long, multi-step government forms that occasionally break static scripts. Explore mode figures out the flow once replay mode repeats it safely.\n\n## **What’s next? Is it perfect today?**\n\nNot quite. The architecture works well, but there are still a few places where we can make it smarter and cheaper:\n\n1. **Analyze groups of runs when generating code**.\n\nRight now, we don’t aggregate insights across failures. Each replay fixes itself in isolation. If we could analyze _many_ runs together, spotting which selectors break, which flows diverge, we could automatically generalize better code and reduce the need for fallbacks. That’s especially useful for workflows that branch like trees (different inputs → different paths).\n\n2. **Cache data extractions**.\n\nDuring data extraction, we still rely on the LLM to “read” the page each time because many of our users want to both extract and summarize information at the same time. For example, if you ask Skyvern to pull the summaries of the top five posts on Hacker News, it currently parses the DOM from scratch. We’d like to trace _how_ the model found those elements (which selectors, which substrings) and reuse that mapping. That alone could make scraping and data-harvesting flows an order of magnitude cheaper.\n\n3. **Expose everything through the SDK.**\n\nWe think it will be valuable for developers using the Skyvern SDK to auto-generate these scripts for any ai actions / workflows they run, and use them automatically for subsequent runs. It currently requires a Skyvern server running, but soon it will be the default behaviour.\n\n## **Give it a try!**\n\nRun it via our [Skyvern Open Source](https://github.com/Skyvern-AI/Skyvern?ref=skyvern.com) or [Skyvern Cloud](http://app.skyvern.com/?ref=skyvern.com) versions and let us know what you think!",
        "url": "https://www.ycombinator.com/launches/Odo-skyvern-asking-ai-to-build-scrapers-should-be-easy-right",
        "votes": 12,
        "created_at": "2025-10-17T18:15:33.896Z"
      },
      {
        "id": 90850,
        "title": "📊 Web Bench - A new way to compare AI Browser Agents",
        "tagline": "Expanding on the foundations of WebVoyager",
        "body": "**TL;DR:** [Web Bench](https://www.webbench.ai) is a new dataset to evaluate web browsing agents that consists of 5,750 tasks on 452 different websites, with [2,454 tasks being open sourced.](https://github.com/Halluminate/WebBench/tree/main) Anthropic Sonnet 3.7 CUA is the current SOTA, with the detailed [results here](https://eval.skyvern.com/?evaluation_target=skyvern_final_eval_skyvern_2_0&page=1).\n\nOver the past few months, Web Browsing agents such as [Skyvern](https://www.skyvern.com), [Browser-use](https://browser-use.com/) and [OpenAI's Operator (CUA)](https://operator.chatgpt.com/) have taken the world by storm. These agents have been used in production for a variety of tasks, from helping people apply to jobs, downloading invoices, and even doing SS4 filings for newly incorporated companies.\n\n![](https://blog.skyvern.com/content/images/2025/05/onlayer-gif.gif)\n\n_Skyvern attempting to purchase a product_\n\n![](https://blog.skyvern.com/content/images/2025/05/irs_demo.gif)\n\n_Skyvern attempting to fill out the IRS form_\n\nMost agents report state of the art performance, but we find that browser agents still struggle with a wide variety of tasks, particularly ones involving authentication, form filling and file downloading.\n\nThis is because the standard benchmark today ([WebVoyager](https://arxiv.org/abs/2401.13919)) focuses on read-heavy tasks and consists of **only** **643 tasks across only** **15 websites (out of 1.1 billion possible websites!).** While a great starting point, the benchmark does not capture the internet’s adversarial nature towards browser automation and the difficulty of tasks involving mutating of data on a website.\n\n![](https://blog.skyvern.com/content/images/2025/05/chase3-ezgif.com-video-to-gif-converter--1-.gif)\n\n_Can’t access [chase.com](http://chase.com)_\n\n![](https://blog.skyvern.com/content/images/2025/05/2025-05-08T02_12_10.137717_a_390786750312029848_recording-ezgif.com-video-to-gif-converter--1-.gif)\n\n_Can’t close a pop-up dialog_\n\nAs a result, we partnered with [Halluminate](https://www.halluminate.ai) and created a new benchmark to better quantify these failures. Our goal was to create a new consistent measurement system for AI Web Agents by expanding the foundations created by [WebVoyager](https://arxiv.org/abs/2401.13919) by:\n\n1. Expanding the number of websites from 15 → 452, and tasks from 642 -&gt; 5,750 to test agent performance on a wider variety of websites\n2. Introduce the concept of READ vs WRITE tasks\n3. READ tasks involve navigating websites and fetching data\n   * WRITE tasks involve entering data, downloading files, logging in, solving 2FA, etc, and were not well represented in the WebVoyager dataset\n4. Measure the impact of browser infrastructure (eg, access the websites, solve captchas, not crash, etc)\n\nWe’re excited to announce Web Bench, a new dataset to evaluate web browsing agents that consists of 5,750 tasks on 452 different websites, with [2,454 tasks being open sourced.](https://github.com/Halluminate/WebBench/tree/main)\n\n# Results TL;DR\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-26-at-6.13.00---PM--1-.png)\n\n* All agents performed surprisingly poorly on write-heavy tasks (eg, logging in, filling out forms, downloading files), which implies that this is the area for the highest opportunity for growth\n* Skyvern 2.0 has the best performance for tasks in this category\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-11.54.34---AM.png)\n\n* Agent performance for read-heavy tasks (eg, extracting information out of websites) was better than we expected\n* Anthropic’s CUA had the highest performance\n* The [entire run is **open source**](https://eval.skyvern.com/) and can be viewed to see the specific performance of each task\n\n# Dataset Creation\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-11.49.12---AM.png)\n\nThe 452 websites are distributed across 17 primary categories. We sampled the benchmark websites from the [top 1000 websites in the world](https://dataforseo.com/free-seo-stats/top-1000-websites) measured by web traffic. We then cleaned this dataset of sites by removing:\n\n* repeat domains\n* sites without English translations\n* sites blocked by paywall\n\n# Results\n\n## Initial conditions\n\n* We ran OpenAI Operator with a human in the loop to establish a baseline for performance\n* We used consistent browser infrastructure (Skyvern’s infrastructure) when comparing the API-only models without a runtime to eliminate variables\n* We also ran Skyvern 2.0 on Browserbase to compare the impact of infrastructure, but found (surprisingly) that Skyvern’s infrastructure was able to reliably access more websites and encountered fewer anti-bot issues during navigation\n* Each agent was allowed a max of 50 steps per execution\n* Each result was validated by a human in the loop to assert evaluation data quality\n\n## Accuracy (Overall)\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-11.53.34---AM--1-.png)\n\nThese results were a little bit surprising, so we decided to cut the data along 2 dimensions to understand where agents may falter:\n\n1. Read-only tasks (ie, extracting visible data from a particular website)\n2. Write heavy tasks (ie, logging into websites, filling out forms, downloading files)\n\n## Accuracy (Read-only tasks)\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-11.54.34---AM--1-.png)\n\nRead-only tasks constitute tasks that involve agents going to different websites and navigating the sitemap until a particular answer/state has been found.\n\nUnsurprisingly, these results matched the WebVoyager dataset more closely, as the WebVoyager dataset was largely curated to help agents navigate websites and answer questions.\n\nThe biggest 2 sources of failures for read-heavy tasks are:\n\n* Navigation Issues (cannot figure out how to navigate a page, can’t solve popups)\n* Information extraction issues (doesn’t pull the correct information)\n\n## Accuracy (Write-heavy tasks)\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-26-at-6.13.00---PM--2-.png)\n\nTasks involving:\n\n* filling out forms\n* logging in\n* solving 2FA\n* downloading files\n\nhad a much lower pass rate across the board.\n\nDigging a bit deeper into the failures, there were two culprits for the failures that popped up:\n\n* Incomplete execution (hallucinating that it’s achieved the goal when it has not)\n* Unable to identify the correct element to interact with (eg can’t close a popup dialog)\n\n![](https://blog.skyvern.com/content/images/2025/05/ezgif.com-speed-1.gif)\n\n_Can’t close subscription pop-up_\n\n![](https://blog.skyvern.com/content/images/2025/05/error2-ezgif.com-speed-1.gif)\n\n_Unable to find and click coupon buttons_\n\nThese issues manifest as agents making adverse changes when filling out forms, or optimistically assuming that clicking a “Submit” button completes the task, when in reality, a captcha appeared that now needs to be solved.\n\nThis issue is very similar to the phenomenon observed in coding agents, where smarter models try to “overhelp” with code changes by either making changes to unrelated parts of the codebase or repeatedly suggesting incorrect things because they’re missing some important context.\n\n## Agent Failure Modes - Deepdive\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-11.56.30---AM.png)\n\nDigging deeper, we were able to qualify the overall failures into two buckets:\n\n1. Agent Failures (eg, Agents hallucinated/made poor decisions or didn’t interact with important elements)\n2. Infrastructure failures (eg, Agent can’t access the website, solve a captcha to log in)\n\n### Agent issues\n\nThe 4 biggest categories of agent errors are:\n\n* Navigation Issue (cannot figure out how to navigate a page, can’t solve popups)\n* Incomplete execution (hallucinating that it’s achieved the goal when it has not)\n* Time-outs (exceeds step limits)\n* Information extraction issue (doesn’t pull the correct information)\n\n### **Infrastructure issues**\n\nThe 3 biggest categories of infrastructure issues are:\n\n* Proxy (Failed to access website/website blocked)\n* Captcha (Verification required to proceed and the infrastructure is unable to solve it)\n* Login/Authentication (Google Auth detecting you’re a bot)\n\nThese findings imply that the browser infrastructure powering the agents is equally as important as the quality of the agent itself.\n\n# Other interesting characteristics\n\nWhile accuracy is the most important characteristic of a web browsing agent, there is also a desire to get “faster” and “cheaper” agents.\n\nFast and cheap agents can be characterized by tracking the following metrics:\n\n1. Runtime duration\n2. Number of steps\n\nWhile the right pricing model for browser agents hasn’t emerged yet, this data gives an important insight into whether pricing per hour (common amongst hosted browsers + older robotic process automation) and pricing per step (common amongst computer use APIs) is the right methodology.\n\n## Agent Runtime Duration\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-28-at-10.14.41---AM.png)\n\nThis metric is important for a few latency-sensitive market segments/situations:\n\n1. Copilot-like products where a human is supervising 1 or many agents executing in parallel\n2. Phone agents referencing information / doing real-time lookups while a user is talking\n3. Websites aggregating information in real-time to show to the user(e.g., looking up flight or domain name availability)\n\n## Steps\n\n![](https://blog.skyvern.com/content/images/2025/05/Screenshot-2025-05-25-at-10.47.56---PM.png)\n\nMost web browsing agents’ costs scale with the number of steps (i.e., page scans) required to complete a specific task.\n\nAgents may use a varied number of steps for a few reasons:\n\n1. Most agents use different architectures to batch actions together to minimize the number of steps\n2. Agents eager to solve the problem may use a lot of steps in error situations to try to independently resolve their issues (e.g., chatting with support for solutions instead of terminating early)\n3. Agents using pessimistic approaches to reduce hallucinations may invalidate batches of actions whenever the website changes after a particular action (i.e., filling in an address field might invalidate the action plan for the rest of the page)\n\n# Next Steps\n\n* We intend to benchmark [Claude 4](https://www.anthropic.com/news/claude-4), [Operator O3](https://openai.com/index/o3-o4-mini-system-card-addendum-operator-o3/), [UI-TARs](https://github.com/bytedance/UI-TARS), and [Mariner API](https://deepmind.google/models/project-mariner/)\n* We intend to expand the benchmark to cover more categories of websites beyond the top 1000 websites in the world\n* We intend to expand this dataset to include other languages to assert multi-lingual browser agent performance\n\n# FAQ\n\n### Why not include a bigger cohort of websites?\n\nThe goal of the first version of Web Bench was to start with a reasonably large number of websites + tasks.\n\nThe next few versions will expand: (1) the number of websites encompassed by this benchmark, (2) the language of websites, and (3) the categories of websites\n\n### Why were only a subset of vendors benchmarked?\n\nThe overall cost to run this benchmark was approximately $3,000 per agent for human evaluations. This cost is high enough to be prohibitive of testing every single agent on the market.\n\nThe halluminate team plans to release an open-source automated evaluation harness to allow teams to self-serve testing against [Web Bench](https://www.webbench.ai). We leave this for future work.\n\nIf you’d like to submit benchmark results for your own agent, please [contact the Halluminate team](mailto:jerry@halluminate.ai)\n\n### Where can I read the technical deepdive?\n\nFor a comprehensive technical debrief on the dataset creation and evaluation methodology, check out the Halluminate team writeup [here](https://halluminate.ai/blog/benchmark).\n\n### Did anything funny happen along the way?\n\nYes.\n\nWe had examples of Skyvern chatting with GitHub’s AI Support bot\n\n![](https://blog.skyvern.com/content/images/2025/05/image--67-.png)\n\nBrowser Use googled how to evade Cloudflare captchas\n\n![](https://blog.skyvern.com/content/images/2025/05/image--68-.png)\n\n",
        "url": "https://www.ycombinator.com/launches/NdK-web-bench-a-new-way-to-compare-ai-browser-agents",
        "votes": 18,
        "created_at": "2025-05-29T18:26:39.628Z"
      },
      {
        "id": 89234,
        "title": "🐉 Skyvern MCP Server: let agents control your browser",
        "tagline": "Meet Skyvern, an open source AI agent that can control your browser and complete complex tasks",
        "body": "We were playing around with MCPs over the weekend and thought it would be cool to build an MCP that lets Claude / Cursor / Windsurf control your browser: <https://github.com/Skyvern-AI/skyvern/tree/main/integrations/mcp>\n\nJust for context, we’re building Skyvern, an open-source AI Agent that can control and interact with browsers using prompts, similar to OpenAI’s Operator.\n\nThe MCP Server can:\n\n* This allows Claude to navigate to docs websites/stack overflow and look up information like the top posts on hackernews\n\n![uploaded image](/media/?type=post&id=89234&key=user_uploads/1120458/9a0259b8-ebb7-44c4-9f4d-a881c31c8d10)\n\n* This allows Cursor to apply for jobs, fill out contact forms, log in and download files, etc.\n\n  ![uploaded image](/media/?type=post&id=89234&key=user_uploads/1120458/f9c936a8-88de-47cc-8209-7983754fcb8a)\n\n* Connect Windsurf to take over your Chrome while running Skyvern in “local” mode\n\n![uploaded image](/media/?type=post&id=89234&key=user_uploads/1120458/40ca033c-fa32-4c7f-bda0-7cd072ae9212)\n\nWe built this mostly for fun, but we can see this being integrated into AI agents to give them custom access to browsers and execute complex tasks like booking appointments, downloading your electricity statements, looking up freight shipment information, etc.",
        "url": "https://www.ycombinator.com/launches/NDG-skyvern-mcp-server-let-agents-control-your-browser",
        "votes": 8,
        "created_at": "2025-04-03T18:28:44.864Z"
      },
      {
        "id": 86895,
        "title": "Skyvern 2.0 - State-of-the-art web navigation with 85.8% on WebVoyager Eval",
        "tagline": "Open source AI Web Agent",
        "body": "We’ve been working hard, cooking up something new to share with you all!\n\nSkyvern 2.0 **scored state-of-the-art 85.85%** on the WebVoyager Eval.\n\nThis is the best-in-class performance of all WebAgents, giving advanced closed-source web agents like Google Mariner a run for their money.\n\n![uploaded image](/media/?type=post&id=86895&key=user_uploads/1120458/4f04949f-e01d-42cb-b568-a2460f7ac7f5)\n\n### TL;DR\n\n* **Real-World Tests:** We ran all of the tests in Skyvern Cloud to get a better representation of autonomous browser operations (ie, they didn’t run on any local machines)\n* **Open-Sourced Results:** All of the runs can be [seen here](https://eval.skyvern.com/) through our UI.\n* **We’re just getting started**. Try [Skyvern Cloud](https://app.skyvern.com/) or [Skyvern Open Source](https://github.com/Skyvern-AI/Skyvern) out for yourself and see Skyvern in action!\n\n## Agent Architecture\n\nAchieving this SOTA result required expanding Skyvern’s original architecture. Skyvern 1.0 involved a single prompt operating in a loop both making decisions and taking actions on a website. This approach was a good starting point but scored \\~45% on the WebVoyager benchmark because it had insufficient memory of previous actions and could not do complex reasoning.\n\nTo solve this problem, we created a self-reflection feedback loop within Skyvern. This resulted in 2 main changes:\n\n1. We added a “Planner” phase, which could decompose very complex objectives down into smaller achievable goals\n2. This allowed Skyvern to have a working memory of things it had completed and things that were still waiting to be finished\n   * This allows Skyvern to work with long, complex prompts without increasing the hallucination rate\n3. We added a “Validator” phase, which confirmed whether or not the original goals the “Planner” generates are successfully completed\n4. This acts as a supervisor function to confirm that the Task executor is achieving its objectives as expected and report any errors/tweaks back to the Planner so it can make adjustments in real-time as needed\n\n![](https://blog.skyvern.com/content/images/2025/01/architecture.png)\n\n## Test Setup\n\nAll tests were run in [Skyvern Cloud](https://app.skyvern.com/) with an **async cloud browser** and used a combination of GPT-4o and GPT-4o-mini as the primary decision-making LLMs. The goal of this test is to assert real-world quality — the quality represented by this benchmark is the same as what you would experience with [Skyvern](https://app.skyvern.com/)’s browsers running asynchronously.\n\n💡 **Why is this important?** Most benchmarks are run on local browsers with a relatively safe IP address and an impressive browser fingerprint. This is not representative of how Autonomous agents will run in the cloud, and we wanted our benchmark to represent how agents would behave in production\n\nIn addition to the above, we’ve made a few minor tweaks to the dataset to bring it up to date:\n\n1. We’ve removed 8 tasks from the dataset because the results are no longer valid. For example, one of the tasks asked to go to [apple.com](http://apple.com) and check when the Apple Vision Pro will be released — in 2025, it’s already been released and forgotten\n2. Many of the flight/hotel booking tasks referenced old dates. We updated both the prompt and the answer to more modern dates for this evaluation\n\n🔍 **For the curious**:\n\nThe full dataset can be seen here: <https://github.com/Skyvern-AI/skyvern/tree/main/evaluation/datasets>\n\nThe full list of modifications can be seen here: <https://github.com/Skyvern-AI/skyvern/pull/1576/commits/60dc48f4cf3b113ff1850e5267a197c84254edf1>\n\n## Test Results\n\nWe’re doing something out of the ordinary. In addition to the results, we’re making our entire benchmark run public.\n\n💡 **Why is this important?** Most benchmarks are run behind closed doors, with impressive results being published without any accompanying material to verify the results. This makes it hard to understand how things like hallucinations or website drift over time play an effect on agent performance\n\nWe believe this isn’t aligned with our open source mission, and have decided to publish the entire eval results to the public.\n\n📊 All individual run results can be seen here: <https://eval.skyvern.com>\n\n🔍 The entire Eval dataset can be seen here: <https://github.com/Skyvern-AI/skyvern/tree/main/evaluation/datasets>\n\n## Limitations of the WebVoyager benchmark\n\nThe WebVoyager benchmark is a comprehensive benchmark testing a variety of prompts on 15 different websites. While this acts as a good first step in testing Web agents, this only captures 15 hand-picked websites of the millions of active websites on the internet.\n\nWe think there is tremendous opportunity here to better evaluate web agents against one another with a more comprehensive benchmark similar to SWE-Bench.\n\n## What’s on the horizon\n\nBrowser automation is still a nascent space with tons of room for improvement. While we’ve achieved a major milestone in agent performance, a few important issues are next to be solved:\n\n1. Can we improve Skyvern’s reasoning to operate efficiently in situations with more uncertainty? Examples include vague prompts, ambiguous or highly complex websites/tools, websites with extremely poor UX (legacy portals)\n2. Can we give Skyvern access to more tools so it can effectively log into websites, make purchases, and behave more like a human?\n3. Can we have Skyvern better memorize things it has already done in the past so it can do them again at a lower price point?\n\n## References\n\n* [Google Mariner Report](https://deepmind.google/technologies/project-mariner/)\n* [Claude Computer Use Report](https://docs.anthropic.com/en/docs/build-with-claude/computer-use)\n* [WebVoyager Report](https://arxiv.org/html/2401.13919v4)\n* [WILBUR Report](https://arxiv.org/html/2404.05902v1)\n* [AgentE Report](https://arxiv.org/html/2407.13032v1)\n* [HCompany Report](https://www.hcompany.ai/blog/a-research-update)",
        "url": "https://www.ycombinator.com/launches/MbX-skyvern-2-0-state-of-the-art-web-navigation-with-85-8-on-webvoyager-eval",
        "votes": 10,
        "created_at": "2025-01-16T16:48:00.855Z"
      },
      {
        "id": 86198,
        "title": "🐉 Skyvern's Contact Form Agent: Automate contact form submissions with AI",
        "tagline": "Cold outbound is the worst. Try this new form of outbound instead!",
        "body": "Companies use [Skyvern](https://www.skyvern.com/) to automate browser-based workflows, doing tasks like form-filling, invoice downloading, and more. One emerging use case for Skyvern has been navigating to thousands of different websites and filling out contact forms... to scale a new untapped outbound channel.\n\nAnd this strategy blew our minds. Why:\n\n1. Companies with contact forms often, guess what, want to be contacted\n2. Contact form submissions often go to a specific team dedicated to help with inquiries\n3. You avoid all of the craziness that comes with setting up email infrastructure: buying up domains, warming inboxes, worrying about bounce rates and other email nuisances\n4. You can send as many as you want whenever you want—forget limiting the number you send per day to increase deliverability\n\nThe demand and success of this use case led us to create a dedicated agent tailored to it: Skyvern Contact Forms. And this is why I’m here, to share this sick outbound strategy and let you know we can help :)\n\nTry it out at [**https://app.skyvern.com/forms**](https://app.skyvern.com/forms), let us know what you think!\n\nLike what you see and want bulk pricing? [**I’d love to chat.**](https://meetings.hubspot.com/skyvern/demo)\n\n![uploaded image](/media/?type=post&id=86198&key=user_uploads/1120458/2e37309e-035e-41cd-b89a-c8552a5019d6)\n\n",
        "url": "https://www.ycombinator.com/launches/MQI-skyvern-s-contact-form-agent-automate-contact-form-submissions-with-ai",
        "votes": 8,
        "created_at": "2024-12-10T18:35:41.619Z"
      },
      {
        "id": 81634,
        "title": "🐉 Skyvern Cloud - Open source AI Agent to automate browser based workflows",
        "tagline": "The easiest way to automate similar tasks on a lot of different websites",
        "body": "### TL;DR\n\n[Skyvern](https://skyvern.com/) helps companies automate browser-based workflows using AI. We provide a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable scripts. We’re open source — check out [our repository here](https://github.com/Skyvern-AI/Skyvern). **We just launched Skyvern Cloud — check it out at **[**app.skyvern.com**](https://app.skyvern.com)**.**\n\n**—**\n\n### 🧙‍♂️🐉 Skip the boring explanation, show me the magic!\n\n**We have good news!** We just launched Skyvern Cloud. You can go to [app.skyvern.com](http://app.skyvern.com) and try playing around with it! You’ll get $5 of credit when signing up and setting up a payment method\n\nHere’s an example of it filling out the job application form and applying to Lever’s job demo website:\n\n![uploaded image](/media/?type=post&id=81634&key=user_uploads/1120458/b3e666b8-5e36-4e7e-91ac-1120de55cd39)\n\n### 🤔 What are browser workflows?\n\nMost companies we’ve talked to have manual or semi-automated workflows that support their core product. Many of these workflows started manually as companies were getting off the ground, [doing things that don’t scale](https://paulgraham.com/ds.html), and evolved to occupy a larger amount of human capital or automated with unreliable scripts.\n\n**Example 1 — Downloading invoices on a large number of websites**\n\nLet’s say it’s the end of the month, and your accountant is hounding you for invoices for all your transactions this month. Traditionally, you would have to log into each portal and download it yourself. Skyvern can help automate this for you! **PS: If you’d like to automate a complex flow like this, we’d [love to help get you set up.](https://meetings.hubspot.com/suchintan)**\n\n![uploaded image](/media/?type=post&id=81634&key=user_uploads/1120458/49e1d569-5a46-4728-a3ec-6a0a4d8c40aa)\n\n**Example 2 — Filling out forms + uploading documents on Government websites**\n\nLet’s say you’re an accounting company or a law firm, and you want to help your clients get set up for Payroll in California — you wouldn’t want to go get registered for EDD manually for each potential customer.. and in each state! Call in a favor from Skyvern to help automate this.\n\n![uploaded image](/media/?type=post&id=81634&key=user_uploads/1120458/66682edb-bdc4-4a03-b157-235501356bfc)\n\n**Example 3 — Automating materials procurement from commerce websites**\n\nLet’s say you’re a B2B marketplace for car parts, and your users submit orders from multiple vendors with you. A subset of vendors you work with don’t support ordering via an API, so you have a human in the loop to transact orders for those vendors. Here’s an example of Skyvern navigating to [finditparts.com](http://finditparts.com/) and ordering an airbag — all via an API call!\n\n![uploaded image](/media/?type=post&id=81634&key=user_uploads/1120458/f4630123-b7d1-4fff-aedb-31dd3ea11f05)\n\n**Example 4 —** **Completing dynamic multi-step workflows**\n\nLet’s say that you’re trying to automate complex workflows, such as generating insurance quotes. Skyvern is able to navigate each step until its specified goal is achieved — even if the steps change depending on the user’s situation. Here’s an example of Skyvern navigating to [Geico.com](http://geico.com/) and generating an auto insurance quote with your personalized information.\n\n![uploaded image](/media/?type=post&id=81634&key=user_uploads/1120458/f800fef8-1a6d-4345-ba5a-043a154ad488)\n\n### 🤩 How does it work??\n\nSkyvern uses LLMs to become an AI Agent capable of interacting with websites like you or I would — all via a simple API call.\n\nTraditional approaches required writing custom scripts for websites, often relying on [DOM parsing](https://developer.mozilla.org/en-US/docs/Web/API/Document_Object_Model) and [XPath-](https://en.wikipedia.org/wiki/XPath)based interactions, which would break whenever the website layouts changed.\n\nSkyvern’s prompt → automation breaks these constraints and relies on multi-modal LLMs to parse items in the viewport and interact with them the way a human would.\n\nThis approach gives us a few advantages:\n\n1. Skyvern can take a single prompt (i.e., “Download an invoice from the order history page”) and repeat it across a large number of similar websites. This would traditionally require one script per website — making tackling the long tail of website interactions very challenging\n2. Skyvern is able to operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow without any customized code\n3. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate\n4. We’re able to circumvent or navigate through many bot detection methods, as many of them rely on looking for outlier behavior\n5. We rely on LLMs to reason through interactions to make sure we can cover complex situations. Examples include:\n   * If you wanted to get an auto insurance quote from Geico, the answer to a common question, “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16\n   * If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!)\n\n### 😮 And you’re open source?\n\nYes! You can [check out our code here](https://github.com/Skyvern-AI/Skyvern). We’re open source for two big reasons:\n\n1. It allows developers to be able to look at, understand, and dive deep into Skyvern’s implementation details to (1) expand their capabilities by adding support for new functionality and (2) decode why they’re doing what they’re doing.\n2. It allows security-minded enterprises to escape “security theater” and keep data on-prem by self-hosting Skyvern.\n   1. If this is at all interesting to you, [let’s chat](https://meetings.hubspot.com/suchintan?uuid=9cb375c0-cb2c-4900-b22d-f52baa74dfb2).\n\n### Our Ask\n\nDo you have any complex workflows that you’d love to automate? We’d love to chat! Shoot me an email at [suchintan@skyvern.com](mailto:suchintan@skyvern.com) or grab some time via [my calendar.](https://meetings.hubspot.com/suchintan)",
        "url": "https://www.ycombinator.com/launches/LEg-skyvern-cloud-open-source-ai-agent-to-automate-browser-based-workflows",
        "votes": 35,
        "created_at": "2024-06-20T14:46:54.762Z"
      },
      {
        "id": 77665,
        "title": "🐉 Skyvern - Automate Browser-based workflows with AI",
        "tagline": "Automate manual workflows within your company with an AI Agent",
        "body": "### TL;DR\n\nSkyvern helps companies automate browser-based workflows using LLMs and computer vision. We provide a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable scripts.\n\n### 🤔 What are browser workflows?\n\nMost companies we’ve talked to have manual or semi-automated workflows that support their core product. Many of these workflows started manually as companies were getting off the ground, [doing things that don’t scale](https://paulgraham.com/ds.html), and evolved to occupy a larger amount of human capital or automated with unreliable scripts.\n\n**Example 1 — Automating materials procurement from commerce websites**\n\nLet’s say you’re a B2B marketplace for car parts, and your users submit orders from multiple vendors with you. A subset of vendors you work with don’t support ordering via an API, so you have a human in the loop to transact orders for those vendors. Here’s an example of Skyvern navigating to [finditparts.com](http://finditparts.com) and ordering an airbag — all via an API call!\n\n![uploaded image](/media/?type=post&id=77665&key=user_uploads/1120458/055a1d70-8ed2-428d-8104-740f7632d3e7)\n\n**Example 2 —** **Completing complex multi-step workflows**\n\nLet’s say you’re trying to automate complex workflows such as generating insurance quotes. Skyvern can navigate each step until its specified goal is achieved — even if the steps change depending on the user’s situation. Here’s an example of Skyvern navigating to [Geico.com](http://Geico.com) and generating an auto insurance quote with your personalized information.\n\n![uploaded image](/media/?type=post&id=77665&key=user_uploads/1120458/747160ae-eb91-48c0-977d-830074a5b93b)\n\n### 🤩 How does it work??\n\nOur focus is bringing stability to browser-based workflows. We leverage LLMs to create an AI Agent capable of interacting with websites like you or I would — all via a simple API call.\n\nTraditional approaches required writing custom scripts for websites, often relying on [DOM parsing](https://developer.mozilla.org/en-US/docs/Web/API/Document_Object_Model) and [XPath-](https://en.wikipedia.org/wiki/XPath)based interactions which would break whenever the website layouts changed.\n\nSkyvern operates like a human — increasing reliability by not relying on fragile scripts, instead relying on computer vision to parse items in the viewport and interact with them the way a human would.\n\nThis approach gives us a few advantages:\n\n1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code\n2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate\n3. We’re able to circumvent or navigate through many bot detection methods as many of them rely on allowing people to access the websites\n4. We rely on LLMs to reason through interactions to ensure we can cover complex situations. Examples include:\n   * If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16\n   * If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!)\n\n### Our Ask\n\nDo you have any complex workflows that you’d love to automate? Were you [doing things that don’t scale](https://paulgraham.com/ds.html) that you now need to scale? We’d love to chat! Shoot me an email at [suchintan@skyvern.com](mailto:suchintan@skyvern.com) or grab some time via [my calendar.](https://meetings.hubspot.com/suchintan)",
        "url": "https://www.ycombinator.com/launches/KCf-skyvern-automate-browser-based-workflows-with-ai",
        "votes": 69,
        "created_at": "2024-01-19T17:34:36.013Z"
      },
      {
        "id": 72744,
        "title": "🐉 Wyvern AI - Increase revenue in marketplaces with better product ranking",
        "tagline": "With Wyvern, not only do marketplaces get better product ranking, they get the ability to fine-tune the ranking with unique insights about their business",
        "body": "**TL;DR:** [Wyvern.ai](https://www.wyvern.ai?utm_source=yc&utm_medium=yc_launch_email&utm_campaign=launch_yc&utm_id=10) is a machine learning platform for marketplaces to help them optimize their product ranking. We do this by providing an API that’s integrated with their product catalog, that optimizes product ranking based on factors such as popularity, relevance, and personalization. In addition, we also provide a framework where customers can fine-tune their product ranking models with custom insights and tailor them to their business. We built this exact platform before, and it unlocked over $100M of value.\n\n## 💸 Product Ranking: **Unleashing Growth Potential**\n\nMarketplaces, such as Amazon, eBay, or Etsy, often have millions of products listed by various sellers, making it crucial to determine how to present these products to users effectively. These companies have teams of data scientists dedicated to improving the quality of product ranking, factoring in things like personalization (ie individual user preferences), relevance, and general product popularity.\n\nOur experience building Faire and Gopuff’s machine learning platform highlighted the value that product ranking derives for the business. Through successive iterations of machine learning models, we witnessed impacts exceeding 10% (equivalent to over $100 million) for these businesses.\n\nA notable portion of these impactful iterations involved providing machine learning models with access to unique insights into user behaviour. For instance, at Faire, gaining a better understanding of what products their retailers stocked in their stores enabled tailoring the shopping experience to suit each store's distinct characteristics.\n\nOther larger marketplaces had their own insights. At eBay, incorporating buyer-seller shipping logistics information into their product ranking models produced notable improvements. Similarly, at Etsy, the users' initial interactions with the site indicate their shopping intention, allowing their product ranking models to transform the entire homepage to deliver a personalized experience.\n\n## 💣 Challenges to navigate along the way\n\nWhile larger marketplaces have the leverage to iterate on their product ranking, smaller marketplaces tend to buy off-the-shelf solutions, allowing them to bootstrap and search for product-market-fit.\n\nOnce a marketplace establishes product-market-fit, transitioning from an off-the-shelf product ranking solution to a highly customizable one is deceptively challenging and requires substantial engineering investment:\n\n1. **Latency:** Effective product ranking models perform better when they can evaluate a large number of products. However, as the number of products increases, so does the user-facing latency. Maintaining fast browsing experiences while running machine learning models on hundreds of products in under 100 milliseconds is a demanding technical hurdle.\n2. **Customizability:** The value derived from machine learning models in product ranking heavily relies on incorporating unique user behavior within the marketplace. Therefore, the system needs to be highly flexible, enabling data science teams to explore and implement new insights they acquire about users.\n3. **Observability:** The importance of data quality and accuracy cannot be overstated in machine learning. Ensuring that that the data used for model training and evaluation is reliable, enabling effective decision-making and accurate product ranking.\n\n## 🐉 Solution: [Wyvern AI](https://www.wyvern.ai?utm_source=yc&utm_medium=yc_launch_email&utm_campaign=launch_yc&utm_id=10)\n\nWyvern’s machine learning platform solves these problems. Not only do we give marketplaces a model that immediately improves their product ranking, we also provide:\n\n1. The ability to iterate on and fine-tune the model by expanding its inputs, such as relevance, personalization, popularity, and more.\n2. The ability to adjust the model's objective, allowing it to optimize conversion, GMV, revenue, profit, or a combination thereof, based on the current focus of the business.\n3. An observability layer is provided to marketplaces, offering essential capabilities for debugging and evaluate historic predictions. This layer also facilitates transforming the data into a feedback loop, enabling continuous improvement of the performance of real-time machine learning models.\n\nThis allows our customers to dedicate data scientists to iterate on models within their marketplace, allowing them to continually improve the quality of product ranking across all of their surfaces.\n\n![uploaded image](/media/?type=post&id=72744&key=user_uploads/1120458/639a4715-c00e-430a-94c3-f5b122dfe255)\n\n## 🙏 Our asks\n\n* Are you a marketplace company? We would love to chat.\n* Do you know anyone working at growing marketplaces? We’d love an introduction.\n* If you have any questions, you can reach us at [suchintan@wyvern.ai](https://mailto:suchintan@wyvern.ai)",
        "url": "https://www.ycombinator.com/launches/IvI-wyvern-ai-increase-revenue-in-marketplaces-with-better-product-ranking",
        "votes": 18,
        "created_at": "2023-07-05T13:51:14.223Z"
      }
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    "website": "https://www.epsilla.com/",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "Epsilla is an all-in-one platform for building AI agents powered by your private data and knowledge. Easy to use for domain professionals, deeply customizable for AI experts, and fully equipped for enterprise customers with security, scalability, and integration.",
    "one_liner": "All-in-one platform to create AI agents with your private knowledge",
    "team_size": 3,
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    "subindustry": "B2B -> Infrastructure",
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    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
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        "full_name": "Richard Song",
        "title": "Founder",
        "founder_bio": "Richard Song is the Co-Founder and CEO of Epsilla. Before Epsilla, Richard was the Senior Director of Cloud Engineering at TigerGraph, led the team to build the DBaaS offering of TigerGraph's graph analytics platform. In addition to this, Richard led the team to build a suite of tools designed to empower developers, data scientists, and DevOps teams, enabling them to extract value from TigerGraph.",
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        "full_name": "Ricki Qin",
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        "founder_bio": "Ricki Qin is the Co-Founder of Epsilla. Before Epsilla, Ricki was the Engineering Manager in Cloud team at TigerGraph, led the team to build the Cloud Portal. Ricki has 5 years experience in the database industry, helped to build a suite of tools designed to empower developers, data scientists, and DevOps teams.",
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        "full_name": "Eric Yang",
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        "founder_bio": "Eric is the Co-Founder of Epsilla. Before Epsilla, Eric was the Architect of Tigergraph Cloud. Eric has 10+ years experience in Cloud Infrastructure with deep insight of System Relability and Scalability.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/eric-yang-a4a743271",
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      {
        "id": 122233,
        "title": "UHP: Unified Harness Protocol by HarnessRouter",
        "tagline": "An open standard for running complete agent harnesses as your product's backend, now officially adopted by Qoder by Alibaba",
        "body": "**TL;DR:** We're launching the Unified Harness Protocol (UHP), an open standard (Apache 2.0) for running complete agent harnesses as your agent product's backend. Qoder by Alibaba now officially supports UHP. Integrate once, and Qoder, Codex, Claude Code, Hermes and 20+ agent harnesses become your product's backend through one contract. No harness engineering required.\n\n\\*\\*Stop building the agent backend. Focus on your product.\\*\\*\\\nYou don't need LangChain, an agent SDK, or a managed agent platform to build your agent's backend. Instead of building your own agent loop, tool execution, sandbox, orchestration, and tracing, plug in a pre-built, configurable harness like Qoder, Codex or Claude Code as your backend. Every task runs in an isolated sandbox, is orchestrated across harnesses, and is fully traced. Tailor your tools, plugins and skills, and focus on your product.\n\n![uploaded image](/media/?type=post&id=122233&key=user_uploads/14642/185decdc-9d10-47be-965c-5f24336a39bf)\n\n**The problem:** A harness is a complete agent runtime that plans, calls tools, edits files and reports back. Every harness already knows how to do the work. What none of them agree on is how a product should drive one: how to start a task, follow its progress, continue it, cancel it, get the files it produced, and understand why it failed.\n\nThat's an N×M problem: N products times M harnesses, each pair wired up by hand. So most teams end up building their own harness instead, and spend months on harness engineering before shipping a single feature.\n\n**What UHP is**\n\n* An HTTP contract. You send a task and get back a running agent, results and files. The unit is a job, not a completion.\n* Shaped like the OpenAI Responses API, so existing SDKs, streaming parsers and UI code work with no changes.\n* Versioned spec, OpenAPI schema, and a conformance suite with 95 runnable checks that defines what \"conformant\" means.\n* No hosted service required: a conformant server can run entirely on your own machine with your own keys.\n\n**Qoder by Alibaba adopts UHP:** Qoder now officially supports UHP, so you can use it as a pre-built, configurable agent harness for your product. Instead of building your own agent loop, tool execution, sandbox, orchestration, and tracing, you get a complete backend: Qoder reads files, runs commands, and delivers finished work, from generated files to completed actions. Every task runs in an isolated sandbox, is orchestrated through HarnessRouter, and is fully traced. Build your frontend and let Qoder handle your users' tasks.\n\n![uploaded image](/media/?type=post&id=122233&key=user_uploads/14642/7be585c9-451d-4618-bd89-1bbf42951eb2)\n\n\n**Run it:** HarnessRouter is the open-source UHP server, and HarnessRouter Cloud runs it for you. You build the frontend; the harness is your backend.\n\n**Links**\n\n* Spec: [unifiedharnessprotocol.org](http://unifiedharnessprotocol.org)\n* Repo: [github.com/HarnessRouter/harnessrouter](http://github.com/HarnessRouter/harnessrouter)\n* Cloud: [harnessrouter.ai](http://harnessrouter.ai)\n\n**Our ask:** If you build agent harnesses, implement UHP and run the conformance suite. If you're building an agent product, skip the harness engineering: use Qoder as your backend through HarnessRouter and tell us which harness you want next.",
        "url": "https://www.ycombinator.com/launches/VnV-uhp-unified-harness-protocol-by-harnessrouter",
        "votes": 4,
        "created_at": "2026-10-14T22:11:14.980Z"
      },
      {
        "id": 111172,
        "title": "HarnessRouter: Open-Sourcing the World’s First Unified Interface for Agent Harnesses and the Unified Harness Protocol",
        "tagline": "Plug Codex, Claude Code, and Hermes into your backend on your own infrastructure. Introducing the Unified Harness Protocol (UHP), an open standard for connecting applications to different agent harnesses.",
        "body": "Hey everyone 👋 Three weeks ago, we[ launched HarnessRouter here on YC](https://www.ycombinator.com/launches/RpL-harnessrouter-bring-the-world-s-best-ai-agents-into-your-app-with-one-api) with one promise:\n\nBring the world’s best AI agents into your app, with one API.\n\nToday, we’re opening the layer underneath that promise.\n\nWe’re releasing HarnessRouter Community Edition and the Unified Harness Protocol (UHP). You can now run Codex, Claude Code, and Hermes as your product’s backend on infrastructure you control, using the same API contract as HarnessRouter Cloud.\n\n**TL;DR：**\n\nHarnessRouter is the world’s first unified interface for agent harnesses.\n\nYour product sends a task through one agent API. HarnessRouter runs the supported harness you choose and returns completed work, including files, artifacts, progress, and results.\n\nSessions, streaming, cancellation, and failure handling follow the same contract across Codex, Claude Code, and Hermes. You can add or switch a supported harness without rebuilding your product integration.\n\n🎥[ Watch the launch video](https://www.youtube.com/watch?v=iqrfCBrb6zY)\n\n### **Why agent harnesses aren't infrastructure yet**\n\nAn agent harness is the runtime layer around a model. It executes tasks, uses tools, manages sessions and files, streams progress, and returns completed work.\n\nHistorically, product teams built this layer themselves: task execution, session management, streaming, file and artifact handling, and error recovery, then maintained everything as models and tools evolved.\n\nNow frontier labs and open-source communities are building increasingly capable harnesses. Codex, Claude Code, Hermes, and DeepSeek Harness are examples. But they're still seen as coding tools rather than reusable infrastructure, so teams keep rebuilding this layer in-house. Just as product teams don't build their own databases, they shouldn't have to build their own harness layer either.\n\nEven teams that do adopt an existing harness hit the next obstacle: every harness works differently, so each new product-harness pair needs its own integration. We call this the N×M harness problem. In practice, it becomes harness lock-in: teams stick with the first harness they integrated, even when better or cheaper options exist.\n\nWe asked a simple question: what if agent harnesses became plug-and-play infrastructure that any product can build on?\n\nPlug-and-play means you integrate once, run any supported harness, and switch anytime a better or cheaper one ships.\n\nThat is what HarnessRouter makes possible.\n\n### **What we’re open-sourcing**\n\nWe’re releasing two core components under the Apache License 2.0:\n\n* **Unified Harness Protocol (UHP):** UHP is an open standard that gives different agent harnesses a shared execution contract. This release includes a versioned specification, schemas, and a conformance suite covering harness configuration, task execution, progress, sessions, files, cancellation, errors, and results.\n* **HarnessRouter Community Edition:** The self-hosted implementation of UHP. Gateway, Runner, and Console ship in a single Docker container. It runs Codex, Claude Code, Hermes, and more through one API and passes the full UHP conformance suite.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/2bea0a11-8aba-49c5-9a85-112001854d96)\n\nYour model-provider credentials, application state, and files remain under your control. Model calls still go to the providers you configure.\n\nMCP connects agents to tools. UHP covers a different boundary: how an application drives a complete agent harness and receives its full execution lifecycle.\n\n### **Start self-hosted. Scale with Cloud when you’re ready.**\n\nHarnessRouter Community Edition and HarnessRouter Cloud implement the same UHP contract, so one integration works for both. Start self-hosted on infrastructure you control, or scale into Cloud when you're ready.\n\nHarnessRouter Cloud provides agent harness as a service (HaaS): fully managed harnesses with serverless execution in isolated sandboxes, so teams can scale concurrent runs without operating sandbox capacity themselves.\n\n### **Agent harnesses can do more than write code: runnable examples**\n\nWe also released a **Starter Kit** with runnable applications that show an end-to-end Community Edition integration.\n\nOne example puts a harness behind an Excel column so each row can trigger agent work. Other examples cover presentations, dashboards, and video generation.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/914297fb-2588-4d8a-9a3b-37d39d82bae7)\n\nAgent harnesses can do much more than write code. We want the Starter Kit to show what becomes possible when you put them inside products.\n\n![uploaded image](/media/?type=post&id=111172&key=user_uploads/14642/f4cff64a-7539-41b3-804e-fa5ee53d77bd)\n\nStarter Kit components are available under their applicable licenses.\n\n### **Choose harnesses in production with evidence**\n\nWe benchmarked eight Harness × Model configurations on the same task and input. Cost varied by 475× and latency by 3.2×. Even with the model fixed, changing the harness moved cost by 1.5–2.1× and latency by up to 1.95×.\n\nHarness selection is a task-level production decision, not a one-time choice. HarnessRouter helps teams compare configurations on their own workloads. This was one controlled test, not a universal ranking; we published the methodology, results, and limitations.\n\n### **What teams are already building**\n\nTeams are using HarnessRouter in very different settings:\n\n* 🏥 Stanford Medicine is connecting its operational data through a shared agent layer, an “academic brain” for the organization.\n* ✅ Readily.ai encodes domain expertise into agent skills and can move a new agent from problem and plan to production within 24 hours.\n* 🎬 Spira.ai drives its generation pipeline through HarnessRouter.\n* 🪄 hibo.ai uses HarnessRouter to execute and evaluate agent skills.\n\n### **Why we built it**\n\nWe’re Kuanze Ma and Richard Song, the co-founders of HarnessRouter.\n\nKuanze is a four-time founder focused on human-AI systems. Richard has spent more than a decade building large-scale systems, including at TigerGraph and Meta.\n\nWe both worked on agent systems before HarnessRouter. We kept seeing product teams rebuild the same application-to-harness layer, so we decided to turn that layer into shared infrastructure.\n\n### **What we want from you**\n\n* If you build an agent harness or already maintain your own runtime, read UHP and tell us where it is wrong. Which parts should be standardized, and which should remain harness-specific? That comparison is worth more to us than a signup.\n* Try the Community Edition Quickstart on a clean machine. Tell us where it breaks or where the execution lifecycle is incomplete.\n* If your compliance requirements prevent you from using a hosted agent runtime, try Community Edition on infrastructure you control.\n* If you want to contribute, we use a proposal-before-code process. Describe the problem, your proposed change, and its expected impact. You do not need to arrive with a finished implementation.\n* Intros are welcome to teams shipping agent-powered features that do not want to own another backend.\n\n### **Run it yourself**\n\n* [Open-source repository](https://github.com/HarnessRouter/harnessrouter)\n* [Unified Harness Protocol](https://unifiedharnessprotocol.org)\n* [Starter Kit](https://github.com/HarnessRouter/starter-kit)\n* [Benchmark and methodology](https://harnessrouter.ai/benchmarks)\n* [Docs and HarnessRouter Cloud](https://harnessrouter.ai)\n\nReach us at [contact@harnessrouter.ai](mailto:contact@harnessrouter.ai).",
        "url": "https://www.ycombinator.com/launches/Sv6-harnessrouter-open-sourcing-the-world-s-first-unified-interface-for-agent-harnesses-and-the-unified-harness-protocol",
        "votes": 6,
        "created_at": "2026-08-17T22:23:24.250Z"
      },
      {
        "id": 106971,
        "title": "HarnessRouter: Bring the World's Best AI Agents Into Your App, With One API",
        "tagline": "One API that puts Codex, Claude Code, Hermes, and more behind your product, so your users get real output: videos, games, code, documents.",
        "body": "Hey everyone 👋 Today we're introducing HarnessRouter.\n\n**TL;DR:** HarnessRouter lets builders bring Codex, Claude Code, Hermes, and other agents into your products through one API. Each task runs in an isolated sandbox with step-by-step tracing and configurable cost caps. It's designed to scale from consumer apps to large-scale model evaluations, orchestrating thousands of isolated sandbox runs in parallel. Start with our [AGENTS.md](https://harnessrouter.ai/agents.md), drop it into your coding agent, describe want you want to build, add an API key, and ship without rebuilding agent runtime infrastructure.\n\n**Try it now:** [harnessrouter.ai](http://harnessrouter.ai?utm=yc)\n\n<https://www.youtube.com/watch?v=vZXvgYS7_lg>\n\n## **The Problem**\n\nThere's a gap between what AI agents can do and what your product can offer.\n\nAgents like Codex and Claude Code can genuinely finish work now, but they're built for developers, not for your users. Bringing that capability to your users inside your own product means building the infrastructure yourself: isolated execution environments, agent runtime, tool orchestration, file handling, streaming, retries, permissions, cost controls. Every team building an AI feature rebuilds this same stack from scratch, and it takes months. After launch, the upgrades and maintenance never stop.\n\nAs a result, most teams ship a chat interface instead, when what users actually need is finished output.\n\n## **The Solution**\n\nHarnessRouter runs the entire agent stack as a service. Your product sends a task through one API; We run it on the agent you choose and return the result in structured, renderable form: reviewable diffs, generated files, images, video, confirmed tool actions.\n\nTeams are already running production workloads on it:\n\n* 🏥 A top medical research institution is connecting its operational data into a unified agent layer, an \"academic brain\" for the organization.\n* ✅ A leading healthcare compliance company encodes domain expertise into agent skills, saving their clients hundreds of hours of manual work per audit.\n* 🎬 An AI video marketing startup drives its content generation pipeline through HarnessRouter, producing viral-grade video at human quality.\n\nIntegration takes three steps: drop our [AGENTS.md](https://harnessrouter.ai/agent.md) into your coding agent, describe the feature you want and add your API key. Switching agents is a one-line configuration change, and the same setup scales from a single task to thousands of concurrent runs.\n\n## **Who We Are**\n\n**Kuanze Ma, Co-founder**\\\nKuanze is a four-time founder focused on human–AI systems, with experience in systems design, product management, algorithms, recruiting, marketing, and engineering. He holds a BSc in Mathematics, a diploma in Software Engineering, and an MSL from Northwestern University.\n\n**Richard Song, Co-founder**\\\nRichard has over a decade of experience building large-scale systems, including vertical AI agents powered by private data and domain knowledge. Previously, he was a Senior Director at TigerGraph and a Senior Software Engineer at Meta. He holds an MS in Computer Science from Cornell University and a BS in Computer Science.\n\n## **Our Ask**\n\n* **If you're building an AI feature**, start with the free 7-day trial. We'll help you integrate.\n* **If you need to run thousands of agents in parallel**, for model evaluations, batch generation, or large-scale pipelines, that's what the sandbox orchestration layer was built for. Talk to us.\n* **If you've built your own agent infrastructure**, we'd value the comparison. Tell us what we're missing.\n* **Intros welcome** to any team that wants to ship agent-powered features without building the backend.\n\nReach us at [contact@harnessrouter.ai](mailto:contact@harnessrouter.ai)",
        "url": "https://www.ycombinator.com/launches/RpL-harnessrouter-bring-the-world-s-best-ai-agents-into-your-app-with-one-api",
        "votes": 29,
        "created_at": "2026-07-23T13:44:28.619Z"
      },
      {
        "id": 99723,
        "title": "ClawTrace — Make Your OpenClaw Agent Better, Cheaper, and Faster",
        "tagline": "The observability platform for OpenClaw agents: execution traces, cost analysis, and Tracy, your OpenClaw's doctor agent.",
        "body": "## 🚀 TL;DR\n\n[ClawTrace](https://clawtrace.ai?utm=yc) is the observability platform for OpenClaw agents. It captures every trajectory your OpenClaw agent runs — every LLM call, tool use, and sub-agent delegation — and gives you three views to understand it: an execution path, a call graph, and a timeline. When you need answers fast, ask Tracy, your built-in AI analyst, in plain English. Watch our demo [here](https://youtu.be/-moG7vCkawQ?si=ivl7Utv3h7ynv5rb):\n\n—\n\nHello, world! I'm Richard, and today I'm excited to introduce **ClawTrace** — the observability platform for OpenClaw agents.\n\nAI agents are going to production. But right now, when something goes wrong, you're flying blind.\n\n## ❌ The Problem\n\nDeploying an OpenClaw agent is easy. Understanding what it actually did is not.\n\nWhen a run costs $104.70 instead of the usual $1.5, you have no idea why. When an agent fails silently, you don't know which step broke or what input caused it. When a trajectory takes 3 minutes instead of 30 seconds, you can't tell where the time went. You end up reading raw JSON logs, manually counting token estimates, and guessing at execution paths. This doesn't scale.\n\nExisting options make this worse, not better:\n\n* **General distributed tracing tools** (Jaeger, Zipkin, Datadog) weren't built for LLM concepts — they have no understanding of token counts, model costs, prompt/completion pairs, or sub-agent hierarchies.\n* **Framework-specific tools** like LangSmith only work within their own ecosystem. If you're on OpenClaw, you're on your own.\n* **Point solutions** cover one slice of the problem — just logs, just cost tracking, just evals — and leave you to stitch everything together yourself.\n* **Every tool** dumps data into a dashboard and expects you to interpret it. There's no analyst. You still have to do all the thinking.\n\n## ⭐️ Our Solution\n\nWe built ClawTrace to give you complete visibility into every OpenClaw agent run — automatically, with zero code changes to your agent.\n\nOne plugin. Three commands. Every trajectory captured.\n\n```bash\nopenclaw plugins install @epsilla/clawtrace\nopenclaw clawtrace setup\nopenclaw gateway restart\n```\n\nThat's it. From that point on, every agent run streams to ClawTrace automatically.\n\n**With ClawTrace, you get:**\n\n* **Execution Path** — a collapsible tree of every step your agent took: LLM calls, tool executions, sub-agent delegations, with full input and output payloads at every node. Metadata badges show model name, duration, token counts, and estimated cost inline.\n\n  ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/d592e3b7-c8d3-4f7c-b71d-a27037551bba)\n\n* **Call Graph** — an interactive force-directed diagram showing the relationships between every agent, model, and tool in a run. Understand the shape of a complex multi-agent workflow at a glance.\n\n  ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/e81258a7-05d0-4c5d-be9a-0ef944b72329)\n\n* **Timeline** — a Gantt chart of every span in the trajectory, positioned by start time and sized by duration. Bottlenecks, parallelism, and idle gaps become immediately visible.\n\n  ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/bf94f46d-90e6-4ea4-8b1e-2971ad79af85)\n\n* **Tracy, your OpenClaw’s Doctor** — every other observability tool gives you data and expects you to figure it out yourself. Tracy is different. Ask her a question in plain English — _\"where is the bottleneck in this trace?\"_ — and she queries your trajectory graph live, generates a visualization, and delivers specific recommendations: which step to fix, why, and how. Tracy is powered by a Claude managed agent and has real-time Cypher access to your full trajectory graph via PuppyGraph graph lake-house architecture.\n\n  ![uploaded image](/media/?type=post&id=99723&key=user_uploads/14642/7cb3bad9-86b3-4795-b761-75b7a390ce62)\n\n* **Cost Estimation** — per-span cost calculation covering 80+ models across OpenAI, Anthropic, Google, DeepSeek, Mistral, Alibaba Qwen, Zhipu GLM, Moonshot, and more. Cache-aware pricing (fresh input vs. cached input vs. output tokens) for accurate estimates.\n* **Consumption-based billing** — pay for what you use with credits. No seat fees, no per-user pricing. Start with 100 free credits.\n\n## 🔦 The Ask\n\nAI agents are moving from demos to production. ClawTrace is the observability layer that makes that transition safe, debuggable, and cost-efficient.\n\n* 🚀 **Sign up for free** at [clawtrace.ai](http://clawtrace.ai) — 100 credits included, your first trajectory shows up in minutes.\n* ⭐ **Star us on GitHub** — ClawTrace is fully open source under Apache 2.0: [github.com/epsilla-cloud/clawtrace](https://github.com/epsilla-cloud/clawtrace)\n* 💬 **Tell us what you're building** — we'd love to hear about your OpenClaw setup and what observability problems matter most to you. [Schedule a call with me](https://epsilla-ai.sg.larksuite.com/scheduler/dc6a62e558c3a1cc).",
        "url": "https://www.ycombinator.com/launches/PwR-clawtrace-make-your-openclaw-agent-better-cheaper-and-faster",
        "votes": 12,
        "created_at": "2026-04-14T15:34:37.032Z"
      },
      {
        "id": 84075,
        "title": "Epsilla — Ship production-ready AI agents on day 1",
        "tagline": "All-in-one platform to create AI agents with your private data and knowledge.",
        "body": "### **🚀 TL;DR**\n\n[Epsilla](https://www.epsilla.com/) is an all-in-one platform for building AI agents powered by your private data and knowledge. Easy to use for domain professionals, deeply customizable for AI experts, and fully equipped for enterprise customers with security, scalability, and integration. Watch our demo [here:](https://www.youtube.com/watch?v=ZhBPsxr2AL0)\n\n---\n\n—\n\nHello, world! We are Richard, Eric, and Ricki. Today, we are super excited to introduce Epsilla, the all-in-one platform for creating AI agents with your private data and knowledge.\n\n### **❌ The Problem**\n\nBuilding a prototype AI agent is easy, but creating production-level AI agents with private knowledge can be overwhelming. It involves managing LLMs, prompt engineering, tool usage, memory management, vector databases, RAG (Retrieval-Augmented Generation), evaluation, and more.\n\nExisting AI agent-building products in the market are:\n\n* Either developer-facing tools that require a significant amount of engineering work;\n* Or lacking depth in customization and flexibility, limiting the final agent's performance and quality;\n* Or just focus on only one small aspect of the problem—such as prompt engineering, workflow, retrieval, evaluation, or hosting—leaving builders to handle the heavy lifting of integrating these components into a cohesive system.\n\n### **⭐️ Our Solution**\n\n![uploaded image](/media/?type=post&id=84075&key=user_uploads/14642/83e9fd99-cf4a-473a-b1ac-57f4935ff4dc)\n\nWe have reimagined the AI agent platform from the ground up and developed an all-in-one solution for creating, iterating, and operating AI agents—without writing a single line of code.\n\nWith Epsilla, you can:\n\n* Build high-quality knowledge bases from diverse data sources, including uploaded files, websites, Google Drive, Notion, cloud storage, and more.\n* Deeply customize AI agents with options for appearance, prompt tuning, workflow customization, and advanced, modular, agentic, and graph-based RAG.\n* Evaluate agent performance, monitor user engagement, and manage user feedback.\n* In addition to providing an out-of-the-box ChatGPT or Perplexity-like UI, we also allow developers to use the agents as their application backend via REST API.\n* Epsilla seamlessly integrates with a growing set of Large Language Model providers, allowing you to choose the best model at any stage of the knowledge base and agent workflow to balance quality, latency, and cost.\n\n### **🔦 The Ask**\n\nThousands of GenAI builders, along with dozens of enterprises and SMBs across various industries, are already building their AI agents on Epsilla. We hope to see you join them soon!\n\n* Sign up for FREE at <https://epsilla.com> to build your first AI agent within 10 minutes!\n* Let’s talk about your use case! Schedule a meeting with us [here](https://epsilla-ai.larksuite.com/scheduler/4aca8159d1224454).\n* Join our community with us on [Discord](https://discord.gg/cDaY2CxZc5) and share what you built on Epsilla!",
        "url": "https://www.ycombinator.com/launches/Ls3-epsilla-ship-production-ready-ai-agents-on-day-1",
        "votes": 35,
        "created_at": "2024-09-17T16:47:45.508Z"
      },
      {
        "id": 73587,
        "title": "Epsilla - A 10x faster open source vector database",
        "tagline": "The information retrieval foundation for the next generation of AI applications",
        "body": "### **🚀 TL;DR**\n\nEpsilla is an open-source vector database. Epsilla offers low-latency searches across billions of vectors, consumption-based database-as-a-service, comprehensive APIs, and multi-language bindings. Our team has 7 years of experience building the high-performance graph database, and our advanced ANN index leverages parallel graph traversal technology, making the vector search 10x faster.\n\n![uploaded image](/media/?type=post&id=73587&key=user_uploads/14642/801e5229-0b56-4eb5-a3fb-e5ff7d2686ec)\n\n### **❌ The Problem**\n\nWith the surge of LLMs and the evolution of AI agents for task planning and decomposition for complex tasks, a **high-performance vector database** for information retrieval and enhancing context in prompts is a **MUST**! Most vector databases utilize HNSW, and its performance significantly degrades when aiming for high search precision.\n\n### **⭐️ Our Solution**\n\nWe’ve reimagined vector search from the ground up for scalability, performance, and cost-effectiveness. We implemented the state-of-art ANN index algorithm from the academia that leverages intra-query parallel graph traversal, which outperforms HNSW by **5x** on high precision query latency on medium size (1M) vector space and outperforms HNSW by **50 times** on large-scale vector search.\n\n![uploaded image](/media/?type=post&id=73587&key=user_uploads/14642/7f276b64-0500-4286-b589-e23d339abe6d)\n\n### **🔦 The Ask**\n\n* Try us at <https://epsilla-inc.gitbook.io/epsilladb/quick-start>\n* Follow our repo at <https://github.com/epsilla-cloud/vectordb>\n* Connect with us on [Discord](https://discord.gg/cDaY2CxZc5)\n* Let’s talk at <https://epsilla-ai.larksuite.com/scheduler/a80f6a3202d4c328>!",
        "url": "https://www.ycombinator.com/launches/J8t-epsilla-a-10x-faster-open-source-vector-database",
        "votes": 41,
        "created_at": "2023-07-31T01:45:04.471Z"
      }
    ]
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    "id": 28960,
    "name": "Apoxy",
    "slug": "apoxy",
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    "website": "https://apoxy.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Apoxy is the programmable edge platform that simplifies connectivity, security, and custom logic deployment for distributed applications. Our platform combines secure site-to-site networking with a high-performance runtime for custom TypeScript and Go filters, allowing developers to deploy business logic at the edge without complex infrastructure. Whether you need instant HTTP/TCP/UDP connectivity, advanced access controls, or custom data processing, Apoxy meets you where you are - from Kubernetes clusters to simple processes - with powerful observability and sub-second troubleshooting built in.",
    "one_liner": "Simpler and cheaper multi-cloud and cross-platform deployments.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1690268211,
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    "nonprofit": false,
    "batch": "Summer 2023",
    "status": "Active",
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    "regions": [
      "United States of America",
      "America / Canada",
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/apoxy",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/apoxy.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/apoxy/",
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        "user_id": 954645,
        "full_name": "Dmitry Ilyevsky",
        "title": "Founder",
        "founder_bio": "Technical co-founder of Apoxy - a programmable traffic controller that helps companies build and operate APIs at scale. Before Apoxy spent 15 years in the industry working on compute clusters and networking fabrics at Render, Mux, Cruise, Google, and Motorola.\n\nGeneralist with a focus on networking and systems. Semi-retired alpinist and current ultra long-distance runner.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/dilyevsky",
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  {
    "id": 29073,
    "name": "Terracotta AI",
    "slug": "terracotta-ai",
    "former_names": [
      "Monitaur",
      "Monitaur (Pivoted)",
      "Monitaur",
      "OpsBerry AI (formally Monitaur)",
      "OpsBerry AI"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7901d3a776bcd60d2813e642a8814ea750bb84ef.png",
    "website": "https://tryterracotta.com",
    "all_locations": "Las Vegas, NV, USA",
    "long_description": "Terracotta AI is an AI-powered Infrastructure as Code PR reviewer for Terraform, OpenTofu, and CDK-TF code, making Infrastructure as Code changes more transparent, less risky, and faster.\r\n\r\nTerracotta stops your Infrastructure as Code deployments from becoming ticking time bombs every time you modify your environment with AI-driven, contextual feedback delivered directly to your pull requests.",
    "one_liner": "AI-powered Infrastructure as Code PR reviews",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1686680826,
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      "Developer Tools",
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    "year_founded": 2023,
    "city": "Las Vegas",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/tryterracotta",
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    "primary_partner": {
      "name": "Garry Tan",
      "url": "https://www.ycombinator.com/people/garry-tan"
    },
    "founders": [
      {
        "user_id": 925045,
        "full_name": "Carlos Feliciano",
        "title": "Founder",
        "founder_bio": "Co-founder and CEO of Terracotta AI. Over a decade of experience in Infrastructure, monitoring, and operations at YC-backed startups and large enterprises. AWS Solutions Architect. Love PC gaming, fitness, the beach, and just good vibes!",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/carlosfelicianoii",
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      {
        "id": 91358,
        "title": "Terracotta AI - AI agents for Infrastructure as Code workflows",
        "tagline": "Intelligent preflight checks for your Infrastructure as Code workflows inside of your PR",
        "body": "# **tl;dr**\n\nPlatform and DevOps teams are still catching Terraform issues after they break production. Terracotta AI brings AI-powered review directly into PRs, analyzing your code, state, and live cloud resources to prevent problems before any workflow triggers.\n\n**Try Terracotta AI for free at [https://tryterracotta.com](https://tryterracotta.com)!**\n\nhttps://www.youtube.com/watch?v=QyzyiJVeZqU\n\nhttps://www.youtube.com/watch?v=GuSfvcYllZo&t\n\n# **Problem**\n\nEven the simplest Terraform changes can break production in unexpected ways and can take a significant amount of time to debug and fix.\n\nPlatform teams have developed sophisticated DevOps practices, including module catalogs, compliance policies, and continuous integration/continuous delivery (CI/CD) pipelines.\n\nHowever, even with senior engineers reviewing pull requests (PRs), critical issues still slip through, including drift conflicts, hidden dependencies, and blast radius across services that you didn't know were connected.\n\nThe problem isn't a lack of processes. It's that humans can't consistently analyze Terraform changes against live cloud state, remote state, and downstream dependencies simultaneously. Traditional tools catch problems after the PR is approved or deployed, not before.\n\n# **Solution**\n\n![uploaded image](/media/?type=post&id=91358&key=user_uploads/925045/89ae62a4-eb80-4d9e-95d5-e2af0684740a)\n\nWe created a purpose-built AI agent for Infrastructure as Code (IaC) workflows that seamlessly integrates into your IaC pipeline, contextualizing and providing real-time feedback directly within your pull requests.\n\nTerracotta AI analyzes every Terraform change against the most critical contexts in your IaC deployment pipeline and provides accurate, contextual, actionable feedback inside of your PR:\n\n* **Your IaC code** — syntax, structure, linting, and more\n* **Local plan state** — what Terraform thinks will happen\n* **Live remote state** — What should be deployed\n* **Live cloud resources** — what actually is deployed\n\nWe instantly surface the issues that break production:\n\n* Drift and state mismatches\n* Threat vectors and misconfigurations\n* Hidden blast radius and downstream risk\n* Cost anomalies and compliance violations\n* Cross-PR conflicts for resource modifications\n\nEverything happens as soon as the PR opens, before any CI/CD workflow triggers, and serves as an early detection system for hidden risks and issues that might enter your production environment.\n\n# **Traction**\n\nWe're working with platform engineering and DevOps teams at SupportLogic, LaminatedLabs, and others who are catching issues they would have missed and deploying with confidence rather than crossing their fingers.\n\n\"Terracotta has helped my DevOps team ship Terraform faster and with more clarity. It catches cost impacts, exposed secrets, potential threat vectors, and even cross-PR conflicts before we trigger our CI/CD pipelines. It's been a huge win for both speed and compliance and an overall boost in confidence in helping manage Terraform across all our environments.\" - **Tyler Pinckard - Head of Software Engineering, DevOps & Security at SupportLogic**\n\n**SupportLogic Case study:** https://tryterracotta.com/case-study/supportlogic\n\n# **Our ask**\n\nIf your team is tired of Terraform, OpenTofu, and CDK-TF surprises in production, let's show you a 15-minute demo or sign up for a free 14 day trial at https://tryterracotta.com.\n\nEmail us: [founders@tryterracotta.com](mailto:founders@tryterracotta.com)",
        "url": "https://www.ycombinator.com/launches/NlW-terracotta-ai-ai-agents-for-infrastructure-as-code-workflows",
        "votes": 16,
        "created_at": "2025-06-16T22:20:23.169Z"
      },
      {
        "id": 82694,
        "title": "OpsBerry AI - Secure your identity sprawl",
        "tagline": "Defending against identity-driven breaches with AI. ",
        "body": "![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/926203d7-e2a0-45cd-b97b-d38419a524b4)\n\n**TL; DR** —[OpsBerry AI](https://d2lssd04.na1.hs-sales-engage.com/Ctc/ZW+23284/d2LsSd04/Jks2-6qcW69sMD-6lZ3lyVmsNqH4X0sgdW5TPVYB7bLMr0W6gCpW-7YVgsNW1bvyKV6VTFTXVcRKMB84G-_WW68Bq2C90_lXpW6_BGLs6C_-0yW4lQkxg4FflCWW8rtXpw1ppdmgW2NfKVX6P8tfhW5nXNYm10nsb3W4lyMqq6LVSVZV_cQzJ8LnY0rW5Vt27w62FFv5W2Q4pWc4Z67KLW3kFQKk6ZC34bW68q3RQ3jfwPVW1ztCL98bbKldW685KMM36rzGDW8SMnJ02zDZD_f5nZ8xj04) is an Identity Security Posture Management Platform that uses AI trained with the latest cybersecurity and compliance frameworks to help organizations proactively control, secure, and defend their human and non-human identity sprawl against identity-driven attacks. We help security and engineering teams quickly unify, analyze, and remediate critical vulnerabilities in their identity sprawl into a single pane of glass across central cloud, identity, and ZeroTrust providers.\n\n# **The team**\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/f8edf5e2-f5ee-4acb-8df8-3284f47601f0)\n\n[**Carlos**](https://www.linkedin.com/in/carlosfelicianoii/) is the company's CEO. He handles the business end, infrastructure, security, architecture, and technical operations. He has over 15 years of experience in Cloud infrastructure, architecture, and observability. Before Y Combinator, Carlos was Director of Solutions Architecture and technical customer success at [OpsRamp](https://opsramp.com), multi-cloud and hybrid observability, and ITSM (acq. by HPE), helping enterprises like Micron, Dolby, Epsilon, and others architect end-to-end observability across their multi-cloud environments. Before that, Carlos was an engineering and technical support manager for [LogDNA](https://mezmo.com) (YC S15).\n\n[**Casey**](https://buildspace.so/) is the company's CTO. With over 16 years of experience architecting and shipping code for his business ventures, Casey started by building and selling Runescape bots when he was 12. This eventually led him into various software engineering roles at YC-backed startups like [Buildspace](https://buildspace.so) and Farmstead. Casey runs the product side of the house, handling UIUX design, software architecture, and development, and ultimately, shipping amazing enterprise code.\n\n# **What is causing the rapid increase in identity-driven breaches and why?**\n\nIdentity security is often overlooked and underestimated, leaving organizations vulnerable to unauthorized access, data breaches, and compliance failures. User-based identities are steadily growing annually at a 30% rate, with most employees having two or more identities that allow access to critical business and engineering systems, which is only part of it. NHIs, or Non-Human identities (think OAuth apps, API keys, Service Accounts, etc.), are increasing even faster at 50%, or said otherwise, four or more NHIs per every employee.\n\nIn 2023 alone, 84% of organizations suffered at least one identity-based attack, which averaged $4.5 million in cost. The number of breaches has grown at a steady 50% yearly since 2020 (Source: ISDA). That's not all; unrealized fallout from these breaches after the fact tends to include legal repercussions, loss of customers, increased security costs, higher insurance premiums, reputational repair, loss of intellectual property, and more.\n\n![](https://www.idtheftcenter.org/wp-content/uploads/2024/01/ITRC-DataBreachReport-2023-Infographic-H-1024x791.jpg.webp)\n\nUser identities are only one attack vector in your identity security posture; machine-based identities like API keys, OAuth apps, service identities, and other NHIs are critical attack vectors for businesses. Partially offboarded identities, orphaned human and non-human identities, identity privilege creep, overprivileged access, and shadow accounts that fly under the radar, like non-sso access, gen-AI tools, and other IT internal systems, eventually snowball out of control and exponentially increase the potential for identity-driven breaches across critical engineering and business environments.\n\nFor businesses struggling to maintain compliance and adhere to regulatory requirements, these unchecked identity issues lead to significant compliance and regulatory problems. Organizations face increased risks of failing audits, substantial fines, and even reputational damage due to their inability to manage and secure their identity sprawl effectively.\n\n# **How does OpsBerry solve this with AI?**\n\nOpsBerry AI defends against human and non-human identity breaches using AI to continuously discover, prioritize, and remediate vulnerabilities and risks in your identity sprawl. Our AI-powered identity security posture management platform, trained with popular cyber security and compliance frameworks, provides comprehensive visibility and contextualization across your user accounts, service accounts, API keys, OAuth apps, and more, keeping your organization safe from identity-driven attacks.\n\n# **How it works**\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/41846940-aa9e-44ae-948b-39ed31b48d7a)\n\n👀 **Automated Identity risk assessments**—We automatically gather all information about each identity and generate in-depth risk assessments with AI, instantly surfacing gaps, vulnerabilities, and threats.\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/019721bf-ba26-4e5d-abd6-af44511dabae)\n\n👥 **Shadow identities**—let AI surface the identities that hide in the shadows and quickly identify “unknown unknown” identity risks in your identity secure posture. Quickly surface and remediate shadow accounts across human and non-human identities, such as non-sso user accounts, API Keys and tokens, service accounts, orphaned identities, unmonitored privileged accounts, and more. This is the quickest way to transform hidden risks into strategic cost savings.\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/ecaaaa23-83bd-40d6-9f1e-d2c3a169e380)\n\n**🌱 Lifecycle management and remediation** are the easiest ways to control and secure your human and non-human identity posture. Drop-and-drop no-code automation lets you build streamlined self-service workflows to quickly onboard and offboard identities, remediate risks, and defend against identity-driven breaches.\n\nAnd much more in the works!\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/968ba4e4-c7d3-4379-b992-3f6781f46563)\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/dda6bfd7-7e40-42e1-aca1-1c14c25ec780)\n\n![uploaded image](/media/?type=post&id=82694&key=user_uploads/925045/119b7479-a979-48a4-ac8b-2d3b7c3a0fb0)\n\n# **Our ask**\n\nNeed help wrangle your identity sprawl with an identity security platform that’s like having your very own in-house identity security expert? We’d love to work with you!\n\nRegardless of your company size, industry, or role, if you’re serious about protecting against identity breaches and want to take real control of your identity sprawl, we’d love to chat and show you a demo! Email [carlos@opsberry.ai,](mailto:carlos@opsberry.ai) or you can even set a time on our [Calendly](https://calendly.com/opsberry/opsberry).\n\n# Socials\n\n[OpsBerry AI (Y S23)](https://opsberry.ai)\n\n[X](https://x.com/opsberryai)\n\n[Linkedin](https://www.linkedin.com/company/opsberryai)",
        "url": "https://www.ycombinator.com/launches/LVm-opsberry-ai-secure-your-identity-sprawl",
        "votes": 34,
        "created_at": "2024-08-02T19:49:51.530Z"
      }
    ]
  },
  {
    "id": 29115,
    "name": "Mocha",
    "slug": "mocha",
    "former_names": [
      "action AI",
      "Action AI",
      "Axilla",
      "Axflow",
      "Srcbook"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e2e9ab5d9d14600d56cfc241294be2559fffc5f6.png",
    "website": "https://getmocha.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "What if you could build web apps as easily as you make a Figma design or edit a Google Doc?\r\n\r\n\r\nEnter Mocha. We've reimagined app development with a state-of-the-art coding agent that builds for our users.\r\n\r\nWant to launch that app idea? Need to build something for your community? Looking to prototype faster? Start here. We've removed the barriers between idea and execution.\r\n\r\nOur mission is to empower everyone to build for the web, our vision is personal software.\r\n\r\n",
    "one_liner": "Build and publish web apps in minutes. No coding required.",
    "team_size": 7,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1690767970,
    "tags": [
      "No-code",
      "Web Development",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2023",
    "status": "Acquired",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/mocha",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/mocha.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/96310770",
    "x_url": "https://twitter.com/buildwithmocha",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/srcbookdev/srcbook",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e2e9ab5d9d14600d56cfc241294be2559fffc5f6.png",
    "app_video_url": null,
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    "company_photos": [
      "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1f3192a68c42ec6ce46753d2035c2f7677266dec.png"
    ],
    "primary_partner": {
      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
    },
    "founders": [
      {
        "user_id": 1399285,
        "full_name": "Nicholas Charriere",
        "title": "Founder",
        "founder_bio": "Currently building Mocha. \n\n\n\nFrench & British, been a developer in the valley for 12 years in smaller & larger startups (Pinterest, Cruise).\nGeneralist but most of my recent experience is in distributed systems, ML platforms and AI systems (agents, harnesses, agent UX).\n\nhttps://nicholascharriere.com",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/nicholas-charriere",
        "x_url": "https://twitter.com/nichochar",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/158068b1cc71592e72d530ace07bd358ca420202.jpg"
      },
      {
        "user_id": 1448621,
        "full_name": "Ben Reinhart",
        "title": "Founder",
        "founder_bio": "Software engineer and entrepreneur. Previously ML infra at Cruise, cofounder at Fam (acquihired by Nextdoor), and Groupon.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/bereinhart",
        "x_url": "https://twitter.com/benjreinhart",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/431f4c4da27f11fb396f174e32f93b73d8d572de.jpg"
      }
    ],
    "jobs": [],
    "news": [
      {
        "title": "Show HN: Srcbook – A TypeScript notebook for rapid prototyping | Hacker News",
        "url": "https://news.ycombinator.com/item?id=41291700",
        "date": "Aug 20, 2024"
      }
    ],
    "launches": [
      {
        "id": 83310,
        "title": "Srcbook: TypeScript notebooks",
        "tagline": "Meet Srcbook, the best way to prototype in TypeScript!",
        "body": "**TLDR**: We recently launched a new open-source application for prototyping in TypeScript: [**Srcbook**](https://srcbook.com/) (pronounced “source-book”). Srcbook is a notebook-style programming environment and the best way to prototype code, explore libraries, learn concepts, and rapidly iterate on your ideas with TypeScript. It runs locally, has powerful AI features, and exports to markdown for maximum portability.\n\n[**https://www.loom.com/share/f0ea7257d9c544848f4128cf0ccba8ae?sid=d5a51920-8c37-4b10-90ee-414170dfeb5d**](https://www.loom.com/share/f0ea7257d9c544848f4128cf0ccba8ae?sid=d5a51920-8c37-4b10-90ee-414170dfeb5d)\n\n### Origin story\n\nWe are very bullish on AI moving from being a Python discipline in the ML era to a TypeScript discipline in the Generative AI era. We previously tried building multiple products to serve this new TypeScript-AI wave, but we quickly realized that most teams don’t want low-code GUI tools to do evals or build agents with drag-and-drop. What they actually want is to write code but do so with rapid iterations. In Python, they automatically reach out to Jupyter for such workflows, but in TypeScript? We found that there was a large gap… We realized that a TypeScript notebook would be useful for many use cases, not just applied gen-AI, and so we started building.\n\n### The product\n\nSrcbook is a beautiful, open-source, local-first application to write and iterate on TypeScript code. It’s available today on npm: `npx srcbook start`\n\n![uploaded image](/media/?type=post&id=83310&key=user_uploads/1399285/1af6e324-e52a-4d49-8e35-27af1b5dd4b5)\n\nSome key features:\n\n* Full npm ecosystem access\n* AI-assisted coding (OpenAI, Anthropic, or local models), with notebook-specific advantages and a natural “pair-programmer” UX\n* Exports to valid markdown for easy sharing and version control\n\nWe recently [launched on HN](https://news.ycombinator.com/item?id=41291700) and devs really seem to like it, all our graphs started looking like this!\n\n![uploaded image](/media/?type=post&id=83310&key=user_uploads/1399285/a040cd03-a61f-4767-b524-50b285aa9bd3)\n\n### Demo\n\nIn this video, you can see Nick using the genAI features to bootstrap a CLI-based chess game in a few minutes.\n\n<https://www.loom.com/share/a212e1fd49a04c548c09125a96a1836f>\n\n### Asks\n\nTry Srcbook out! It’s free, just `npm srcbook start` in any terminal.\n\nStarring us on [GitHub](https://github.com/srcbookdev/srcbook/tree/main) also helps, although what we really want is for you to build cool stuff with Srcbook. If you make a good Srcbook, send it our way, and we will showcase it in the [Hub](https://hub.srcbook.com).\n\n![uploaded image](/media/?type=post&id=83310&key=user_uploads/1399285/8bab03fa-f71f-43bb-9639-152a83cfb733)\n\nPS: We’re [Ben](https://linkedin.com/in/bereinhart) and [Nick](https://linkedin.com/in/nicholas-charriere), been building together for 5+ years at Cruise and other startups, and we love TypeScript. If you do too, reach out and let’s chat!",
        "url": "https://www.ycombinator.com/launches/Lfi-srcbook-typescript-notebooks",
        "votes": 31,
        "created_at": "2024-08-22T17:06:51.212Z"
      },
      {
        "id": 73588,
        "title": "Axilla — an AI framework for TypeScript",
        "tagline": "Axilla is an end-to-end framework to develop LLM applications for enterprise",
        "body": "**tl;dr** Axilla is a framework for the AI engineer of the future: a product-focused TypeScript developer. Axilla provides a family of modular libraries, which can be incrementally adopted, and together form an **end-to-end opinionated framework for LLM development**.\n\n### The team\n\nHey there 👋, we’re [**@Ben Reinhart**](https://linkedin.com/in/bereinhart) and [**@Nicholas Charriere**](https://linkedin.com/in/nicholas-charriere).\n\n![uploaded image](/media/?type=post&id=73588&key=user_uploads/1399285/8bda8bff-dc8a-4979-9c4b-a73b4c49a5f1)\n\nWe first started working together on ML platforms at Cruise, a leading self-driving car company, where we spent 4 years on the ML platform team. This is also where we became great friends!\n\nAt Cruise, we’ve seen the productivity losses that result from a fragmented ML organization. We then spent years designing and building a standardized framework and personally experienced the productivity superpowers that resulted from the framework’s adoption.\n\n### 🤨 The problem\n\nAn accelerating trend in the AI landscape is the shift from in-house model training to foundational models abstracted away behind APIs (chatGPT, Midjourney, llama2…).\n\nA consequence of this trend is that AI teams are morphing from Python-focused data scientists to resemble typical product engineering teams: full-stack web developers increasingly coding AI applications in TypeScript.\n\nWe’ve spoken to a number of enterprise customers who’ve confirmed their product teams are now responsible for designing and launching new AI initiatives, especially ones involving LLMs. However, given how recent the explosion of LLMs is, **these product engineers don’t yet have the tools** **to ship AI features quickly and with confidence**.\n\n### 💡 The solution\n\n[**Axilla**](https://www.axilla.io/) provides a family of modular and coherent libraries which together form an end-to-end opinionated framework for LLM development. It is aimed at TypeScript developers who want to build and operate production-grade AI applications.\n\nIt’s early, but in the last three weeks we launched our first two modules:\n\n1. [**axgen**](https://github.com/axilla-io/ax/tree/main/packages/axgen) is focused on data ingestion and retrieval\n2. [**axeval**](https://github.com/axilla-io/ax/tree/main/packages/axeval) is focused on continuous evaluation & quality monitoring\n\nNext, we are excited to begin work on model serving and monitoring, which are two of our most critical components. Together, these integrate production data back into the continuous evaluation pipelines, enable enterprises to run open-source models internally, and **allow developers to stop flying blind** when it comes to their AI applications.\n\n### Demo\n\nAs part of our first release, we built a proof of concept that demonstrates how to build a “chat with your documents” application using axgen.\n\n<https://youtu.be/8KEbP7LL_sM>\n\n### Asks\n\n[**Try axilla**](https://github.com/axilla-io/ax) today! It’s open source and available on npm. If you like it, **give it a star** ⭐️, and come join the conversation on [twitter](https://twitter.com/axilla_io).\n\nAre you a product engineer building AI applications (especially with TypeScript)? We’d love to [chat](https://calendly.com/nichochar/15min-chat-with-nicholas)!",
        "url": "https://www.ycombinator.com/launches/J8u-axilla-an-ai-framework-for-typescript",
        "votes": 54,
        "created_at": "2023-07-31T01:48:09.241Z"
      }
    ]
  },
  {
    "id": 29120,
    "name": "Morph",
    "slug": "morph",
    "former_names": [
      "AutoInfra",
      "Lingtual",
      "innkeeper"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dedc5ca68422aae62a4f58f3a0823d0903b71997.png",
    "website": "https://morphllm.com",
    "all_locations": "Columbus, OH, USA",
    "long_description": "Specialized inference, built for agent workloads\r\n\r\nFast Open-Source models: Kimi k3, Deepseek v4 flash, qwen 397b, at 200+ tps\r\nSpecialized models for applying edits, code search, compaction, and model routing",
    "one_liner": "Fast Models Optimized for Coding Agents",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1686706939,
    "tags": [
      "Developer Tools",
      "Generative AI",
      "API",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2023",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/morph",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/morph.json",
    "year_founded": 2025,
    "city": "Columbus",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/morphllm",
    "x_url": "https://twitter.com/morphllm",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/dedc5ca68422aae62a4f58f3a0823d0903b71997.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 405977,
        "full_name": "Tejas Bhakta",
        "title": "Founder",
        "founder_bio": "inference thats optimized for agents",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/tejas-bhakta",
        "x_url": "https://twitter.com/tejasybhakta",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c6f50e938c0f7e3a90a8e68bbcc0f5d615160f77.jpg"
      }
    ],
    "jobs": [
      {
        "id": 81462,
        "title": "Founding Account Executive",
        "url": "https://www.ycombinator.com/companies/morph/jobs/PSesvJB-founding-account-executive",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$80K - $140K",
        "equity_range": "0.50%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Growth design"
        ]
      },
      {
        "id": 102427,
        "title": "Member of Technical Staff",
        "url": "https://www.ycombinator.com/companies/morph/jobs/0Z8vI3K-member-of-technical-staff",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$180K - $350K",
        "equity_range": "0.40%",
        "experience": "6+ years",
        "visa": "US citizen/visa only",
        "skills": []
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 108269,
        "title": "Kimi K3 on Morph: near-Fable performance at 1/5 the cost",
        "tagline": "Live on OpenRouter and Vercel's AI Gateway",
        "body": "Kimi K3 is now live on Morph.\n\n![uploaded image](/media/?type=post&id=108269&key=user_uploads/405977/9bf53656-3db4-42fe-92a9-48a4a412df40)\n\nMost open-weight models look promising in benchmarks, then fall apart in production: unreliable tool calls, weak long-context performance, or inconsistent outputs.\n\nGLM-5.2 and Kimi K3 are the first two I’ve tested that feel genuinely production-ready.\n\nIn our benchmarks, Kimi K3 approaches Fable-level performance at roughly half the cost of Opus. On Morph, it comes with:\n\n* 1M-token context\n* 90–200 output tokens/sec\n* Tool calling and structured outputs\n* OpenAI- and Anthropic-compatible APIs\n\nWe want to see how it performs on real workloads, not toy demos.\n\nIf you currently run Anthropic or OpenAI models in production, we’ll cover the cost of routing 5% of your production traffic to Kimi K3. Compare quality, latency, and cost against your existing model with minimal integration work.\n\nWe’d love brutally honest feedback: where it works, where it breaks, and what it would take for you to route more traffic to it.\n\nTry it here: <https://docs.morphllm.com/sdk/components/fast-models>\n\nComment or DM me if you want us to fund your production test.\n\n![uploaded image](/media/?type=post&id=108269&key=user_uploads/405977/cd809dfe-c7e5-406f-9707-f7c250c7113a)\n\n",
        "url": "https://www.ycombinator.com/launches/SAH-kimi-k3-on-morph-near-fable-performance-at-1-5-the-cost",
        "votes": 2,
        "created_at": "2026-07-29T06:05:58.276Z"
      },
      {
        "id": 104095,
        "title": "Reflexes API: Catch what agent logs miss",
        "tagline": "Most Agent failures are behavioral. Catch them at scale ",
        "body": "Behavioral observability for AI agents, built and priced to run on millions of production turns.\n\nMost agent failures don’t show up as errors.\n\n![uploaded image](/media/?type=post&id=104095&key=user_uploads/405977/00b2cef4-ce0d-4a93-a145-0209d28eb3a6)\n\n\nThe API returns 200.\\\nThe tool call succeeded.\n\nBut the agent is looping. The user is frustrated. A jailbreak slipped through. The answer technically completed, but the experience failed.\n\nThat is the problem Reflexes solves.\n\nReflexes are fast classifiers that run on every turn of every agent conversation and catch the behavioral signals normal observability misses:\n\n* user frustration\n* infinite loops\n* jailbreak attempts\n* refusals\n* hallucinations\n* task failures\n* policy violations\n* custom signals specific to your product\n\nTraditional observability tells you when software breaks. Reflexes tells you when the agent experience breaks.\n\nThe reason this has not worked before is cost and latency. Frontier-model LLM-as-judge is fine for offline evals, but it is too slow and expensive to run across production traffic.\n\nReflexes are built and priced to run at scale: millions of turns, inline, across 100% of production conversations.\n\nUnder the hood, Reflexes uses a shared backbone with many small heads, so multiple behavioral signals can run on the same conversation without multiplying latency or cost.\n\nYou can use our default reflexes out of the box, or train a custom reflex for the failure modes that matter to your agent.\n\nWe built this after working with production agent teams and seeing the same pattern repeatedly: agents usually fail long before they throw an error.\n\nThey fail in the behavior.\n\nTry Reflexes: <https://www.morphllm.com/products/reflex>",
        "url": "https://www.ycombinator.com/launches/R4x-reflexes-api-catch-what-agent-logs-miss",
        "votes": 5,
        "created_at": "2026-06-27T14:31:15.899Z"
      },
      {
        "id": 98898,
        "title": "FlashCompact: A specialized model for context compaction",
        "tagline": "33k tok/sec context compaction for coding agents",
        "body": "We built **FlashCompact** because context compaction is one of the most broken parts of modern coding agents.\n\nRight now, compaction is:\n\n* slow (often 2+ minutes)\n* lossy (kills useful context)\n* expensive (wastes tokens and steps)\n\nSo people avoid it… and agents degrade over time.\n\n---\n\n![uploaded image](/media/?type=post&id=98898&key=user_uploads/405977/77e4cb4f-c326-4f2b-8602-2aca1e0fa9c3)\n\n\n### What we built\n\nA **specialized model for context compaction**:\n\n* **33,000 tokens/sec throughput**\n* **200k → 50k tokens in \\~1.5s**\n* Designed specifically for agent workflows, not general summarization\n\nCompaction should feel invisible. This actually does.\n\n---\n\n### Why this matters\n\nWe analyzed **200+ real agent sessions** across **40+ top coding agent harnesses**.\n\nThe core insight:\n\n> Most context bloat comes from tool responses, not model generation.\n\nThat means naive summarization fails, because it compresses the wrong things.\n\nFlashCompact:\n\n* removes redundant tool output\n* preserves execution-relevant state\n* reduces context without hurting task performance\n\nResult:\n\n* no performance drop\n* fewer tokens\n* fewer steps\n\n---\n\n### The deeper point\n\nAgents don’t fail because models are weak.\n\nThey fail because their **context degrades over time**.\n\nIf you want agents to:\n\n* run longer\n* take more steps\n* stay coherent across complex tasks\n\nYou need **clean, compressed context**.\n\n<https://docs.morphllm.com/sdk/components/compact>",
        "url": "https://www.ycombinator.com/launches/Pj8-flashcompact-a-specialized-model-for-context-compaction",
        "votes": 6,
        "created_at": "2026-03-18T14:42:28.223Z"
      },
      {
        "id": 98329,
        "title": "WarpGrep v2: Code Search Subagent -> #1 on SWE-Bench Pro",
        "tagline": "Code search limits coding capabiltiies. Moving it out to a specialized model wins.",
        "body": "### WarpGrep v2 is a fast code search subagent that works via parallel tool calls. Runs up to 36 grep/read tool calls in under 5s.\n\n![uploaded image](/media/?type=post&id=98329&key=user_uploads/405977/ba17c624-92ca-4502-8c56-00c22e31ad4e)\n\n### SWE-Bench Pro: WarpGrep v2\n\n#### Scores\n\n* Opus 4.6 → 55.4\n* GPT-5.3 Codex → 56.0\n* MiniMax 2.5 → 55.4\n* Opus 4.6 + WarpGrep → **57.5** (**+2.1**)\n* Codex 5.3 + WarpGrep → **59.1 (+3.1)**\n* MiniMax 2.5 + WarpGrep → **57.6** (**+3.7**)\n\n#### Efficiency\n\nTokens:\n\n* Input tokens: **\\~17% less** (Opus, Codex, MiniMax, etc..)\n* Output tokens: **13% less** (fewer turns)\n\nTurns (MiniMax):\n\n* Avg turns per task: **157 → 135**\n\nRuntime (Opus):\n\n* Opus 4.6: **518s**\n* Opus 4.6 + WarpGrep: **545s**\n* **178s saved** (**12% faster**)\n\nCost (Opus):\n\n* Opus 4.6: **$3.06**\n* Opus 4.6 + WarpGrep: **$2.51**\n* **$0.55 saved** (**15.6% cheaper**)\n\n#### TLDR\n\nWarpGrep v2 raises SWE-Bench Pro scores (**+2.1 Opus**, **+3.7 MiniMax**) while using **\\~17% fewer input tokens**, **13% fewer turns**, running **12% faster**, and costing **15.6% less**. The median WarpGrep codebase search takes 5s end to end compared to 75s for the Claude Code Explore subagent\n\nOn normal usage on production repos these numbers improve across the board \\~40% faster\n\n\\\ninstall it in your favorite coding agent here <https://www.morphllm.com/mcp>\n\nor integrate it into your product here <https://docs.morphllm.com/sdk/components/warp-grep/tool>",
        "url": "https://www.ycombinator.com/launches/PZx-warpgrep-v2-code-search-subagent-1-on-swe-bench-pro",
        "votes": 4,
        "created_at": "2026-03-02T15:39:58.078Z"
      },
      {
        "id": 95846,
        "title": "WarpGrep: A 20x faster subagent to grep for code",
        "tagline": "50% faster than Cognition's SWE-grep - improve Claude Code or Codex via MCP ",
        "body": "**Problem**\\\nCoding agents don’t feel fast because they aren’t.\n\nIn our benchmarks, agents spend **60%+ of their time searching for the right code**, not generating any. They why they do more than you want, and break developer flow.\n\nThe bottleneck isn’t “agent intelligence.”\\\nIt’s **speed, context retrieval** and the **irrelevant code** that gets shoved into the prompt.\n\nMost agent stacks today are basically **sequential grep pipelines**:\n\n1. Ask the model where to look\n2. Call a tool\n3. Read output\n4. Repeat 10–20x\n\nIt’s slow, noisy, and compounds latency every step.\n\nWarpGrep is built to do that dirty job correctly and fast.\n\n**Our Insight**\n\n\\*\\*We value human attention.\\*\\*\\\nYou can’t build responsive coding agents until retrieval is treated as its own learning and inference optimization problem.\\\nWe optimized for a simple goal: keep both the developer and the agent inside the sub-10-second “flow window.” Anything slower and usage collapses.\n\n**What we built**\\\nWarpGrep is an RL-trained retrieval model designed specifically to be used as a tool by a coding agent. It operates under a strict budget:\n\n![uploaded image](/media/?type=post&id=95846&key=user_uploads/405977/cbc2f17d-1ff9-4d7c-bfa1-38d3d36b3ac7)\n\n* Up to eight parallel tool calls per turn (grep, glob, file read, semantic search)\n* A reward function that only cares about two things: did it fetch the correct files and did it hit the correct line ranges.\n\nWarpGrep is an expert at deciding what to grep, and what context is relevant for the task. That’s it. This combination reduces context rot by more than fifty percent in production and eliminates the “forty irrelevant files in your prompt” failure mode.\n\n**Performance**\\\nSWE-Grep runs at around 650 tokens per second on Cerebras.\\\nWarpGrep hits around 900 tokens per second on B200.\\\n\\\nWe worked closely with NVIDIA to optimize WarpGrep. CUDA gives us the stability and customization ability to push non-standard inference workloads for parallel search.\n\n![uploaded image](/media/?type=post&id=95846&key=user_uploads/405977/8e04cd3d-9646-4f25-a0f6-96d2a440471b)\n\n**RL Training**\\\nRL for MOEs is notoriously inefficient, so we built infrastructure to eliminate dead time:\n\n* Dedicated inference and training GPU pools, continuous rollout streaming to the trainer\n* Controlled policy staleness without collapsing effective sample size\n* Partial rollout interruption so slow sequences don’t stall sync\n* In-flight weight updates so vLLM workers ingest new weights mid-generation with only millisecond pauses\n\nThose optimizations delivered a 1.6 to 2.35 times training throughput boost with essentially no sample efficiency loss.\n\n**Why this matters**\\\nEvery company building coding agents is running into the same wall.\\\nOnce your agent touches a large codebase, retrieval dominates latency and derails reasoning.\\\nYou solve it by giving the agent a retrieval system that behaves like a specialist, not a bottleneck.\n\nIf you want an agent that actually performs on large codebases, doesn’t have crippling context rot, and stays within real-time latency, reach out!\n\nhttps://docs.morphllm.com/api-reference/endpoint/mcp\n\nhttps://docs.morphllm.com/sdk/components/warp-grep",
        "url": "https://www.ycombinator.com/launches/Ovu-warpgrep-a-20x-faster-subagent-to-grep-for-code",
        "votes": 17,
        "created_at": "2025-11-28T19:27:09.731Z"
      },
      {
        "id": 90352,
        "title": "Morph: The \"other models\" you need to build coding agents like Cursor",
        "tagline": "Instantly apply AI output into code and files (5000+ tok/sec). Build your own coding agents. ",
        "body": "**TLDR: Morph: the “other models” you need to build the best coding agents like Cursor and Windsurf.**\n\n**Retrieve and rerank code, stuff context, and apply edits to files FAST. Relevant context, fast applies, every time.**\n\n**Morph Apply:** The fastest way to apply updates from Claude, GPT-4o, and others into any file - codebases, docs, etc - 5000+ tok/sec\n\n[Try API for Free →](https://morphllm.com/dashboard)\n\n![uploaded image](/media/?type=post&id=90352&key=user_uploads/405977/887a632b-bd61-4ace-9af5-d6abc166c319)\n\n> Benchmarked on a 9,000 token file, \\~2 seconds\n\n# **The Problem**\n\nThere’s no good way to apply edits that models want to make into files. Outputting the full file again is slow and expensive. Diff/Patch edits are brittle and a poor product experience.\n\nIn a production setting, AI agents need to update thousands of files. What about when you have a 50k token docx to update? Or when you need to be world-class at retrieving relevant info from a 500+ file repo?\n\n# **Solution:**\n\nMorph is the foundational infrastructure for AI Coding Agents that **_work and feel amazing_** _-_ not a quick demo.\n\n**~~Tired~~**~~: Chunked RAG and having Claude re-output full files~~\n\n**Wired**: Syntax-aware embeddings, reranking, and Fast Apply models = the perfect product experience\n\n**Cursor and Windsurf _roughly_ do this:**\n\n1. For a query, search for top-k relevant embeddings.\n2. Re-rank the corresponding content with a re-ranking model → stuff prompt using Claude/Gemini tool calls\n3. Use Claude/Gemini to generate small, focused changes\n4. Use a Fast Apply model to apply those changes back to the original file\n\n**We provide:**\n\n**Morph Apply**: _Fast Apply_ model: Merge updates from GPT-4o, Claude, and others into your files in under 2s (5000+ toks/second) \\\n**Morph Embeddings:** Syntax-aware embeddings, built for code\n\n**Morph Reranking:** Rerank functions/classes or file snippets to stuff your context with only the relevant context - every time.\n\n[Try it Now →](https://morphllm.com/dashboard)\n\n**Morph SDK:** Intelligently watch for file changes + smarter embeddings.\n\n![uploaded image](/media/?type=post&id=90352&key=user_uploads/405977/20ff3242-a426-4ec3-8c62-4e8633a5cc74)\n\n> Morph Fast Apply dropped errors by 8x vs. patch-based edits in our internal IDE and worked on our largest files - **Staff Eng @ Fortune-50**\n\nIf you’re building the Cursor for \\__\\_, or building agents that modify code:\n\nEmail us: [info@morphllm.com](mailto:info@morphllm.com)\n\n[Grab a Time Here](https://calendar.app.google/PW4nayCdGanN9iEKA)\\\nGet Started for Free: https://morphllm.com\n\n[Self Host Morph](https://morphllm.com/blog/self-hosting)",
        "url": "https://www.ycombinator.com/launches/NVI-morph-the-other-models-you-need-to-build-coding-agents-like-cursor",
        "votes": 31,
        "created_at": "2025-05-13T16:56:19.950Z"
      }
    ]
  },
  {
    "id": 29123,
    "name": "Glide",
    "slug": "glide-2",
    "former_names": [
      "WalletKit"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d1c76d50e7b1ba3c361d312f4e312c3e5f8f54b3.png",
    "website": "https://buildwithglide.com",
    "all_locations": "New York City, NY, USA; Remote",
    "long_description": "Glide lets apps accept crypto deposits from any chain, wallet, or exchange using pre-built wallet funding flows. Users can fund wallets directly — no bridges, no swaps, no repeated KYC.",
    "one_liner": "Crypto Deposits, Wallet Funding, and Pay-with-crypto Infra",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1689702823,
    "tags": [
      "Developer Tools",
      "Crypto / Web3",
      "B2B",
      "API",
      "Infrastructure"
    ],
    "tags_highlighted": [
      "Crypto / Web3"
    ],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2023",
    "status": "Acquired",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Fully Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/glide-2",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/glide-2.json",
    "year_founded": 2023,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/paywithglide",
    "x_url": "https://twitter.com/tusharsoni1205",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/usewalletkit",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/d1c76d50e7b1ba3c361d312f4e312c3e5f8f54b3.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Harj Taggar",
      "url": "https://www.ycombinator.com/people/harj-taggar"
    },
    "founders": [
      {
        "user_id": 139706,
        "full_name": "Tushar Soni",
        "title": "Founder",
        "founder_bio": "Co-founder, CEO @ Glide (buildwithglide.com)\n\nPrior to starting Glide, I was a tech lead at Robinhood Crypto building the Robinhood Wallet app. Previously, I worked on large-scale consumer apps such as Uber Eats, Apple Music, and App Store.\n\nI also created and maintain Copper (https://gocopper.dev) - a fullstack toolkit to build web apps in Go.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/tusharsoni",
        "x_url": "https://twitter.com/tusharsoni1205",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4eaba884107d19c2cd6d94abab93483bc503a360.jpg"
      },
      {
        "user_id": 1440060,
        "full_name": "Qinyu Tong",
        "title": "Founder",
        "founder_bio": "I'm the co-founder at WalletKit building the best developer platform for web3 consumer app developers. Prior WalletKit, I was at Robinhood Crypto building the Robinhood Wallet. Previously, I worked at LinkedIn on enterprise infra such as Ads API, AI Infra. ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/tongqinyu",
        "x_url": "https://twitter.com/qtofrivia",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/111d623f397ce92074da29e898a8e59f7e8bd78f.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 73221,
        "title": "WalletKit — Developer platform for building consumer-friendly web3 apps",
        "tagline": "Build web3 apps with wallets, tokens, and NFTs using simple HTTP APIs",
        "body": "**_⏳ TLDR;_** WalletKit is a developer platform, that helps you build decentralized apps without any crypto knowledge. It is best used for building user-friendly consumer apps aimed at mass adoption. Come talk to us if you’re building in this space or if you’re curious about adding decentralized features to your product.\n\nHey everyone! This is [**@Tushar Soni**](https://bookface.ycombinator.com/user/139706) and [**@Qinyu Tong**](https://bookface.ycombinator.com/user/1440060) from [**@WalletKit**](https://bookface.ycombinator.com/company/29123), and we are super excited to tell you what we’ve been working on.\n\n![uploaded image](/media/?type=post&id=73221&key=user_uploads/139706/1b7b6d81-ebee-42dd-a5bf-c926342dc9d6)\n\n---\n\n### **🤔 Problem**\n\nCrypto has gone through several ups and downs in its decade-long history. However, we have yet to build meaningful use cases outside of trading. There are a few reasons that are slowing us down:\n\n1. **Low Adoption**: People find crypto products hard to use, confusing, risky, or scammy. We believe this is because it is incredibly difficult to build trusted brands and high-quality user experiences that everyone has come to expect from the products they use.\n2. **High Complexity**: Blockchain is a collection of really complicated technology and building consumer products directly on top of it will take a lot of expertise and a long time.\\\n   Imagine trying to build a Rewards & Loyalty Program - you have to pick a chain, work with smart contracts, figure out how users will manage their wallets, handle transactions, reorgs, gas management, and so much more. You have to do this while making sure everything is done securely so you and your users don’t lose their digital assets.\n3. **Developer Tooling**: The popular developer platforms are too low-level. They are usually a mirror of Blockchain Node APIs aimed at node operations, not building consumer apps.\n\nOur goal is to elevate the developer experience for building decentralized consumer apps and enable builders to focus on their use case, not blockchain infrastructure.\n\n---\n\n### **🔧 Solution**\n\nWe are rethinking how to build decentralized apps by providing engineers with simple high-level abstractions to create amazing products.\n\nWalletKit wraps popular blockchain building blocks into easy-to-use HTTP APIs. Today, we support building with wallets, tokens, and NFTs.\n\n![uploaded image](/media/?type=post&id=73221&key=user_uploads/139706/77b906df-a7b3-4387-ad5f-43630e739bdf)\n\nWe took it a step further with WalletKit Dashboard - a digital asset management tool for your app-related digital assets. Use WalletKit Dashboard to create wallets, tokens, mint NFTs, make transfers, and interact with user wallets.\n\n![uploaded image](/media/?type=post&id=73221&key=user_uploads/139706/feafd2f1-3d58-493f-bc5e-3ee76bf2f14e)\n\n---\n\n### **👽 Founders**\n\nQinyu and I met at Robinhood, where we led the Web3 team responsible for building Robinhood Wallet. We experienced firsthand the lack of tools and infrastructure needed to build a consumer-friendly product.\n\nIt became clear to us that this gap in infrastructure is holding back the entire community from creating amazing products that have a chance at mass adoption. It acts as a barrier for founders to build decentralized products. WalletKit aims to remove this barrier entirely.\n\n---\n\n### **🤝 Ask & Deal**\n\nConnect us with founders/builders who are building decentralized apps or want to add decentralized features! Feel free to send an intro email to [**tushar@usewalletkit.com**](mailto:tushar@usewalletkit.com). We would love to help out.\n\nFor all YC companies and connections that come through the YC network, we offer a white-glove service and discounted rates :)",
        "url": "https://www.ycombinator.com/launches/J2z-walletkit-developer-platform-for-building-consumer-friendly-web3-apps",
        "votes": 31,
        "created_at": "2023-07-19T15:30:20.915Z"
      }
    ]
  },
  {
    "id": 29231,
    "name": "Ubicloud",
    "slug": "ubicloud",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/621ac44e76ec8f002f47c567b4533df1d318abcc.png",
    "website": "https://www.ubicloud.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Ubicloud is an open source cloud that can run anywhere.\r\n\r\nOur cloud services include elastic compute, block storage, virtual networking, managed Postgres, K8s, AI inference, and powerful IAM. Ubicloud provides these services on bare metal providers, such as Hetzner, Leaseweb, or AWS Bare Metal.\r\n\r\nYou can self-host our software or use our managed service to reduce your cloud costs by 3-10x.",
    "one_liner": "Open source alternative to AWS",
    "team_size": 15,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1700481295,
    "tags": [
      "Cloud Computing",
      "Infrastructure",
      "AI",
      "Databases"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/ubicloud",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/ubicloud.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/ubicloud",
    "x_url": "https://twitter.com/UbicloudHQ",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/ubicloud/ubicloud",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/621ac44e76ec8f002f47c567b4533df1d318abcc.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Garry Tan",
      "url": "https://www.ycombinator.com/people/garry-tan"
    },
    "founders": [
      {
        "user_id": 26240,
        "full_name": "Umur Cubukcu",
        "title": "Founder",
        "founder_bio": "Previously visiting partner at YC for W23 and S23; now co-founder at Ubicloud. Co-founder and CEO of Citus Data (S11), the creators of the distributed PostgreSQL database Citus. Led the PostgreSQL product team at Azure, after Citus Data was acquired in 2019 by Microsoft.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/umurc",
        "x_url": "https://twitter.com/umurc",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/811b92be504c42ee56cf6aad47e9f0f260d0fc7c.jpg"
      },
      {
        "user_id": 26241,
        "full_name": "Ozgun Erdogan",
        "title": "Founder",
        "founder_bio": "I’m the cofounder of Ubicloud (YC W24), an open source alternative to AWS. We're on a mission to rebuild the cloud market with different values.\n\nPreviously, I was a partner at Microsoft, leading PostgreSQL engineering teams. I came to Microsoft through its acquisition of Citus Data (S11). I was the cofounder and CTO at Citus, and I learned a lot about doing startups there. Before all this, I built distributed systems at Amazon.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/ozgune",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d633f56431a747505416d1e826a81fb1377926ee.jpg"
      },
      {
        "user_id": 49554,
        "full_name": "Daniel Farina",
        "title": "Founder/CTO",
        "founder_bio": "I am one of the founders of Ubicloud.\n\nI've contributed to a number of control planes from an early phase, starting at Heroku, in what would become Heroku Postgres. Since then, I've serially created new control planes and infrastructure cloud products with some close colleagues, at Citus Cloud and Crunchy Bridge. Ubicloud is the fourth such system.\n",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/danfarina",
        "x_url": "",
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      {
        "id": 89544,
        "title": "Software Engineer",
        "url": "https://www.ycombinator.com/companies/ubicloud/jobs/j4bntEJ-software-engineer",
        "location": "CA, US / Remote (NL; TR)",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$95K - $250K",
        "equity_range": "0.15% - 0.50%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
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    "news": [
      {
        "title": "Ubicloud wants to build an open source alternative to AWS | TechCrunch",
        "url": "https://techcrunch.com/2024/03/05/ubicloud-wants-to-build-an-open-source-alternative-to-aws/",
        "date": "Mar 05, 2024"
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    "launches": [
      {
        "id": 79315,
        "title": "Ubicloud - Open source alternative to AWS",
        "tagline": "Ubicloud is an open source cloud that can run anywhere.",
        "body": "**TL;DR**: Ubicloud provides IaaS cloud features on bare metal providers, such as Hetzner, Leaseweb, or AWS Bare Metal. You can self-host our software or use our managed service to reduce your cloud costs by 3-10x.\n\nHey everyone 👋 We’re Umur, Daniel, and Ozgun from [Ubicloud](https://www.ubicloud.com). We’re on a mission to rebuild the cloud market with different values. More on us below.\n\n## The Problem\n\n![uploaded image](/media/?type=post&id=79315&key=user_uploads/26241/10a339e1-dafe-421e-99f4-9484960966a7)\n\nAWS, Azure, and Google Cloud make life easier for start-ups and enterprises. But they are closed source, have you rent computers at a huge premium, and lock you in. It gets worse after your credits expire.\n\nAlthough the cloud came in with savings in its early years, the market evolved into a few players that overcharge for non-differentiated services, with increasingly complex products for standard, essential services. Each vendor optimizing for their walled gardens leaves customers stuck maintaining their apps and infra across incompatible clouds.\n\nThe cloud is here to stay. We believe a simpler, secure, and open source version of it will have a lasting impact.\n\n## The Solution\n\nUbicloud offers an open source alternative, reduces your costs, and returns control of your infrastructure back to you — all without sacrificing the cloud’s benefits. We’re starting with elastic compute, virtual networking, block storage (non-replicated), managed PostgreSQL, and powerful IAM.\n\nOur managed cloud runs on cost-effective bare metal providers, such as Hetzner. As a result, you can reduce your costs by 3-10x. Example use-cases:\n\n1. GitHub Actions: Change 1-line in your workflow file. Reduce costs by 10x\n2. Compute for batch processing: Run data pipelines for AI, scientific computing, and video processing - on-demand and encrypted. Reduce costs by 3x (vs AWS VMs)\n3. Managed PostgreSQL: Open source and portable. Uses local NVMes for higher performance, ideal for analytics. Reduce costs by 3x (vs AWS RDS)\n\n## The Team\n\n![uploaded image](/media/?type=post&id=79315&key=user_uploads/89898/3ce2495a-3697-4f8e-a61e-a6aa4a64bde7)\n\n_“Never tell me the odds.” — Han Solo_\n\nWe think the cloud galaxy deserves a solution that stands for open source values. We’re building to those values as part of the Rebel Alliance (yes, an alliance is needed to outmatch the empire :). From left to right:\n\n[Umur](https://www.linkedin.com/in/umurc/) - 5 years at startups. Then co-founded and led Citus Data (S11) to Microsoft acquisition. Led product teams within Azure. YC Visiting Partner for the previous two batches\n\n[Ozgun](https://www.linkedin.com/in/ozgune/) - 4 years at Amazon. Then co-founded and led Citus Data (S11) to Microsoft acquisition. Led engineering teams within Azure.\n\n[Daniel](https://www.linkedin.com/in/danfarina/) - Instrumental in building Heroku PostgreSQL. Amazed everyone with his skills in building managed cloud services at Citus Data, Azure, and Crunchy Bridge.\n\n## Our Ask\n\n1. Help us build the Rebel Alliance. [**Follow us (@UbicloudHQ)**](https://twitter.com/UbicloudHQ) on Twitter and tweet [**this post**](https://twitter.com/UbicloudHQ/status/1767594825023721858)!\n2. Start using Ubicloud for [GitHub Actions](https://www.ubicloud.com/use-cases/github-actions), [secure virtual machines](https://www.ubicloud.com/use-cases/ubicloud-compute), or [managed PostgreSQL](https://www.ubicloud.com/use-cases/postgresql). We reduce costs by 3-10x and we are open source!\n\n## The Deal\n\nTweeting and following us on Twitter gets 5,000 free compute minutes (standard-2). Using Ubicloud for one of the above use cases before demo day gets 10,000 compute minutes. Claim those minutes by sending us an email at [founders@ubicloud.com](mailto:founders@ubicloud.com)",
        "url": "https://www.ycombinator.com/launches/KdH-ubicloud-open-source-alternative-to-aws",
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        "created_at": "2024-03-12T16:11:25.761Z"
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    "id": 29281,
    "name": "Breez Labs",
    "slug": "breez-labs",
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    "one_liner": "Helping Companies Deploy Email Agents in High Trust Domains",
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    "subindustry": "B2B -> Infrastructure",
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    "batch": "Winter 2024",
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      "America / Canada"
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        "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)",
        "answer": "We met online in 2011 because we were two of the biggest players in an online math racing game. After spending a thousands of hours competing on the site, we got really good at math.\r\n\r\nWe then met in person at the Math Olympiad Summer Program and then at MIT. At MIT, we learned to build webapps and were head instructors in the official web development course. We launched an app that over half the MIT undergrad population used, and realized that we love building and impacting people’s lives.\r\n\r\nWe realized that people care a lot about getting better at math, and math competitions are extremely fun, but there is no good way to do math. MathDash is the best way to compete in math and have fun while doing so."
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      {
        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "MathDash is going to be a viral e-sport that people all around the world compete in on a daily basis. Just like how people run to improve their physical fitness, people will do MathDash to improve their mental fitness. People will see math not as a niche nerd activity, but a legitimate mainstream esport. And it will be fun."
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    "url": "https://www.ycombinator.com/companies/breez-labs",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/breez-labs.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
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      "name": "David Lieb",
      "url": "https://www.ycombinator.com/people/david-lieb"
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        "user_id": 1593855,
        "full_name": "Daniel Sun",
        "title": "Founder",
        "founder_bio": "Daniel is the co-founder and CEO of MathDash.\n\nHe was a math competition champion in high school, qualifying for the Math Olympiad Summer Program, which opened doors to MIT and Jane Street. Daniel is now creating MathDash to create an online playground for math games and to get students addicted to learning.",
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        "user_id": 677156,
        "full_name": "Akshaj Kadaveru",
        "title": "Founder",
        "founder_bio": "Akshaj is the co-founder and CTO of MathDash. \n\nHe is a former serial math competition participant (two-time top-12 finisher in the ESPN nationally broadcasted MATHCOUNTS Countdown Round) who spent thousands of hours of his childhood playing an online math game. He studied CS at MIT, and built (with Daniel) a MIT class comparison site with 2000 users, a guess-the-song game with 6000 players, and worked at Gather, Jump Trading, and Wormhole Labs as a software engineer.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/akshaj-kadaveru",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2b2875a7404544131c90ca4162080723b209b794.jpg"
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    "news": [],
    "launches": [
      {
        "id": 79440,
        "title": "MathDash: Turning math into a sport",
        "tagline": "Making competitive learning mainstream",
        "body": "Hi everyone! We’re Daniel and Akshaj. We met 14 years ago playing an online math game and are co-founders of [MathDash](http://mathdash.live).\n\n![uploaded image](/media/?type=post&id=79440&key=user_uploads/1593855/1663a39c-f3a8-41e5-9bff-66008f78ba04)\n\n_From left to right:_\n\n[Daniel](https://www.linkedin.com/in/daniel-sun-440493171/): 3x USA Math Olympiad and 1x Math Olympiad Training Program. Computer Science @ MIT, Trading at Jane Street, SWE at Kalshi and Scale AI. \n\n[Akshaj](https://www.linkedin.com/in/akshaj-kadaveru/): 7x USA Math Olympiad and 2x Math Olympiad Training Program. Computer Science @ MIT, with Trading & SWE experiences at Jump Trading, Wormhole, Jane Street, and Gather.\n\n### Competition Math - the best way to learn math\n\nCompetition math involves solving challenging and creative problems within a competitive setting, covering topics beyond standard school curricula. This format sends competitors to the frontiers of math and science by encouraging innovative thinking, fostering a community, and promoting a love for math.\n\nFounders of [Scale AI](https://scale.com/), [Perplexity AI](https://www.perplexity.ai/), and [Cognition AI](https://www.cognition-labs.com/) all started their journeys as **competitive learners**, and picked up the problem solving skills they would eventually need by doing these competitions.\n\nThis is why 300,000 high schoolers take the [American Math Competitions](https://maa.org/math-competitions) annually, and elite universities recruit high performers. In fact, competitive math and programming are the primary skills tested in trading and software job interviews!\n\n![uploaded image](/media/?type=post&id=79440&key=user_uploads/1593855/a0b3e111-b0f2-4acf-bcb0-dbd3be7ac146)\n\n_Daniel’s middle school math team: college and grad school/first job_\n\n### Competitive Learning hasn't reached most Humans\n\nHumans want to learn, and humans are competitive. Why, then, do most not enter competitive learning circuits? We talked to parents, teachers, coaches, and students, and they told us: \n\n* “The problems are too hard”\n* “The barrier to entry is too high”\n* “What if I look stupid”\n* \"I'm too busy\"\n\n### MathDash makes Competitive Math accessible for everyone\n\n* Problem difficulty ranges from addition all the way to advanced theory\n* MathDash only requires a device with an internet connection\n* You compete against others at your level, and your main goal is to improve your rating\n* The contests happen every day, and you can play an arena match live instantly at any time! Total time for a match is 2 minutes.\n\n![uploaded image](/media/?type=post&id=79440&key=user_uploads/1593855/e1a6ab1e-4daf-440f-8136-ab70204d8e2f)\n\nWe already have **200 of the best competitive mathletes** participating in our tournaments, and our users have solved **over 400,000** **arithmetic questions** - one even played in our Arena for **10 hours straight**!\n\nCompetitive Learning is already a growing community despite the significant barriers preventing most from participating. Having been addicted to competitive learning and educational games when we were young, we are the best positioned team to unlock the power of competitive learning for all.\n\n### Asks\n\nShare [MathDash](http://mathdash.live) with any math teachers, coaches, or students you know! We would love intros to any teachers or coaches as well.\n\nIf you are feeling ambitious, our next big synchronous [contest](http://mathdash.live/contests) is Monday, March 18 at 4:30pm PDT.",
        "url": "https://www.ycombinator.com/launches/KfI-mathdash-turning-math-into-a-sport",
        "votes": 467,
        "created_at": "2024-03-17T04:24:26.836Z"
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    "name": "PointOne",
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    "website": "https://pointone.com/",
    "all_locations": "New York City, NY, USA",
    "long_description": "PointOne is the leading AI Time platform. With automated time entry, compliance, and analytics, PointOne offers AI-native",
    "one_liner": "AI Time Platform for Law Firms",
    "team_size": 35,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1705177845,
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      "B2B",
      "Legal",
      "AI"
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    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Active",
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/pointone",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/pointone.json",
    "year_founded": 2023,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/pointoneai",
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    "primary_partner": {
      "name": "David Lieb",
      "url": "https://www.ycombinator.com/people/david-lieb"
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    "founders": [
      {
        "user_id": 1644994,
        "full_name": "Katon Luaces",
        "title": "Founder/CEO",
        "founder_bio": "Working on PointOne, automating timekeeping and billing. Previously worked on a legal tech product at Google before joining Applied Intuition where I worked on a platform for the verification and validation of autonomous vehicles.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/katon-luaces",
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      },
      {
        "user_id": 1623919,
        "full_name": "Jeremy Ben-Meir",
        "title": "Founder/CTO",
        "founder_bio": "Working on PointOne to automate attorney timekeeping and billing. Was previously a software engineer at Amazon and Google where I worked on search relevance and Ads ML, respectively.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/jeremyben-meir",
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        "id": 92918,
        "title": "Account Executive",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/XfnHuBG-account-executive",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$180K - $280K",
        "equity_range": "0.01% - 0.02%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
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        "id": 93010,
        "title": "GTM Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/lEDIBUL-gtm-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Devops",
        "salary_range": "$140K - $180K",
        "equity_range": "0.03% - 0.05%",
        "experience": "3+ years",
        "visa": "Will sponsor",
        "skills": []
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      {
        "id": 93046,
        "title": "Senior Software Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/unIyfPV-senior-software-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$180K - $230K",
        "equity_range": "0.02% - 0.30%",
        "experience": "6+ years",
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        "skills": []
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      {
        "id": 93047,
        "title": "Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/1VUq9m1-forward-deployed-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$160K - $230K",
        "equity_range": "0.10% - 0.30%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 93048,
        "title": "Software Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/nY8WP5e-software-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$160K - $190K",
        "equity_range": "0.10% - 0.15%",
        "experience": "1+ years",
        "visa": "Will sponsor",
        "skills": []
      },
      {
        "id": 93088,
        "title": "Customer Success Manager",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/n3nydhf-customer-success-manager",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$90K - $150K",
        "equity_range": "0.02% - 0.10%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 93089,
        "title": "Customer Success Lead",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/KLexKAg-customer-success-lead",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$140K - $170K",
        "equity_range": "0.03% - 0.20%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 93090,
        "title": "New Grad Software Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/SJ9AFBo-new-grad-software-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$130K - $160K",
        "equity_range": "0.03% - 0.05%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 93091,
        "title": "Security & Infrastructure Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/UOnViNj-security-infrastructure-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$180K - $230K",
        "equity_range": "0.20% - 0.30%",
        "experience": "6+ years",
        "visa": "Will sponsor",
        "skills": []
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      {
        "id": 93092,
        "title": "Product Reliability Engineer",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/fYiPc4v-product-reliability-engineer",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$100K - $160K",
        "equity_range": "0.01% - 0.05%",
        "experience": "1+ years",
        "visa": "Will sponsor",
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      },
      {
        "id": 93099,
        "title": "Sales Development Representative",
        "url": "https://www.ycombinator.com/companies/pointone/jobs/KUme05Y-sales-development-representative",
        "location": "New York, NY, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$110K - $130K",
        "equity_range": "0.01% - 0.02%",
        "experience": "1+ years",
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    ],
    "news": [
      {
        "title": "PointOne Raises $16 Million Series A - PointOne",
        "url": "https://pointone.com/blog/pointone-raises-16-million-series-a",
        "date": "Mar 23, 2026"
      },
      {
        "title": "Timekeeping Startup PointOne Secures $3.5M Seed Round, Including Investment From Cooley Fund | Legaltech News",
        "url": "https://www.law.com/legaltechnews/2024/07/10/timekeeping-startup-pointone-secures-3-5m-seed-round-including-investment-from-cooley-fund",
        "date": "Jul 10, 2024"
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    "id": 29291,
    "name": "ROE",
    "slug": "roe",
    "former_names": [
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    "website": "https://getroe.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "ROE AI solve risks and compliance problems for financial industry.",
    "one_liner": "AI for risk and compliance",
    "team_size": 9,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1705558893,
    "tags": [
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      "Generative AI",
      "Compliance",
      "Regtech"
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We came from Snowflake, so we know how it enables companies to get value out of their data and we are here to help get value from even more, harder-to-reach data.\n\n![](https://bookface.ycombinator.com/media/?type=post&id=78571&key=user_uploads/1065666/b268a0a7-4a58-43b9-8b25-bfd8318a5d7a)\n\n### **Who it’s for:**\n\nIf you work with data, you should check us out!\n\n**Connect your data -** Roe AI supports various unstructured data types - natural languages, documents, websites, images, videos, etc. Connect with your data sources from S3, Snowflake, Databricks, and many more.\n\n**Create smart, multi-modal agents** **\\-** Create a powerful, customizable AI agent within 30 seconds.\n\n**Query your data with SQL -** apply AI agents on **unstructured data** using SQL queries + natural language!\n\n### **👩‍💻 A data scientist’s 3 minutes**\n\n**9:30AM Monday, San Francisco.**\n\nAnna, the data scientist at Oak Ventures, needs to understand this YC batch to help GPs make investment decisions in YC W24. \n\n**9:31 AM.** Anna logs onto Roe AI data warehouse, where she has collected tens of thousands of crawled **homepages** and **pitch decks** from startups. \n\nFrom those data, Anna hopes to find who these startups are selling to, market size, and revenue. More importantly, Anna **only** wants to target YC-backed startups because they’re the best.\n\n**9:32 AM.** Anna wrote a simple SQL with some English. \n\nThe query finishes within 6 seconds. She sees the results of over 200 YC W24 companies and their ICP, TAM, and ARR extracted from the pitch decks. Moreover, Anna checks out the pitch deck side by side to assist her analysis!\n\n![](https://lh7-us.googleusercontent.com/Q8CN1oMnEd1Xd91F0KEUYCA-nHpPrdoM4mHHUHlUgHWiML9b-QVO3j2GKTLEZ650tisYVlH8dVXfIcgaMpRqqpcWUzpv7Xp4dcI2BhskL6ArSaByMExJqM__0JHYWcQD8WHm5s_mDn9gcudMPJJGzAY)\n\nNow it’s **9:33 AM.**\n\nAnna’s Monday is just getting started, but she has achieved so much, so she decides to pour herself some coffee while enjoying the data magic of Roe AI.\n\n### **❓Why a new data warehouse?**\n\n**20% of data is structured; what about the remaining 80%?** Transcripts, Images, documents, videos, audio, people call it unstructured data.\n\nWe interviewed data teams across industries, and 85%+ of them are just leveraging that 20%! Based on that partial view, they need to make business-critical decisions. This is wrong! But why??\n\n**High barrier to leveraging AI on Data:** processing unstructured data requires a lot of AI. 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So we can send you the login instructions!**\n\nIf you want a live demo from us, please book us some time <https://calendly.com/roe-ai/intro> \n\nOr email us at [founders@roe-ai.com](mailto:founders@roe-ai.com)\n\nOr text or call us directly at (510)-325-2872\n\n![uploaded image](/media/?type=post&id=78571&key=user_uploads/1065666/96fec7c9-f390-4369-84a9-db484b7e0c68)\n\n",
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        "body": "When I was at AWS building Amplify Flutter, I found myself wanting to write more and more of my code in Dart. I loved being able to maintain my flow state and use a single programming language—one I’d grown quite fond of—for everything. CLIs, tooling scripts, HTTP servers… if I needed a piece of code, I would find a way to write it in Dart.\n\nThe ecosystem for these use cases was immature but that just made it a challenge, which I loved. _Use the right tool for the job_, you may say. Nah… I want to use Dart. I thought I was alone in this quest but, I was okay being the crazy one.\n\nAnd then, to my surprise, our customers began asking\n\n“When can I write my Lambdas in Dart?”\n\n“When is AWS going to release an SDK for Dart so I can use it in my backends?”\n\nIt dawned on me that there was perhaps something deeper we were all experiencing, something more fundamental about coding being expressed.\n\nJavaScript is one of the most popular server-side languages in use today. A reasonable person with no knowledge of JavaScript may conclude from this that it’s because JavaScript is performant, efficient, or otherwise designed for the server.\n\nThe reality, though, is that developers will often seek the path of least resistance in an environment replete with friction. It’s easy to deride a choice in language, but it’s hard to downplay the numbers. Software engineers are human, after all—and humans are constantly optimizing their flows.\n\nFlutter was born from a desire to solve this age-old quest of optimization—first, as a way to build iOS and Android apps from the same codebase, saving teams the burden of managing two separate products. Then, to desktop and web applications, dramatically reducing the barrier to entry for these platforms too. The vision is largely realized. And, thanks to Dart, it’s been done without sacrificing performance.\n\n![uploaded image](/media/?type=post&id=79354&key=user_uploads/251048/d2f6624a-21cf-4080-93ba-a61020b1070b)\n\nOur mission with [Celest](https://celest.dev/) is to fill in the last piece of this puzzle—the cloud. We do that, simply, by starting and ending with Dart.\n\nHere’s how you define a cloud function with Celest.\n\n![uploaded image](/media/?type=post&id=79354&key=user_uploads/251048/78043071-9e1e-4349-907e-ffc1c1f08c3e)\n\nAnd here’s how you add authentication.\n\n![uploaded image](/media/?type=post&id=79354&key=user_uploads/251048/8ca2deb1-8964-4b0b-b08b-734e58a02c6b)\n\nTo test it out, you run `celest start` and iterate on your backend locally as you build your Flutter app. Your functions hot reload as you save your changes, and integration comes free with an auto-generated client library.\n\n![uploaded image](/media/?type=post&id=79354&key=user_uploads/251048/6222b983-d692-43f1-8f83-58c42a131053)\n\nAnd when you’re ready to turn this into a fully managed and auto-scaled backend, you run `celest deploy`. That’s it.\n\n![uploaded image](/media/?type=post&id=79354&key=user_uploads/251048/ad25a06e-a7c8-4fb9-b6d9-b591519c8b98)\n\nAs a Flutter developer, I’ve never left the Dart programming language, never thought about the many layers of abstraction beneath, and didn’t even need to leave my IDE to solve an otherwise laborious and flow-breaking task. I’m back to writing my app in less than 5 minutes. It feels like I’d never left.\n\n---\n\nCelest is the product I always wanted as a Flutter developer, the tool I had felt crazy for wanting at one point. It feels a little less crazy now, but only time will tell :-)\n\nYou can try and play with Celest for free today. Our cloud offering has started rolling out to the waitlist and will be publicly available in the coming weeks. For this and all the other goods in store, check us out @ [celest.dev](https://celest.dev?utm_source=ycombinator&utm_medium=blog&utm_campaign=launch_yc)\n\nAnd if this story rings true for you (or not), I’d love to hear your thoughts\n\n[dillon@celest.dev](mailto:dillon@celest.dev)\n\nTo the crazy ones 😎 🎯\n\nDillon, Chief Celestial",
        "url": "https://www.ycombinator.com/launches/Kdu-celest-turning-every-flutter-developer-into-a-full-stack-developer",
        "votes": 45,
        "created_at": "2024-03-13T14:29:37.676Z"
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      {
        "id": 79874,
        "title": "Keywords AI - Unified DevOps platform to build AI applications",
        "tagline": "Dead simple way to deploy & monitor AI apps with 2 lines of code.",
        "body": "### **‼️ The Problem**\n\nLLM developers face many challenges that hinder the ability to quickly build and scale high-quality products that people love:\n\n* Time-consuming infrastructure setup and maintenance for deploying and scaling LLM applications\n* Difficulty collaborating on prompt engineering and evaluating models for specific use cases\n* Challenges monitoring and ensuring consistent performance in production for optimal user experience\n* Tedious process of creating datasets for fine-tuning and iteratively improving output quality\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/d44261ea-e426-4e84-9f5f-5e5c9e217417)\n\n\n### **🌟 Our Solution**\n\nKeywords AI is built for developers. It has every feature needed to build, deploy, and scale LLM applications:\n\n**Infrastructure and Deployment**\n\n* OpenAI-compatible integration with 2 lines of code\n* Supports 100+ LLMs with built-in load balancing, error handling, and dynamic routing\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/60b64a75-0987-4bbb-93e2-f946760abbee)\n\n\n**Model Playground**\n\n* Interactive playground for comparing prompts and A/B testing different models\n* Find the best LLM for your use case through experimentation and evaluation\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/bc21dd8f-740b-4600-8930-f887a86c81fa)\n\n\n**Prompt Management**\n\n* Intuitive prompt management system for versioning and deploying prompts\n* Collaborative workspace for teams to share and iterate on prompts\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/d943ff84-f908-4744-80b9-4b5dcafc4ac7)\n\n\n**Request Logging**\n\n* Request logs for tracing and debugging user-model interactions\n* Automated output evaluation for detecting hallucination and other quality issues\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/9761abd2-48c2-4dfd-92df-5f6b054f02ec)\n\n\n**Performance Evaluations**\n\n* Comprehensive evaluations to compare model performance with customized metrics\n* Monitor response quality over time to build product confidence\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/59f074ba-0d90-4eae-93f8-3fa2328d5750)\n\n\n**Usage Dashboard**\n\n* Real-time monitoring of model performance and user analytics\n* Comprehensive overview of product usage trends and user behavior\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/cc4903db-9d46-4c98-a44a-babe133fc848)\n\n\n**User Analytics**\n\n* Track growth, retention, and unit economics with detailed usage analytics\n* Sentiment analysis and topic/intent classification for user insights\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/eec04dbb-d665-4bee-8333-285de818a6bc)\n\n\n**Data Curation and Fine-Tuning**\n\n* ​Easily collect and curate production golden dataset for model optimization\n* One-click model fine-tuning for improved performance and cost savings​\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/d44261ea-e426-4e84-9f5f-5e5c9e217417)\n\n\n### **🤠 The Team**\n\n[Andy](https://linkedin.com/in/hanheli) and [Raymond](https://www.ycombinator.com/companies/www.pareon.com) both studied mechanical engineering at UIUC and have been best friends since college. \n\nAndy took a gap year to work as a product design engineer at Apple after freshman year, where he designed parts for the next-gen & next-next-next-gen AirPods. He dropped out after realizing his dream job wasn’t that fun.\n\nRaymond is so good at learning things. He graduated with the highest honor in just three years and self-mastered software engineering within a year. He dropped his robotics PhD offers from top schools.\n\nWe’ve worked on a bunch of very bad ideas together in the past year. Now we think we've stumbled upon a good one :)\n\n![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/b3ead780-3541-4b92-9e26-df04c3e891cc)\n\n![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/d44261ea-e426-4e84-9f5f-5e5c9e217417)\n\n### **🔥 Keywords AI is available today!**\n\n* [Get started free](https://keywordsai.co) with $15 in credits\n* [Chat with us](https://cal.com/keywordsai/demo) if you’re building with LLMs - we’d love to be helpful\n* Join our [Discord community](https://discord.com/invite/KEanfAafQQ) of AI builders\n* Follow us on [LinkedIn](https://www.linkedin.com/company/keywordsai/) and [X](https://twitter.com/keywordsai)\n\n  ![uploaded image](/media/?type=post&id=79874&key=user_uploads/927016/d44261ea-e426-4e84-9f5f-5e5c9e217417)\n\n\n[team@keywordsai.co](mailto:team@keywordsai.co)",
        "url": "https://www.ycombinator.com/launches/KmI-keywords-ai-unified-devops-platform-to-build-ai-applications",
        "votes": 20,
        "created_at": "2024-04-02T14:24:37.708Z"
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    "name": "Guide Labs",
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        "title": "Guide Labs: Interpretable foundation models",
        "tagline": "Foundation models that can explain their reasoning, and are easy to align",
        "body": "At [Guide Labs](https://www.guidelabs.ai/), we build interpretable foundation models that can reliably explain their reasoning and are easy to align.\n\n### **The Problem: foundation models are black-boxes and difficult to align**\n\nCurrent transformer-based large language models (LLMs) and diffusion generative models are largely inscrutable and do not provide reliable explanations for their output. In medicine, lending, and drug discovery, it is not enough to only provide an answer; domain experts would also like to know **_why_** the model arrived at its output.\n\n* **Current foundation models don’t explain their outputs**. Would you trust a black-box model to propose medications for your illness or decide whether you should get a job interview?\n* **You can't debug a system you don't understand**: When you call a model API and the response is incorrect, what do you do? Change the prompt? What part of your prompt should you change? Switch to a new model API?\n* **Difficult to reliably align or control model outputs**: Even when you've identified the cause of the problem. How do you control the model so that it no longer makes the mistake you identified?\n\n### **Our Solution**\n\n**We’ve developed interpretable foundation models that can explain their reasoning, and are easy to align.**\n\nThese models:\n\n* provide human-understandable explanations;\n* indicate what part of the prompt is important; and,\n* specify which tokens led to the model's output.\n\nUsing all these explanations, we can:\n\n* identify the part of the prompt that causes the model to err;\n* isolate the samples that cause those errors; and,\n* use explanations to control and align the model to fix its errors.\n\n### **About Us**\n\nWe are  interpretability researchers and engineers that have been responsible for major advances in the field. We have set out to rethink the way machine learning models, AI agents, and systems broadly are developed.\n\n### **Our Ask**\n\nPlease reach out to us at [info@guidelabs.ai](mailto:info@guidelabs.ai).",
        "url": "https://www.ycombinator.com/launches/Kk9-guide-labs-interpretable-foundation-models",
        "votes": 68,
        "created_at": "2024-03-27T14:56:29.406Z"
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    "id": 29347,
    "name": "Vibrant Labs",
    "slug": "vibrant-labs",
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    "website": "https://vibrantlabs.com/",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "We work on methods to autonomously scaling evals/environments for post-training agents. ",
    "one_liner": "Autonomously scaling environments",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1709606147,
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      "Generative AI",
      "Reinforcement Learning",
      "Open Source",
      "AI"
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    "top_company": false,
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    "batch": "Winter 2024",
    "status": "Active",
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      "Infrastructure"
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    "regions": [
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      "America / Canada",
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      "Fully Remote"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/vibrant-labs",
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    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/vibrant-labs-ai",
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      "name": "Nicolas Dessaigne",
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      {
        "user_id": 387451,
        "full_name": "Jithin James",
        "title": "Founder/CEO",
        "founder_bio": "Jithin James (jjmachan), believes that life's purpose is found in the toil in mastering a craft and the fulfilment one gets from using it for the greater good. For him, this means excelling in software development, particularly in open-source and AI tooling, to empower fellow developers and help materialize their ambitious visions into reality. His journey is a testament to skill, dedication, and a commitment to community enrichment.",
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        "full_name": "Shahul ES",
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        "founder_bio": "Shahul (aka ikka) discovered his fascination for AI in his sophomore year while pursuing his computer science degree. This early interest laid the foundation for his journey to becoming a Kaggle GrandMaster and led to his significant contributions to open-source AI, particularly in initiatives like OpenAssistant AI.",
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      {
        "id": 79074,
        "title": "Ragas: Open-source evaluation and testing Infrastructure for LLM applications",
        "tagline": "Deploy your LLM application with confidence. ",
        "body": "### **TL;DR**\n\nWe are building [Ragas](https://www.ragas.io/) — an open-source evaluation and testing infrastructure for LLM application developers to deploy their applications in production with confidence.\n\n### About us\n\n![uploaded image](/media/?type=post&id=79074&key=user_uploads/889503/21a941e3-4798-4628-95cc-15332facb47e)\n\nWe’re [Jithin](https://linkedin.com/in/jjmachan) and [Shahul](https://linkedin.com/in/shahules)! Having met in college, we’ve collaborated on various projects for almost a decade now.\n\nJithin takes care of building the software and infrastructure. He was an early employee at Bento ML, where he built and maintained tools like [Bentoctl](https://github.com/bentoml/bentoctl), [Bentoml](https://github.com/bentoml/BentoML), and [Yatai](https://github.com/bentoml/Yatai). Shahul is responsible for AI research and engineering. He is a [Kaggle Grandmaster](https://www.kaggle.com/shahules) and a lead contributor to different open-source AI projects, including [Open-Assistant AI.](https://github.com/laion-ai/open-assistant)\n\n### Problem\n\nBefore 2023 software used to be written in code but with the emergence of foundational models software and applications are going to be compound systems containing code, prompts, and other components. This introduces several new problems\n\n* How do you select the best model or component suitable for your application from the abundance of available resources?\n* How do you test these systems and ensure continuous quality?\n* How do you derive insights from production to measure and improve your system?\n\nAs early adopters of this technology to build applications, we faced this problem while we were building RAG systems early last year.\n\n### Solution\n\nWe at [Ragas](https://docs.ragas.io/) make use of model-graded evaluations and testing techniques to ensure quality. This includes automated synthesis of test data points, explainable metrics, and adversarial testing.\n\nWe started by building this for RAGs, which is the most popular application of LLM as of today. Ragas is now the default open-source standard for evaluating RAG applications, processing over 4.7 million responses last month and used by engineers from enterprises like AWS, Microsoft, Databricks, Moody’s, UHG, and Tencent.\n\n![uploaded image](/media/?type=post&id=79074&key=user_uploads/889503/4da2d427-8bfe-4e4b-a76e-01c7a28ce747)\n\n### Our Ask\n\n* Checkout ragas on [GitHub](https://github.com/explodinggradients/ragas)\n* If you’re building RAG applications, consider [applying](https://forms.gle/TnqUHuY176m2juqL9) for the Ragas office hours program",
        "url": "https://www.ycombinator.com/launches/KZO-ragas-open-source-evaluation-and-testing-infrastructure-for-llm-applications",
        "votes": 94,
        "created_at": "2024-03-05T18:38:36.494Z"
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    "id": 29379,
    "name": "Trieve",
    "slug": "trieve",
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    "website": "https://trieve.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Infrastructure for search teams building retrieval and RAG. Trieve combines search language models with tools for tuning ranking and relevance.\r\n\r\nBuilding excellent search is difficult and can take months to implement then even more time to maintain. Trieve offers production-ready infrastructure that works out of the box to help search teams build adjustable AI search and RAG into their products. With tools for custom models, relevancy weighting, date-recency biasing, semantic full-text hybrid search, recommendations, and more Trieve covers the full spectrum of what a team wants from their discovery infrastructure. ",
    "one_liner": "All-in-one API for search, RAG, & recommendations",
    "team_size": 7,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1709233497,
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      "Open Source",
      "API",
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Acquired",
    "industries": [
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      "Infrastructure"
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "app_answers": null,
    "question_answers": [
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        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "Power every retrieval experience on the internet"
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    "url": "https://www.ycombinator.com/companies/trieve",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/trieve.json",
    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/trieveai/",
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      "name": "Pete Koomen",
      "url": "https://www.ycombinator.com/people/pete-koomen"
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    "founders": [
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        "user_id": 1655076,
        "full_name": "Nick Khami",
        "title": "Founder/CEO",
        "founder_bio": "Find me at https://github.com/skeptrunedev . My primary hobbies are reading progression fantasy, riding motorcycles, and running. ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/nicholas-khami-5a0a7a135",
        "x_url": "https://twitter.com/skeptrune",
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      {
        "user_id": 1655175,
        "full_name": "Denzell Ford",
        "title": "Founder/CTO",
        "founder_bio": "Find me at https://github.com/cdxker. I'm mostly active on x",
        "is_active": true,
        "linkedin_url": "https://linkdin.com/in/denzell-ford",
        "x_url": "https://twitter.com/cdxker",
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    "news": [
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        "title": "Trieve Secures $3.5M to Power AI Search and RAG — Trieve",
        "url": "https://trieve.ai/trieve-fundraise-announcement/",
        "date": "Sep 11, 2024"
      }
    ],
    "launches": [
      {
        "id": 78949,
        "title": "Trieve 🔎 Infrastructure for search teams",
        "tagline": "We want to power every search bar on the internet.",
        "body": "Howdy, y’all!\n\nWe’re [Denzell](https://linkedin.com/in/denzell-ford) and [Nick](https://github.com/skeptrunedev), founder engineers of [Trieve](https://trieve.ai/).\n\n![uploaded image](/media/?type=post&id=78949&key=user_uploads/1655175/c03dd5a8-16e9-4d39-8885-fdbb3d7e0626)\n\n**Trieve offers source-available all-in-one infrastructure for entire search teams. Engineers implement a pipeline that connects their data source to our backend then non-technical staff can fine-tune relevance using included frontends.**\n\nCustomers have the option to either **(1)** use the managed cloud version of the software suite or **(2)** buy a license to host it themselves in their own cloud or on-premise.\n\nWe want to power every search bar on the internet.\n\n### Problem - Poor search quality, lack of modularity, inaccessibility\n\n1. Engineers first have to build APIs on top of DBs and AI models which then need to be maintained, scaled, and improved.\n2. Engineers then need to build UI's for tuning which then need to be maintained, scaled, and improved.\n3. Improving retrieval quality requires hiring more engineers such that you have enough bandwidth to both maintain and improve your custom and complex infrastructure.\n4. Ineffective spend of your search budget as you have to hire engineers to build and maintain things before you can even start adjusting retrieval quality.\n\n### Solution - Quality retrieval & source-available license\n\nTrieve provides a complete infrastructure suite that is both easy for a single engineer to integrate and allows businesses to hire more non-technical staff who can use the included front-end tools for fine-tuning relevance.\n\nWith Trieve's solution, businesses can get more value out of their search budget whether it's $10k or $10M.\n\n### WE have two asks!\n\n1. Could you introduce us to developers and teams building search and relevancy into their products?\n2. [Can you star our GitHub](https://github.com/devflowinc/trieve)?\n\n![uploaded image](/media/?type=post&id=78949&key=user_uploads/1655175/b72dc26d-867b-4637-8df5-111b2f869fa0)\n\n### About us\n\nDenzell and I are software engineers who walked into this problem trying to build semantic search for a knowledge management system that helped business teams argue better.\n\nWhen we went to go sell the arguing app we found out that the underlying search infrastructure was what people really wanted to buy.\\\n\\\n[founders@trieve.ai](https://mailto:founders@trieve.ai)",
        "url": "https://www.ycombinator.com/launches/KXN-trieve-infrastructure-for-search-teams",
        "votes": 137,
        "created_at": "2024-03-01T18:52:01.827Z"
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    "id": 29433,
    "name": "Hatchet",
    "slug": "hatchet-run",
    "former_names": [
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      "Inc."
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    "website": "https://hatchet.run",
    "all_locations": "New York City, NY, USA",
    "long_description": "Hatchet is an orchestration platform for managing and observing AI agents, background tasks, and mission-critical workflows. Our mission is to provide developers with a simple, powerful, and reliable orchestration layer, enabling them to focus on building great applications.",
    "one_liner": "The orchestration platform for teams who ship.",
    "team_size": 7,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1709312849,
    "tags": [
      "Developer Tools",
      "Open Source",
      "Infrastructure"
    ],
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    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Active",
    "industries": [
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/hatchet-run",
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    "year_founded": 2023,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/hatchet-run",
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    "github_url": "https://github.com/hatchet-dev",
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      "name": "David Lieb",
      "url": "https://www.ycombinator.com/people/david-lieb"
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      {
        "user_id": 363082,
        "full_name": "Alexander Belanger",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder & CEO at Hatchet (W24), previously co-founder & CTO at Porter (S20). M.S. in Physics, B.A. in Physics and Computer Science from the University of Pennsylvania. \n\nAlways happy to chat anything devtools, OSS, infrastructure!  ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/alexander-belanger-aa3974135",
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      {
        "user_id": 491474,
        "full_name": "Gabe Ruttner",
        "title": "Founder",
        "founder_bio": "Co-founder & CTO at Hatchet (W24), previously co-founder & CTO at ClearMix (S20). Masters in CS/AI from Cornell. Always game to chat anything dev tools, OSS, AI Architecture!\n\n",
        "is_active": true,
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      {
        "id": 79258,
        "title": "Hatchet - Background task orchestration and visibility platform",
        "tagline": "We help engineers run fast and reliable background tasks",
        "body": "**tl;dr** - At a certain scale, all applications need a task queue - think Celery for Python, BullMQ for Node.js, and Sidekiq for Rails. [Hatchet](https://hatchet.run) is an [open-source](https://github.com/hatchet-dev/hatchet), managed task queue focused on scale, reliability, and improved developer experience. \n\n<https://youtu.be/3myPYVyaqwM>\n\n**The problem**\n\nBuilding a reliable application is difficult — network connections can fail or be interrupted, the application could run out of memory, or a bad commit on production can take down your entire service. To reduce the pressure on APIs, task queues are typically used to offload different parts of the application logic to other services in the system.\n\nHowever, these queues introduce more points of failure — they require new infrastructure, a new set of integrations to monitor and debug your tasks, and are difficult to use in development. As applications begin to scale, application developers are forced to implement solutions to avoid queue overflow and overcrowding and to think about how to optimize their queues.\n\nWith LLM APIs — which are high latency and highly variable in response quality — these problems are even worse. It’s now typical to ingest, sync, vectorize, and index the entirety of multiple data sources on account creation, and as LLM systems get more agentic, developers need to chain together unreliable LLM calls to fulfill requests. These requests are often slow and without the right architecture delivering a responsive and reliable user experience is challenging.\n\n**The solution**\n\nHatchet is an **end-to-end platform** for task orchestration. It includes a web UI for monitoring tasks, an API for interacting with tasks and workflows, and SDKs for Python, Typescript, and Golang.\n\n![uploaded image](/media/?type=post&id=79258&key=user_uploads/363082/1617c928-fc04-4482-9b13-98c342e14694)\n\nHatchet has all the features you’d expect — like **retries, timeouts**, and **cron schedules** — along with features that developers are usually asked to build, like handling different **fairness and concurrency patterns**, **durable execution**, and **result streaming**.\n\n**The team**\n\nWe’re both ex-CTOs and founders of multiple YC-funded companies — [Alexander](https://linkedin.com/in/alexander-belanger-aa3974135) was co-founder and CTO at [Porter](https://porter.run) (S20) while [Gabe](https://linkedin.com/in/gruttner) was co-founder and CTO at [Clearmix](https://www.ycombinator.com/companies/clearmix) (also S20). Before that, Alexander got his Master’s in Physics from UPenn while Gabe got his Master’s in AI from Cornell.\n\nAlexander started working on this after years of wrangling task queues and distributed systems at both Porter and [Oneleet](https://oneleet.com), where he experienced the issues of building complex task orchestration and workflows. Gabe came at this from another perspective, having spent the last few years working with ML and more recently LLM stacks, where he saw the architectural problems that many of these applications have.\n\nWe immediately bonded over our excitement about what infrastructure will look like as AI becomes core to nearly every business.\n\n![uploaded image](/media/?type=post&id=79258&key=user_uploads/363082/e77d894d-1e99-4128-9bff-65f905d97571)\n\n**How do I get started?**\n\nHatchet is fully open source with an [invite-only cloud offering](https://hatchet.run/request-access). Feel free to request access or check out the [self-hosting Hatchet](https://docs.hatchet.run/self-hosting) docs.\n\nIf you’re dealing with observability problems in your queue, are running batch processing of documents for LLM workflows, or are experiencing queue fairness problems in multi-tenant apps, we’d love to chat.\n\nAnd if you have any questions — or you’re just interested in distributed systems, task queues, and scheduling — we have a [public Discord](https://discord.gg/ZMeUafwH89). See you there!",
        "url": "https://www.ycombinator.com/launches/KcM-hatchet-background-task-orchestration-and-visibility-platform",
        "votes": 57,
        "created_at": "2024-03-11T14:21:38.752Z"
      }
    ]
  },
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    "id": 29436,
    "name": "Downlink",
    "slug": "downlink",
    "former_names": [
      "Titan"
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    "website": "https://downlink.dev/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Downlink is a platform that continuously improves your AI.\r\n\r\nBoost rate limits, slash latency, and raise accuracy.\u0003All through one simple API.",
    "one_liner": "Make your LLMs 3x faster.",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1708740244,
    "tags": [
      "Developer Tools",
      "SaaS",
      "API",
      "AI"
    ],
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2024",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
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      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "url": "https://www.ycombinator.com/companies/downlink",
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        "role_type": null,
        "salary_range": "$250K - $350K",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102353,
        "title": "Enterprise Product Manager",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/6XYMsPA-enterprise-product-manager",
        "location": "New York City",
        "type": "Full-time",
        "role": "Product",
        "role_type": null,
        "salary_range": "$200K - $240K",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102354,
        "title": "Brand Designer",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/2qIxkKo-brand-designer",
        "location": "SF / NYC",
        "type": "Full-time",
        "role": "Design",
        "role_type": "Web design",
        "salary_range": "$180K - $220K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102355,
        "title": "GTM & Ops Recruiter",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/AeVjAAc-gtm-ops-recruiter",
        "location": "SF / NYC",
        "type": "Full-time",
        "role": "Recruiting & HR",
        "role_type": "Recruiter",
        "salary_range": "$185K - $225K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102356,
        "title": "Enterprise Product Manager",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/sVsVfcs-enterprise-product-manager",
        "location": "New York City",
        "type": "Full-time",
        "role": "Product",
        "role_type": null,
        "salary_range": "$200K - $240K",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102357,
        "title": "Brand Designer",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/zxZqPQw-brand-designer",
        "location": "SF / NYC",
        "type": "Full-time",
        "role": "Design",
        "role_type": "Web design",
        "salary_range": "$180K - $220K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 102413,
        "title": "Technical Storyteller",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/KGmaCqg-technical-storyteller",
        "location": "New York City",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$150K - $180K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104627,
        "title": "Startups Lead",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/V9KDz2Y-startups-lead",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$180K - $200K",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104628,
        "title": "Account Executive, Digital Natives",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/f7ANJlU-account-executive-digital-natives",
        "location": "SF / NYC",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 110245,
        "title": "Office Manager",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/rTpU06x-office-manager",
        "location": "New York City",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$115K - $150K",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 113078,
        "title": "Executive Assistant (to the CEO)",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/ks7LrAV-executive-assistant-to-the-ceo",
        "location": "San Francisco / Remote (US)",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$160K - $185K",
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        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 114961,
        "title": "Recruiting Coordinator ",
        "url": "https://www.ycombinator.com/companies/blacksmith/jobs/iujp36C-recruiting-coordinator",
        "location": "New York City",
        "type": "Full-time",
        "role": "Recruiting & HR",
        "role_type": "Recruiter",
        "salary_range": "$100K - $120K",
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        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
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      {
        "title": "AI code-testing startup Blacksmith's valuation jumps almost 10x in less than a year | TechCrunch",
        "url": "https://techcrunch.com/2026/08/12/blacksmiths-valuation-jumps-10x-to-550m-as-ai-coding-fuels-software-validation/",
        "date": "Aug 12, 2026"
      },
      {
        "title": "Google Ventures doubles down on dev tool startup Blacksmith just 4 months after its seed round | TechCrunch",
        "url": "https://techcrunch.com/2025/09/17/google-ventures-doubles-down-on-dev-tool-startup-blacksmith-just-4-months-after-its-seed-round/",
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      {
        "id": 79029,
        "title": "Blacksmith: Run your GitHub Actions 2x faster, at half the cost",
        "tagline": "Blacksmith runs GitHub Actions on high-performance gaming CPUs on a software stack optimized for CI. ",
        "body": "![uploaded image](/media/?type=post&id=79029&key=user_uploads/760850/813c2fd4-6f8b-4da0-a014-b91ac29f1a89)\n\n**tl;dr**\n\n[Blacksmith](https://blacksmith.sh/) helps companies run their GitHub Actions up to twice as fast, at half the cost. We do this by running their Github Actions on high-performance gaming CPUs on a software stack optimized for CI.\n\n**What problem are we solving?**\n\nMore engineers writing more tests over time leads to quadratic growth of a company’s CI run time. On top of this, platforms like GitHub Actions have staggering markups while providing compute backed by decade-old server hardware. These platforms are disincentivized from investing in software optimizations to make CI faster since they bill by the minute.\n\n**How is Blacksmith 2x faster?**\n\nBlacksmith runs GitHub Actions on high-performance gaming CPUs. These machines offer significantly better single-core performance, ideal for tasks like code compilation, testing, and single-threaded runtimes like Node.js.\n\nThe hardware is only part of the puzzle. Blacksmith runs a software stack optimized for CI.\n\n* We colocate warm caches with our GitHub Action runners resulting in [4x faster cache reads and writes](https://x.com/aayush_shah15/status/1759253159514374552?s=20)\n* Local NVMe-backed filesystem for highly performant disk reads and writes\n* Docker layers caching across all builds in a GitHub repository resulting in faster docker builds (coming soon)\n* Shared volumes to turn every CI run into an incremental build (coming soon)\n\nThe numbers speak for themselves\n\n![uploaded image](/media/?type=post&id=79029&key=user_uploads/760850/b996eba7-800c-42d5-9f1e-a9f42da2d7ef)\n\nHere’s Blacksmith being used in a [public repo](https://github.com/axelarnetwork/axelar-amplifier/pull/271) by one of our customers, [Axelar.](https://axelar.network/)\n\n![uploaded image](/media/?type=post&id=79029&key=user_uploads/760850/6500f095-9553-4393-aa2c-0995ad7f5155)\n\n\\\n**How would I switch from Github to Blacksmith?**\n\nAll you need to do is [install our GitHub app](https://app.blacksmith.sh/) and change one line of code in your workflow file.\n\n![uploaded image](/media/?type=post&id=79029&key=user_uploads/760850/a3def935-6370-472c-b6fe-da85c2839a26)\n\n**Our vision**\n\nWe want to be the compute layer for CI (think AWS EC2 for CI). Although we are starting with GitHub Actions, in the future, we plan on expanding to other CI platforms such as BuildKite, GitLab, Jenkins, etc.\n\nWe are going after the $10B CI compute market currently captured by the hyperscalers (AWS, GCP, Azure).\n\n**Our team**\n\nWe are a team of three engineers who met at the University of Waterloo eight years ago. [Aayush Shah](https://linkedin.com/in/aayushshah15) and [Aditya Maru](https://linkedin.com/in/maruaditya) were early engineers on CockroachDB, working on replication and disaster recovery, respectively. Aayush was then an early engineer at Superblocks, working on enterprise readiness. [Aditya Jayaprakash](https://linkedin.com/in/adijp) (JP) did research in theoretical computer science and later joined Faire (YC W17), where he worked on search infra and was a founding member of the ads team.\n\n![uploaded image](/media/?type=post&id=79029&key=user_uploads/760850/72125173-2a56-41a6-a286-194624a51db7)\n\nIf you’re using GitHub Actions, **we can help your team move a lot faster while reducing spend**. [**Try us out**](https://blacksmith.sh/) - we are a one-line change to integrate, no credit card required.\n\nWe love talking to companies about their CI infra. You can reach us at [hello@blacksmith.sh.](mailto:hello@blacksmith.sh)",
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        "created_at": "2024-03-05T18:47:44.000Z"
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    "website": "https://www.mirurobotics.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Miru makes it easy for robotics teams to manage, version, and deploy robot configurations. Developers use our well-documented APIs and UI components to automate configuration updates for both engineers and operators.\r\n\r\nIn the long term, we're building software infrastructure to accelerate the automation of the physical world.",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "status": "Active",
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    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/miru-ml",
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    "city": "San Francisco",
    "country": "US",
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    "github_url": "https://github.com/miruml",
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        "full_name": "Armel Talla",
        "title": "Founder/CEO",
        "founder_bio": "Armel is the Co-Founder and CEO of Miru.\n\nHe graduated from UT Austin with a B.S. in Computer Science and Mathematics. He previously founded Clips AI, an AI video dev tool that automatically repurposes long videos into clips. During college, Armel interned at Google and YouTube, focusing on building internal tools.",
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        "linkedin_url": "https://linkedin.com/in/armeltalla",
        "x_url": "https://twitter.com/armelwtalla",
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        "full_name": "Vedant Nair",
        "title": "Founder",
        "founder_bio": "Vedant is the Co-Founder and COO of Miru.\n\nHe graduated from UT Austin, where he studied Business and Computer Science. He previously founded Clips AI, an AI video developer tool that automatically repurposed long videos into clips. During college, he interned at Bain & Co. and EY-Parthenon, doing GTM consulting for software companies.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/vedantnair",
        "x_url": "https://x.com/vedantnair__",
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        "user_id": 1521401,
        "full_name": "Benjamin Smidt",
        "title": "Founder/CTO",
        "founder_bio": "Ben is the Co-Founder and CTO of Miru. \n\nHe holds a B.S. in Electrical and Computer Engineering from UT Austin, where he initially spent two years studying Mechanical Engineering, igniting his passion for hardware and robotics. Prior to Miru, Ben founded Clips AI, where he developed production-ready AI models and video processing servers using Kubernetes.",
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        "linkedin_url": "https://linkedin.com/in/benjamin-smidt",
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    "launches": [
      {
        "id": 82564,
        "title": "Miru - Deployment infrastructure for Robotics and IoT 🦾",
        "tagline": "Deploy software updates over the air to fleets of devices",
        "body": "**tl;dr:** Miru helps robotics and IoT teams painlessly deploy software over the air, provision devices, and manage versioning for their fleets.\n\nFor engineers using Miru, _updating software on thousands of devices is as easy as pushing a change to GitHub._\n\n**Our Ask:** Engineers in robotics and IoT, [get started with Miru today!](https://www.miruml.com/)\n\n![uploaded image](/media/?type=post&id=82564&key=user_uploads/1035647/c64f81c9-695b-472e-bab9-5687c4eef19f)\n\n**❌ The Problem**\n\nRobotics and IoT companies don’t have robust, off-the-shelf DevOps tools.\n\nAs a result, to deploy their software, they’re forced to manually SSH/SCP into their devices or build a hacky solution internally.\n\n**But building this infrastructure is costly.** These companies often need to dedicate more than half of their software team to build and maintain their custom internal tools. This balloons engineering costs and decreases product velocity.\n\nIt’s one of the reasons **why many of these companies die.**\n\n**🦾 Miru**\n\nMiru helps engineers build and maintain their deployment pipelines, saving them months of work.\n\nWith Miru, you can:\n\n1. Push changes to GitHub, and let Miru handle building, staging, and deploying your software to your devices across the world\n2. Manage devices and track software versioning on a centralized dashboard\n3. Enjoy obsessive, fast, 24/7 customer support 🫡\n\n**Installation is easy.** If your device already uses Ubuntu, the Miru installation is just one command **🪄**\n\nMiru users have an unfair advantage. They enjoy reliable, scalable, and secure deployments _without all the pain of building it themselves_.\\\n\\\n<https://youtu.be/N0N6R5Gtjx4?si=2P6385myxlbDnB2j>\n\n🧍🏼🧍🏿🧍🏾 **About Us**\n\nHi 👋🏾 - we’re [Vedant](https://linkedin.com/in/vedantnair), [Armel](https://linkedin.com/in/armeltalla), and [Ben](https://linkedin.com/in/benjamin-smidt)!\n\n![uploaded image](/media/?type=post&id=82564&key=user_uploads/1035647/a48e7554-cf3c-493b-b5c7-d8aa5a03eb84)\n\nAfter meeting at our freshman dorm at UT Austin, we instantly clicked and became best friends. Whether we were playing intramural basketball or scaling our previous startup, [Clips AI](https://www.clipsai.com/), to 7000 MAUs in just three months, we've proven we make an exceptional team.\n\nOutside of Clips AI, Armel worked as a software engineer at Google and YouTube, focusing on building internal tools and infrastructure. Vedant brings experience in software GTM consulting from Bain & Co. and EY-Parthenon. Ben, our hardware fanatic, has a background in mechanical and electrical engineering, and he’s built production-ready AI and video processing infrastructure using Kubernetes.\n\nWe’re passionate about a future where robots can free humans from doing the dull, dirty, and dangerous tasks that dominate billions of people’s lives. We founded Miru to provide the infrastructure to accelerate this transition.\n\nWe’re obsessed with solving this problem. If you’re a software engineer in robotics or IoT who is struggling with deployment, we’d LOVE to make your life easier.\n\nJust reach out at \\[vedant\\] @ miruml \\[dot\\] com, or better yet, [start using Miru today](https://www.miruml.com/)",
        "url": "https://www.ycombinator.com/launches/LTg-miru-deployment-infrastructure-for-robotics-and-iot",
        "votes": 235,
        "created_at": "2024-07-30T16:20:31.446Z"
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    "id": 29585,
    "name": "Mem0",
    "slug": "mem0",
    "former_names": [
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    "website": "https://mem0.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Mem0 enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. It remembers user preferences, adapts to individual needs, and continuously learns over time - ideal for customer support chatbots, AI assistants, and autonomous systems.",
    "one_liner": "The Memory layer for AI Agents",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1716511817,
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      "Open Source",
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      "AI"
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    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Active",
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    "regions": [
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    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/mem0",
    "x_url": "https://twitter.com/mem0ai/",
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        "full_name": "Taranjeet Singh",
        "title": "Founder/CEO",
        "founder_bio": "Taranjeet Singh is the co-founder & CEO of Mem0. He joined Khatabook (YC S18) as first growth engineer & quickly transitioned to Senior PM. He began his software engineering career at Paytm (India's Paypal) & witnessed its meteoric rise to become a household name. He built an AI-powered tutoring app that was featured at Google I/O. He co-created EvalAI, an open source Kaggle alternative with Deshraj & grew it to 1.6K GitHub stars. He also created the first GPT app store, scaling it to 1M+ users.",
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        "linkedin_url": "https://linkedin.com/in/taranjeet7114",
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      {
        "user_id": 299508,
        "full_name": "Deshraj Yadav",
        "title": "Founder/CTO",
        "founder_bio": "Deshraj Yadav is the co-founder and CTO of Mem0. He is broadly interested in the field of Artificial Intelligence and Machine Learning Infrastructure. He led the AI Platform at Tesla Autopilot, which enabled large-scale training, model evaluation, monitoring, and observability for Tesla's full self-driving development. Prior to that, Deshraj created EvalAI, an open source ML platform, as his master's thesis at Georgia Tech. He has published papers at top AI conferences like CVPR, ECCV, AAAI etc.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/deshrajdry",
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    "jobs": [
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        "id": 97144,
        "title": "Full Stack Engineer",
        "url": "https://www.ycombinator.com/companies/mem0/jobs/i1LyerD-full-stack-engineer",
        "location": "San Francisco Bay Area",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $250K",
        "equity_range": "0.10% - 0.50%",
        "experience": "3+ years",
        "visa": "Will sponsor",
        "skills": []
      },
      {
        "id": 99945,
        "title": "Research Engineer - Agent Memory",
        "url": "https://www.ycombinator.com/companies/mem0/jobs/fuNIwmP-research-engineer-agent-memory",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$175K - $250K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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      {
        "id": 99947,
        "title": "Developer Relations ",
        "url": "https://www.ycombinator.com/companies/mem0/jobs/mpmlfnN-developer-relations",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$140K - $180K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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      {
        "id": 99951,
        "title": "Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/mem0/jobs/85yRXxn-forward-deployed-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$150K - $190K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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    ],
    "news": [
      {
        "title": "This Startup Is Trying To Solve The AI Memory Problem",
        "url": "https://www.youtube.com/watch?v=Sr1STQP0cds",
        "date": "Jan 23, 2026"
      },
      {
        "title": "Mem0 raises $24M from YC, Peak XV and Basis Set to build the memory layer for AI apps | TechCrunch",
        "url": "https://techcrunch.com/2025/10/28/mem0-raises-24m-from-yc-peak-xv-and-basis-set-to-build-the-memory-layer-for-ai-apps/",
        "date": "Oct 28, 2025"
      },
      {
        "title": "Mem0's scalable memory promises more reliable AI agents that remembers context across lengthy conversations | VentureBeat",
        "url": "https://venturebeat.com/ai/mem0s-scalable-memory-promises-more-reliable-ai-agents-that-remembers-context-across-lengthy-conversations/",
        "date": "May 08, 2025"
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    ],
    "launches": [
      {
        "id": 83896,
        "title": "Mem0 - Open Source Memory Layer for AI Apps",
        "tagline": "Mem0 adds memory to AI applications, solving the problem of repetitive, stateless interactions and enabling personalized, cost-efficient AI experiences.",
        "body": "**TL;DR**\n\n[Mem0](https://mem0.ai) is an open-source memory layer for AI applications. It solves the problem of stateless LLMs by efficiently storing and retrieving user interactions, enabling personalized AI experiences that improve over time. Our hybrid datastore architecture combines graph, vector, and key-value stores to make AI apps personalized and cost-efficient. Watch our explainer video [here](https://youtu.be/Rg3_Mpr3Kcs).\n\n—\n\nHey everyone! We're Taranjeet and Deshraj, and we built [Mem0](https://github.com/mem0ai/mem0) to solve a big problem we faced with LLMs while building Embedchain (an open-source RAG framework with 2M+ downloads). LLMs don’t have memory, so they forget everything after each session. This leads to inefficient and repetitive interactions, making it hard to create personalized AI experiences. Think about having to repeat your preferences over and over again, and how frustrating that gets! Mem0 changes that.\n\n❌ **The Problem**\n\nLLMs are stateless—they don’t remember anything between sessions. Every time you interact with them, you have to provide the same context, which gets repetitive and wastes computational resources. This makes AI apps less useful and personalized over time.\n\n✨ **Our Solution**\n\n![uploaded image](/media/?type=post&id=83896&key=user_uploads/661173/ed531d50-421e-4d2f-a361-088587c27198)\n\nMem0 adds a **memory layer** to AI applications, making them stateful which allows them to store and recall user interactions, preferences, and relevant context. This way, AI apps evolve with every interaction, delivering more personalized and relevant responses without needing large context blocks in each prompt.\n\nTo make this possible, we needed to create a system that could efficiently manage and retrieve all the relevant information AI apps collect over time. That’s where Mem0’s **hybrid datastore architecture** comes in, making AI smarter and more efficient as it learns.\n\n⚙️ **How it works**\n\n[Mem0](https://mem0.ai) employs a hybrid datastore architecture that combines graph, vector, and key-value stores to store and manage memories effectively. Here’s the breakdown:\n\n* **Adding memories:** When you use Mem0 with your AI App, it automatically detects and stores the important parts of your messages or interactions.\n* **Organizing information:** Mem0 categorizes memories in three ways:\n  * **Key-value stores** for quick access to structured data (facts, preferences).\n  * **Graph stores** for understanding relationships (like people, places, objects).\n  * **Vector stores** for capturing the overall meaning and context of conversations, allowing AI apps to find similar memories later.\n* **Retrieving memories:** When an input query is received, Mem0 searches for and retrieves relevant memories using a combination of graph traversal techniques, vector similarity, and key-value lookups. It prioritizes the most important, relevant, and recent information, ensuring that the AI has the right context, no matter how much memory is stored.\n\nWatch this video for a demo of our playground in action [here](https://youtu.be/VtRuBCTZL1o)\n\n🙏 **Our Asks**\n\n* Try out [Mem0](https://mem0.ai)! We guarantee that your users will have personalized interactions after adding Mem0 in your AI apps. Moreover, you will save on your LLM costs.\n* If you are looking for a memory provider for your AI app and want to schedule a demo, please feel free to block some time on [my calendar](https://cal.com/taranjeetio/mem0) or email me at [taranjeet@mem0.ai](mailto:taranjeet@mem0.ai)\n* Checkout our platform and open-source offering and give feedback:\n  * Platform: <https://app.mem0.ai/playground>\n  * GitHub: <https://github.com/mem0ai/mem0>",
        "url": "https://www.ycombinator.com/launches/LpA-mem0-open-source-memory-layer-for-ai-apps",
        "votes": 634,
        "created_at": "2024-09-10T14:36:03.296Z"
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  },
  {
    "id": 29586,
    "name": "Glasskube",
    "slug": "glasskube",
    "former_names": [],
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    "website": "https://distr.sh/",
    "all_locations": "",
    "long_description": "Managing enterprise software deployments shouldn’t feel like a balancing act. At Glasskube, we make deploying, managing, and updating software in on-premises, VPC, and air-gapped environments easier than ever.\r\n\r\nOur Mission:\r\nWe empower software vendors to deliver exceptional customer experiences with less complexity. Whether you’re onboarding new clients, rolling out updates, or troubleshooting issues, our Software Distribution Platform streamlines every step.\r\n\r\nThe founders have been best friends for over 15 years, lived together in college, and even had a startup together. Philip was the CTO of a European Kubernetes-based company that was acquired in 2022.",
    "one_liner": "Ship self-hosted software with Distr",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1717765543,
    "tags": [],
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Active",
    "industries": [
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    "regions": [
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      "Partly Remote"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/glasskube",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/glasskube.json",
    "year_founded": 2023,
    "city": null,
    "country": null,
    "linkedin_url": "https://www.linkedin.com/company/glasskube/",
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    "cb_url": "https://www.crunchbase.com/organization/glasskube",
    "github_url": "github.com/glasskube/distr/",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
    },
    "founders": [
      {
        "user_id": 1278777,
        "full_name": "Louis Weston",
        "title": "Founder/President",
        "founder_bio": "Business, Law, and Compliance background. Before Glasskube, I advanced from intern to legal counsel at a fintech startup with over 100 employees in three years, negotiating contracts with Apple, Deutsche Bank, and Volkswagen, and building a compliance and security system according to ISO standards.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/louiswaefler",
        "x_url": "https://twitter.com/thekubernaut",
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      },
      {
        "user_id": 1910548,
        "full_name": "Philip Miglinci",
        "title": "Founder",
        "founder_bio": "Open-Source and Kubernetes ❤️\n\nIronman finisher 🏊 🚴 🏃\n\nBefore working on Glasskube, I built:\n - Mission-critical alerting infrastructure in Europe as CTO\n - PCR testing and analytics software as part of my Master thesis\n",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/pmigat",
        "x_url": "https://twitter.com/pmigat",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0e2d994dcc7cb590958470b860803e3445e8bcf4.jpg"
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    "news": [],
    "launches": [
      {
        "id": 90349,
        "title": "Distr – The Open Source Control Plane for Self-Managed, BYOC, and On-Prem Deployments",
        "tagline": "Connect Distr to GitHub and deploy anywhere in hours—not months.",
        "body": "Hi everyone! We’re [Philip](https://www.linkedin.com/in/pmigat/) and [Louis](https://www.linkedin.com/in/louisnweston/), the co-founders of [Glasskube](https://glasskube.dev/) and we are excited to share Distr.\n\n![uploaded image](/media/?type=post&id=90349&key=user_uploads/1278777/8ef8adb5-e11a-40bf-9b04-efa9c19be58e)\n\n**TLDR:** Distr is an Open source control plane for AI companies. Our customers plug us into their existing CI/CD pipeline to offer BYOC and self-managed in hours, not months.\n\n**Distr is fully open source.** Check out our repository on [GitHub](https://github.com/glasskube/distr). (We'd appreciate a star 🌟)\n\n### **“Self-managed is making a big comeback.” — Shay Banon, Founder of Elastic**\n\n![uploaded image](/media/?type=post&id=90349&key=user_uploads/1278777/33f877d3-6adf-49eb-aa00-add8dcbcdde3)\n\nWe spoke with Shay earlier this month, and I couldn’t agree more.\n\n* Enterprises want control.\n* Data is valuable\n* Security concerns are rising\n* Data sovereignty is now critical, especially in the age of AI.\n\nThis shift has been building for a while and is accelerating rapidly, particularly in Europe, Singapore, and regulated industries around the world.\n\nIf you're building AI tooling and selling to large customers, how often have you been asked: _“Can we run this inside our VPC?”_\n\n### So why isn’t everyone offering self-managed?\n\nBig players like ClickHouse, Snowflake, and Redpanda figured it out early. But for smaller teams, supporting self-managed deployments often feels out of reach. Too much effort, not enough return.\n\nSelf-managed often means:\n\n* Too much complexity, too early.\n* Every customer wants something different.\n* Shipping updates is slow and painful.\n* You’re flying blind, no usage insights.\n* Troubleshooting without visibility is brutal.\n\n### \n\n### Our Solution\n\nDistr is your Open Source control plane for self-managed, BYOC, and on-prem deployments. It gives you everything you need out of the box to support self-managed customers without building a custom solution from scratch. Distr fits right into your existing stack and lets you onboard, update, and support self-managed customers in minutes, not months. Just connect your GitHub repo and you’re up and running.\n\n![uploaded image](/media/?type=post&id=90349&key=user_uploads/1278777/0a5b78df-5000-421a-8d6d-24a4f820b0db)\n\n* **Simple GitHub integration:** Connect your repo and start shipping in minutes.\n* **Works with your stack:** Supports Docker Compose, Terraform, Docker Swarm, and Kubernetes.\n* **Deploys anywhere:** AWS, GCP, Azure, on-prem, and even air-gapped environments.\n* **White-label customer portal:** Give your customers full control and visibility into their deployments.\n* **Built-in OCI registry:** Track image downloads and updates at the user and tag level.\n* **Advanced telemetry:** Collect detailed usage and performance metrics with OpenTelemetry.\n* **Flexible licensing:** Restrict access to versions and features with our Open Feature-compatible licensing server.\n* **API-first and automation-ready:** Fully integratable via API and SDK for custom workflows.\n\nhttps://youtu.be/FkSWkudlHJw\n\n### Coming soon:\n\n* Built in CVE scanning and automated security checks for all artifacts\n* HubSpot Integration\n* Workflows with Slack Notifications\n\n### First self managed customer?\n\nZero to one is the hardest. From your first PoC to a working Helm Chart and your first self-managed production deployment—we’ve done it all, many times over. That’s why we support you during your first self-managed customer and why using Distr is an unfair advantage.\n\n### Our Ask\n\n* [Try it for free](https://signup.distr.sh/signup.html) and give us feedback or\n* [Book a Demo](https://cal.glasskube.com/team/gk/demo)",
        "url": "https://www.ycombinator.com/launches/NVF-distr-the-open-source-control-plane-for-self-managed-byoc-and-on-prem-deployments",
        "votes": 19,
        "created_at": "2025-05-13T16:30:04.177Z"
      },
      {
        "id": 81592,
        "title": "Glasskube - Open source package manager for Kubernetes",
        "tagline": "Deploy, update, and configure Kubernetes packages 20x faster ",
        "body": "Hi everyone! We’re [Philip](https://www.linkedin.com/in/pmigat/) and [Louis](https://www.linkedin.com/in/louisnweston/), the co-founders of [Glasskube](https://glasskube.dev/).\n\n**TLDR:** Glasskube is an open source package manager for Kubernetes that makes deploying, updating, and configuring packages on Kubernetes **20x faster than** tools like **Helm or Kustomize.** Inspired by the simplicity of Homebrew and npm, our goal is to build enterprise-ready infrastructure software that can run mission-critical workloads across the more than 3 million Kubernetes clusters globally.\n\n**Glasskube is fully open source.** Check out our repository on [GitHub](https://github.com/glasskube/glasskube). (We'd appreciate a star 🌟)\n\n![uploaded image](/media/?type=post&id=81592&key=user_uploads/1278777/b43af44c-5372-4995-a383-3a84cdc9941c)\n\n# **The Backstory & Problem**\n\nKubernetes packages have been growing rapidly, from around 100 packages in 2016 to over 800 packages on the CNCF landscape today. With more than 7 million developers working with Kubernetes, Helm, an open-source tool created during a hackathon nine years ago, has become the go-to solution. However, Helm struggles to keep up with the increasing demand, leading to complex workflows and non-standardized solutions.\n\nOver the past five years, writing and deploying packages on Kubernetes, we found ourselves defaulting to Helm despite its shortcomings due to a lack of alternatives. While working on other cloud-native projects, users consistently highlighted common pain points with Helm. This realization prompted us to tackle the larger issue of package management in Kubernetes, leading to the development of Glasskube.\n\n# **What is Glasskube?**\n\nGlasskube brings the experience of Homebrew or npm to your Kubernetes cluster. It simplifies installation, updates, and configuration, making it easier and faster to manage your packages.\n\n![uploaded image](/media/?type=post&id=81592&key=user_uploads/1278777/f1208e18-d6ab-40ca-ae88-9b1be886b50a)\n\n**Focusing on simplicity and reliability with our CLI and UI**\n\nEasily install packages in your cluster via the Glasskube UI, where all packages are conveniently located, eliminating the need to search for a Helm repository.\n\n**Safe Package Updates**\n\nPreview and perform pending updates to your desired version with a single click (or CLI command). All updates are pre-tested by the Glasskube test suite.\n\n**Package configurations**\n\nConfigure packages with typesafe input values via the UI or interactive CLI questionnaire. Inject values from other packages, ConfigMaps, and Secrets easily. No more untyped and undocumented values.yaml files.\n\n**GitOps Integration**\n\nAll Glasskube packages are custom resources, manageable via GitOps. We're also integrating with [renovate](https://github.com/renovatebot/renovate/issues/29322). \\\n\\\nRead also more about [dependency management](https://glasskube.dev/docs/design/dependency-management/), [multiple repositories and private packages](https://glasskube.dev/docs/design/repositories/) on our [website](https://glasskube.dev/).\n\nInterested? Check out our [Demo.](https://youtu.be/aIeTHGWsG2c)\n\n# **Our Vision**\n\nCurrently, we are focused on enhancing the user experience, aiming to save engineers as much time as possible by building on top of existing technologies. In the future, we plan to develop an entirely new packaging and bundling format for all cloud-native packages. This will provide package developers with a straightforward way to define how to install and configure packages, offer simple upgrade paths, and enable us to provide feedback, crash reports, and analytics to every developer working on Kubernetes packages, similar to how Android supports Play Store developers.\n\n# **Presignup - Glasskube Cloud**\n\nWe are currently working on Glasskube Cloud, for which you can now [pre-signup](https://glasskube.cloud/signup.html). Key features include:\n\n* Cloud Hosted Private Packages\n* Security Alerts and Update Notifications\n* Advanced GitOps Integrations (intelligent insights on upgrade success probability, smart config migration suggestions, enhanced open PRs showing detailed changes at manifest/resource level)\n\n# **Our Asks**\n\nWe love talking to engineers and anyone interested in DevOps who uses Kubernetes. You can schedule a call with Louis and Philip [here](https://cal.glasskube.eu/team/founder/30min).\n\nIf you find this interesting and would like to try it out:\n\n* Presignup for [Glasskube Cloud](https://glasskube.cloud/signup.html)\n* Check out our repository [on GitHub](https://github.com/glasskube/glasskube).",
        "url": "https://www.ycombinator.com/launches/LE0-glasskube-open-source-package-manager-for-kubernetes",
        "votes": 39,
        "created_at": "2024-06-18T16:27:51.444Z"
      }
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  },
  {
    "id": 29591,
    "name": "Synnax",
    "slug": "synnax",
    "former_names": [
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    "website": "https://www.synnaxlabs.com/",
    "all_locations": "Denver, CO, USA",
    "long_description": "We build software that helps hardware teams quickly and clearly evaluate how a component (engine, battery, valve, etc.) or process (assembly line, manufacturing plant) performs. We prevent mistakes, reduce costs, and accelerate time-to-market. Our platform unifies industrial control with sensor data storage and automated post-processing, delivering an end-to-end operations and observability stack. To put it comparatively, take LabVIEW, InfluxDB, and Grafana, wrap them into a single package, add a bit of magic, and you get Synnax.",
    "one_liner": "Unified Hardware Control and Data Processing Software",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1721072332,
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    "batch": "Summer 2024",
    "status": "Active",
    "industries": [
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    "regions": [
      "United States of America",
      "America / Canada"
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    "stage": "Early",
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      {
        "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)",
        "answer": "During his time in industry, Emiliano witnessed outdated control software and bad data review processes lead to multi-million dollar failures of critical path hardware. Frustrated with proprietary file formats, slow data pipelines, and unintuitive, complex tools like LabVIEW, he set out to built a modern control and data processing stack for hardware. Patrick and Elham were Emiliano’s first beta testers on the University of Michigan’s student rocketry team. They played an essential role in early iterations of the product, giving feedback through experience gained during their internships at SpaceX, NASA JPL, and MIT’s Lincoln Labs. Over time, their conviction in the problem Synnax is solving and the quality of the product grew. In early 2024, they joined forces as co-founders."
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    "url": "https://www.ycombinator.com/companies/synnax",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/synnax.json",
    "year_founded": 2024,
    "city": "Denver",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/synnaxlabs/",
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    "primary_partner": {
      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
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      {
        "user_id": 529055,
        "full_name": "Emiliano Bonilla",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder & CEO at Synnax. I'm classically trained in Aerospace Engineering and have a passion for building and selling great software to improve the way teams develop hardware. I led a team in the development of data processing tools for the first launches of Firefly Aerospace's Alpha rocket, one of the few new-space companies to repeatedly reach orbit. I've also worked on automous electric vehicle prototypes and developed banking software for European startups.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/emiliano-bonilla-8a0a95187/",
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      },
      {
        "user_id": 1849518,
        "full_name": "Patrick Dotson",
        "title": "Founder/COO",
        "founder_bio": "Co-Founder & COO at Synnax. I worked on propulsion systems for the Starship program at SpaceX and have experience in both hardware and software engineering. I graduated from the University of Michigan with a degree in aerospace engineering.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/patrick-dotson/",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eb84bf453eff4df1402b984a4acc4e85e58e04a1.jpg"
      },
      {
        "user_id": 2215787,
        "full_name": "Elham Islam",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder & CTO at Synnax. I wrote flight software for the Mars Sample Return Lander at NASA JPL and developed embedded control systems for laser communication at MIT Lincoln Laboratory. I studied computer engineering at the University of Michigan.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/elham-islam-/",
        "x_url": "https://x.com/ElhamIslam_",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/15129d123788fa6e760ef0a1ac1be47c44f62ea2.jpg"
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    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 82381,
        "title": "Synnax - The software infrastructure for operating & observing complex hardware",
        "tagline": "Detect anomalies, build mission control interfaces, and automate at scale.",
        "body": "### **Summary**\n\nTeams operating hardware from rockets to manufacturing lines use outdated, unintuitive control software that disregards the importance of rapid, quality data processing. This leads to unexpected downtime, disastrous anomalies, and delayed development.\\\n\\\n[Synnax](https://synnaxlabs.com) delivers a modern mission control stack with integrated data storage & processing. Wrap [LabVIEW](https://www.ni.com/en/shop/labview.html?cid=PSEA-7013q000001fLK5AAM-CONS-GOGSE_161597904920&utm_keyword=labview&gad_source=1&gclid=CjwKCAjwhvi0BhA4EiwAX25ujz2zA6xF3VRQaQaUKSnIWT-PQLXdmQGvejLgYkhZ7wgR4DdhgZxLyRoClgkQAvD_BwE#521715&s_kwcid=AL!6304!3!691563588228!b!!g!!labview), [Snowflake](https://www.snowflake.com/en/), and [Grafana](https://grafana.com/) into a single package, tune it for hardware, and you get Synnax.\n\n### **The Problem**\n\nWe’re a group of Aerospace Engineers hailing from NASA JPL, SpaceX, and Firefly Aerospace. (From left to right: [Patrick Dotson,](https://www.linkedin.com/in/patrick-dotson/) [Elham Islam](https://www.linkedin.com/in/elham-islam-/), and [Emiliano Bonilla](https://www.linkedin.com/in/emiliano-bonilla-8a0a95187/))\n\n![uploaded image](/media/?type=post&id=82381&key=user_uploads/529055/2aa036ec-8e14-4f42-b5e3-7715e93d17a1)\n\nWe’ve seen the best engineers blow up multi-million dollar systems because their mission control software didn’t give them a comprehensive picture of how their hardware was behaving. A director of test & launch operations once told our team:\n\n> It’s amazing what people guess at just because loading data is so hard\n\nTo put their quote in context, here’s what the leading aerospace control software, LabVIEW, shows as a UI screenshot on their landing page:\n\n![uploaded image](/media/?type=post&id=82381&key=user_uploads/529055/fe8ee6b7-a6bb-43c6-8cda-ee5c596d40b8)\n\nRemember [Scratch](https://scratch.mit.edu/about) or [Lego Mindstorms](https://www.nxtprograms.com/help/learn.html) from elementary school? LabVIEW is kind of like that, except people use it to fly humans to space. LabVIEW and its cross industry counterparts (such as [AVEVA Scada](https://www.aveva.com/en/solutions/operations/scada/) and [Rockwell FactoryTalk](https://www.rockwellautomation.com/en-us/products/software/factorytalk.html)) were first developed in the 20th century and have evolved little since. These tools come with:\n\n* Outdated, unintuitive user interfaces\n* High price tags\n* Closed ecosystems with no integrations\n* Poor documentation\n* Expensive certifications\n\n**Most importantly, they disregard the importance of tightly integrated and automated data processing that drives the innovation of today’s software giants.** Most solutions spit out massive CSV files or mindlessly write data to an SQL table. They’re disorganized, difficult to locate, inaccessible to most engineers, and are challenging to integrate with analysis tools.\n\n### **Our Solution - The Product**\n\nAt Synnax, we’re building a modern alternative to tools like LabVIEW by developing tightly integrated hardware control, sensor data storage, and automated post-processing software. Here’s our UI:\n\n![uploaded image](/media/?type=post&id=82381&key=user_uploads/529055/012764f7-8440-406b-b51e-fc82ee687b6c)\n\nWe deliver:\n\n* Intuitive interfaces for humans to manually operate everything from automotive test stands to manufacturing lines.\n* Tools for categorizing and reviewing historical data.\n* Automated post-processing and report generation pipelines.\n* Direct integration with existing sensor systems so teams don’t need to buy new hardware.\n* A simple API for writing automated control sequences in Python, C++, and TypeScript.\n\nIt’s this easy to open a valve based on temperature in Synnax:\n\n![uploaded image](/media/?type=post&id=82381&key=user_uploads/529055/197cb0a0-dc36-41fb-a273-816dd06d67f8)\n\nHere’s a comparable sequence in LabVIEW:\n\n![uploaded image](/media/?type=post&id=82381&key=user_uploads/529055/64d695d8-a786-49a1-975d-cecb7facf9f7)\n\n### **Our Solution - The Tech**\n\nWe’ve built one of the fastest time-series database engines available. Synnax can stream millions of samples per second in real time, making it possible to control systems with extremely tight timing requirements, such as starting a rocket engine.\n\n### **Our Solution - The Impact**\n\nBy bringing together hardware control and sensor data processing, organizations operating large, complex systems tighten the feedback loop between recording sensor data and using it to make actionable decisions.\n\nWith Synnax, teams:\n\n* Reduce risk by detecting and reacting to anomalies in real time.\n* Spend less time developing in-house data processing solutions.\n* Reduce the manual labor involved in extracting insights from sensor data.\n\n### **Our Asks**\n\nDo you work on hardware and need updated control software? Do you know someone who does? Let us know at [founders@synnaxlabs.com](mailto:founders@synnaxlabs.com).",
        "url": "https://www.ycombinator.com/launches/LQj-synnax-the-software-infrastructure-for-operating-observing-complex-hardware",
        "votes": 32,
        "created_at": "2024-07-23T20:54:19.457Z"
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    "id": 29699,
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    "slug": "pulse-3",
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      "Pulse",
      "Pulse AI"
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    "website": "https://www.runpulse.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Pulse converts complex information into LLM-ready inputs. Our API supports all document formats, from PDFs to Word, Excel, etc. Pulse integrates seamlessly with any existing data pipeline in minutes without any training or complexity. ",
    "one_liner": "Production-grade unstructured document extraction",
    "team_size": 22,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1720821338,
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      "America / Canada"
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    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/pulse-3.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/pulse-ai-corp",
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    "primary_partner": {
      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
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    "founders": [
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        "user_id": 954117,
        "full_name": "Sid Manchkanti",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder/CEO of Pulse (S24). Prev NVIDIA, D.E. Shaw, Berkeley CS",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/sidmanchkanti/",
        "x_url": "https://x.com/sid_mnk",
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      },
      {
        "user_id": 1979575,
        "full_name": "Ritvik Pandey",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder/CTO of Pulse (S24). Prev Tesla ML, Goldman Sachs, Georgia Tech CS/Math",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ritvikpandey22/",
        "x_url": "https://twitter.com/ritvikpandey21",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/051da6c6c5ac21c8cfa1b9f5afcac5d405d513d5.jpg"
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        "id": 79495,
        "title": "Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/v5CgKQG-forward-deployed-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$140K - $200K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 79496,
        "title": "Software Engineer",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/DGmw1Gt-software-engineer",
        "location": "San Francisco",
        "type": "Full-time",
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        "role_type": "Full stack",
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      {
        "id": 79497,
        "title": "DevOps Engineer",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/WJU0MZY-devops-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $210K",
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        "experience": "3+ years",
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      {
        "id": 79498,
        "title": "Machine Learning Engineer",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/onL2M1m-machine-learning-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
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        "id": 79499,
        "title": "Software Engineer, Inference",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/9UxFE8b-software-engineer-inference",
        "location": "San Francisco",
        "type": "Full-time",
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        "experience": "3+ years",
        "visa": "US citizen/visa only",
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        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/SG2w3qy-design-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$110K - $150K",
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        "experience": "3+ years",
        "visa": "US citizen/visa only",
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      },
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        "id": 79502,
        "title": "Account Executive",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/Ohcm7TJ-account-executive",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$100K - $150K",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 86138,
        "title": "Business Development Representative (BDR)",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/oOkCIeg-business-development-representative-bdr",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$80K - $120K",
        "equity_range": "0.10% - 0.40%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 109047,
        "title": "Growth and Marketing",
        "url": "https://www.ycombinator.com/companies/pulse-3/jobs/nPfmn5m-growth-and-marketing",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
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    ],
    "news": [
      {
        "title": "AI Startup Pulse Raises $3 Million From Nat Friedman and Daniel Gross - Business Insider",
        "url": "https://www.businessinsider.com/unstructured-data-extraction-startup-pulse-funding-nat-friedman-daniel-gross-2025-2",
        "date": "Feb 19, 2025"
      },
      {
        "title": "Pulse AI Blog - Announcing our $3.9M Seed Round",
        "url": "https://www.runpulse.com/blog/pulse-seed-nfdg-venture",
        "date": "Feb 19, 2025"
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    ],
    "launches": [
      {
        "id": 84833,
        "title": "Pulse STUDIO Vision API: Production-Grade Unstructured Document Extraction",
        "tagline": "An API + playground for production-grade unstructured document extraction, turning complex information into LLM-ready inputs. No training required.",
        "body": "Hey everyone, we’re Sid and Ritvik — the founders of [Pulse AI.](https://www.trypulse.ai/)\n\nWe first developed this API for a very specific use case in supply chain and got it to work across various unstructured documents used in the field. We were simply unable to find another solution on the market that worked well. Our original plan was to keep it in-house, but we soon started getting requests from dozens of companies to use this API on their own. \n\n# **❌ The Problem**\n\n**Most enterprise data is unstructured, making it difficult to parse with LLMs**\n\nApproximately 75% of enterprise data is unstructured, the majority of this is directly within PDF files. This makes it extremely difficult to build RAG applications with this data, and ingestion is often the bottleneck.\n\n**Current solutions are slow, inaccurate, and expensive**\n\nWe personally tested nearly every other tool on the market and found they lack accurate contextual understanding, multi-column PDFs, and multimodal documents. Most of the current technologies are simply wrappers on Textract or Gemini — which have their own inherent flaws. \n\n# **✅ The Solution**\n\n**Pulse STUDIO Vision API, a SOTA document/spreadsheet vision model** \n\nWe’ve trained our own set of Vision Language Models (VLMs) and OCR techniques to bridge this gap. We achieved what we think to be a state-of-the-art (SOTA) vision model for documents and spreadsheets. You’ll get bounding boxes across your documents and spreadsheets, alongside incredible OCR on tables and graphs. \n\nWe’re also actively working on a novel reasoning tool on spreadsheets using this technology – stay tuned!\n\n![uploaded image](/media/?type=post&id=84833&key=user_uploads/954117/61283c46-e471-4053-b80f-30fa7025981e)\n\n# **🧩 Team**\n\nThe founding team has deep machine learning experience at Tesla, NVIDIA, D. E. Shaw, and AWS — as well as research experience at world-class AI labs at Berkeley and Georgia Tech.\n\n# **🙋Our Ask**\n\nOur API is deployed in companies across hardware, healthcare, manufacturing industries and more. We’re expanding rapidly! [Fill out this form for access](https://studio.trypulse.ai/signup) and we will reach out within 24 hours with playground access. More info on the product page [here](https://studio.trypulse.ai/). \\\nPlease share this post, as you never know who it may help! Feel free to contact us directly at [sid@trypulse.ai](mailto:sid@trypulse.ai) if you would like to try it out and follow us on [Linkedin](https://www.linkedin.com/company/pulse-ai-corp/).",
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        "created_at": "2024-10-17T22:05:15.980Z"
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    "id": 29751,
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      "Octopipe"
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        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/abhishek-ray-29210145",
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      {
        "id": 82453,
        "title": "Opslane – Making on-call suck less",
        "tagline": "We reduce alert fatigue for on-call teams.",
        "body": "**TL;DR**\n\nOur [Slack bot](https://github.com/opslane/opslane) classifies your Datadog alerts and flags noisy alerts, reducing alert fatigue.\n\nAdditionally, we generate insights based on all your alerts to give you a picture of your overall alert hygiene.\n\n### The Problem\n\n**Picture this:** It's 3AM, and you're woken up by a page. It's an error you've seen before, and you know it wasn't worth waking up for.\n\nAnyone who's been on-call knows the infamous middle-of-the-night unactionable alert all too well.\n\nEven with modern tools, it's still tough to determine when something has actually gone wrong, the user impact, and how to fix it quickly.\n\n**Why are existing solutions not cutting it?**\n\n* Lack of context - it's really hard to understand the customer and business impact from looking at an alert.\n* Alerts are noisy and not actionable.\n* Debugging requires context switching between multiple siloed tools.\n\n### Our solution\n\nWe're starting with our open source Slack bot that reduces alert noise.\n\nOnce you add our bot to your Slack channel, we pull your alert history from Datadog.\n\nWe analyze your alert history across various parameters:\n\n* Alert frequency\n* How quickly have the alerts resolved in the past\n* Alert priority\n* Alert response history\n\nBased on these, we classify your alert as actionable or noisy.\n\n![User-uploaded](https://slabstatic.com/prod/uploads/s97i19pj/posts/images/6P01k6pFnPq5repaabLC5_-G.png)\n\nIn addition, we generate a report on your team's overall alert health, pinpointing which noisy alerts are prime candidates for tuning or disabling.\n\n![User-uploaded](https://slabstatic.com/prod/uploads/s97i19pj/posts/images/1e4X0_iAo7eLJh7PLEo1oZoS.png)\n\nOur vision is to stitch together all the relevant context engineers need to debug incidents in one place and automate root cause analysis.\n\n### Team\n\n![uploaded image](/media/?type=post&id=82453&key=user_uploads/6530/ba62e789-6ccf-4b5c-a555-90e5c932d81c)\n\nWe ([Amaresh](https://linkedin.com/in/amareshray/) and [Abhishek](https://linkedin.com/in/abhishek-ray-29210145)) have firsthand experience dealing with the challenges of on-call and alert fatigue at companies like Robinhood and Atlassian. With Opslane, we’re reducing the alert fatigue that overwhelms on-call engineers and leads to millions in lost productivity and downtime.\n\n### Ask\n\n🔥 Are noisy alerts a hair-on-fire problem for your company or someone you know? We'd love to hear from you - email [founders@opslane.com](mailto:founders@opslane.com) or [schedule a call](https://cal.com/team/opslane/demo) with us.\n\n⭐ Please star us on Github: <https://github.com/opslane/opslane>",
        "url": "https://www.ycombinator.com/launches/LRt-opslane-making-on-call-suck-less",
        "votes": 10,
        "created_at": "2024-07-26T14:59:47.999Z"
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    "all_locations": "San Francisco, CA, USA",
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    "one_liner": "GPUs for Agents",
    "team_size": 10,
    "industry": "B2B",
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    "launched_at": 1723245936,
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    "question_answers": [
      {
        "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)",
        "answer": "Carl Peterson and Brian Model met as freshmen at Georgia Tech and became close friends. The idea for Thunder Compute sparked from Brian's experience in his Systems for AI lab, where researchers made GPU reservations weeks in advance via Google Sheets, severely hindering research progress. This inefficiency inspired us to explore ways to improve GPU access."
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    "url": "https://www.ycombinator.com/companies/thunder-compute",
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    "year_founded": 2022,
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    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/thundercompute/",
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        "body": "![uploaded image](/media/?type=post&id=82910&key=user_uploads/1276433/30b5b896-6109-4aa6-8a05-fb9a138f61a8)\n\n### **The backstory**\n\nHi everyone, we’re Carl and Brian, aka the team behind [Thunder Compute.](https://thundercompute.com)\n\n![uploaded image](/media/?type=post&id=82910&key=user_uploads/1276433/07db7c92-f1f2-4356-80f8-9dabf9c2fbcd)\n\nBefore Thunder Compute, [Carl](https://www.linkedin.com/in/cpeterson42) was a Management Consultant at Bain & Company, and [Brian](https://www.linkedin.com/in/brian-model/) was a Quantitative Developer at Citadel Securities. Carl and Brian met at Georgia Tech and have been close friends for over 6 years.\n\n### **Problem**\n\nGPU access sucks. Today’s options are expensive and hard to use. Developers are crowded out of the market by large corporations and can’t get the GPUs they need for reasonable prices. Even if you can afford GPUs, they are scarce and hard to use.\n\n### **Solution**\n\nThunder Compute sells cloud GPUs for the lowest rates on the market and provides a simple interface to access them.\n\nYou can connect in one click through VSCode, without SSH. We have “prototyping” mode, with prices 50% lower than anywhere else, and “production” mode, with higher uptime and multi-GPU nodes.\n\n### **Our offer**\n\nSimple: low-cost, easy GPUs. \\\n\\\nIf you’re looking for a better GPU cloud, join Thunder Compute’s public beta today at [www.thundercompute.com](https://www.thundercompute.com).\\\n\\\nIf you have questions, need technical support, or want to rant about the current state of GPUs, join our discord at [**https://discord.gg/nwuETS9jJK**](https://discord.gg/nwuETS9jJK) or contact us at [**founders@thundercompute.com.**](https://mailto:founders@thundercompute.com/)",
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        "title": "Actionbase: An SDK for automating popular sites",
        "tagline": "Automate LinkedIn, Amazon, Uber, and Resy in a few lines of code",
        "body": "Hey everyone! We are [Sam](https://linkedin.com/in/samcrombie) and [Adam](https://www.linkedin.com/in/adam-mcquilkin/) building Actionbase. Actionbase builds tools that make the web agent-interpretable. Today, we are launching our first product, the [Web Action SDK](https://actionbase.co/).\n\n# **TL;DR**\n\nThe [Web Action SDK](https://actionbase.co/) is a TypeScript / JavaScript SDK that lets developers run automations on popular websites with only a few lines of code.\n\n# **What’s the problem?**\n\nWeb automations, particularly for high-traffic websites, **suck to build and maintain**. Yet engineering teams find themselves having to build them from scratch, over and over.\n\nSpinning your own automations is an easy path to unnecessarily complex codebases and enormous tech debt. Automations break down, hit edge cases, and become exponentially unruly.\n\nBut this is business-critical functionality, so what else can you do?\n\n# **Solution: Web Action SDK**\n\nWe’ve built an SDK that allows you to run automations on popular sites with only a few lines of code. Simply add your API key, define an action, and run it.\n\nHere’s how you can use our SDK to send a LinkedIn message.\n\n![uploaded image](/media/?type=post&id=83184&key=user_uploads/448194/5978c043-af17-4af7-8b20-5c3ef74d7277)\n\nInitialize, send, and that’s it!\n\nNow that you’ve made your plans, you can use other services supported by the SDK to prepare for dinner: buy a notebook to take notes (Amazon), make a restaurant reservation (Resy), and get a ride to the restaurant (Uber).\n\n![uploaded image](/media/?type=post&id=83184&key=user_uploads/448194/5875c5ac-d64a-4bb7-9f1f-0eddf8b25a95)\n\nIn less than 10 lines of code, you’ve planned a pretty cool night out.\n\n# **Value Proposition: Simplicity**\n\nWeb Action SDK does all the heavy-lifting for you: authentication, anti-botting, task creation and execution, self-healing, and hosting. Our goal is to enable engineering teams to focus on **what to do with web actions**, not how to make them work.\n\n# **Asks**\n\n**\\#1:** If you currently have code that programmatically interacts with: LinkedIn, Facebook, Resy, Uber, DoorDash, Amazon, United, or any popular, high traffic sites, we’d love for you to sign-up and [try the SDK for yourself](https://actionbase.co). If you want to discuss bringing Actionbase to your company/use case, book time [here](https://actionbase.co/book-demo).\n\n**\\#2:** If you know someone who scrapes or automates sites, please forward them this URL or our [email blurb.](mailto:your_recipients_email_here@email.com?subject=Does%20this%20look%20useful%3F%20A%20web%20automation%20SDK&body=Hey%2C%20a%20YC%20company%20called%20Actionbase%20just%20launched%20their%20Web%20Action%20SDK%20(https%3A%2F%2Fwww.ycombinator.com%2Flaunches%2FLdg-actionbase-an-sdk-for-automating-popular-sites)%2C%20which%20lets%20you%20to%20automate%20popular%20websites%20like%20LinkedIn%20in%20a%20few%20lines%20of%20code.%0A%0AIs%20this%20something%20you%27d%20find%20useful%3F)\n\n# **Our Background**\n\nAdam and I met at Dartmouth where worked together on our senior thesis project. After graduation, we worked at Microsoft, building expertise in web automation and infrastructure on the Edge Copilot and PowerBI Copilot teams. We’ve joined forces once again to make the web agent-interpretable with Actionbase.\n\n![uploaded image](/media/?type=post&id=83184&key=user_uploads/448194/cbe18c26-732c-40c3-aab0-f9d935192ba8)\n\n![uploaded image](/media/?type=post&id=83184&key=user_uploads/448194/59f21dd2-616b-4afa-82e0-b0229d9dfe48)\n\n",
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        "full_name": "Phillip Nadjafov",
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        "title": "Staff Engineer, IOI (Internet of Industrials)",
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        "location": "San Francisco, CA, US",
        "type": "Full-time",
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        "title": "Senior Full-Stack Engineer, IOI Applications",
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        "title": "Forward Deployed Engineer, Enterprise",
        "url": "https://www.ycombinator.com/companies/oway/jobs/AqsthKE-forward-deployed-engineer-enterprise",
        "location": "San Francisco, CA, US",
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        "title": "Strategic Partnerships Lead, Enterprise",
        "url": "https://www.ycombinator.com/companies/oway/jobs/bGqgrpO-strategic-partnerships-lead-enterprise",
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        "id": 115290,
        "title": "Quantitative Researcher, IOI Index",
        "url": "https://www.ycombinator.com/companies/oway/jobs/LHEjh2d-quantitative-researcher-ioi-index",
        "location": "San Francisco, CA, US",
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        "role": "Engineering",
        "role_type": "Data science",
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        "title": "5 Founders Building the Future of Logistics, AI, and Industrial Technology - The San Francisco Tribune",
        "url": "https://thesanfranciscotribune.com/5-founders-building-the-future-of-logistics-ai-and-industrial-technology/",
        "date": "Oct 15, 2026"
      },
      {
        "title": "How Oway uses AI to fill empty trucks and cut shipping costs - FreightWaves",
        "url": "https://www.freightwaves.com/news/how-oway-uses-ai-to-fill-empty-trucks-and-cut-shipping-costs",
        "date": "Sep 30, 2025"
      },
      {
        "title": "YC-backed Oway raises $4M to build a decentralized ‘Uber for freight’ | TechCrunch",
        "url": "https://techcrunch.com/2025/08/22/yc-backed-oway-raises-4m-to-build-a-decentralized-uber-for-freight/",
        "date": "Aug 22, 2025"
      },
      {
        "title": "9 startups that stood out on YC Demo Day 2 | TechCrunch",
        "url": "https://techcrunch.com/2024/09/26/9-startups-that-stood-out-on-yc-demo-day-2/",
        "date": "Sep 26, 2024"
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      {
        "id": 122375,
        "title": "Oway: Building the Internet of Industrials",
        "tagline": "Oway connects industrial systems through a common protocol, giving AI the context to coordinate the physical economy.",
        "body": "**TL;DR:** We’re building the Internet of Industrials (IOI), a shared context layer connecting industrial systems and giving AI a common basis of structured data across companies. IOI already powers Oway, where our first model, Juno, uses it to coordinate shipments through unused truck capacity. We’re opening that same foundation to robotics, automated factories and other industrial systems to accelerate US industrial automation.\n\n![](/media/?type=post&id=122375&key=user_uploads/993914/9346b6ba-f710-49c9-9ecb-72882cc13bca)\n\n**An example:** A factory asks its AI for eight units of mild steel by tomorrow morning. Through IOI, the AI coordinates production with a connected certified supplier and books a connected truck for delivery. All three systems share the context needed to execute and track the order.\n\n### The problem\n\nAmerica is building new factories, deploying automation, and developing autonomous vehicles. But the systems connecting industrial operations still speak different languages.\n\nInventory, capacity, equipment, orders, and operating constraints are scattered across ERP systems, TMSs, warehouses, carriers, sensors, and closed systems. That fragmentation extends across the companies that need to work together. AI cannot reliably coordinate physical work without understanding what exists, what is available, and what each system can do.\n\nProductive capacity goes unused while businesses pay more to make, store, and move goods.\n\n### Introducing IOI\n\nWe built IOI as the backend that runs Oway. It already connects every Oway customer and carrier. We’re now making it available to other businesses as a common structured context layer.\n\nOur thesis is that logistics provides the foundational language for an industrial network. Nearly every physical business can be described through a common set of primitives: **what exists, where it is, where it needs to go, available capacity, time, cost, constraints, storage, and transformation.**\n\nIOI translates those states into structured data that enterprise software, physical hardware and AI agents can share across company boundaries. Businesses can build nodes alongside their existing systems instead of replacing them. \n\nSetting up an IOI node is extremely simple, our default setup allows you to simply plug Oway in as a “carrier” into your stack that monitors and optimizes your supply chain quotes and orders - automatically gaining the context to build a full IOI data lake for you that already has action capabilities and can infer your inventory levels and SKU-level performance. Data can then automatically be fed back to SAP, Oracle Netsuite, or any data system / AI agent you’d like.\n\n![uploaded image](/media/?type=post&id=122375&key=user_uploads/993914/c9f841f6-3976-429b-9f25-81e63bc0c113)\n\nIOI will be open source soon, with Oway maintaining the standard so developers can build compatible systems. It is designed around widely adopted enterprise security practices, with each business controlling its data, access permissions and authorized actions. Nodes can run in enterprise clouds or at the edge.\n\n**Below: Ask IOI anything -** Each Oway enterprise customer also gets search functionality out of the box. \n\n![](/media/?type=post&id=122375&key=user_uploads/993914/07bea764-9c83-47a6-8c71-97f5130d28b5)\n\n### Our first model: Juno\n\nJuno is our first AI model. It reasons over IOI to price, coordinate and execute pooled shipments on trucks already traveling the route.\n\nBy ridesharing shipments into capacity already moving, we lower costs, shorten transit times and reduce damage from handling. Carriers earn from their existing capacity, with legal paperwork, insurance and DOT compliance all automatically monitored and handled.\n\n**Below: Juno in Oway OS.** A full API, MCP and EDI-compatible suite that enables businesses to leverage Juno’s rideshare capabilities however they’d like for freight shipping.\n\n![](/media/?type=post&id=122375&key=user_uploads/993914/f2a8e9b3-a635-4c69-b9a8-4c5d3f8eb1f7)\n\nWe estimate this is a **$200B+ annual optimization opportunity within America’s roughly $1T trucking industry**.\n\nWith Oway Rideshare, we deliver average savings of 30% per transaction, while also reducing damage rates, and transit times as well for cross-country shipments.\n\nFreight gives IOI an operating environment where its primitives have to produce real physical outcomes.\n\n### Where we’re going\n\nEvery connected system and shipment adds context about capacity, pricing, routes, facilities, and industrial operations.\n\nThat shared context gives robotics companies, automated factories and self-driving trucks a way to communicate and coordinate across companies. Every company on Oway, and even Oway’s model - are already running on IOI. We believe that providing these capabilities at a national level will lead to significant improvements to the GDP and global competitiveness of US-made products.\n\nOway + IOI already serves Fortune 500 companies, connects over 10,000 vehicles nationwide, and indexes hundreds of millions of dollars in annualized shipping demand.\n\nIf you’re working at a company that makes, stores, or moves physical goods, [we’d love to show you what we’re building](https://www.shipoway.com).",
        "url": "https://www.ycombinator.com/launches/Vpn-oway-building-the-internet-of-industrials",
        "votes": 23,
        "created_at": "2026-10-15T15:50:53.501Z"
      }
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    "id": 29950,
    "name": "Pipeshift",
    "slug": "pipeshift",
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    "website": "https://pipeshift.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Pipeshift offers ultra-low latency infrastructure for engineering teams to run real-time inference in production. We offer latency-optimized runtimes for voice, chat, and physical AI, paired with distributed orchestration that routes and auto-scales across clusters and regions — typically 3 to 5X lower time-to-first-audio, so teams serve 2x the users on the same hardware.",
    "one_liner": "Inference for real-time voice and chat",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1717199794,
    "tags": [
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2024",
    "status": "Active",
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    "regions": [
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      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/pipeshift.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://linkedin.com/company/pipeshift",
    "x_url": "https://x.com/pipeshiftai",
    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/pipeshift",
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6dc8136429eadec250bda86f1102143f81a24beb.png",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
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    "founders": [
      {
        "user_id": 951072,
        "full_name": "Arko C",
        "title": "Founder/CEO",
        "founder_bio": "CEO @ Pipeshift.\nBuilding inference for real-time voice and chat.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/arkoc",
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      {
        "user_id": 960185,
        "full_name": "Enrique Ferrao",
        "title": "Founder/CTO",
        "founder_bio": "CTO @ Pipeshift. Focused on squeezing out max LLM performance from GPUs",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/enrique-ferrao",
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        "user_id": 960170,
        "full_name": "Pranav Reddy",
        "title": "Founder/CIO",
        "founder_bio": "CIO @ Pipeshift. Making LLMs go brrrr at Pipeshift",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/pranavreddyg",
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        "title": "Democratising Access To Open-Source GenAI With Pipeshift",
        "url": "https://www.forbes.com/sites/davidprosser/2025/01/23/democratising-access-to-open-source-genai-with-pipeshift/",
        "date": "Jan 23, 2025"
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    "launches": [
      {
        "id": 83260,
        "title": "Pipeshift AI - Fine-tuning and inference for open-source LLMs",
        "tagline": "Replace GPT/Claude in production with specialized LLMs that are fine-tuned on your context, offering higher accuracy, lower latencies and model ownership.",
        "body": "**_TL;DR:_** Pipeshift is the cloud platform for finetuning and inferencing open-source LLMs, helping teams get to production with their LLMs faster than ever. With Pipeshift, companies making >1000 calls/day on frontier LLMs can use their data and logs to replace GPT/Claude with specialized LLMs that offer higher accuracy, lower latencies, and model ownership. [_Connect with us._](https://cal.com/arkoc/pipeshift)\n\n![uploaded image](/media/?type=post&id=83260&key=user_uploads/951072/55b3d2ab-6279-4002-9bc7-2e7527c8578b)\n\n# **🧨 The Problem: Building with Open-source LLMs is hard!**\n\nThe open-source AI stack is missing, forcing most teams to experiment by **duct-taping things like TGI/vLLM but having nothing ready for production.** As you scale, it requires expensive ML talent, long build cycles, and constant optimizations.\n\nThe **gap between open-source and closed-source models is shrinking** (Meta's Llama 3.1 405B is a testament to that)! And open-source LLMs offer multiple benefits over their closed-source counterparts:\n\n🔏 Model ownership and IP control\\\n🎯 Verticalization and customizability\\\n🏎️ Improved inference speeds and latency\\\n💰 Reduction of API costs at scale\n\n# **🎉 The Solution: Heroku/Vercel for Open-source LLMs**\n\nPipeshift is the **cloud platform for fine-tuning and inferencing open-source LLMs**, helping developers get to production with their LLMs faster than ever.\n\n**🎯 Fine-tune Specialized LLMs**\\\nRun multiple LoRA-based fine-tuning jobs to build specialized LLMs.\n\n**⚡️ Serverless APIs of Base and Fine-tuned LLMs**\\\nRun inference for your fine-tuned LLMs and pay as per your token usage.\n\n**🏎️ Dedicated Instances for High Speed and Low Latency**\\\nUse our optimised inference stack to get max throughputs and utilisation on GPUs.\n\n[Product Demo: https://youtu.be/z8z5ILyXxCI](https://youtu.be/z8z5ILyXxCI)\n\n_Our inference stack is one of the best globally, hitting **150+ tokens/sec on 70B parameter LLMs** without any model quantization. And, since our private beta access was opened (<2 weeks back), we have already seen **25+ LLMs being fine-tuned with over 1.8B tokens in training data** across 15+ companies._\n\n![uploaded image](/media/?type=post&id=83260&key=user_uploads/951072/b91ba4bb-dca1-4567-8a0c-cf637e03b561)\n\n# **👋 Ask: How you can help**\n\nIf you’re building an AI co-pilot/agent/SaaS product and **are looking to move to open-source LLMs** or know someone who’s looking to do that same, then [book a call](https://cal.com/arkoc/pipeshift) or mail us at [_founders@pipeshift.ai_](mailto:founders@pipeshift.ai) - whichever you’d like!",
        "url": "https://www.ycombinator.com/launches/Leu-pipeshift-ai-fine-tuning-and-inference-for-open-source-llms",
        "votes": 703,
        "created_at": "2024-08-21T01:29:39.204Z"
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    "id": 29966,
    "name": "Mica AI",
    "slug": "mica-ai",
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    "website": "https://usemica.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Today, as employees teach AI how to perform valuable company work, the resulting capabilities often remain trapped on individual laptops or scattered across chats and repositories. \r\n\r\nMica gives each company a managed, agent-native internal marketplace where employees can publish capabilities, discover them by describing the task they need help with, and install them through the AI tools they already use. Mica tracks ownership, requirements, permissions, and team customizations while letting employees contribute improvements back to the shared version for evaluation and approval.\r\n\r\nThe result: employees stop rebuilding workflows their coworkers have already solved, useful improvements spread across the organization without overwriting team-specific customizations, and security gains visibility without blocking bottom-up innovation. Mica turns scattered AI work into institutional knowledge that compounds.",
    "one_liner": "Turn employees' AI skills into shared company infrastructure",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1720831958,
    "tags": [
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      "B2B",
      "Infrastructure",
      "AI"
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    "isHiring": false,
    "nonprofit": false,
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    "regions": [
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/mica-ai",
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    "year_founded": 2023,
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      "name": "Brad Flora",
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        "user_id": 281665,
        "full_name": "Achyuta Iyengar",
        "title": "Founder/CEO",
        "founder_bio": "Achyuta is the co-founder and CEO of Mica AI.\n\nPrior to Mica, Achyuta worked as a Product Manager at Lob (YC S13) where he led the New Product Development, Address Verification, and Developer Experience teams. He has a BS in Business Administration from the Haas School of Business at UC Berkeley. \n",
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        "user_id": 1626603,
        "full_name": "Bharadwaj Swaminathan",
        "title": "Founder/CTO",
        "founder_bio": "Bharadwaj is the founder and Head of Engineering at Mica AI. Previously, he worked as a Data Scientist at Hewlett Packard Enterprise, working on creating machine learning products in the networking space. He has a Bachelor's in Data Science and Economics from UC Berkeley.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/bharadwajswaminathan",
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      {
        "id": 82748,
        "title": "Mica AI - AI Agents that Automate B2B Sales",
        "tagline": "Helping SaaS sales teams close more deals.",
        "body": "Hi everyone, we’re Achyuta, Jai, and Bharadwaj!\n\n**TLDR:**\n\n[**Mica**](https://usemica.com/) helps B2B sales teams close more deals by automating their entire sales follow-up process. Our AI agents analyze sales calls, generate tailored collateral, and follow up with prospects until the deal is closed.\n\n**The Problem:**\n\nThe best sales reps aren’t just great at selling—they are elite content creators. They crafted tailored collateral like decks, 1-pagers, and product demos to help champions advocate internally. However, creating this content often takes over 30 minutes per call, and with multiple calls a day, even the best reps can only prioritize a few clients, leaving others with subpar follow-ups and millions in lost deals.\n\n**Our Solution:**\n\nMica automates the sales follow-up process by generating deal-closing collateral in less than a minute. Our AI analyzes sales calls, extracts key insights, builds tailored collateral, and sends this to prospects in a centralized deal summary page.\n\n![uploaded image](/media/?type=post&id=82748&key=user_uploads/281665/a654eebe-5ef9-48ee-ab9d-ca41efc83bc4)\n\n**Key Benefits**\n\n1. **Quicker Follow-up Time:** Mica creates personalized follow ups in under 1 minute. This lets top performers do A+ work on every deal and helps the rest of the team sell like a top performer.\n2. **Faster Sales Cycles:** The point of contact can easily review important information from the call, share it internally, and make decisions faster.\n\n**How it Works**\n\n1. **Upload:** Mica automatically pulls in your sales call from existing recording or notetaking tools.\n2. **Extract Key Insights:** Mica identifies the key points the prospect was most interested in.\n3. **Generate Tailored Collateral:** Mica creates tailored decks, product demos, and 1-pagers for each key topic.\n4. **Share:** Mica generates a shareable link containing all the collateral, which can be sent directly to the prospect in follow-up emails that Mica also creates.\n\nHere’s a demo of Mica that we created from an actual Mica sales call:\n\n<https://www.youtube.com/watch?v=xIHwwnYKj5o>\n\n**Our Ask:** We’re looking to onboard customers who want to automate and improve their sales follow-ups.\n\nWe’re looking for:\n\n* Sales reps and heads of sales at B2B SaaS companies.\n* Founders who are doing founder-led sales.\n\nAnyone who is interested can book a demo [**here**](https://calendly.com/achyutaiyengar/mica-15-minute-demo).\n\n![uploaded image](/media/?type=post&id=82748&key=user_uploads/281665/307627c1-b261-47d7-b07a-b78ac54926be)\n\n",
        "url": "https://www.ycombinator.com/launches/LWe-mica-ai-ai-agents-that-automate-b2b-sales",
        "votes": 16,
        "created_at": "2024-08-05T20:19:18.712Z"
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    "id": 29976,
    "name": "Freestyle",
    "slug": "freestyle",
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    "website": "https://www.freestyle.sh",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Freestyle builds the most powerful VMs for AI Agents. \r\n\r\n- **Instant Startup**: Freestyle VMs provision in milliseconds, with p99s under 400ms.\r\n- **Persistence**: Freestyle VMs can run forever, they can be hibernated with their exact memory in place and come back to life weeks later instantly, in that exact state.\r\n- **Powerful**: Freestyle VMs go up to 64GB of RAM for public tiers and much further for enterprise. Freestyle does high quality hardware virtualization supporting nested virtualization, FUSE, eBPF, full Linux networking and more. If it can run in an EC2, it can run in a Freestyle VM.\r\n- **Cheaply Snapshottable and Branchable**: Freestyle VMs are instantly and cheaply snapshottable (sub 1ms interruption, sub 50ms to snapshot ready). These snapshots can be used to create new VMs in the exact state of the original VM (memory and disk). This enables powerful workflows like branching, cloning, and rapid recovery from failures.\r\n\r\n",
    "one_liner": "The most powerful sandboxes for AI Agents",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1719115501,
    "tags": [
      "Developer Tools",
      "Cloud Computing",
      "AI"
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    "batch": "Summer 2024",
    "status": "Active",
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/freestyle",
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    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
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      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
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        "user_id": 800509,
        "full_name": "Benjamin Swerdlow",
        "title": "Founder/CEO",
        "founder_bio": "I learned to code in elementary school.\n\nIn high school I served as a code owner and technical lead for the World Health Organization COVID-19 App Collective Team.\n\nI did a year of college, then took a job as a consultant for Apple. In 3 months I became a senior. \n\nI left Apple to found Freestyle. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/benswerdlow/",
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      {
        "user_id": 1860855,
        "full_name": "Jacob Zwang",
        "title": "Founder/CTO",
        "founder_bio": "I dropped out of college and became an Apple senior full-stack engineer in Conversational Engineering at 19. During the year and a half I worked there, I delivered multiple projects to production at scale.\n\nI love building products that inspire and delight customers, but I'm frequently frustrated by poor back-end developer tools. My co-founder and I are building a new cloud and backend workflow so that we and others can build exceptional software in a fraction of the time.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jacobzwang/",
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        "id": 83627,
        "title": "Freestyle: A new way to build fullstack TypeScript apps",
        "tagline": "Empowering TypeScript developers to build with TypeScript",
        "body": "Today, most new apps are written with Fullstack TypeScript, but the tooling around them is still built as though the frontend and backend are separate codebases. We built an open source framework, runtime, and cloud that acknowledges this and removes these unnecessary layers from the development process.\n\n**What?**\n\nAny JavaScript class can be marked as [@cloudstate](https://github.com/freestyle-sh/cloudstate), and:\n\n1. The data in it is now persisted. You query the data with regular JavaScript.\n2. The methods on it can now be called from the front end. No Express/API Routes/tRPC needed!\n\n**Coolest Side Effect:** By building our tooling around Fullstack TypeScript, we enable **full-stack feature packages,** a way for JavaScript developers to package any feature they build and share it in a way that it can be embedded in other projects. Think **npm install mui**, except it’s **npm install chat**, or **npm install auth**. Imagine a world where every third-party SaaS provider today can be an open source package that runs in your application.\n\n**Our Ask:**\n\n**Build with us!** Contribute to our [Open Source Repository,](https://github.com/freestyle-sh/cloudstate) or try [deploying an app with us](https://docs.freestyle.dev)!\n\n![uploaded image](/media/?type=post&id=83627&key=user_uploads/89898/0a2412f8-6b6e-4906-9d8f-256badd8b332)\n\n",
        "url": "https://www.ycombinator.com/launches/Lkp-freestyle-a-new-way-to-build-fullstack-typescript-apps",
        "votes": 22,
        "created_at": "2024-08-30T19:44:46.817Z"
      },
      {
        "id": 81748,
        "title": "Freestyle: TypeScript for EVERYTHING",
        "tagline": "A fundamental shift in how we approach web development",
        "body": "# **TLDR; You can use TypeScript for EVERYTHING**\n\n[**Freestyle**](https://www.freestyle.sh) is a fundamental shift in how we approach web development. With Freestyle, you write your entire application—frontend and backend—in TypeScript. No context switching. No mental gymnastics. Just pure, consistent TypeScript throughout.\\\n\\\nRight now, building an application has a ton of disjointed layers. Imagine you’re writing a book in English for English readers. Now, imagine being told that all the connecting words—the “ands,” “buts,” and “ors”—must be written in Latin. Sounds absurd, right? Yet, this is often what web development feels like: you write your frontend in one language, only to switch to another for the backend, another for your database, and yet another to let your app connect to others.\n\n![alt](https://blog.freestyle.dev/images/blogs/multiplayer-infinite-craft/meme-backend-frontend.png)\n\nDuring our work for Apple, we lost hundreds of hours trying to integrate different layers and teams together to make our application run. That time should’ve been spent on features, but instead was spent on stitching features together. We created [Freestyle](https://www.freestyle.sh) not as a new stitching tool but as a way for whole organizations to not need those tools and instead function as a single unit.\n\nWhen you use Freestyle, you write your frontend and backend in JavaScript. Then, when you wanna call functions from your backend on your frontend, you just use them — no more dealing with REST APIs. When you want to store data, you just mark your JavaScript as [@cloudstate](https://docs.freestyle.dev/guides/cloudstate/) and when you’re ready to deploy, you run **_npx freestyle deploy_** — and in one command you’re up and running.\n\nNo more SQL, no MongoDB, no Firebase, no GraphQL or REST, or anything else taking time away from building features. With Freestyle, all you need is TypeScript.\n\n### Ready to try it? [**Sign up for a demo here**](https://www.freestyle.sh/join), or [**CHECK OUT THE DOCS HERE**](https://docs.freestyle.dev/getting-started/astro)",
        "url": "https://www.ycombinator.com/launches/LGW-freestyle-typescript-for-everything",
        "votes": 87,
        "created_at": "2024-06-25T22:12:58.661Z"
      }
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    "id": 30032,
    "name": "Darkgrade",
    "slug": "darkgrade",
    "former_names": [
      "Coblocks"
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/cdfc2cbde82e44ba7028fcc0e806374a31e74349.png",
    "website": "https://darkgrade.com",
    "all_locations": "Denver, CO, USA",
    "long_description": "We're building a protocol for large language models to directly interface with image sensors.",
    "one_liner": "Let your camera understand the world",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1731328761,
    "tags": [
      "Consumer",
      "Social Media",
      "Video",
      "Media",
      "AI"
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2024",
    "status": "Active",
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    "regions": [
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      "America / Canada",
      "Remote",
      "Partly Remote"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/darkgrade",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/darkgrade.json",
    "year_founded": 2024,
    "city": "Denver",
    "country": "US",
    "linkedin_url": "",
    "x_url": "",
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    "founders": [
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        "user_id": 1242764,
        "full_name": "Kevin Schaich",
        "title": "Founder/CEO",
        "founder_bio": "Let your camera understand the world with Darkgrade",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/kevinschaich/",
        "x_url": "https://twitter.com/kevin_schaich",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e4a7f36cfe27ece4494690865ae375e99304778e.jpg"
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    "id": 30041,
    "name": "Revyl",
    "slug": "revyl",
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    "website": "https://www.revyl.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Revyl is the mobile development platform for agents, developers, product, and design. It brings development, build infrastructure, verification, and application intelligence together in one platform, giving teams and agents a shared source of truth for every mobile application.\r\n\r\nTeams can build and run mobile applications in the cloud, understand how their applications work, collaborate across functions, and verify changes against the running app. AI agents gain the context and runtime they need to move beyond simply generating code and understand whether their work actually works.\r\n\r\nBy connecting the people and agents building software to the application itself, Revyl helps teams move faster, collaborate more effectively, and build mobile with confidence.",
    "one_liner": "The Mobile Development Platform",
    "team_size": 16,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1729643205,
    "tags": [
      "Developer Tools",
      "B2B",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Fall 2024",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/revyl",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/revyl.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/revylai/",
    "x_url": "https://x.com/tryrevyl",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/RevylAI/",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1eca61dd3b32e7b0f7174161cd567ec823504e49.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Andrew Miklas",
      "url": "https://www.ycombinator.com/people/andrew-miklas"
    },
    "founders": [
      {
        "user_id": 1321343,
        "full_name": "Landseer Enga",
        "title": "Founder/CEO",
        "founder_bio": "Building the mobile source of truth @ Revyl",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/landseerenga",
        "x_url": "https://x.com/landseerenga",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/610a94112586436ed1f68e60566a57df17956a1a.jpg"
      },
      {
        "user_id": 1432741,
        "full_name": "Anam Hira",
        "title": "Founder/CTO",
        "founder_bio": "building proactive observability - catching bugs before they reach production @ Revyl. \n\n\nex Uber AI - dragoncrawl team \n\n\nUBC cs + stat\n\n\n\n",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/anam-h-5b37a2125",
        "x_url": "https://x.com/anamhira",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9f711adc18ebc09fe193cac71ac8fd0504054be3.jpg"
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    ],
    "jobs": [
      {
        "id": 93254,
        "title": "Engineering Intern",
        "url": "https://www.ycombinator.com/companies/revyl/jobs/SduBVhc-engineering-intern",
        "location": "San Francisco, CA, US",
        "type": "Internship",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$5K - $10K / monthly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104528,
        "title": "GTM Intern",
        "url": "https://www.ycombinator.com/companies/revyl/jobs/d5wDFIK-gtm-intern",
        "location": "San Francisco, CA, US",
        "type": "Internship",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "$5K - $10K / monthly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
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      },
      {
        "id": 105186,
        "title": "Staff Infrastructure Engineer — Device Cloud",
        "url": "https://www.ycombinator.com/companies/revyl/jobs/7csBGxt-staff-infrastructure-engineer-device-cloud",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Devops",
        "salary_range": "$150K - $250K",
        "equity_range": "",
        "experience": "6+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Amazon Web Services (AWS)",
          "Android",
          "Go",
          "Google Cloud",
          "iOS",
          "Kubernetes",
          "MacOS",
          "AI Agents"
        ]
      },
      {
        "id": 106901,
        "title": "Software Engineer",
        "url": "https://www.ycombinator.com/companies/revyl/jobs/E3sb54p-software-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$125K - $200K",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 85015,
        "title": "Revyl - Proactive observability",
        "tagline": "Automatically catch and triage bugs before they reach production.",
        "body": "# **TL;DR:**\n\n[Revyl](https://www.revyl.ai/) creates resilient end-to-end tests linked to telemetry traces to catch and triage bugs before they reach production.\\\n<https://www.youtube.com/watch?v=Zy7yXTomh7M&ab_channel=AnamHira>\n\n# **Problem:**\n\n* E2E tests frequently break due to UI changes\n* Finding the root cause of a bug can be extremely time-consuming\n* Bugs in production cause revenue loss and erode customer trust\n* Testing practices are fragmented across web and mobile\n\n![uploaded image](/media/?type=post&id=85015&key=user_uploads/1321343/0b6d5568-fd23-4cd7-8d5f-1feca7fc1d57)\n\n# **Solution:**\n\nRevyl was developed to simplify your QA pipeline. Provide us with your user flows, and we'll handle the rest.\n\nAt its core, Revyl uses resilient end-to-end tests connected to telemetry traces to enable companies to catch and triage bugs before they reach production. Revyl supports tests across Web, iOS and Android.\n\nThis approach:\n\n1. Catches bugs across your whole tech stack\n2. Prevents bugs from reaching production\n3. Accelerates triaging by pinpointing UI and/or microservice failures\n4. Cuts test maintenance to near zero\n\n![uploaded image](/media/?type=post&id=85015&key=user_uploads/1321343/07c6f0c2-6d2f-4cc2-882c-edd303380eb4)\n\n# **Why Us?:**\n\n* [**@Anam Hira**](https://linkedin.com/in/anam-h-5b37a2125): Built [**DragonCrawl**](https://www.uber.com/en-CA/blog/generative-ai-for-high-quality-mobile-testing/), an LLM-based mobile testing framework, while working as a machine learning engineer at Uber. Dragoncrawl saved $25M in 4 months, detecting 11 P0 incidents.\n* [**@Landseer Enga**](https://linkedin.com/in/landseerenga): Experienced in growth-stage startups (PM/Engineer), did some web scraping for PE firms.\n\n# **Our Ask:**\n\n1. [**Book a meeting**](https://cal.com/landseer-enga/book-a-demo) or email us at [founders@revyl.ai](mailto:founders@revyl.ai) if you struggle with testing and want to start pushing to prod on Fridays.\n2. If you know anyone on QA/Observability teams we would love to talk to them!\n\n![uploaded image](/media/?type=post&id=85015&key=user_uploads/1321343/d2cbf495-3460-4f97-98e8-6ca8154ef8b8)\n\n",
        "url": "https://www.ycombinator.com/launches/M7D-revyl-proactive-observability",
        "votes": 122,
        "created_at": "2024-10-24T13:35:15.397Z"
      }
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  },
  {
    "id": 30045,
    "name": "Archil",
    "slug": "archil",
    "former_names": [
      "Neptune Storage",
      "Regatta Storage"
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0695554aaa43f831a6d970c73486aad8f7a9101f.png",
    "website": "https://archil.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Archil transforms S3 buckets into a 30x faster, unlimited, local disk. Archil enables AI, analytics, and serverless applications to instantly access massive data sets without waiting for data transfer. Researchers use Archil for shareable, local storage of data set and model versions that never runs out of capacity.",
    "one_liner": "The high-performance file system that connects AI to data",
    "team_size": 11,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1727196277,
    "tags": [
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      "Machine Learning",
      "Big Data",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2024",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Growth",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/archil",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/archil.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/archil",
    "x_url": "https://x.com/archildata",
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    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
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    "founders": [
      {
        "user_id": 2542,
        "full_name": "Hunter Leath",
        "title": "Founder/CEO",
        "founder_bio": "I'm building Archil, a cloud file system with unlimited capacity, local-like performance, and instant access to massive data sets in your S3 buckets. I've spent the past 9 years building cloud storage products at AWS and Netflix, and I'm thrilled that teams can finally stop managing disks.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/hleath/",
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    "jobs": [],
    "news": [
      {
        "title": "Archil - All of your data, on every instance, instantly",
        "url": "https://archil.com/post/archil-file-system-to-data-company",
        "date": "Jun 02, 2025"
      },
      {
        "title": "The Quest for a Distributed POSIX-Compatible Filesystem",
        "url": "https://materializedview.io/p/the-quest-for-a-distributed-posix-fs",
        "date": "Dec 05, 2024"
      }
    ],
    "launches": [
      {
        "id": 85093,
        "title": "Archil: Transform S3 into an infinite, local file system",
        "tagline": "AI and analytics teams use Archil to instantly access massive S3 data sets, without waiting for data transfer or worrying about capacity.",
        "body": "_TL;DR:  Archil transforms S3 buckets into infinite, local file systems. It’s 30x faster than accessing S3 directly, and 90% cheaper than manually managing EBS disks. Archil provides instant access to data in S3, and it powers AI applications that can’t wait for massive data sets to download. Archil doesn’t require code changes because it’s just a file system. You can try it today at [archil.com](https://archil.com)._\n\nI’m Hunter, the founder of Archil. I’ve spent the past 9 years building cloud storage products on the frontier of what’s possible – leading both Amazon’s Elastic File System product and Netflix’s cloud storage team. During this time, I worked with thousands of application developers, cloud teams, and software providers to deeply understand how they use storage.\n\nHere's the problem: I found that AI and analytics teams love storing their data in S3 because it’s low-cost and scalable, but their applications can only use this data once it’s in a local file system. As a result, these teams are constantly moving data back and forth from their local instance file systems to S3. For larger data sets, applications can wait for hours to download and decompress data before starting. Unlike S3, local file systems have a limited amount of capacity that you can’t easily change after creation, they don’t support sharing data, and they’re 10x more expensive.\n\nArchil transforms your existing S3 buckets into a new kind of infinite, local file system. With Archil, your applications can instantly use petabyte-scale data sets in S3 without waiting for manual data download. Archil automatically scales with your data, and you only pay for the data you’re actively using – 90% cheaper than using EBS. With Archil’s shared, high-speed cache, all your instances and containers can take advantage of 30x faster, consistent reads and writes. When you’re done editing data, it automatically flows back to S3 within a few minutes, so you can easily share results with teammates. Archil has full POSIX compatibility (including renames, appends, and locking), so it’s already compatible with your applications – no code changes needed. Teams move faster when they use Archil because they don’t have to build custom data movement infrastructure.\n\n[Check out how easy Archil is to set up and use.](https://www.tella.tv/video/cm2num964000e03jng6jl5d9c/view)\n\nWe are really excited about how Archil will help teams simplify their data management. We are currently working with: AI teams who want to start model inference without waiting for downloads to complete, researchers who want to experiment on models and create new versions without running out of disk capacity, analytics teams who want to speed up their batch pipelines, and cloud teams who want lower-cost local storage without worrying about utilization. We’d love to work with you, too. If you have any questions, feel free to email me at [hleath@archil.com](https://mailto:hleath@archil.com). You can try Archil today by signing up at [archil.com](https://archil.com).\n\nLet’s build the future of the cloud together.\n\n![uploaded image](/media/?type=post&id=85093&key=user_uploads/2542/df85a282-87df-4986-b3c7-9cb58acc84ab)",
        "url": "https://www.ycombinator.com/launches/M8T-archil-transform-s3-into-an-infinite-local-file-system",
        "votes": 65,
        "created_at": "2024-10-28T17:59:23.946Z"
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    "id": 30054,
    "name": "CTGT",
    "slug": "ctgt",
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bcc702434416a96306d0394fd2a0226a38a87e98.png",
    "website": "https://www.ctgt.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "CTGT is an applied AI research laboratory fundamentally solving the alignment and reliability bottleneck for enterprise AI.\r\n\r\nFor enterprises, especially highly regulated industries, deploying Generative AI is historically a compromise between capability and catastrophic risk. Standard enterprise approaches, such as RAG, fine-tuning, and prompt engineering, operate at the wrong abstraction layer. They are inherently probabilistic, carry massive engineering overhead, and fail to deliver the mathematical certainty required by the Fortune 500.\r\n\r\nWe focus on the science of representation engineering and have productized mechanistic interpretability. By opening the \"black box\" of neural networks, CTGT has developed a proprietary architecture that intervenes directly at the model's representation layer. We convert complex corporate SOPs, SEC/FINRA regulations, and strict editorial rulebooks into machine-readable \"Policy as Code,\" enforcing deterministic constraints and defensible audit trails without requiring expensive model retraining.\r\n\r\nThe result is a step-function breakthrough in enterprise AI economics and capability. Our fundamental architecture allows organizations to run secure, self-hosted open-source models that mathematically match the reasoning and performance of frontier models. Benchmarks from our enterprise deployments demonstrate a 96.5% prevention of hallucinations, up to a 3.3× accuracy multiplier in complex domain-specific tasks, and an 80-90% reduction in human-in-the-loop manual review.\r\n\r\nBacked by an $8M seed round from Gradient Ventures (Google), General Catalyst, and Y Combinator, CTGT is currently deployed with Fortune 500 companies, including Tier-1 financial institutions and global media conglomerates, giving them the deterministic control necessary to deploy enterprise AI with zero margin for error.",
    "one_liner": "The deterministic layer for frontier intelligence",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1730040950,
    "tags": [
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      "Enterprise",
      "AI"
    ],
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    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Fall 2024",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/ctgt",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/ctgt.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/ctgt/",
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        "body": "We're building the future of trustworthy AI deployment. CTGT helps enterprises eliminate hallucinations and unwanted behavior in LLMs while dramatically accelerating time-to-production.\n\n[Utilizing CTGT's Platform for Model Evaluation and Monitoring 🚀 - Watch Video](https://www.loom.com/share/7e4948fa0ccf4256b2f7036ee8d7baf4)\n\n## **The Problem: “Black Box” of AI**\n\nThe status quo of deep learning involves throwing more compute at models with the expectation that they will achieve true intelligence. The questionable nature of this assumption aside, this will certainly lead to more complex models that we don’t meaningfully understand.\n\nI’ve been obsessed with AI and its potential to change the world since I was a kid. In high school, I took apart old laptops to eke out just a bit more performance to train models, so I’m exceedingly aware how important access to and understanding AI is. (For more background, check out the great piece [TechCrunch did on us](https://techcrunch.com/2024/10/29/ctgt-aims-to-make-ai-models-safer/).)\n\nEnterprise companies are sitting on powerful AI models but can't deploy them due to hallucinations and unpredictable behavior.\n\n* Healthcare providers can't risk medical chatbots giving dangerous advice\n* Financial firms need guarantees their AI won't recommend fraudulent investments\n* Tech companies need AI that consistently aligns with their brand voice\n\n## **Our Solution: Principled AI Development**\n\nCTGT takes a fundamentally different approach. Rather than black-box approximations, we've developed breakthrough technology that understands [how neural networks actually learn](https://openreview.net/forum?id=8WiNDyXgj6). \n\nThis allows us to:\n\n* Automatically detect and eliminate hallucinations\n* Train models 10x faster with 10x less compute\n* Give enterprises foundational control over AI behavior\n\nHow it works:\n\nLet’s imagine we ask an LLM about cooking food. \n\n_I started lunch by simmering water on the stove. I prepared dough for a homemade pizza dish. Using a knife, I sliced fresh tomatoes and cheese. I spread glue on the bread for tomorrow's breakfast. The dough rose in the oven. I brewed tea while stirring the soup with a spoon. A plate was set, and lunch was nearly complete. Every pot and utensil had its place._ \n\nWe can determine, for example, that pizza and bread is part of the “food” concept in the model, along with other related things in the output.\n\n<https://www.youtube.com/watch?v=WSDro9tEz_w> \n\nAnd we can determine that “glue” shouldn’t be part of this concept.\n\n<https://www.youtube.com/watch?v=skW5Z5CPr9o> \n\nOur technology ensures “glue” doesn’t appear in queries regarding food, reducing hallucinations by 80-90%.\n\n<https://www.youtube.com/watch?v=sH8x9boOtF4> \n\n## **Real Results with Fortune 10 Companies**\n\nWe're already helping major enterprises deploy AI with confidence:\n\n* Removed bias from facial recognition systems in one step\n* Eliminated investment advice hallucinations for portfolio managers\n* Enabled fraud detection LLMs with precise error identification\n\n## **The Team**\n\nFounded by UCSD researchers pushing the boundaries of AI:\n\n* Cyril Gorlla (CEO): Recipient of the Endowed Chair's Fellowship at UCSD. 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        "body": "**TL;DR:** [**Ryvn**](https://ryvn.ai) **_empowers SaaS startups to seamlessly deploy, monitor, and manage their applications within customer clouds_** _and on-premises environments. We streamline on-prem delivery with integrated logging, monitoring, and simplified updates and rollbacks across all customer installations._ [_Watch our demo_](https://youtu.be/1wTY1a0KKPU)\n\n   \n&nbsp;\n# **🔥 The Team**\n\n[Albert](https://www.linkedin.com/in/simplyalam/) and [Shardool](https://www.linkedin.com/in/sp97/) collectively bring over a decade of expertise in infrastructure and DevOps.\n\nAt Palantir, Shardool led engineering teams building deployment infrastructure for managing **hundreds of environments**, including cloud, on-premises, and air-gapped systems—facilitating over 10,000 deploys per day.\n\nAlbert led engineering teams at Palantir, developing disaster prevention products that secured $xxM in contracts. At Capital One, he architected modular deployment pipelines and resource management tools.\n\n   \n&nbsp;\n# **❌ The Problem**\n\n**Building infra is not building product**\\\nSetting up infrastructure for each customer—learning tools like Terraform and Kubernetes, creating configurations, and making Docker images—is time-consuming. Instead of building new features, your team is tied up with these tasks just to get deployments working.\n\n**Customer DevOps slows you down**\\\nIf you're sending code, docker images, or Helm charts to customers, you're relying on their DevOps team to prioritize your updates and correctly follow installation instructions. This can lead to **delays of 4-6 weeks** before updates are applied, hurting your delivery cycle.\n\n**Support sucks without monitoring and logging**\\\nHearing about outages or bugs over email or Slack leaves you scrambling to react, putting customer trust at risk. Secure access to logs and metrics is critical to providing reliable service.\n\n   \n&nbsp;\n# **🔮 Our Solution**\n\n**🚢 Easily Install and Ship Updates**\\\nRyvn handles on-prem and VPC deployments automatically from your GitHub code, like Vercel, allowing you to push updates **multiple times a day** with little to no customer involvement.\n\n![uploaded image](/media/?type=post&id=85379&key=user_uploads/1690248/a32c6ee9-98c9-4289-b62f-83ec48cda238)\n \\\n**🎮 Controlled Updates & Instant Rollbacks**\\\nRoll out updates with ease across release stages, including **instant rollbacks** to previous stable versions, ensuring uptime and customer confidence.\n\n![uploaded image](/media/?type=post&id=85379&key=user_uploads/1690248/a6862582-b463-415b-9239-39ff5d161cf0)\n \\\n**👁️ Integrated Logging & Monitoring**\\\nSecure, **centralized access to logs** and health metrics across customer environments. Logs are streamed to your cloud or accessible on-demand from your customer’s cloud.\n\n![uploaded image](/media/?type=post&id=85379&key=user_uploads/1690248/1bffb4e3-5a28-44d0-a75c-8e92ebf639d6)\n\n   \n&nbsp;\n# **💭 Why Deploy to Customer Clouds?**\n\n**💸 Charge more, bigger markets**\n\n* Charge **3–10x** more for customer cloud / on-prem deployments\n* Sell to regulated industries — health, finance, enterprise, etc.\n\n**🥷 Skip the security review & SOC 2**\n\n* Land **PoCs** _faster_ by deploying on-prem to bypass lengthy security reviews\n* You don’t need SOC 2 or HIPAA to deploy on-prem\n\n**❤️ Make your bank and lawyers happy**\n\n* Customers pay directly for infrastructure cost\n* Not hosting customer data reduces your liability\n\n   \n&nbsp;\n# **📣 Our Ask**\n\n**Follow** - we’re on [LinkedIn](https://www.linkedin.com/company/105480942/)\\\n**Share** - If you know any startups looking to deploy to customer clouds, we’d appreciate a warm intro using our email [founders@ryvn.ai](mailto:founders@ryvn.ai)\\\n**Demo** - [book here](https://cal.com/albertlam/ryvn-demo)\n\n![uploaded image](/media/?type=post&id=85379&key=user_uploads/1690248/7b4e5b4d-30d0-4b34-8762-1f8664c960b6)\n\n",
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        "body": "Hi, I’m Josh, founder of Synth.\n\n**TL;DR**\n\nWe log what your agent is doing, identify where it’s coming up short, and fine-tune the AI models your agent uses to prevent those failures.\n\nYou add a few decorators to your code, run your agent, and get back fine-tuned models (via OpenAI or the provider of your choice) that perform better. Our product, when applied to SWE-Agent, an academic baseline, delivers a **33%** relative improvement on the [SWE-Bench agent benchmark](https://www.swebench.com) with just 100 training data points. [ ](https://www.youtube.com/watch?v=DwC8iBLpWdU&feature=youtu.be)\n\n**Why is this important?**\\\nTeams want to deploy software that automates economically meaningful tasks over multiple steps. Iterating on those systems is hard: an agent taking 100 steps to complete a task will often create a newspaper’s worth of text data over just one task run, and reviewing a representative evaluation dataset can be daunting. Moreover, getting language models to demonstrate context-specific agentic behavior - such as following strong plans and making the best use of their environment - can be challenging, just with manual prompt tweaking. \n\nWorking as an agent researcher at a startup automating accounting tasks, I struggled to parse through the reams of logs my systems would generate every time I wanted to test a potential improvement. Manually tweaking dozens of prompts and reasoning about how changes would propagate through the system was also difficult— and, for many problems, ineffective. As agents are deployed in more challenging applications and to more customers, these problems will grow. Agent developers need a tool that scales with their agent’s complexity and scope in order to build state-of-the-art systems.\n\n**Our solution**\\\nSynth works in 3 steps\n\n1. Communicate: We summarize your agent’s logs to help you understand how it’s performing at a glance.\n2. Flag: We identify common failure modes your agent is demonstrating.\n3. Address: We fine-tune base models your agent uses to address AI-related issues and suggest changes to your code when gaps in agent scaffolding are the root cause.\n\nWe worked with a startup that was working on automating code generation to increase their agent’s success rate at an editing task from ~**85% to 100%** on a representative evaluation set.\n\n**Why Now?**\\\nDevelopers and researchers have been building AI agents with language models for years, but only somewhat recently have base models provided a strong enough foundation to enable the most ambitious applications.\n\nMoreover, some of the most powerful and consistent approaches for tuning agents to address domain-specific challenges— the ones powering Synth— have only been published in the last year or so. The market is ready for our solution, as is the research literature.\n\n**Team**\\\nI recently worked at [Basis](https://www.getbasis.ai), a startup automating accounting tasks with AI agents, as a researcher developing and maintaining pipelines and agents used to serve top accounting firms. While there, I had the opportunity to publish [academic research](https://arxiv.org/abs/2406.11695) on the topic of agent optimization at EMNLP, a respected AI conference.\n\nBefore Basis, I built a startup doing ML for cyber with some friends that got acqui-hired right out of the gate for a modest amount. Before then, I studied math and machine learning at Yale.\n\n**Ask**\\\nSend folks building agents our way at [founders@usesynth.ai.](https://mailto:founders@synth.ai) We’d love to talk to anyone using AI agents to solve hard, multi-step tasks - the more ambitious and complex the approach, the better.\n\n![uploaded image](/media/?type=post&id=85819&key=user_uploads/89898/5d64c8db-48e1-423a-a020-f5ace9ea7b82)\n\n",
        "url": "https://www.ycombinator.com/launches/MKB-synth-automated-training-for-vertical-ai-agents",
        "votes": 82,
        "created_at": "2024-11-23T14:56:40.105Z"
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    "name": "Flowtel",
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    "long_description": "Flowtel is building the next generation operating system for hospitality, powered by modern AI agents that can handle everything from booking to room service. ",
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    "industry": "B2B",
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        "full_name": "Eylon Miz",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder & CEO @ Flowtel - AI Voice agents for hotels",
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        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$180K - $250K",
        "equity_range": "0.70% - 3.00%",
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      {
        "id": 87581,
        "title": "Casixty - Your Reddit Marketing Agent for Technical Audiences",
        "tagline": "Turn Reddit discussions into marketing wins",
        "body": "![uploaded image](/media/?type=post&id=87581&key=user_uploads/824876/a9f19804-29f1-4589-8bd7-2d5cb592c8b1)\n\n**TL;DR** — Technical companies struggle to authentically connect with their audiences on Reddit, missing valuable opportunities for engagement. We built an AI-powered platform that helps you identify and engage in relevant technical discussions, generate authentic responses, and measure impact - all while maintaining your brand voice.\n\nHey! We're Eylon and Yoav, both engineers who discovered firsthand how challenging it is for technical companies to effectively market on Reddit. After successfully growing multiple open-source projects to thousands of stars (including [reactagent.io](http://reactagent.io) and [pezzo.ai](http://pezzo.ai)), we built Casixty to help other technical companies unlock Reddit's potential.\n\n**The Problem** \\\nTechnical companies face four major challenges with Reddit marketing:\n\n1. **Finding the Right Discussions** is like searching for needles in a haystack - manually scanning for relevant conversations is incredibly time-intensive\n2. **Creating Authentic Responses** requires deep technical knowledge and platform understanding - especially challenging for marketing teams\n3. **Measuring Impact** is nearly impossible without proper analytics tied to specific engagements\n4. **Maintaining Consistency** demands significant resources, leading to sporadic engagement that hurts brand presence\n\n**Our Solution** \\\n<https://www.youtube.com/watch?v=zzby3_YHbCs>\\\nCasixty is a digital marketing platform that:\n\n* Indexes your company's knowledge (blogs, docs, etc.) to identify perfect engagement opportunities\n* Generates draft responses that match your brand voice and technical expertise\n* Delivers notifications via Slack for quick action\n* Takes just 3 minutes to onboard and minutes per day to maintain\n* Creates evergreen content (Reddit discussions index well in Google!)\n\n**Customer Success Story** \\\nWe recently worked with a cybersecurity company targeting infosec and MSP audiences. In just 10 weeks, we helped them:\n\n* Generate over 5,000 karma in technical discussions\n* Create thought leadership content that resonated with their audience posted by C levels (CEO, CTO, CMO, CPO, CISO) and SMM team (10 accounts total)\n* Build brand awareness that amplified their Reddit ad campaigns\n\n**Why Us?**\n\n* We're engineers building for engineers - we understand the importance of quality and authenticity\n* We're expanding beyond Reddit soon - Twitter, LinkedIn, Bluesky and more platforms coming in the next few months\n* Our platform delivers measurable results while respecting Reddit's community standards\n\n**YC Special Offer** For YC companies signing up before W25 ends:\n\n* Know someone who needs this? Share code YCFRIENDSREF for 20% off 6 months\n* Unlimited Reddit research, strategy & consultation with founders via Slack Connect for 3 months (Pro plan)\n\nStart free on <https://casixty.com/> or talk to us at [success@casixty.com](mailto:founders@casixty.com)\n\nEylon & Yoav",
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        "votes": 79,
        "created_at": "2025-02-11T17:25:14.438Z"
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "We are on a mission to build the world’s most accurate, fastest, and cheapest task-specific models for every production AI system.\r\n\r\nMost AI products, whether copilots, agents, or search systems, depend on frontier LLMs to handle every step of their pipeline. \r\n\r\nYet, the vast majority of these steps are narrow, repeatable workloads: reranking, embedding, classification, routing, query rewriting, context compression, where frontier intelligence is overkill. \r\n\r\nRunning them on general-purpose models is slow and expensive, capping what production AI can achieve. \r\n\r\nThat is why we're building ZeroEntropy: to train small, task-specific models that replace frontier LLMs on these workloads, and empower developers to ship AI products that are more accurate, faster, and cheaper.",
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    "team_size": 6,
    "industry": "B2B",
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    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/zeroentropy-inc/",
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      "name": "Harj Taggar",
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        "full_name": "Ghita Houir Alami",
        "title": "Founder/CEO",
        "founder_bio": "cofounder & CEO @ ZeroEntropy (YC W25)\n-\nmoroccan founder with 2 masters in applied maths from École Polytechnique and UC Berkeley\n",
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        "linkedin_url": "https://linkedin.com/in/ghita-houir-alami",
        "x_url": "https://www.x.com/ghita__ha",
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        "user_id": 1802968,
        "full_name": "Nicholas Pipitone",
        "title": "Founder/CTO",
        "founder_bio": "Cofounder & CTO @ ZeroEntropy (YC W25) -\n\nMy background is in theoretical mathematics and computer science.\n\nDropped out of CMU to pursue startups. Worked on low-level C/C++/Assembly/GPU Code. CTO and/or main dev for 5 different startups. Created stat-arb algorithms and audited blockchains for bug bounties and hedge fund due diligence.\n\nBuilt the AI @ myko.ai, manifestapp.xyz, magibook.co",
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    "news": [
      {
        "title": "Moroccan founder raises $4.2M for her YC-backed startup building the next layer of AI search  | TechCrunch",
        "url": "https://techcrunch.com/2025/07/09/moroccan-founder-raises-4-2m-for-her-yc-backed-startup-building-the-next-layer-of-ai-search/",
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      {
        "id": 86779,
        "title": "ZeroEntropy 🔎 - Advanced AI Search Over Complex Documents",
        "tagline": "ZeroEntropy is the first search engine designed from the ground up to answer natural language queries by deeply understanding the language in long, complex documents.",
        "body": "Hey there YC!\n\nWe’re [**Ghita**](https://www.linkedin.com/in/ghita-houir-alami/) and [**Nicholas**](https://www.linkedin.com/in/nicholas-pipitone-5736a2107/), the founders of ZeroEntropy.\n\n![uploaded image](/media/?type=post&id=86779&key=user_uploads/1276815/916d931e-36dd-4c92-828a-e98d35fadf6a)\n\n**We are on a mission to build the world’s most accurate search engine over complex and unstructured documents, behind a simple API.**\n\n👉 _If you are building RAG, a search bar, or search tools for your AI Agents, please reach out to us here_ [_founders@zeroentropy.dev_](https://mailto:founders@zeroentropy.dev)\n\n<https://youtu.be/VxwnZDlEz2U>\n\n# **Why?**\n\nMost AI products - whether Q&A bots or AI agents - depend on retrieval systems to provide relevant context from knowledge bases.\n\nYet, the vast majority of these systems rely on basic semantic or hybrid search methods, which still frequently fail.\n\nThese mistakes lead to inaccurate responses and hallucinations by LLMs, frustrating developers and end users alike.\n\nThat’s why we’re building ZeroEntropy: to add intelligence to retrieval, and empower developers to build reliable and accurate AI products!\n\n# **A few common failure modes**\n\nThe most common examples are:\n\n* **Negated Queries**, like: _“Which electric vehicle articles **do not** include any reference to Elon Musk?”._\n* **Multi-Hop Queries**, like:_“Where was the suspect who was identified in the report born?”_\n* **Fuzzy Filtering Queries**, like: _“What diagnostic methods are suggested for early-stage cancer, in papers **that mention a sample size of over 2000**?”_\n\nThese types of queries are common in industries like legal, healthcare and manufacturing, where documents are very complex. Semantic similarity and keyword-based approach fail on all these examples. You can read more about why [**here**](https://medium.com/@zeroentropy/agi-requires-better-retrieval-not-just-better-llms-72f5288c37ef).\n\n# **Our Solution**\n\nSearch AI agents can solve these complex queries. Not only can they understand the user’s intent, but also route each query to the most suitable retrieval system, ensuring precise and relevant answers. Moreover, they adapt and personalize their performance over time, continuously improving with use.\n\nAll of this capability is accessible through a simple API that you can try out [here](https://dashboard.zeroentropy.dev).\n\n# **We need you!**\n\n👉 If you are building RAG, a search bar, or search tools for your AI Agents, please reach out to us here: [founders@zeroentropy.dev](mailto:founders@zeroentropy.dev)\n\n👉 Give our [product](https://dashboard.zeroentropy.dev) a try, and join our [Slack](https://join.slack.com/t/zeroentropy-community/shared_invite/zt-2venuk3yt-Of\\~\\~NtGIQn_hsD_QJe4fkQ) to follow our journey!\n\n# **About us**\n\nWe both have a background in math and AI: Ghita has two masters in Applied Maths from Ecole Polytechnique (Paris) and from UC Berkeley, and Nicholas grew up doing math and coding competitions at CMU before becoming the CTO of several startups.\n\nWe’ve both experienced the pain of building great retrieval systems from working as AI engineers and researchers in several industries from healthcare, to finance, to B2B SaaS, and consumer AI.\n\n# **PS/Fun Fact**\n\nIn case you’re curious about our name, in information retrieval, entropy refers to the expected variability of information contained in a variable or message. ZeroEntropy refers to knowing exactly what the message contains and is associated to a high predictability, low chaos state. In physics, absolute zero entropy is impossible to achieve. But we’ll try!",
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    "name": "Mundo AI",
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    "long_description": "Mundo develops data, evaluations, and applied research for frontier labs and AI companies to advance perceptual intelligence across audio, video, and emerging modalities.",
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        "full_name": "Jason Liao",
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        "founder_bio": "Hi! I'm Jason. Last year I worked on ML research abroad, where I discovered how impossibly challenging it is to build good multi-lingual AI models. Before that, I was the youngest quant researcher at a $60B hedge fund in Canada. jason@mundoai.world",
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        "full_name": "Kenneth Wu",
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        "title": "Mundo AI - High Quality Multilingual Training Data for AI Models",
        "tagline": "We're building the world's largest and highest-quality multilingual data library.",
        "body": "# **TL;DR**\n\nAI models are great at English, but struggle with almost every other language. So, we are building the world’s largest and highest quality multilingual data library to help AI labs build better non-English models.\n\n# **The Story**\n\nWhen Jason was working on AI research abroad, he found that it was incredibly difficult to find training data in non-English languages. Because of this, his peers were all working on English models rather than ones in their native language.\n\n# **The Problem**\n\nAfter speaking with researchers and entrepreneurs around the world, it became clear to us that AI usability was dramatically behind in non-English languages - even for major languages like Hindi and Arabic. This is because of the severe shortage of high quality training data in non-English languages. That leaves the 75% of the world that does not speak English out of the AI revolution.\n\nData has been a major bottleneck for researchers and AI labs building multilingual AI models, and the demand for better and larger datasets is only increasing.\n\nCurrent workarounds such as synthetic data and machine translation simply don’t achieve the desired results, and open-source efforts fail to produce datasets in the quantity and quality required.\n\n# **How are we solving this**\n\nWe work directly with native speakers to build and create completely novel and high quality datasets. We do this by setting up end-to-end operations in the country where native speakers of a language reside, and by using our proprietary software platform to streamline data collection, generation, annotation, and quality assurance.\n\n# **Demo Video**\n\n[**https://www.youtube.com/watch?v=zZiilPrhDJs**](https://www.youtube.com/watch?v=zZiilPrhDJs)\n\n# **The Team**\n\n[**Jason Liao**](https://www.linkedin.com/in/jasonliao02/) helped build a record-breaking fraud detection AI model at Tsinghua University. Before that, he led a quant research team at a $60B quant hedge fund.\n\n[**Kenneth Wu**](https://www.linkedin.com/in/wukenneth/) was a quant at Canada’s largest quant fund. Previous roles in SWE at Amazon Web Services and Analyst at the Ontario Teachers’ Pension Plan.\n\n[**Naijide Anwaer**](https://www.linkedin.com/in/naijide-anwaer) was the youngest Platform PM at Binance US. He speaks 4 languages.\n\n[**Garreth Lee**](https://www.linkedin.com/in/garrethlee) was an ML engineer and the first Indonesian at Hugging Face, where he helped build the world’s best open pre-training dataset. Previously a member of technical staff at Cohere.\n\nAlso shoutout to our founding PM @Ahnaf Muqset Haque\n\n# **Our Ask**\n\nDo you know any researchers or data partnership managers at any AI labs? We’d love to get in touch! We’re trying to learn as much as we can about the data bottlenecks that are preventing researchers from making progress.\n\nYou can reach us at [**contact@mundoai.world**](mailto:contact@mundoai.world)\n\n![uploaded image](/media/?type=post&id=88044&key=user_uploads/89898/1f30211a-9e17-45c0-bab2-e5477ece04a1)\n\n",
        "url": "https://www.ycombinator.com/launches/Mu4-mundo-ai-high-quality-multilingual-training-data-for-ai-models",
        "votes": 67,
        "created_at": "2025-02-26T01:50:57.921Z"
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    "id": 30284,
    "name": "Exla",
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    "website": "https://exla.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Exla is your 24/7 AI SRE agent. It reads your production logs, traces, and deploys, and holds full context on your codebase. It establishes a live baseline of normal behavior, so when something silently regresses, it catches the deviation immediately, pins it to the exact commit that caused it, and opens a fix PR. No paging a human at 3am, no digging through dashboards to find what changed.\r\n\r\nhttps://cal.com/exla-ai/schedule",
    "one_liner": "Building agents that reads logs and fixes issues on your behalf.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1740012412,
    "tags": [
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    "nonprofit": false,
    "batch": "Winter 2025",
    "status": "Active",
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    "regions": [
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        "full_name": "Viraat Das",
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        "founder_bio": "CEO @ Exla. Previously machine learning engineer @ Amazon.",
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        "linkedin_url": "https://linkedin.com/in/viraatdas",
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        "full_name": "Pranav Nair",
        "title": "Founder/CTO",
        "founder_bio": "CTO at Exla. Previously an OS engineer at Apple leading sleep/hibernation for all Apple devices.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/pranavnair311/",
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    "launches": [
      {
        "id": 88090,
        "title": "Exla – Run datacenter models on edge devices",
        "tagline": "Optimize models to run on edge devices (e.g. Jetsons) with 3-20x faster inference and 80% less memory requirements",
        "body": "Hey YC! We’re Viraat and Pranav – cofounders of Exla.\n\n![uploaded image](/media/?type=post&id=88090&key=user_uploads/2212412/6f2a4f23-207a-4ba8-84dd-1be1039c46f7)\n\n# **TL;DR**\n\nThe Exla SDK optimizes models for edge devices (e.g. NVIDIA Jetsons), **cutting memory usage by up to 80%, with 3-20x faster inference**. We’re focusing on optimizing and deploying **LLMs, VLMs, VLAs, and other CV models** on the edge.\\\n[Here’s Viraat showcasing our SDK](https://www.loom.com/share/d8fbb4faef87493c9806610fff6ff86c?sid=10b54831-159b-4157-95b7-8b9f7a5c8d8e)\n\n# **‼️ The Problem**\n\n**Frontier models are unlocking new applications on constrained edge devices** – Vision-Language Models in manufacturing defect detection, Vision-Language-Action Models to control robots via natural language, and LLMs to power in-car assistants are a few examples.\n\n**But these** **models are now shy of a _trillion_ parameters**, and with the emergence of inference-time scaling, **they are more computationally demanding than ever**. This limits their adoption to edge devices with beefy GPUs and sufficiently large VRAM, and even then, a Jetson Orin Nano Super is completely saturated attempting to run a 13B model, leaving little room for other tasks.\n\n# **🏎️ The Solution: The Exla SDK**\n\n**We’re building mixed-precision low-bit quantization software** to dramatically cut the compute footprint of these models, leading to **80% less memory usage, 3-20x faster inference**, and reduced energy consumption.\n\n**Today we’re starting with the Exla SDK** which applies our optimizations to a catalog of transformer-based and CV models, with growing support for your custom models. We’re primarily targeting deployment on NVIDIA Jetsons, followed by CPU-based platforms like Raspberry Pis and other embedded platforms.\n\n![uploaded image](/media/?type=post&id=88090&key=user_uploads/2212412/68c46641-ffb9-4dc9-a0a0-8facdba4e132)\n\nOur roadmap includes building custom silicon that takes advantage of the quirks of low bit compute – which we expect to bring in another order of magnitude in compute savings. _We’re bringing frontier models everywhere._\n\n# **🥷 The Team**\n\nWe met each other on the first day of college under questionable circumstances and have built several projects together since. Exla is the latest in that series!\n\n[Viraat](https://www.linkedin.com/in/viraatdas/) graduated in 2.5 years and joined Amazon as machine learning engineer, where he worked on building personalized search and infrastructure to optimize models. In a previous life, he used to marathon around the world. Now he marathons at home, coding.\n\n[Pranav](https://www.linkedin.com/in/pranavnair311/) previously worked at Apple as an OS engineer, hacking the iOS/macOS kernel to improve the sleep/wake experience for over a billion devices. In his non-existent free time, he tends to his 5 year old baby – an operating system he’s built from scratch.\n\n# **🙏 Our Ask!**\n\nPlease reach out to [**founders@exla.ai**](mailto:founders@exla.ai) if you’re facing issues optimizing your models on Jetsons, other edge devices, or on-prem deployments! We’re happy to onboard you to our private beta.\n\nWe’re particularly looking to solve model optimization at companies working on robotics, manufacturing & industrial automation, and camera-based systems – your help would make a world of difference <3",
        "url": "https://www.ycombinator.com/launches/Muo-exla-run-datacenter-models-on-edge-devices",
        "votes": 27,
        "created_at": "2025-02-27T01:55:49.203Z"
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    "long_description": "AfterQuery is an applied research lab curating data solutions for frontier foundation model development. Serving every frontier AI lab.",
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    "batch": "Winter 2025",
    "status": "Active",
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    "regions": [
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      "America / Canada"
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    "stage": "Growth",
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    "url": "https://www.ycombinator.com/companies/afterquery",
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/afterquery",
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    "primary_partner": {
      "name": "Gustaf Alstromer",
      "url": "https://www.ycombinator.com/people/gustaf-alstromer"
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    "founders": [
      {
        "user_id": 2382342,
        "full_name": "Spencer Mateega",
        "title": "Founder/CEO",
        "founder_bio": "Building AfterQuery. Previously interned in technology private equity at Silver Lake, technology investment banking at Morgan Stanley, and software engineering at Meta and Google.\n\nB.S. in Finance and Statistics at Wharton and a M.S. Computer Science at Penn.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/spencermateega",
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      {
        "user_id": 503252,
        "full_name": "Carlos Georgescu",
        "title": "Founder/CTO",
        "founder_bio": "Building AfterQuery. Previously interned at Citadel Securities (2x), Meta & Google as a software engineer. Past acquired ed-tech founder. \n\nStudied for a B.S. in Computer Science from the University of British Columbia. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/carlossgg/",
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        "title": "Software Engineer Intern",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/6a7szXP-software-engineer-intern",
        "location": "San Francisco, CA, US / Remote (US)",
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        "equity_range": "",
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        "id": 82771,
        "title": "Software Engineer - Applied AI / Platform",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/7tsT0ks-software-engineer-applied-ai-platform",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$140K - $200K",
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        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": [
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        "id": 87019,
        "title": "Software Engineer - Hiring Infra (LatAM)",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/PohDLWh-software-engineer-hiring-infra-latam",
        "location": "San Francisco, CA, US / Remote (BR; CL; MX; AR; CO; PE)",
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        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$7K - $10K / monthly",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
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        "id": 91403,
        "title": "Strategic Projects Lead",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/iTt13cJ-strategic-projects-lead",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$200K - $400K",
        "equity_range": "",
        "experience": "1+ years",
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        "id": 91406,
        "title": "Technical Strategic Projects Intern",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/8NagVRQ-technical-strategic-projects-intern",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$90K",
        "equity_range": "",
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        "visa": "US citizen/visa only",
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        "id": 91408,
        "title": "Strategic Projects Associate",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/qwNKclV-strategic-projects-associate",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$200K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
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      {
        "id": 91409,
        "title": "Strategic Projects Intern",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/Ep6eNax-strategic-projects-intern",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
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        "id": 91415,
        "title": "Technical Strategic Projects Associate",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/ziJE7ZY-technical-strategic-projects-associate",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
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        "id": 91417,
        "title": "Marketing Lead",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/Ww01atn-marketing-lead",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Marketing",
        "role_type": null,
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        "id": 91418,
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        "url": "https://www.ycombinator.com/companies/afterquery/jobs/t0uj8SF-technical-strategic-projects-lead",
        "location": "San Francisco",
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        "id": 91613,
        "title": "Senior Software Engineer, Infrastructure & Platform",
        "url": "https://www.ycombinator.com/companies/afterquery/jobs/TU2SAvz-senior-software-engineer-infrastructure-platform",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
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        "title": "AfterQuery Becomes YC’s Fastest Unicorn At $3.2 Billion",
        "url": "https://www.forbes.com/sites/annatong/2026/09/01/afterquery-becomes-ycs-fastest-unicorn-at-32-billion/",
        "date": "Sep 01, 2026"
      },
      {
        "title": "This 23 Year-Old’s New AI Data Company Has Already Hit A $100 Million Run Rate",
        "url": "https://www.forbes.com/sites/annatong/2026/04/09/this-23-year-olds-new-ai-data-company-has-already-hit-a-100-million-run-rate/",
        "date": "Apr 09, 2026"
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      {
        "id": 87549,
        "title": "AfterQuery - High-quality AI starts with high-quality human data",
        "tagline": "Powering AI models with expert-level human data",
        "body": "## **TL;DR**\n\nAI models need high-quality training data to effectively automate complex professional tasks - we provide that data. If you are building an AI model or need fine-tuning data for your agent, we’d love to help. \n\n\\------------\n\n## **Why We’re Building AfterQuery**\n\nThere is a dearth of high-quality training data.\n\nCurrent AI models are trained to be smart but are not trained on the work of actual professionals in many fields. \n\nFor example, current models are largely unusable to professionals working in finance (private equity, hedge funds, investment banks, etc). See our [paper](https://arxiv.org/pdf/2501.18062), where LLMs fail 60% of realistic daily tasks – but with some fine-tuning on high-quality training data, significantly improve.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeJndLBLBOQJVpf7yPJNA2WFddpffkxqdE1rFqmEfxo3FRmcSCVWqHnsOR793XuN1iCUm-ibeWfFj-FV_3FaswWhXetsEhembFLjln5Lwc9LA0tJV6uNq7MPWyjdDq7FTZSRdmekQ?key=_udDbu5NRw5fwLzMYyqM9i52)\n\n_Chart showing LLM performance against FinanceQA_\n\nAnd the lack of high-quality training data is everywhere:\n\n* Domain-specific AI (legal, consulting, healthcare, etc.)\n* Multimodal data (audio, multilingual, etc.)\n* Foundational models looking to push the boundary on reasoning and grading\n\n## **Solution**\n\nWe are building a platform to onboard experts to work on custom datasets. Think working professionals at top firms and the smartest students in various subjects (PhDs). Then, whenever you have a request for a certain type of data, we will construct and lead a team to produce:\n\n* Post-training / fine-tuning data\n* Human-Led Reinforcement Learning (RLHF) data\n* Custom benchmarks\n\nYou will know the people making your dataset and their qualifications while we coordinate with you to ensure quality standards. We save you time and energy, so you can focus on the model and not the data.\n\n## **Example Datasets We Provide**\n\n_Foundational Model Advancing Coding Capabilities:_\n\n* New data structures and algorithms problems with test cases and step-by-step answer reasoning \n* Computer architecture and system design reasoning\n* Performance optimization code with benchmarks and reasoning\n\n_Enterprise SaaS Company Building Internal AI Developer Tool:_\n\n* Codebase-wide code snippets with corresponding refactoring suggestions and explanations \n* System and runtime errors paired with step-by-step debugging solutions \n* Database query optimization solutions and reasoning steps\n\n_Startup Building an AI Agent for Law:_\n\n* Answered legal questions with expert reasoning\n* Computer use data for common tasks on legal project management software\n* Contract redlines with expert corrections\n\n**If AI is one day to replace jobs entirely, it can’t just be good, it needs to be near perfect. This transformation will require an immense amount of high-quality training data.**\n\n## **Our Ask**\n\n* Are you **working on a foundational model** advancing multimodal applications, nuanced reasoning, or domain-specific expertise?\n* Are you **implementing AI in a specialized domain** where current models lack expertise (e.g., medicine, legal, finance, software, government, healthcare)?\n\nWe would love to meet you! \n\n## **Our Team**\n\nCarlos and Spencer first met in high school at a summer program at Google and then interned together at Meta. Danny and Spencer met in high school, where they built a startup. They then both got into Wharton, sold that startup, and became roommates.\n\n[Carlos](https://www.linkedin.com/in/carlossgg/) has built multiple software businesses on his own, exited an ed-tech startup, and has worked as a software engineer at Citadel Securities. He’s won the largest possible academic scholarship in all of Canada, competed in state and national sailing competitions, and has 8x his bankroll in poker.\n\n[Spencer](https://www.linkedin.com/in/spencermateega) wrote an award-winning research paper and interned at a private equity firm in high school, before deciding to intern at Meta (SWE), Google (SWE), Morgan Stanley (IB), and Silver Lake (PE) in the next 3 years. He was the sole summer analyst at Silver Lake globally and has programmed numerous apps in his spare time (and sold some of them). He trains for half marathons and lifts in his free time between completing a master’s in computer science concurrently with his Wharton undergraduate degree.\n\n[Danny](https://www.linkedin.com/in/danny1898/) competed and won international competitions in public speaking during high school while pursuing his interest in AI, winning an international Microsoft AI competition and conducting research at a machine learning neuroscience lab. He then learned how people build real estate, from grocery stores to billion-dollar luxury hotels, eventually working on the largest real estate IPO in history and the largest IPO in 2024. He also really likes movies and attended the world's largest film festival, catching premieres of films like Anora and The Substance.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfkN7pvcMyTHjf5fZe7qJrF9xiyAvI0yAJ0PHNDdxWcOcU0gw5cDFUzOq20x68YJeb40Pk8dPXsNGsPVSnhYNZu3AHEAoZHcSTQ3nYyLYvMNcthHoAkoERsmzARwkGFS-fRnslLNw?key=_udDbu5NRw5fwLzMYyqM9i52)\n\n",
        "url": "https://www.ycombinator.com/launches/Mm5-afterquery-high-quality-ai-starts-with-high-quality-human-data",
        "votes": 114,
        "created_at": "2025-02-10T21:58:10.460Z"
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    "name": "TensorPool",
    "slug": "tensorpool",
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    "website": "https://tensorpool.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Our CLI makes ML model training effortless - just describe your job, and we handle GPU orchestration and execution at half the cost of major cloud providers.",
    "one_liner": "Vercel For GPUs",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "full_name": "Tycho Svoboda",
        "title": "Founder",
        "founder_bio": "Co-founder & CEO of Tensorpool. Ex-Blackstone, Stanford",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/tycho-svoboda/",
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        "full_name": "Joshua Martinez",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder & CTO of TensorPool (W25)\nPreviously NVIDIA, Apple, Chan Zuckerberg Initiative\nStanford BS & MS in Computer Science + Statistics",
        "is_active": true,
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        "full_name": "Hlumelo Notshe",
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        "founder_bio": "Co-Founder & CPO of TensorPool\nEx-DeepMind & NextDoor\nStanford BS & MS in Computer Science + Mathematics",
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      {
        "id": 87792,
        "title": "TensorPool: the easiest way to use GPUs 🌊",
        "tagline": "With TensorPool, just describe your job and we handle all the GPU orchestration and execution at half the cost",
        "body": "Hi Everyone! We’re [Joshua](https://bookface.ycombinator.com/user/969804), [Hlumelo](https://bookface.ycombinator.com/user/1709047), and [Tycho](https://bookface.ycombinator.com/user/2359695) - cofounders of TensorPool!👋\n\n**TLDR;** Our CLI makes ML model training effortless - just describe your job, and we handle GPU orchestration and execution at half the cost of major cloud providers.\n\n[Get setup in 3 minutes](https://github.com/tensorpool/tensorpool)\n\nHere’s a quick demo of how easy it is to train ML models with TensorPool!\n\n[**TensorPool Demo -- Fine-Tuning DeepSeek**](https://youtu.be/RqqLIVzP780)\n\n# **Problem**\n\nSince high school, when we trained our first ML models, **accessing remote compute has always been a massive hurdle**. As the years went by and we moved further into academia/industry, a good solution never came. Whether it was AWS EC2’s absurd amount of configuration, or Google Colab’s inability to run jobs without your computer being on, nothing left us satisfied. We talked to our co-workers and friends and they all felt the same. We wanted using GPUs to be as easy as programming locally.\n\n# **Solution**\n\nWe got together and built a way to **access industry-scale compute from your IDE**. No more SSHing. No more massive data migrations. No more ML Ops headache. Just a simple CLI. \\[We also **cut your costs by 50%** which will be explained below :)\\]\n\n# **How It Works**\n\nTensorPool has 2 main advantages:\n\n1. **Ease of Use:** \n   * **Don’t leave your IDE:** TensorPool deploys your code directly to GPUs and ships the results back, just as if you were developing locally. \n   * **Intuitive CLI & Configuration:** With TensorPool you can run your job with a single command, kick off several experiments with different tp.config.toml configurations, and version control training jobs with your code.\n2. **Price Advantage**\n   * **Multi-cloud integration:** We’ve developed technology to do a real time analysis of all available GPU cloud providers, and put your job on the cheapest one.\n   * **Spot node resummation tech:** We’ve created a way to give users the price advantage of spot nodes with the reliability of on-demand instances. This leads to \\~50% savings!\n\n# **Our ask**\n\nWe’re currently giving away $5/week of **free compute** to early users, and would love for you all to **try us out**! Whether your start-up needs an ML infra upgrade or you love messing around with models in your free time – reach out to us we’d love to chat ([team@tensorpool.dev](mailto:team@tensorpool.dev))! You can find all the information to get started [on our GitHub](http://github.com/tensorpool/tensorpool) (**drop us a star while you’re there** 😊) or [our website](https://tensorpool.dev).\n\n**We would also love intros to enterprises** that you believe could benefit from cheaper / easier access to GPUs.\n\n# **Try TensorPool today!**\n\nBelow is a quick start:\n\nAfter completing [the prerequisites](https://github.com/tensorpool/tensorpool?tab=readme-ov-file#prerequisites), we create a “tp.config.toml” ([explained here](https://github.com/tensorpool/tensorpool?tab=readme-ov-file#configuration)), either manually with: \n\n```\ntp config new\n```\n\nOr auto-generate one with natural language: \n\n```\ntp config <describe your task>\n```\n\nTo then deploy and run your job – simply run:\n\n```\ntp run\n```\n\nand that’s it! Your model is training on cloud GPUs! \n\n# **About Us**\n\nJoshua, Tycho, and Hlumelo all met their freshman year at Stanford where they got placed on the same dorm floor. Since then, they’ve taken all their classes together and have worked at companies like Apple, Blackstone, DeepMind, Nextdoor, CZI, and PIMCO.",
        "url": "https://www.ycombinator.com/launches/Mq0-tensorpool-the-easiest-way-to-use-gpus",
        "votes": 697,
        "created_at": "2025-02-18T22:18:38.093Z"
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    "name": "Butter",
    "slug": "butter",
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    "website": "https://butter.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Butter is building bVisor, an incredibly lightweight sandbox runtime for AI agents.\r\nIt embeds directly into your local process, providing a safe bash environment without the overhead of VMs or remote infra.",
    "one_liner": "Embedded Sandboxes for Agents",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1737747738,
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2025",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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      "America / Canada"
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    "stage": "Early",
    "app_video_public": false,
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    "question_answers": [
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        "question": "What's the history of your company from getting started until the present day? What were the big inflection points?",
        "answer": "* Dec 11: First line of code* Jan 21: Launched serverless VM cloud platform → learned users need to self-host* Feb 16: Launched Piglet computer driver for self hosting → learned implementing agent loops is high friction* Feb 27: Launched Agent layer (chat, soon API) to get users to successful automations faster"
      },
      {
        "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?",
        "answer": "RPA is brittle, and gatekept by consultants who benefit from the need to constantly rewrite automations. For more AI-forward developers building products on top of legacy apps, they have to resort to insecure hacks, like exposing backdoors on their users’ computers, simply to get an automation pushed down it, after which they still must deal with the brittleness inherent to hardcoded automations.I’m deeply excited about this problem because, despite all the change in the world with AI, one thing is for certain: enterprises will keep using Windows."
      },
      {
        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "As the global labor output increases tenfold with agents, we’ll be the personal computers they use to interface with the business world."
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    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/butter.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/butter-dot-dev",
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        "full_name": "Erik Dunteman",
        "title": "Founder/CEO",
        "founder_bio": "Founder and CEO at Butter.dev (acquired by Modal 2026). I work on sandboxing, filesystems, and virtualization. Previously founded Banana.dev, a serverless GPU platform where we hosted production-critical inference jobs for more than 3,000 AI teams. I run ultras and enjoy travelling with zero plans.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/edunteman/",
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      {
        "id": 87161,
        "title": "Pig 🐷 - Automate Windows apps with AI",
        "tagline": "Pig is an API for automating workflows in Windows applications.",
        "body": "# TL;DR\n\n[Pig](https://pig.dev) is an API for automating Windows desktops with AI.\n\nTeams can connect their own Windows machines to Pig, open up a chat, and instruct an AI to perform workflows on that computer, over the internet.\n\nPig’s agent operates computers as humans would, performing high-value automations that were previously not possible. \n\n[Sign up here](https://www.pig.dev/login#auth-sign-up)\n\n# The Problem\n\nMany enterprise tools, from electronic health records to manufacturing supply-chain apps, are desktop-based and do not have a public API, preventing automation.\n\nTraditional hardcoded RPA automations can be brittle. Subtle changes in the desktop apps, like popups or slow loading, can derail an automation. And complex workflows which require planning and judgment are not possible.\n\nAs a result, human office workers are employed to do highly repetitive tasks, often involving transferring text between different desktop apps, spreadsheets, and emails.\n\n# Our Solution\n\n<https://youtu.be/qC8WTFtKYOA>\n\nIn this example, we show what’s possible building on Pig. \n\nWe first connect our own Windows machine using our open-source [Piglet](https://github.com/pig-dot-dev/piglet) computer driver\n\nAnd then we start an AI chat powered by the [Claude Computer-Use](https://docs.anthropic.com/en/docs/build-with-claude/computer-use) model:\n\n![Chat interface for automation](https://www.pig.dev/_next/image?url=%2FChatFeature.png&w=3840&q=75&dpl=dpl_GKdZd2HZi9eL2zCkbzmv3rzJACTW)\n\nThe AI is then given high-level tasks, and it begins to work, using Pig’s human-emulating APIs such as:\n\n* Screenshotting the desktop screen\n* Clicking at coordinates\n* Entering keystrokes\n\nAll of which are enough to complete the task.\n\nPig also provides a developer SDK, allowing teams to build their own agents, manage their connected machines, and send automations to their desktops. Purpose-built agents can be incredibly powerful, so [contact us here](https://www.pig.dev/talk) to talk through custom implementation.\n\n![uploaded image](/media/?type=post&id=87161&key=user_uploads/265552/644dc367-8cac-45ba-b919-22e6664088b4)\n\nLastly, Pig includes tools for agents to pass control to human operators during critical operations, such as clicking send on important emails, inputting payment information, or solving captchas.\n\n![uploaded image](/media/?type=post&id=87161&key=user_uploads/265552/d4cec1ed-390a-4ba1-8c48-b5d5173e9668)\n\nWe intend to be the personal computers for AI, and the embeddable observability window for managers.\n\n# Use Cases\n\nKiller use-cases of Pig involve the following factors:\n\n* the workflow requires a Windows desktop app\n* The app does not have a public API\n\nI expect to be surprised by what people build on Pig, but I’m most optimistic for use of:\n\n* Office Suite\n* Electronic Health Record Software\n* Claims Processing Software\n* Factory Control Systems\n* Lab Software\n\n# Team & Background\n\nI’m Erik, a second-time founder, with five years spent building cloud infrastructure.\n\n![uploaded image](/media/?type=post&id=87161&key=user_uploads/89898/6b51d5af-79d8-4d30-ad7e-f3e221dd3b53)\n\nMost notably, I built [Banana](https://banana.dev), a serverless GPU product used by more than three thousand teams to host production inference jobs.\n\nPrior to Banana, I built a cloud desktop product for accelerating 3D design apps. Following Banana, I had the privilege to work with our former competitor, [Modal](https://modal.com), on their serverless Python cloud.\n\nWhy am I building Pig?\n\nThroughout 2024, I spent my time experimenting with new technologies and building up a thesis for what the future of AI tooling would be. After poking around a few wild ideas (such as data centers in space and internet-over-laser), I boomeranged back to the area that I know and love: compute infrastructure.\n\nAn AI founder friend called me up in Nov 2024, pitched me on this idea as something he needed to see in the world, and said, “Erik, you’re the single best founder to build this.”\n\nAnd I agree. I am.\n\n# How to become a user\n\nPig just launched open and free access to users self-hosting their machines.\n\nGet started today at [**pig.dev/docs**](http://pig.dev/docs), or [**contact us**](https://pig.dev/talk) for help building custom agents and I’ll reach out!\n\nAnd if you don’t need Pig, please tell at least one friend about it.\n\nOink!\\\nErik\n\n![uploaded image](/media/?type=post&id=87161&key=user_uploads/265552/70b01c11-9da9-4db0-976c-1162ca31f323)\n\n",
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    "website": "https://assistant-ui.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "assistant-ui helps frontend developers add AI chat to their apps. Our product is used by several dozen companies, like LangChain, Stack AI, Browser Use, and Athena Intelligence. We're building an open source Typescript/React library and a backend as a service, with first class integrations into popular agent frameworks like LangGraph and Vercel AI SDK.",
    "one_liner": "Open Source React.js Library for AI Chat",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "batch": "Winter 2025",
    "status": "Active",
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/assistant-ui",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/assistant-ui.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://linkedin.com/company/assistant-ui",
    "x_url": "https://x.com/assistantui",
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      "url": "https://www.ycombinator.com/people/jared-friedman"
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    "founders": [
      {
        "user_id": 1152943,
        "full_name": "Simon Farshid",
        "title": "Founder/CEO",
        "founder_bio": "Simon is the Founder and CEO of assistant-ui. He previously co-founded READO, a book recommendation engine, and grew it to 140k MAUs. Simon started coding at age 12 and has won 8 hackathons in 5 countries.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/simon-farshid",
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        "id": 87644,
        "title": "💬 assistant-ui - Open Source Typescript/React Library for AI Chat",
        "tagline": "The UX of ChatGPT in your own app, connected to your own AI agents.",
        "body": "Hi YC! I’m Simon, building **assistant-ui**, an open source Typescript/React library for AI chat.\n\nHundreds of projects use assistant-ui to build in-app AI assistants, including companies like:\n\n![Logos of LangChain, AthenaIntelligence, Browser Use, Stack AI](https://bookface.ycombinator.com/media/?type=post&id=87644&key=user_uploads/1152943/d3eda556-de8c-4404-b4be-c0295f9aeb8e)\n\nWith >50k+ monthly downloads, assistant-ui is the most popular UI library for AI chat.\n\n## **Getting Started**\n\nYou can get started by running **\\`npx assistant-ui create\\`** (new project) or **\\`npx assistant-ui init\\`** (existing project) in your terminal.\n\n![Gif showing the setup process, which takes under 30 seconds](https://bookface.ycombinator.com/media/?type=post&id=87644&key=user_uploads/1152943/17c78ae1-d8cf-4fc4-a8aa-056fe449da02)\n\n## **Features**\n\n* **Pre-built components**, fully customizable\n* **Connects to your backend**\n  * We have first class integration for **AI SDK** and **LangGraph** or build your own\n* **Generative UI**: Map LLM tool calls and JSONs to custom UI components\n* **Frontend Tool Calls:** Let LLMs take action in your frontend app\n* **Human Tool Calls:** Human approvals and input collection\n* **Chat history and analytics** via assistant-cloud (the hosted offering)\n\n## **Customization**\n\nThe API of assistant-ui is inspired by libraries like **Radix UI** and **cmdk**. Instead of a single monolithic chat component, we give you composable primitives and a great starter configuration. You have full control over the look and feel of every pixel while leaving auto-scrolling, LLM streaming and accessibility to us.\n\n![Photo showing the modular component structure. ComposerPrimitive.Input is the chat input field, ComposerPrimitive.Send is the send button.](/media/?type=post&id=87644&key=user_uploads/1152943/d8eacc69-2d94-4c43-8fab-2dcac5e7f029)\n\nSample customization to make a perplexity lookalike:\n\n![Perplexity lookalike Chat UI](/media/?type=post&id=87644&key=user_uploads/1152943/16e96ae2-d563-4f6c-9047-6c3485a1cb3d)\n\n## **Demo Video**\n\n[Demo Video](https://youtu.be/9eLKs9AM4tU?si=jm9GzoFAcyM_tPT-)\n\n## **About**\n\nI’ve wanted to build my own dev tool company since high school. Last year, I built the first version of assistant-ui because I needed the UX of ChatGPT in my own app. The app I was building didn’t work out, but few friends asked me about the chat UI. Since then, AI chat has became increasingly popular as a means for delegating tasks to AI agents. My focus is on enabling human <> agent collaboration through better UX and AX (agent experience).\n\n![Photo of Simon, the founder](https://bookface.ycombinator.com/media/?type=post&id=87644&key=user_uploads/1152943/d43c8332-b160-40d8-8f27-849bd82806e3)\n\n## **Ask**\n\n* [Check out example demos](https://www.assistant-ui.com/)\n* [Star us on GitHub](https://github.com/assistant-ui/assistant-ui)\n* [Join our Discord](https://discord.com/invite/S9dwgCNEFs)\n* [Book a sales call](https://cal.com/simon-farshid/assistant-ui)",
        "url": "https://www.ycombinator.com/launches/Mnc-assistant-ui-open-source-typescript-react-library-for-ai-chat",
        "votes": 30,
        "created_at": "2025-02-13T15:37:24.704Z"
      }
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  },
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    "id": 30356,
    "name": "Artil",
    "slug": "artil",
    "former_names": [
      "Fira"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e50d8dfa254f692b3d51a22620ca8519141e9ed2.png",
    "website": "https://artil.dev/",
    "all_locations": "London, England, United Kingdom",
    "long_description": "Artil gives AI agents their own email addresses and phone numbers so they can communicate and use online services. Agents can send and receive emails and text messages, sign up for services, and complete email and SMS verification. We’re building the identity and communication infrastructure for teams developing agents and people putting them to work.",
    "one_liner": "Agent identity and communication infrastructure",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1736800806,
    "tags": [
      "Developer Tools",
      "B2B",
      "Email",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2025",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United Kingdom",
      "Europe"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/artil",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/artil.json",
    "year_founded": 2025,
    "city": "London",
    "country": "GB",
    "linkedin_url": "",
    "x_url": "",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/artilai/",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e50d8dfa254f692b3d51a22620ca8519141e9ed2.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Gustaf Alstromer",
      "url": "https://www.ycombinator.com/people/gustaf-alstromer"
    },
    "founders": [
      {
        "user_id": 626328,
        "full_name": "Alex Tkachenko",
        "title": "Founder/CEO",
        "founder_bio": "CEO at Artil. Previously founded Hashscan, blockchain analytics startup. Created Hodler, crypto portfolio tracker app (sold in 2022).",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/alextcn",
        "x_url": "https://twitter.com/jackqack",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5a8afd23b084a0bd4885df51efd9f1c676d29ac4.jpg"
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      {
        "id": 100809,
        "title": "Fira MCP: Financial datasets in Claude and Excel",
        "tagline": "Connect SEC filings, UK Companies House, market data and earnings transcripts to your AI with source-cited answers",
        "body": "Reliable financial data access and trust in outputs are still the bottleneck in adopting AI in banking, consulting and investment firms. We built Fira MCP to solve that, starting from for teams adopting Claude. Our data connector lets your AI read and cite SEC and international filings, UK Companies House, market data, and earnings transcripts. It works with Claude, Claude for Excel, ChatGPT, and Copilot, while setup takes 3 minutes and it's free to try - <https://firaresearch.com/mcp>.\n\n<https://youtu.be/JoEY1imdVhw>\n\n### Problem\n\nMany banking, consulting, and investment firms are moving day-to-day AI work to Claude. But reliable data access and trust in the outputs are still the bottleneck.\n\n### Solution - Fira MCP\n\nFira MCP plugs directly into Claude, ChatGPT, Copilot, and Claude for Excel as a data connector. Your AI gets access to filings, transcripts, and market data, with numbers linked back to the exact paragraph or row in the source document. It handles scanned PDFs, nested tables, and works across tens of documents in a single research thread.\n\n## Available datasets\n\n* **SEC Filings** — 8,000+ active US companies (10-K, 10-Q, 8-K, and more)\n* **UK Companies House** — 5M+ private companies, including scanned accounts\n* **International filings** — 10,000+ companies across 50+ countries\n* **Earnings call transcripts** — full transcripts with analyst Q&A\n* **Market data** — 20+ years of daily stock and FX, US and global\n* **Custom datasets** — proprietary and third-party integrations for enterprise\n\n### Use cases\n\n**I. Peer comparison in Excel** — pull financials, strategy, and risk factors across competitors directly from filings.\n\n![uploaded image](/media/?type=post&id=100809&key=user_uploads/626328/99b5fdce-2a4b-4053-a6c1-6b94ec87d578)\n\n**II. Corporate structure mapping** — trace ownership and financials through complex group structures from UK filings.\n\n![uploaded image](/media/?type=post&id=100809&key=user_uploads/626328/c1665625-8a07-414d-ac76-b7ecd356b12b)\n\n**III. Earnings call analysis** — review management commentary and analyst Q&A for sentiment and evasive answers.\n\n![uploaded image](/media/?type=post&id=100809&key=user_uploads/626328/7fb98502-0b18-4486-b7f7-5b6655161588)\n\n**IV. Document verification** — cross-reference every number against source, catch rounding deltas and inconsistencies.\n\n![uploaded image](/media/?type=post&id=100809&key=user_uploads/626328/c11d5c2d-d1d3-4328-a4bb-63dd13aba8d6)\n\n### Asks\n\nLooking to connect with finance professionals (banking, PE, credit, hedge funds, wealth) who want their AI assistant to actually do useful work.",
        "url": "https://www.ycombinator.com/launches/QDx-fira-mcp-financial-datasets-in-claude-and-excel",
        "votes": 14,
        "created_at": "2026-05-05T19:11:01.299Z"
      },
      {
        "id": 88048,
        "title": "Fira: Agentic AI Analyst",
        "tagline": "Accelerating financial analysts with source-cited insights and verified calculations from complex company filings",
        "body": "**Fira** is building an agentic AI analyst for financial research and automation, starting with UK investment firms. Today, we accelerate analysts with source-cited insights and verified calculations from complex company filings.\n\n<https://youtu.be/a8C9TifdUOg>\n\n# Problem\n\nAnalysts at investment banks and boutique M&A firms waste tons of time digging through hundred-page annual reports to prepare pitch decks and models. General LLM tools aren't good enough for the job — lacking accuracy, domain-specific data, and output verification. The problem extends to senior professionals who need to quickly understand new topics deeply while staying grounded in truthful data sources.\n\n---\n\n# Solution\n\nFira saves analysts time by instantly searching, analyzing and citing UK company filings. It reads multiple filings and reports (including 500 page long and scanned documents) to provide source-cited answers and verifiable calculations. We cover hundreds of public UK companies, integrate with Companies House for private company data, and allow to upload your documents.\n\n---\n\n# Use cases\n\n**I. Breakdown detailed financial and operational KPIs in seconds**\n\n![uploaded image](/media/?type=post&id=88048&key=user_uploads/626328/d03ed2eb-89ea-49f2-af3e-895cf89dd3a7)\n\n**II. Perform general research on data scattered across reports and filings**\n\n![uploaded image](/media/?type=post&id=88048&key=user_uploads/626328/af63b5d1-c1ba-4551-9168-91c8b3a8ca8f)\n\n**III. Fira calculates financial metrics it can't find, providing transparency in formula and variables used**\n\n![uploaded image](/media/?type=post&id=88048&key=user_uploads/626328/d65b3dba-a6ce-4653-86e9-9ddedcb5d0a4)\n\n# Asks\n\nLooking to connect with senior professionals and analysts at investment banks, M&A advisory firms, hedge funds, and wealth management firms. Especially in the UK. If you work in finance or know someone who does, we'd love an intro. Book a demo [here](https://firaresearch.com) or email us at [alex@firaresearch.com](mailto:alex@firaresearch.com).\n\n# About us\n\nAlexey and Alex are both technical founders, met 9 years ago at university and been close friends since.\n\nAlexey was CTO and co-founder of [resume.io](http://resume.io), one of the largest resume builder websites (acquired in 2021). He’s also an author of wouter, a React router library with 7k stars and 580k downloads/month, and a regular tech conference speaker.\n\nAlex was the founder and CEO of Hashscan, on-chain user analytics startup. Previously, he built a popular crypto porftolio tracker app Hodler (sold in 2022).\n\n![uploaded image](/media/?type=post&id=88048&key=user_uploads/626328/13c0b9c8-06c4-4307-b446-8edc81b35aab)\n\n# Links\n\nWebsite: <https://firaresearch.com>\n\nLinkedIn: <https://www.linkedin.com/company/fira-ai/>",
        "url": "https://www.ycombinator.com/launches/Mu8-fira-agentic-ai-analyst",
        "votes": 35,
        "created_at": "2025-02-26T01:51:02.509Z"
      }
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    "id": 30403,
    "name": "Aravolta",
    "slug": "aravolta",
    "former_names": [
      "CentralAxis"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fef90b2fedf3b6bcd07d770648c6074b9717d2a0.png",
    "website": "https://www.aravolta.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Aravolta provides a single pane of glass software for data centers to increase uptime, reduce costs, and improve performance.\r\n\r\nA single utility node onboards every asset within an hour and detects hardware failures, hotspots, and other issues.\r\n\r\nIt also generates reports on important metrics like Power Usage Effectiveness (PUE), energy metering, resource utilization, and much more. ",
    "one_liner": "Software to monitor, control, and optimize data centers.",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1746569409,
    "tags": [
      "Enterprise Software",
      "Cloud Computing",
      "Monitoring"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2025",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/aravolta",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/aravolta.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/centralaxis",
    "x_url": "https://x.com/CentralAxis_X25",
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    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 1833246,
        "full_name": "Margarita Groisman",
        "title": "Founder/CEO",
        "founder_bio": "Worked on data center deployments at Microsoft, now my company Aravolta builds software for data centers. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/margarita-groisman/",
        "x_url": "https://x.com/Marg_Groisman",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5150584412ea3eb0182f8e63c266e0f281c95f7c.jpg"
      },
      {
        "user_id": 2213346,
        "full_name": "Jack Sutton",
        "title": "Founder/CTO",
        "founder_bio": "Lifelong software engineer",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/sutton-jack",
        "x_url": "https://x.com/JackSutton_SF",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8af4649e155b78aa37df060b30c652788ea65252.jpg"
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    ],
    "jobs": [
      {
        "id": 76973,
        "title": "Senior Software Engineer",
        "url": "https://www.ycombinator.com/companies/aravolta/jobs/5mXBTwN-senior-software-engineer",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$140K - $200K",
        "equity_range": "0.40%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Bash/Shell",
          "Node.js",
          "PostgreSQL",
          "TypeScript",
          "Next.js",
          "Tailwind"
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      },
      {
        "id": 80505,
        "title": "Intern",
        "url": "https://www.ycombinator.com/companies/aravolta/jobs/xNqAuCd-intern",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Internship",
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        "role_type": "Full stack",
        "salary_range": "$4K - $15K / monthly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
        "skills": [
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          "Node.js",
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          "TypeScript"
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      },
      {
        "id": 92973,
        "title": "Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/aravolta/jobs/F5owCzV-forward-deployed-engineer",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Full-time",
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        "role_type": "Full stack",
        "salary_range": "$150K - $240K",
        "equity_range": "0.25% - 0.50%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 111389,
        "title": "Sales Development Representative",
        "url": "https://www.ycombinator.com/companies/aravolta/jobs/ySYYxBA-sales-development-representative",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Full-time",
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        "role_type": null,
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        "visa": "US citizen/visa only",
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    "id": 30448,
    "name": "PgDog",
    "slug": "pgdog",
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    "website": "https://pgdog.dev",
    "all_locations": "San Francisco, CA, USA; Remote",
    "long_description": "PgDog is an application for sharding PostgreSQL. It understands SQL and can distribute queries automatically between databases. It's built for managed databases, like AWS RDS, and doesn't require any changes to application code or schema.\r\n\r\nIn addition to sharding, PgDog is a load balancer and pooler, so it can act as a replacement for PgBouncer, RDS Proxy, and other Postgres scaling products. It brings the simplicity and performance of HTTP load balancing to the database.\r\n\r\nBuilt from the experience of sharding Postgres at Instacart during peak growth in 2020, PgDog is the answer to the old question: does Postgres scale? It does now.",
    "one_liner": "Scaling engine for PostgreSQL",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1745009582,
    "tags": [
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    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2025",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Fully Remote"
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    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/pgdog",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/pgdog.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/pgdog/",
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    "cb_url": null,
    "github_url": "https://github.com/pgdogdev/pgdog",
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      "name": "Garry Tan",
      "url": "https://www.ycombinator.com/people/garry-tan"
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    "founders": [
      {
        "user_id": 989617,
        "full_name": "Lev Kokotov",
        "title": "Founder/CEO",
        "founder_bio": "Second time technical founder, sharded Postgres at Instacart in 2020 during peak growth. Previously built PostgresML (AI + PostgreSQL = 🚀).",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/levkk",
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    "jobs": [
      {
        "id": 78009,
        "title": "Founding Software Engineer",
        "url": "https://www.ycombinator.com/companies/pgdog/jobs/uWymUYy-founding-software-engineer",
        "location": "San Francisco, CA, US / CA, US / OR, US / Remote (San Francisco, CA, US; CA, US; OR, US)",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $225K",
        "equity_range": "1.00% - 3.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "PostgreSQL",
          "Rust"
        ]
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    "news": [],
    "launches": [
      {
        "id": 90596,
        "title": "PgDog: Scaling PostgreSQL",
        "tagline": "Proxy for scaling Postgres without app changes or database extensions.",
        "body": "Hey YC,\n\nI’m Lev and I invented time travel… for your database.\n\n“Huh?”\n\n**TLDR:** PgDog is a proxy for scaling PostgreSQL. It can load balance traffic and shard databases, without changes to app code or using database extensions. What does this have to do with time travel? Keep reading.\n\nhttps://www.youtube.com/watch?v=trSVJel3gyc\n\n# ❌ The Problem\n\nScaling Postgres is a full time job. Your app is writing data but the database can’t keep up. You’re adding more databases and thinking of breaking the app into services to spread the load.\n\nPgDog is for you if:\n\n* You had an outage caused by a bad query\n* Your database is unpredictable; app latency and uptime are all over the place\n* You know more about Postgres internals (like vacuum) than you’d like, and you just want it to work so you can go back to building your product\n\nAnd you catch yourself day dreaming about the good old days, when you were pre-PMF and your DB was at 10% CPU.\n\n# 👷 The Solution\n\nDatabase problems should be solved at the database layer. PgDog is a load balancer, connection pooler, bouncer, and sharder. It speaks fluent SQL and understands every single Postgres query. It uses this knowledge to:\n\n* Distribute queries between shards, horizontally scaling your relational database\n* Load balance traffic between replicas\n* Block or rewrite queries you don’t like, e.g. queries that forgot an index or are missing the customer_id and were about to leak data\n\nWith PgDog, your DB is young again and ready to grow 10x. Written in Rust, for security and performance, PgDog is fully tested and ready for production.\n\n# 🙏 The Ask\n\nIf any of this sounds familiar, **get in touch!** I’m happy to come work for you to fix your DB. I’ll stay in the basement and make PgDog scale Postgres, while you work on your startup.\n\nYou’re my ideal design partner if:\n\n* Postgres is your production database (naturally)\n* Despite your troubles, you love it and want to do more with it\n* You believe in first principles solutions to hard problems\n\n![uploaded image](/media/?type=post&id=90596&key=user_uploads/989617/91f087f7-4175-47fe-9753-57ed4e9bda62)\n\nEmail [lev@pgdog.dev](mailto:lev@pgdog.dev), join our [Discord](https://discord.gg/CcBZkjSJdd), and give us a star on [GitHub](https://github.com/pgdogdev/pgdog). Everything we build is open source.\n\n# 👬 The Team\n\nI’m a second time technical founder (previously co-founded [PostgresML](https://github.com/postgresml/postgresml)) and started the Instacart storage infra team. I scaled 100TB+ databases when Instacart grew 5x in 3 months. This is my second pet-inspired project for scaling Postgres (have you heard of [pgcat](https://github.com/postgresml/pgcat)?)\n\n![uploaded image](/media/?type=post&id=90596&key=user_uploads/989617/86ff8181-b991-4bf9-bd05-8b0a387ed978)\n\n",
        "url": "https://www.ycombinator.com/launches/NZE-pgdog-scaling-postgresql",
        "votes": 40,
        "created_at": "2025-05-21T17:22:59.749Z"
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    "id": 30464,
    "name": "Sixtyfour",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Sixtyfour deploys AI agents that investigate people and companies across the open and dark web, official records, proprietary databases, and unstructured sources. We resolve identities, map entity relationships, and surface risk signals for teams where data quality is essential.",
    "one_liner": "Intelligence Infrastructure on People and Entities",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "country": "US",
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        "full_name": "Saarth Shah",
        "title": "Founder/CEO",
        "founder_bio": "CEO @ Sixtyfour. Previously, engineering @ Whatnot (W20) and Deepgram AI (W16). UC Berkeley alum. Ex-Data Science Researcher @ Stanford Snyder & San Diego Supercomputer Center.",
        "is_active": true,
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        "full_name": "Christopher Price",
        "title": "Founder/COO",
        "founder_bio": "Cornell CS, AI CalHacks Winner, Prev @ Onyx (YC W24), Currently Building AI Agents for GTM",
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        "linkedin_url": "https://linkedin.com/in/christopher-price-59a24b178",
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        "title": "Growth Intern - Fall 2026",
        "url": "https://www.ycombinator.com/companies/sixtyfour/jobs/lPEgSOT-growth-intern-fall-2026",
        "location": "San Francisco, CA, US",
        "type": "Internship",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "$30 - $50 / hourly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
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      {
        "id": 112379,
        "title": "Product Marketing Lead",
        "url": "https://www.ycombinator.com/companies/sixtyfour/jobs/WR7Fp66-product-marketing-lead",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "$100K - $200K",
        "equity_range": "0.05% - 0.30%",
        "experience": "1+ years",
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    "launches": [
      {
        "id": 102050,
        "title": "Sixtyfour - People Intelligence Infrastructure",
        "tagline": "Deploy thousands of AI agents to research any person or company.",
        "body": "👋 Hi, we're Saarth and Chris, co-founders of Sixtyfour (YC X25). \n\n<https://www.youtube.com/watch?v=9Q0aVTtXFM4>\n\n**The problem**\n\nIn 2025-2026 alone, Texas, Nevada, Indiana, and New Jersey all sued Discord for failing to identify predators on the platform.\n\nAnonymous accounts on Polymarket made $2.4M with a 98% win rate on Iran war bets.\n\nOnur Aksoy ran 19 shell companies and 25 Amazon storefronts, selling $100M in counterfeit Cisco gear that ended up on US military aircraft.\n\nToday, teams that need to follow it (Trust &amp; Safety, KYB, threat intel, due diligence) throw analyst teams at it. Still not trusting the answer after days or weeks of research. Not to mention it doesn't scale.\n\nBad actors slip through. People get harmed.\n\n**What we built** \n\nThe child predator's username. The scammer's banned accounts. The fraudster's shell company. \n\nAll of them leave a trail.\n\nImpossible to connect, but the trail was always there.\n\nOur agents have now made it possible to follow it.\n\nGive us one identifier (a name, email, phone number, username, or company name), and we deploy agents to recursively search the open web, dark web, social platforms, court records, and regulatory filings. Every signal is cross-referenced across thousands of sources. Every data point cited.\n\nCompanies using us today:\n\n* Cloaked uses us to uncover highly sensitive, hidden data about people, identifying attack vectors against their users.\n* TRM Labs uses us to attribute bad actors to real-world identities.\n* Airwallex uses us to screen counterparties for shell companies and AML risk.\n* Whatnot uses us to find qualified marketplace sellers that no platform can surface.\n* Surge, Turing, and Datacurve use us to source niche domain experts for AI training data.\n\nTo name a few.\n\n**Two ways to use it**\n\n1. App at [app.sixtyfour.ai](http://app.sixtyfour.ai)\n2. [API](https://docs.sixtyfour.ai/introduction) + [MCP](https://docs.sixtyfour.ai/developer-tools/mcp-setup) server to plug into Claude, Codex, Cursor, or your own stack\n\n\\\n**The numbers**\n\nIn the next couple of weeks, we’ll publicly release our benchmark results with independent validation from one of the world’s leading data labs.\n\nOur new deep people research system sets a new state-of-the-art, outperforming Gemini 3.1 Pro, Grok 4.20, GPT 5.4, Exa Deep Reasoning, and Parallel Ultra while running 3x faster than the closest competitor.\n\n![uploaded image](/media/?type=post&id=102050&key=user_uploads/804300/9d779b0d-a669-4606-991d-d7a595ab098e)\n\n\\\n**Our ask**\n\nWould really love Intros :) \n\n* Heads of Trust &amp; Safety, \n* Fraud, AML, or KYB at marketplaces, banks, and fintechs \n* Heads of Risk and Threat Intel at cybersecurity teams \n* Heads of Talent and Recruiting at hypergrowth companies\n* Companies needing people and company data enrichment solutions\n\n\\\nReach out at [saarth@sixtyfour.ai](mailto:saarth@sixtyfour.ai)\n\nHiding just got harder. Try it now at [app.sixtyfour.ai](http://app.sixtyfour.ai).\n\nSaarth, Chris &amp; the Sixtyfour team\n\n![uploaded image](/media/?type=post&id=102050&key=user_uploads/804300/b56fa1e1-efb9-4695-9e4f-d1af45d331dd)\n\n",
        "url": "https://www.ycombinator.com/launches/QXy-sixtyfour-people-intelligence-infrastructure",
        "votes": 21,
        "created_at": "2026-05-27T19:22:20.361Z"
      },
      {
        "id": 90535,
        "title": "Sixtyfour - Bloomberg Terminal for sales",
        "tagline": "We help hypergrowth companies deploy custom research agents that map their TAM and surface real-time, tailored market signals.",
        "body": "Enterprise sales teams spend 50% of their time on research that misses the mark. Here's why — and how we're fixing it.\n\nHey everyone! We’re Saarth & Christopher – the founders of Sixtyfour. 👋\n\n**TL;DR**\n\nSixtyfour AI is the Bloomberg Terminal for sales. We work with hypergrowth companies to deploy custom research agents that map their Total Addressable Market and deliver real-time market signals, fully tailored to what matters most.\n\nCustomers like **Mercor**, **Traba**, **Hockeystack**, and **Conduit** use Sixtyfour to stay ahead of the curve.\n\n<https://youtu.be/s45NR6diBPo>\n\n**🧠** **The Enterprise GTM Problem**\n\nIf you’re selling into a niche or regulated market ( health systems, manufacturing, fintech, govtech, and so on), you already know: generic data platforms don’t work.\n\nHere’s why:\n\n**💡 “Intent signals” are too broad**\n\nMost tools will tell you someone visited a pricing page. That’s not useful when you’re selling a $250K solution into a regional hospital network or a logistics hub in the Midwest.\n\n**🧱 The nuance is everything**\n\nEnterprise sellers ask real questions:\n\nDid that manufacturing facility just expand its fleet?\n\nDoes it have two or three parking bays?\n\nDid that nursing home file a new Medicare cost report?\n\nIs the new VP of Ops a better entry point than the outgoing admin director?\n\nThese are the signals that drive pipeline and predict fit, and no off-the-shelf platform can give you that.\n\n**🔍 Static filters can’t model your ICP**\n\nTry finding “companies that just adopted Drift, expanded their marketing team by 30%, and hired a new CRO in the past 90 days” in traditional tools. Impossible. Our agents live in that world\n\n**✨ The Sixtyfour Advantage**\n\nWe don’t sell contact lists. We build intelligence infrastructure for your GTM engine.\n\n**📍 Custom TAMs, always current**\n\nWe work with your team to define and continuously update your market using real-world, dynamic data — regulatory filings, facility attributes, staff movements, and even track infrastructure like parking lot size.\n\n**📡 Proprietary real-time signals**\n\nFrom Medicare cost report changes to decision-maker exits to new vendor adoption — we detect what matters to your team, as it happens.\n\n**🧠 Deployable custom ML models**\n\nHave internal data on close rates, deal velocity, or expansion triggers? We plug it in.\n\nWe can train and deploy custom ML models to estimate buyer readiness, ICP fit, churn risk, and more, directly tailored to your pipeline data.\n\n**🤖 Agents that think like your best rep**\n\nOur AI agents mirror your go-to-market motion, tracking, qualifying, and notifying you at the exact right time.\n\n**👥The Team**\n\nWe’re both repeat founders who have built and sold into complex markets.\n\nSaarth is a UC Berkeley Data Science grad and ex-Whatnot (YC W20), Deepgram (YC W16), and Stanford researcher. He built one of the earliest conversational AI agents (Dart) and has been shipping products since he was 10.\n\nChristopher, a Cornell dropout, led GTM at Onyx (YC W24) and built sales AI tools at Symbl. He’s a CalHacks AI winner and ex-analyst at Cornell Atkinson, with deep experience across AI, ops, and consumer tech.\n\nWe’ve lived the pain of building GTM systems by hand, and we’re building the platform we always wished existed.\n\n**👋 Our Ask**\n\n🏢 Know a GTM, RevOps, or sales leader at an enterprise or hypergrowth vertical SaaS company? Send them to [sixtyfour.ai](http://sixtyfour.ai) — we’d love to chat.\n\n💬 Frustrated with generic signals and shallow filters? Let’s build something that works for your team.\n\nEvery intro, share, and shoutout means the world. Thank you — we’re just getting started.\n\n![uploaded image](/media/?type=post&id=90535&key=user_uploads/804300/0c80ad45-4d97-43f6-8fed-9a5782416b19)\n\n",
        "url": "https://www.ycombinator.com/launches/NYF-sixtyfour-bloomberg-terminal-for-sales",
        "votes": 156,
        "created_at": "2025-05-19T22:27:28.413Z"
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    "name": "RunRL",
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    "website": "https://runrl.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "RunRL improves LLMs and AI agents with reinforcement learning. Do you have a model that needs to get better at a certain task? Are you tired of messing around with prompts? Do you spend a lot of money on observability, and wish that all this data could let your model self-improve? Well, head on over to RunRL.com, and see what we have to offer! If you give us a model, a prompt, and a reward, we’ll make your model’s reward go up.",
    "one_liner": "Reinforcement learning as a service",
    "team_size": 2,
    "industry": "B2B",
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    "launched_at": 1743582739,
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      "America / Canada"
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    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/runrl.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/runrl/",
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      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
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    "founders": [
      {
        "user_id": 1161067,
        "full_name": "Andrew Gritsevskiy",
        "title": "Founder",
        "founder_bio": ":')",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/agritsevskiy",
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      {
        "id": 90475,
        "title": "RunRL: Improve LLMs and agents with reinforcement learning.",
        "tagline": "Make AIs that know how to do their jobs",
        "body": "Hi! We’re Derik and Andrew, and we’re building RunRL: the platform that lets you run reinforcement learning on your AI.\n\nWe used to run an AI research lab together, and Andrew dropped out from his RL PhD to provide you the greatest reinforcement learning experience possible.\n\nhttps://www.youtube.com/watch?v=rPP5YNMA4GE\n\n# The problem\n\nFor a generic LLM agent, every day is like the first day on the job. Tools and tasks need exhaustive explanations. Even still, agents are too unreliable for deployment.\n\n# Our fix\n\nRL fine-tuning produces specialist models that intuitively know what to do. It uses the algorithms that produced DeepSeek R1, but customized for your use case.\n\nSimply give us a reward function that tells us what’s good and what’s bad—and we’ll make your model improve.\n\n### Example 1: chemistry\n\nYou’re trying to make a model that generates potential drug candidates for COVID. You want them to bind as tightly to the active site of Sars-CoV-2 protease as possible, but using Claude+thinking+web-search only gets you a binding performance of 22. Making a better model is as simple as defining the reward to be the binding performance of the model’s outputs:\n\n```\ndef reward(output):\n    molecule = parse_SMILES(output)\n\n    if valid_molecule(output):\n    return binding_performance(output)\nelse:\n    return -1\n```\n\nloading it up on [RunRL.com](http://RunRL.com), and hitting run:\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdybuN1eu2Y3fSZi4m9Wc9YR4a-YYACWTt_jxx611-UVsNZ_G-UriMLuSW0JDalz65ixrgYYEIQcc8p1ni8bz2RpLp1HZcX0YIfTVfa1mE-TqFNL787gI94VNHHfcIy4mVoFxlqKQ?key=Wlf0KwcYIPK-rxwiB_U0-Q)\n\nSo, a model 175x smaller than Claude learns to outperform the frontier models!\n\n### Example 2: agents\n\nYou have an OpenAI or Anthropic-based agent, but it’s not as reliable as you’d like it to be. Maybe your agent already does what you want most of the time, but isn’t consistent enough to be trusted in production. RL can help with that too: it reinforces good behavior and makes your agent better at rule-following, information extraction, and document navigation in high-stakes environments. Join our beta [here](https://www.runrl.com/products/agentflow/)!\n\n# Run RL!\n\nIf you’re using AI and you want your model to be better, and you have a way of describing what “better” is: you’re ready to [run RL](https://runrl.com).\n\n![uploaded image](/media/?type=post&id=90475&key=user_uploads/1161067/2e061652-2c07-473b-a95d-8fb07cdc36cd)\n\n",
        "url": "https://www.ycombinator.com/launches/NXH-runrl-improve-llms-and-agents-with-reinforcement-learning",
        "votes": 40,
        "created_at": "2025-05-16T17:42:48.054Z"
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    "all_locations": "New York City, NY, USA; Remote",
    "long_description": "ParaQuery is a fully-managed GPU-accelerated Spark solution providing double the performance at half the cost compared to solutions like Databricks and BigQuery. It is fully Spark-compatible, cloud agnostic, and has seamless integrations everywhere, all without vendor lock-in. ParaQuery makes it trivial to run large SQL and Spark workloads efficiently and serverlessly, without any data migrations.",
    "one_liner": "Managed GPU-accelerated Spark + SQL at 2x speed and half the cost",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1745603504,
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      "Developer Tools",
      "Analytics",
      "Big Data",
      "Enterprise Software",
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    "year_founded": 2023,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/paraquery",
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      "name": "Diana Hu",
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        "user_id": 1091672,
        "full_name": "Win Wang",
        "title": "Founder/CEO",
        "founder_bio": "Focused on efficiency, effectiveness, and (user) experience.\nPreviously a tech lead @ Twitter, building massively-parallel distributed build systems.\nNow building a cheaper, faster big data platform by using the right hardware for job. Fully-managed, GPU-accelerated, petabyte-scale at double the speed and half the cost.\n",
        "is_active": true,
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        "id": 90162,
        "title": "ParaQuery - 5x your data warehouse's efficiency",
        "tagline": "ParaQuery is the GPU-accelerated Spark + SQL engine for your big data needs.",
        "body": "![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/94e086c6-918d-4975-94ca-5aece8bff85a)\n\nHey, I'm Win, founder of [ParaQuery](http://www.paraquery.com).\n\nWe're building a bigger query engine with up to 5x cost efficiency compared to BigQuery by using cost-efficient cloud GPUs.\n\n**Ask:** Do you know someone optimizing their data warehouse? Email [win@paraquery.com](mailto:win@paraquery.com) or sign up [here](https://paraquery.com/waitlist)!\n\n**4x'ing** BigQuery efficiency with ParaQuery: <https://www.youtube.com/watch?v=uu379YnccGU> \n\n# **The Problem**\n\nData is the new oil and you're feeding your products with a bunch of it. But to do so, you need to build, maintain, and optimize your data infra. That's either a huge timesink, or you're bleeding cash to ignore it, or someone is breathing down your neck about this month's cloud bill. Why can't we just ship features?\n\n![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/419acb3d-1195-4842-a80e-df02ac0d332f)\n\nWhether it's those 20-minute+ Presto queries, or your exploding BigQuery bill (which happens to be tracked in real time), your data infra is letting you down.\n\n# **Our Solution**\n\nParaQuery runs on highly-parallel, throughput-optimized cloud-available hardware, perfect for data processing workloads: **GPUs**!\n\nOur system abstracts out the infra required to serverlessly accelerate your logic. With the extreme efficiency of GPUs, it's half the cost and double the performance of competing solutions like Databricks and BigQuery, without any platform commitments.\n\nFor SQL-based data warehouses like BigQuery, you can use ParaQuery with minimal changes to your queries. And since it's fully Spark-compatible, there's no vendor lock-in. It integrates with literally anything which has a Spark connector, on any major cloud. We can even deploy on GPU clouds.\n\nParaQuery makes data processing as simple as specifying:\n\n1. Inputs\n2. Output\n3. Logic\n4. Run!\n\n![uploaded image](/media/?type=post&id=90162&key=user_uploads/1091672/a6cded39-2da7-432b-9129-014a593caaae)\n\n# **Why?**\n\nI've been looking into GPUs and parallel programming for over 12 years – starting out with crypto. I left that field pretty fast, but I never stopped thinking about efficient compute and GPU acceleration. Even more so when, years later, I had to wait for my laptop to finish 18-hour physics simulations. And during my professional career, I've also had to wait on my fair share of 20-minute+ analytical queries.\n\nIn fact, I hate waiting. ParaQuery is productizing an arbitrage of hardware efficiency, and I'm not waiting any longer to do so.\n\n# **Ask**\n\n1\\. If you know anyone who is running any big data processing scenarios (including MLOps), I'd love to learn more about their use case and see if we're a fit! \n\n2\\. If you want to try it out, sign up for our waitlist at [www.paraquery.com](http://www.paraquery.com), or just email me ([win@paraquery.com](mailto:win@paraquery.com))",
        "url": "https://www.ycombinator.com/launches/NSE-paraquery-5x-your-data-warehouse-s-efficiency",
        "votes": 51,
        "created_at": "2025-05-07T02:14:51.329Z"
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    "website": "https://usenaive.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Naïve is an AI research lab building autonomous companies.\r\n\r\nWe develop the systems, environments, and agent infrastructure required for AI to operate real businesses end-to-end, then release increasingly capable autonomous companies into the world.",
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      "America / Canada"
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    "city": "San Francisco",
    "country": "US",
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        "full_name": "Sean Dorje",
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        "linkedin_url": "https://linkedin.com/in/seandorje",
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        "full_name": "Dennis Zax",
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    "jobs": [
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        "id": 110960,
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        "url": "https://www.ycombinator.com/companies/naive/jobs/rpnVz7a-founding-growth-engineer",
        "location": "Mountain View, CA, US",
        "type": "Full-time",
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        "role_type": "Full stack",
        "salary_range": "$130K - $180K",
        "equity_range": "2.00% - 4.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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        "id": 110961,
        "title": "Founding Product Engineer",
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        "location": "Mountain View, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $200K",
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        "id": 110962,
        "title": "Founding Member of Technical Staff — RL",
        "url": "https://www.ycombinator.com/companies/naive/jobs/OTnFKjw-founding-member-of-technical-staff-rl",
        "location": "Mountain View, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$190K - $225K",
        "equity_range": "2.00% - 4.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
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        "id": 110963,
        "title": "Founding Member of Techincal Staff - Agents",
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        "location": "Mountain View, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$190K - $225K",
        "equity_range": "2.00% - 4.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "AI Agents"
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        "id": 112022,
        "title": "Growth Engineer Intern",
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        "location": "Mountain View, CA, US / Remote (US)",
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        "id": 115481,
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        "url": "https://www.ycombinator.com/companies/naive/jobs/MZLbwFK-in-house-video-editor",
        "location": "Remote (US)",
        "type": "Contract",
        "role": "Marketing",
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        "title": "Naïve raises $28.5M to automate the grunt work of setting up and running a company | TechCrunch",
        "url": "https://techcrunch.com/2026/08/06/naive-raises-28-5m-to-automate-the-grunt-work-of-setting-up-and-running-a-company/",
        "date": "Aug 06, 2026"
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        "id": 98930,
        "title": "Naive - Hire Autonomous Employees that actually run your company.",
        "tagline": "Describe your business. Naive runs it. ",
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        "created_at": "2026-03-19T20:33:57.095Z"
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    "id": 30549,
    "name": "Airweave",
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    "all_locations": "San Francisco, CA, USA",
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        "user_id": 1896666,
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        "user_id": 1971956,
        "full_name": "Rauf Akdemir",
        "title": "Founder/CTO",
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        "linkedin_url": "https://www.linkedin.com/in/orhan-rauf-akdemir/",
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    "jobs": [
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        "id": 86032,
        "title": "Founding Software Engineer, Full Stack",
        "url": "https://www.ycombinator.com/companies/airweave/jobs/objmrAG-founding-software-engineer-full-stack",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$120K - $160K",
        "equity_range": "0.25% - 1.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
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      },
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        "id": 86115,
        "title": "Founding Software Engineer, Data Infrastructure",
        "url": "https://www.ycombinator.com/companies/airweave/jobs/MwvkkF7-founding-software-engineer-data-infrastructure",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
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        "id": 86116,
        "title": "Founding Software Engineer, Data Infrastructure",
        "url": "https://www.ycombinator.com/companies/airweave/jobs/Xluuse7-founding-software-engineer-data-infrastructure",
        "location": "Amsterdam, North Holland, NL",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "€80K - €100K EUR",
        "equity_range": "0.25% - 0.75%",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
        "skills": [
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      },
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        "id": 86117,
        "title": "Founding Software Engineer, Full Stack",
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        "role_type": "Full stack",
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        "id": 86119,
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        "location": "Amsterdam, North Holland, NL",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
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        "equity_range": "0.25% - 0.75%",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
        "skills": [
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        "title": "Airweave Raises $6M Seed Round  - Airweave - Turn Apps Into Agent-Ready Knowledge",
        "url": "https://airweave.ai/blog/seed-round",
        "date": "Jul 03, 2025"
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        "id": 90465,
        "title": "Airweave - Let Agents Search Any App",
        "tagline": "Open-source tool enabling AI agents to search any app",
        "body": "**TL;DR:** Airweave transforms app data into agent-ready knowledge.\n\n![uploaded image](/media/?type=post&id=90465&key=user_uploads/1896666/bf878d34-b781-4f5b-beb9-17b71a95e527)\n\n# **🎬 Demo**\n\n_Check out our demo below to see Airweave’s G-Drive and Slack search in action used by Claude Desktop, showcasing how Airweave outperforms Anthropic’s own built-in Google Drive connector ⤵️_\n\nhttps://youtu.be/XkC5z0AkW8E\n\n# ❌ **The Problem**\n\n* **Agents struggle with vague or informal requests** like _”Resolve that one Asana ticket about auth configs”_ or _“cancel Alex’s Stripe payment for Taylor Swift tickets”_\n* **Typically, then agents hallucinate when they can't find data,** or rely on inefficient chains of function calls\n* **MCP encourages agent-app interactions, but MCP servers alone don’t make workspaces semantically searchable**\n\n# ✅ **Our Solution: Airweave**\n\n![uploaded image](/media/?type=post&id=90465&key=user_uploads/1896666/c686c20f-62ac-454c-8d3b-d43ce9c857c4)\n\n* **Airweave connects to productivity tools, databases, or document stores makes their contents searchable for agents**\n* **Bottom line:** Your agent gets a simple search endpoint. Your users get reliable, personalized experiences\n\n# **Features**\n\n![uploaded image](/media/?type=post&id=90465&key=user_uploads/1896666/d8e186b6-cc60-4b97-802b-f3ef29cf2a4a)\n\n* **Open-Source**\n* **Simple yet powerful search interface** via REST or MCP\n* **MCP-compatible:** Turn any app into a semantically searchable MCP server\n* [**Python & TypeScript SDKs**](https://docs.airweave.ai/sdks)\n* **Connect to your agent to your favorite apps** like Google Drive, Gmail, Outlook, Google Calendar, HubSpot, Notion, Jira, Slack, Dropbox, Stripe, Github, etc.\n* [**White-Labeled Multi-Tenancy**](https://docs.airweave.ai/white-labeling) to provide OAuth2-based integration across user accounts with secure boundaries\n* **File Handling:** Supported formats: DOCX, PDF, PPTX, XLSX, HTML, TXT, PNG, JPEG\n\n# **Examples of How Customers Use Airweave:**\n\n* Legal AI assistants searching Google Drive/OneDrive to give accurate Perplexity-like answers\n* Agents for engineering managers scanning GitHub, Notion, and Jira to understand codebases and draft design docs\n* Compliance agents verifying marketing materials against current Dropbox financial data in regulated industries\n\n# **🤝 The Team**\n\nLennert and Rauf met in uni 7 years ago, and have been friends since. Lennert did AI research at Amazon and IBM, and has been working with LLMs since 2020. Rauf is a CS grad and worked as a Data Platform Engineer at fast-growing startups and large enterprise.\n\n![uploaded image](/media/?type=post&id=90465&key=user_uploads/1896666/d9d8d633-63a6-4c7b-9a3b-1e7ccaefaca8)\n\n# **📢 Our Asks:**\n\n* **⭐️ [Star us on GitHub](https://github.com/airweave-ai/airweave) ⭐️**\n* **Book a call** if you’re building agents that need to interact with apps, productivity tools, or databases: https://cal.com/lennert-airweave/airweave-q-a-demo\n* **Intro us** to PMs, engineering managers, or more senior at agent product teams and companies: [lennert@airweave.ai](mailto:lennert@airweave.ai)\n* [**Join our Discord community**](https://discord.gg/6wDWUhhuu2)",
        "url": "https://www.ycombinator.com/launches/NX7-airweave-let-agents-search-any-app",
        "votes": 126,
        "created_at": "2025-05-16T14:42:40.410Z"
      }
    ]
  },
  {
    "id": 30605,
    "name": "Dedalus Labs",
    "slug": "dedalus-labs",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7090c5a2d73a4c3057030995d02dc87abae964a5.png",
    "website": "https://www.dedaluslabs.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Dedalus Labs builds persistent compute for AI agents. Our flagship product, Dedalus Machines, gives agents isolated computers where they can run software, keep files and state, and do useful work over time.\r\n\r\nWe build across virtualization, distributed storage, networking, scheduling, and orchestration. Our goal is to make persistent compute dependable and straightforward to use through APIs and developer tools, so developers can build agents that work with real software and maintain a working environment across sessions.",
    "one_liner": "Persistent computers for AI agents",
    "team_size": 8,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1756399069,
    "tags": [
      "Developer Tools",
      "DevOps",
      "Cloud Computing",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2025",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/dedalus-labs",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/dedalus-labs.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/dedalus-labs",
    "x_url": "https://x.com/dedaluslabs",
    "fb_url": "https://www.facebook.com/dedaluslabs",
    "cb_url": "https://www.crunchbase.com/organization/dedalus-labs",
    "github_url": "https://github.com/dedalus-labs",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7090c5a2d73a4c3057030995d02dc87abae964a5.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [
      "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/54c7bc34d7f644f26f4d2fb8aea33cfc23d4684b.png",
      "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/16c9af42d9adfb160dd3a15005b124edb50fd1fc.png"
    ],
    "primary_partner": {
      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
    },
    "founders": [
      {
        "user_id": 2684979,
        "full_name": "Cathy Di",
        "title": "Founder/CEO",
        "founder_bio": "Let us build the wings\n      so your agents can fly 🪽",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/cathydi",
        "x_url": "https://x.com/itsCathyDi",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e6105f830d0c2b1243019fb14c0f57b4130c8298.jpg"
      },
      {
        "user_id": 1126811,
        "full_name": "Windsor Nguyen",
        "title": "Founder/CTO",
        "founder_bio": "Moving electrons really fast.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/windsornguyen",
        "x_url": "https://x.com/WindsorNguyen",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5dc47e2245af96f7a157a8480c9a6f536a01ab2e.jpg"
      }
    ],
    "jobs": [
      {
        "id": 107466,
        "title": "Systems Engineer",
        "url": "https://www.ycombinator.com/companies/dedalus-labs/jobs/694An25-systems-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$120K - $250K",
        "equity_range": "0.50% - 1.00%",
        "experience": "Any (new grads ok)",
        "visa": "Will sponsor",
        "skills": []
      },
      {
        "id": 107469,
        "title": "Infrastructure Engineer Intern",
        "url": "https://www.ycombinator.com/companies/dedalus-labs/jobs/012beI1-infrastructure-engineer-intern",
        "location": "San Francisco",
        "type": "Internship",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$7.8K - $8.67K / monthly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 107661,
        "title": "Systems Engineer Intern",
        "url": "https://www.ycombinator.com/companies/dedalus-labs/jobs/EQcMolJ-systems-engineer-intern",
        "location": "San Francisco",
        "type": "Internship",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$7.8K - $8.67K / monthly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 112907,
        "title": "Software Engineer, Infrastructure & Performance",
        "url": "https://www.ycombinator.com/companies/dedalus-labs/jobs/KSsPCqY-software-engineer-infrastructure-performance",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$120K - $250K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "Will sponsor",
        "skills": []
      }
    ],
    "news": [
      {
        "title": "Amid the AI boom, Princetonians swap school for Silicon Valley - The Princetonian",
        "url": "https://www.dailyprincetonian.com/article/2026/04/princeton-features-profiles-dropout-school-startups-silicon-valley-ai-boom",
        "date": "Apr 10, 2026"
      },
      {
        "title": "Disrupting Industries: Highlights from the Latest YC Demo Day",
        "url": "https://nextunicorn.ventures/disrupting-industries-highlights-from-the-latest-yc-demo-day/",
        "date": "Sep 15, 2025"
      },
      {
        "title": "Y Combinator's Summer 2025 Demo Day Highlights Over 160 Startups with a Focus on AI Solutions",
        "url": "https://news.ssbcrack.com/y-combinators-summer-2025-demo-day-highlights-over-160-startups-with-a-focus-on-ai-solutions/",
        "date": "Sep 15, 2025"
      },
      {
        "title": "The 9 most sought-after startups from YC Demo Day | TechCrunch",
        "url": "https://techcrunch.com/2025/09/15/the-9-most-sought-after-startups-from-yc-demo-day/",
        "date": "Sep 14, 2025"
      },
      {
        "title": "Dedalus Labs Review: Is This The “Vercel for AI Agents” We’ve Been Waiting For?",
        "url": "https://medium.com/design-bootcamp/dedalus-labs-review-is-this-the-vercel-for-ai-agents-weve-been-waiting-for-d52e764f3756",
        "date": "Aug 28, 2025"
      }
    ],
    "launches": [
      {
        "id": 94675,
        "title": "Dedalus Labs: Build & Deploy Complex Agents in 5 Lines of Code",
        "tagline": "Build and deploy complex AI agents with any model and any tool in just 5 lines of code. No Docker files. No YAMLs. No wasted weeks.",
        "body": "# [**TL;DR:**](https://www.youtube.com/watch?v=E0FR4LBTSZo)\n\n_We’re excited to launch [Dedalus Labs](https://www.dedaluslabs.ai/), an AI-native cloud platform for developers to build agentic AI applications. Our [SDKs](https://github.com/dedalus-labs/dedalus-sdk-python) allows you to connect any LLM to any MCP servers – local or remotely hosted by us. No Docker files or YAML configs required._\n\n* **Blog:** https://dedaluslabs.ai/blog/dedalus-seed-round\n* **Product Demos:**\n  * https://www.youtube.com/watch?v=s2khf1Monho\n  * https://www.youtube.com/watch?v=4B1xuRCgLgA\n\n# **The Problem: Building Agents Is Hard**\n\nBuilding tool-using agents ≠ calling **_v1/chat/completions._**\n\nToday, developers are still stuck with:\n\n* Running their MCP servers locally.\n* If deploying remotely, wiring up AWS and config hell.\n* Juggling model quirks, streaming issues, and auth across GPT-4o, Claude 3.5, Sonnet, Flash, etc. (♾️ API keys).\n* Rebuilding everything just to add a new tool.\n\nWe were **_tired_** of writing hundreds of lines of code and battling Docker files and infra—all for a spaghetti agent that breaks under stress.\n\n**So, we built the tool we wished existed.**\n\n# **What We Offer: Complex Agents in 5 Lines of Code**\n\nDedalus invented the simplest way to build and deploy agents—a drop-in infrastructure layer that unifies models, tools, and orchestration.\n\nOur open-source SDK supports:\n\n* Vendor-agnostic model handoffs\n* Chaining both local tools and hosted MCPs\n* Real-time streaming across any provider\n\n—all in 5 lines of code.\n\nAnd our managed infra lets you **deploy an MCP server in 3 clicks**, so anyone can equip their agent with tools. No Docker files, no setup.\n\n# **Why MCP: Tools Made for Agents**\n\n[MCP (Model Context Protocol)](https://spec.modelcontextprotocol.io/) is becoming the standard for how models talk to tools. Think of it as an API that AI models already know how to call—reliable, predictable, and language-agnostic.\n\nAs more companies adopt MCP, exposing your product as an MCP server means you’re not just serving humans anymore—you’re serving _agents._\n\nWe’re building the infrastructure that powers that shift.\n\n# **Our Story: Two Princeton CS Students with High Standards**\n\nWe’re **Cathy** (ex-Voyage AI / Salesforce) and **Windsor** (ex-DeepMind / Sentient AGI).\n\nWe first ran into this problem while working with MCP during Windsor’s time at Sentient AGI. Every solution felt wrong: drag-and-drop GUIs, brittle configs, frameworks that broke under real workloads. Nothing met our standards for what good developer infrastructure should be.\n\nSo we built what we always wanted—a developer-first platform that makes agentic workflows composable, scalable, and effortless to ship.\n\n**If no one else is going to build it right, we will.**\n\n# **Get Started + Early YC Deal**\n\n* **Quick install:** **_pip install dedalus-labs_**\n* **Docs:** [docs.dedaluslabs.ai](http://docs.dedaluslabs.ai)\n* **YC Deal:** we’re giving $200 free credits to every YC company. Email [founders@dedaluslabs.ai](mailto:founders@dedaluslabs.ai) to claim your deal!\n\n![uploaded image](/media/?type=post&id=94675&key=user_uploads/1126811/4a1b700a-a1ab-41a3-a730-7b9a88b65500)\n\n_Let us build your wings_ so your agents can fly. 🪽",
        "url": "https://www.ycombinator.com/launches/Od1-dedalus-labs-build-deploy-complex-agents-in-5-lines-of-code",
        "votes": 23,
        "created_at": "2025-10-15T18:10:39.053Z"
      }
    ]
  },
  {
    "id": 30611,
    "name": "AgentMail",
    "slug": "agentmail",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5c18d385fb2e41299574abf5d25f5ba98485d781.png",
    "website": "https://agentmail.to",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "AgentMail is the email inbox API for AI agents. It gives agents their own email inboxes, like Gmail does for humans.",
    "one_liner": "Email Inboxes for AI Agents",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1750990316,
    "tags": [
      "Developer Tools",
      "API",
      "Email"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2025",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/agentmail",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/agentmail.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/agentmailto",
    "x_url": "https://x.com/agentmail",
    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/agentmail-10e0",
    "github_url": "https://github.com/agentmail-to",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5c18d385fb2e41299574abf5d25f5ba98485d781.png",
    "app_video_url": null,
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    ],
    "primary_partner": {
      "name": "Ankit Gupta",
      "url": "https://www.ycombinator.com/people/ankit-gupta"
    },
    "founders": [
      {
        "user_id": 698162,
        "full_name": "Haakam Aujla",
        "title": "Founder/CEO",
        "founder_bio": "Founder at AgentMail\nPrev quant researcher at Optiver",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/haaaujla/",
        "x_url": "https://x.com/haakamaujla",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/49e356f9fbf79e77da670ff6c0df9fa27f1c193c.jpg"
      },
      {
        "user_id": 1149366,
        "full_name": "Michael Kim",
        "title": "Founder/CTO",
        "founder_bio": "Founder at AgentMail\nPrev @NVIDIA on Autonomous Vehicles\n\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/hyun-michael-kim-281242220/",
        "x_url": "https://x.com/michaelhyunkim",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fca64e6a76cd628a51463a3d516235c35583849e.jpg"
      },
      {
        "user_id": 1310091,
        "full_name": "Adi Singh",
        "title": "Founder",
        "founder_bio": "Founder at AgentMail. Previously invested at Accel, StepStone Group,and Flex Capital. Former operator across accounting, edtech, and e-commerce.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/adivirsingh13",
        "x_url": "https://x.com/adisingh",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a23a6aea2b77be113b65dfe676fd771bbb4ffd33.jpg"
      }
    ],
    "jobs": [
      {
        "id": 84801,
        "title": "Founding Engineer",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/x66bmSk-founding-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$150K - $250K",
        "equity_range": "0.50% - 2.00%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": [
          "Amazon Web Services (AWS)",
          "Node.js",
          "TypeScript",
          "DynamoDB"
        ]
      },
      {
        "id": 90991,
        "title": "Senior Engineer, Backend/Infra",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/952yKNS-senior-engineer-backend-infra",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$250K - $400K",
        "equity_range": "1.00% - 2.00%",
        "experience": "3+ years",
        "visa": "Will sponsor",
        "skills": [
          "Amazon Web Services (AWS)"
        ]
      },
      {
        "id": 99327,
        "title": "GTM Engineer",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/MZN8Drx-gtm-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$120K - $200K",
        "equity_range": "0.10% - 0.40%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104427,
        "title": "Founding GTM & Operations Lead",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/c2CPSue-founding-gtm-operations-lead",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$110K - $175K",
        "equity_range": "0.20% - 0.80%",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104428,
        "title": "Founding PLG & Community Lead",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/tlOY6Ny-founding-plg-community-lead",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Support",
        "role_type": null,
        "salary_range": "$110K - $175K",
        "equity_range": "0.20% - 0.70%",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 104429,
        "title": "Agent Experience Engineer",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/bTtTUQZ-agent-experience-engineer",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$125K - $190K",
        "equity_range": "0.30% - 0.80%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 113765,
        "title": "Developer Support Engineer",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/BrmNn3p-developer-support-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$100K - $140K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 115147,
        "title": "Founding Product Engineer",
        "url": "https://www.ycombinator.com/companies/agentmail/jobs/uBDHJL8-founding-product-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$200K - $250K",
        "equity_range": "0.50% - 0.75%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      }
    ],
    "news": [
      {
        "title": "AgentMail raises $6M to build an email service for AI agents | TechCrunch",
        "url": "https://techcrunch.com/2026/03/10/agentmail-raises-6m-to-build-an-email-service-for-ai-agents/?utm_source=dlvr.it&utm_medium=twitter",
        "date": "Mar 10, 2026"
      }
    ],
    "launches": [
      {
        "id": 91972,
        "title": "AgentMail - The API-first email provider for AI agents",
        "tagline": "Give AI agents their own email inboxes",
        "body": "# TLDR\n\nAgentMail is the first email provider for AI agents. Use our API to give your agents their own email inboxes.\n\n# Why?\n\nWe started building email agents because they can live within users’s inboxes in their inboxes and authenticate themselves with third-party applications. But building on top of legacy providers such as Gmail is a struggle. They\n\n* Lack API support for creating new inboxes\n* Charge monthly subscriptions per inbox\n* Impose restrictive rate and sending limits\n* Offer poor developer experience\n\nThese providers don’t scale with both inbox creation or volumes of emails sent/received that AI agents often face, so we’re rebuilding email infrastructure from scratch to enable this.\n\n# Solution\n\nAgentMail is an API-first email provider that gives agents their own inboxes to send, receive, and act upon emails. Think of it like Gmail, but with\n\n 1. Programmatic inbox creation and management\n 2. Usage-based pricing scaling with emails sent/received\n 3. No restrictive rate/sending limits\n 4. Real-time events over webhooks and websockets\n 5. Simple API key auth – no OAuth headaches\n 6. Built-in API permissions and agent guardrails\n 7. Semantic search across every inbox in organization\n 8. Automatic labeling with user-defined prompts\n 9. Structured data extraction\n10. And more on the way!\n\n# Demo\n\nIn this demo, we show how easy it is to build an email agent with AgentMail: https://youtu.be/1V7BISeFUTM\n\nIn this demo, we show an example of a voice + email agent built with AgentMail: [https://youtu.be/eG2fCsRK4RY](https://youtu.be/eG2fCsRK4RY?feature=shared)\n\n# Ask\n\nIf you are building agents that could use email capabilities, we would love to learn more about your company. You can reach us at [founders@agentmail.cc](mailto:founders@agentmail.cc)\n\nhttps://agentmail.to",
        "url": "https://www.ycombinator.com/launches/NvQ-agentmail-the-api-first-email-provider-for-ai-agents",
        "votes": 91,
        "created_at": "2025-07-08T22:02:41.115Z"
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    "id": 30612,
    "name": "truthsystems",
    "slug": "truthsystems",
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    "website": "https://www.truthsystems.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Truth Systems provides a compliance agent to govern the use of generative AI. It translates operational policies into intelligent, real-time guardrails that monitor every AI interaction. This empowers companies to proactively block non-compliant actions and align access to AI tools with user competency. By eliminating the risks and operational blind spots associated with uncontrolled AI adoption, our platform enables enterprises to fully leverage the technology's potential without compromising security. \r\n\r\nWe are establishing the new standard for AI governance, building a future where every professional can innovate safely and confidently.",
    "one_liner": "Automatically blocking risky behavior with real-time governance agents",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1753229882,
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    "batch": "Summer 2025",
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    "regions": [
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    "year_founded": 2023,
    "city": "San Francisco",
    "country": "US",
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        "user_id": 1314921,
        "full_name": "Alex Mac",
        "title": "Founder/CEO",
        "founder_bio": "Former AI Research Lead @ Stanford Law, focusing on Agentic Compliance. Published research on graph neural networks at 14. Lecturer @ University of Warsaw at the age of 15. Published with UCLA NLP in the NAACL at age 17. Industry advisor for Stanford LiftLab.",
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        "full_name": "Nam Nguyen",
        "title": "Founder",
        "founder_bio": "Former AI Researcher @ Stanford Law and presented to State Congress, EU Parliament, & Risk Functions at AmLaw 50. Former Product Manager @ MoMo (SEA fintech unicorn) shipping products to 300,000 MAU. Former Strategy Consultant at EY, advising nearly $30mn in transactions.",
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      {
        "id": 92878,
        "title": "truthsystems: AI Governance Agent for Regulated Industries",
        "tagline": "Building the Chief Compliance Officer at every single AI Interactions",
        "body": "![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdVRq9G6zxM14SvFxiMBUF0pK8ArLuZpJJePm0rkpzFPHtEaCYoGI4uz_9DnEJ7YYY4QM5FsGVm5DOO3Ja05maM1DW0_2TQHPom4vPTWbbpDuMg3qZNbtmYD9hOeyZZnu8OFh2muQ?key=RHZIlJDEf1i3HROkoIzKug)\n\n## **💬 TL;DR: GenAI introduces compliance & governance nightmares to all firms in regulated industries. We solve this by building an AI governance agent that monitors AI use and flags potential policy violations to end users.**\n\n## **💬 The Problem**\n\nLaw firms, banks, and other firms in regulated industries want to use more AI, but doing so without robust governance tools risks compliance breaches and client trust loss.\n\nThe problem is most acute in law firms, which we observed firsthand as AI researchers at Stanford Law School. **It stems from three primary challenges:**\n\n* **Unmanaged Liability:** There is no room for mistakes at law firms. Simple actions, such as uploading confidential files to AI tools or relying on AI-generated citations, can result in millions of dollars' worth of breaches and, more importantly, erode client trust.\n* **No Operational Control:** Firms can't see or control how employees use different AI tools, and they have no way to enforce the policies they write. \n* **Conflicting Client Mandates:** One client may require prior written approval for all AI tools, another might forbid their data on a specific matter from leaving the country, and a third bans AI entirely. Manually enforcing these unique rules for every client and their respective matter is impossible.\n\n**The result:** Firms hesitate to fully embrace AI, fearing reputational damage and costly mistakes; many ban AI or have already paid millions for errors discovered too late.\n\n**🏆 The Solution**\n\nTruth Systems provides firms with a compliance AI agent that reads firm policies and client guidelines, then converts them into dynamic guardrails that can be enforced on top of all AI usage\n\nHere’s an example of how it works:\n\n[Truth Systems - August 2025 Demo](https://www.loom.com/share/7cb045b4d9134727936f25dec2083c0f)\n\n## **🖖 Why We Build Truth Systems**\n\nWe are a team of AI nerds, researchers, and product builders who believe that responsible deployment of technology is the key to unlocking meaningful innovation in regulated industries.\n\nOur co-founders, [Alex Mac](mailto:alex@truthsystems.ai) and [Nam Nguyen](mailto:nam.nguyen@truthsystems.ai), were childhood best friends and AI researchers at Stanford Law under Dr. Megan Ma, collaborating with top law firms to address their concerns about this technology. Our founding engineer, Mikolaj Bochenski, was the 2nd hire at Legora, one of the largest Legal AI companies in the world, and understands intrinsically how legal AI is built and deployed. \n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdbC9GGtu-LK9MRGBCZ221JfsrfUvbLH0mwzGkSbLxxc6uVoBNvQrpedx3mIwvKClEP_K388qiV0_BoOYrYv29ysqqYI375twgSmROoTYp5Dd36Q33W5Fl0wQkgge5OTfn3TihV0g?key=RHZIlJDEf1i3HROkoIzKug)\n\nWe’re already working with two law firms with $2 billion in revenue combined, and helping nearly 1,500 attorneys use generative AI safely and responsibly. \n\n## **📈 The Opportunity**\n\nOur vision is to continue being the leading software that governs all AI use in regulated industries. Law firms are only the first steps for us. They are trusted advisors on governance and an important customer that helps us expand into other regulated industries. We envision a future where every knowledge professional can confidently and safely adopt the latest AI capabilities.\n\n## **⭐ Our Ask**\n\nWe are live and are onboarding law firms daily. If you know any:\n\n* Lawyers, Innovation folks, or Governance/ Info Sec Teams at Big Law, we should talk to\n* Front-end Devs who are passionate about cracking the hardest security problems in regulated industries\n\n \n\nPlease drop us a note at [nam.nguyen@truthsystems.ai](mailto:nam.nguyen@truthsystems.ai)\n\nMany thanks for your support! ❤️",
        "url": "https://www.ycombinator.com/launches/OA2-truthsystems-ai-governance-agent-for-regulated-industries",
        "votes": 39,
        "created_at": "2025-08-10T20:27:26.507Z"
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    "name": "LLM Stats",
    "slug": "llm-stats",
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    "website": "https://llm-stats.com",
    "all_locations": "New York City, NY, USA",
    "long_description": "We build independent and contamination-proof benchmarks that measure real world performance.\r\n\r\nLLM Stats is the most complete LLM leaderboard. We have the most complete archive of LLM benchmark results and also run independent evaluations that are not the classical ones that are already in the training data of most models.\r\n\r\nOur mission: become the biggest community dedicated to AI transparency.",
    "one_liner": "Independent AI evaluations lab",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1755052091,
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2025",
    "status": "Active",
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    "year_founded": 2025,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/llm-stats",
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        "full_name": "Jonathan Chávez",
        "title": "Founder",
        "founder_bio": "Co-Founder at LLM-Stats.",
        "is_active": true,
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        "full_name": "Sebastian Crossa",
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        "title": "ZeroEval - Build self-improving agents",
        "tagline": "A tool to evaluate and optimize AI agents using human feedback.",
        "body": "![uploaded image](/media/?type=post&id=93136&key=user_uploads/208921/328fd273-24d5-4307-9322-075a26467fe9)\n\nHey everyone, we're Sebastian and Jonathan - founders of ZeroEval.\n\n### **TL;DR**\n\nZeroEval is a tool that helps you build reliable AI agents through evaluations that learn from their mistakes and get better over time.\n\nhttps://www.youtube.com/watch?v=hSkpdHE7mCs\n\n### **The problem**\n\nEvaluating complex AI systems is hard and time consuming. The more complex your agents get, the harder this issue becomes. This is especially the case when building:\n\n1. Long-running, multi-turn agents with dozens of intermediate tool calls \n2. Agents where you want to measure the quality of images, video, generated UI, audio, personality, taste, etc\n\n**Current offline eval methods are high-friction**, a lot of work is needed to continuously curate labeled data and write experiments and evaluators. \n\nOn the other hand, **current LLM judges are static and often have terrible performance**, they lack context on how they fail and the nuances of the task at hand. \n\nYour AI agents are as good as your evals. Without them, surpassing the quality threshold your product needs will feel like a never-ending task.\n\n### **What we’re building**\n\nA way to create **calibrated LLM judges** that get better over time the more production data they see and the more incorrect samples are labeled. The more you teach it on where it's failing, the more reliable it becomes.\n\nOnce you have a judge that matches the human preference baseline, you can continue using it on production data or in offline experiments.\n\n![uploaded image](/media/?type=post&id=93136&key=user_uploads/208921/f815b7cf-a84e-4285-974d-1ffc943889ad)\n\nWe’re also introducing **Autotune**, a way to do automatic evaluation on dozens of models and prompt optimization based on a few human samples.\n\n![uploaded image](/media/?type=post&id=93136&key=user_uploads/208921/954461b0-5b4a-42b9-b464-0a698c3f6fd0)\n\nWe envision a future where AI software improves based on human feedback, where developers define the evaluation criteria as a starting point and errors back propagate to find the optimal implementation.\n\n### **The team**\n\nWe met during their first year of college in Mexico over 7 years ago. During that time they worked on side projects together, joined a leading fintech startup as first engineers and most recently built [llm-stats.com](http://llm-stats.com), a leading LLM leaderboard website that reached 60k MAU and  ⅓  million unique users since its launch a few months ago.\n\n* **Sebastian** was founding engineer at Micro building the future of email (backed by a16z), as well as founding engineer at Atrato (YC W21).\n* **Jonathan** was an early employee on the LLM observability team at Datadog. He did undergrad research on vision transformers for particle physics and RL for robotics.\n\nFoundational models have transformed the world. We’re building the second line of offense to fill their capability gaps and create AI products that actually work. We are determined to build the engine behind self-improving software for the following decades. \n\n![uploaded image](/media/?type=post&id=93136&key=user_uploads/208921/b912b213-762c-42ba-8fe5-dd39de8dd748)\n\n### **Our ask**\n\nIf you have AI agents in production and are struggling to measure their quality and/or achieve the reliability needed for your product's success, we’d love to chat!\n\nWe don't just deliver a tool, but will sit with you to understand your pain points and help you build high quality evals.\n\nFeel free to reach out at [founders@zeroeval.com](mailto:founders@zeroeval.com) or [book a demo](https://cal.com/team/zeroeval/demo).",
        "url": "https://www.ycombinator.com/launches/OEC-zeroeval-build-self-improving-agents",
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    "name": "Nuntius",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Applying artificial intelligence to economically productive use-cases requires bulletproof alignment. We’re solving that.",
    "one_liner": "Making Models Follow Rules.",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1754692935,
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    "year_founded": 2025,
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    "country": "US",
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    "id": 30650,
    "name": "Imprezia",
    "slug": "imprezia",
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    "website": "https://imprezia.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "The first advertising infrastructure designed specifically for AI apps. Seamlessly integrate contextual brands & products that enhance user experience while generating revenue to pay the inference bills.",
    "one_liner": "The World's First AI Ad Network.",
    "team_size": 4,
    "industry": "B2B",
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        "full_name": "Bishesh Khadka",
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        "role_type": "Machine learning",
        "salary_range": "$150K - $250K",
        "equity_range": "0.25% - 1.75%",
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        "visa": "US citizen/visa only",
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        "title": "🚀 Imprezia - World’s First AI Ad Network",
        "tagline": "We help brands get mentioned in AI responses – and we help developers get paid.",
        "body": "# **TL;DR:** MIT & Ex-Meta founder building the first ad network for AI chatbots. Brands pay to get mentioned and linked inside AI responses, and developers can monetize their AI apps.\n\n# **The Problem: Subscription only gets you so far…**\n\nMost AI apps today try to monetize with subscriptions, but just can’t find enough users who are willing to pay. \n\n→ **Inference bills are crushing** - AI compute costs 10-30x more than traditional apps\\\n→ **Subscription gates kill growth** - Only 3% of users typically convert\n\n→ **Can't scale to billions** - Free tiers = bankruptcy\\\n\\\nMeanwhile, Brands are dying to get mentioned organically inside of AI chat results.\n\nIt’s time for a new model.\n\n# **Our solution:** Imprezia brings contextual advertising directly into AI conversations.\n\n\\\nhttps://www.youtube.com/watch?v=wI7k7Byftks\n\n### **How It Works:**\n\n1. **One-line integration** - Drop in our SDK with an API key\n2. **Contextual ads** - We subtly include relevant brand mentions in your AI chat\n3. **Revenue sharing** - Developers get paid per impression or per click\n\n![uploaded image](/media/?type=post&id=92592&key=user_uploads/1228775/60ee6f8f-6eaf-40c1-b13f-72ac9e3b8aa6)\n\n# \n\n\\*\\*The Magic:\\*\\*\\\nIf a user asks about luxury stays in Tokyo, Imprezia might insert: \"Park Hyatt Tokyo” as an in line mention.\n\n![uploaded image](/media/?type=post&id=92592&key=user_uploads/1228775/2ff7e8cf-da9c-4074-bf5f-b6d9ee47349e)\n\n\\\n\\*\\*Brand Portal:\\*\\*\\\nAdvertisers can create campaigns and launch across our network of AI apps. \n\n![uploaded image](/media/?type=post&id=92592&key=user_uploads/1228775/3678028f-92fe-4e9c-8620-4dc9e45af275)\n\n**Win, Win, Win:**\n\n**For developers:** Monetize without subscriptions - earn premium CPMs\\\n**For users:** Valuable offers instead of noisy spam, within a free tier!\\\n**For brands:** Direct access to high-intent AI audiences\n\nIf you’re building an AI app or a brand looking to reach users within AI:\\\n✅ **Apply to get early access:** https://start.imprezia.ai \\\n💡 **Questions?** Please reach out to our founder, [bishesh@imprezia.ai](mailto:bishesh@imprezia.ai)\\\n\\\n**Team**:\\\nSince graduating MIT, [Bishesh](https://www.linkedin.com/in/bisheshkhadka/) has scaled billion dollar ad management & optimization systems Amazon ads and Meta.\n\n![uploaded image](/media/?type=post&id=92592&key=user_uploads/1228775/e2cfeb97-cf32-40af-a9bd-b0b18c336f32)\n\n",
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        "votes": 93,
        "created_at": "2025-07-30T23:49:33.889Z"
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    "long_description": "Pangolin delivers seamless access and connectivity across your apps, infrastructure, and AI workloads while continuously enforcing enterprise-grade security. Built for agile IT teams, Pangolin eliminates the friction of bloated, legacy systems with an open platform that’s easier to deploy for your workforce.\r\n\r\nIt's open-source.",
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    "industry": "B2B",
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        "full_name": "Milo Schwartz",
        "title": "Founder",
        "founder_bio": "I am the co-founder of Pangolin. Previously, I worked as a founding engineer on intelligent transportation systems and connectivity and monitoring solutions for public works infrastructure.",
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        "full_name": "Owen Schwartz",
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        "founder_bio": "I am the co-founder and CTO of Pangolin, an open source zero trust networking and infrastructure company. Before this, I worked as a founding engineer for 4 years on intelligent transportation systems and connectivity and monitoring solutions for public works. ",
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        "equity_range": "0.50% - 1.25%",
        "experience": "3+ years",
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      {
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        "title": "Pangolin – Open-source secure gateway to private networks",
        "tagline": "Pangolin tunnels your services to the internet so you can access anything from anywhere.",
        "body": "**TL;DR: [Pangolin](https://digpangolin.com) is an [open-source](https://github.com/fosrl/pangolin) cloud and self-hostable alternative to Cloudflare Tunnels, Zscaler ZPA, and Ngrok. Pangolin provides a simple way to securely expose applications on private networks with identity-aware access control and high availability.**\n\nWe’ve seen incredible early traction since we launched 5 months ago: **12,600+ GitHub stars, 140,000+ installs, 2,700+ Discord members**. \n\nhttps://youtu.be/0nv91PgCm9M\n\n\"Pangolin isn't just a fantastically easy-to-configure VPN and reverse proxy; it's a revelation in self-hosting simplicity.\" XDA Developers\n\n**The problem:**\n\nProviding users with secure access to internal tools and infrastructure is cumbersome for both end users and administrators. They're forced to manage complex VPN clients, servers, and network configurations that create friction and operational overhead. Traditional and mesh VPN approaches require layered ACL management to control which users can access specific resources, turning simple access requests into complex network engineering problems.\n\n**Our solution:**\n\nDrop in the Pangolin connector in your network, and:\n\n* **Expose anything to the internet:** Instantly create a secure tunnel with your domain and SSL to internal dashboards like Grafana, or any other services on your private network and access via web browsers.\n* **Manage access control with ease:** Grant access to your resources with user and role-based permissions, PIN codes, temporary share links, or email OTPs. Bring your existing users by integrating an external identity provider.\n* **Own your infrastructure:** Self-host your own Pangolin Nodes, so data flows through your servers, and our cloud will handle coordination and failover for out-of-the-box high availability.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfMGfOAnChfvZ2vFva0IbL-s3dj1O50IP8cfgGf7h9IDz3ViH_tHbtHEwr_bdecGPerQPY-HSRMtoRLmJew9IlaQqpQQY_1JJBluOAEKCqsWjvusZMgy_9Y5FPVvjo68nGNaZ0Uig?key=2GXukewhPJ8smnttAaub3w)\n\n\"A self-hosted version of Cloudflare Tunnels on your terms. By your terms, I mean on the infrastructure of your choice.\" Lawrence Systems\n\n**Deployment options:**\n\n* **Enterprise:** Self-host the entire system on-premises with complete control over all system components.\n* **Hybrid:** Self-host Pangolin Nodes on internal infrastructure while leveraging our cloud service for automatic failover coordination.\n* **Cloud:** Fully managed service where organizations only run network connectors to expose resources.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXc8cH83Sxqj5M57XuqQm_sofqW-Qn1sfTvTEsaTg4xYVAfRh_33XNMI8QDJrqibWqIQNVLLFHBob1vASk5ZnaX8KF5nZfY2eXYOwMp-n9UzOzyEHQUiocK89KT8izCeFG1CaEJ5xA?key=2GXukewhPJ8smnttAaub3w)\n\n**Get started:**\n\n* Website:[ https://digpangolin.com](https://digpangolin.com)\n* GitHub: https://github.com/fosrl/pangolin\n\n\"Its streamlined deployment has left janky WireGuard configurations and Cloudflare tunnels in its dust.\" [selfh.st](http://selfh.st)\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdXJ0wOsKzIrKFRWntnI4mgQRM3dcsgCMe7-t3poZP4A56J7SxiqqTdPU85bzNvvTXCO5Nuu7hRM2fXyvHrAbn55MzAd2N0J374OuDMoY8PdFgfa9jg9f2eRBgYzQqfCl-Srx3t?key=2GXukewhPJ8smnttAaub3w)\n\nWe are Owen and Milo Schwartz (brothers) who previously worked together as founding engineers at a successful smart mobility startup, working on connectivity and automation solutions for public infrastructure. We are passionate about building the next generation of simple networking tools.\n\n“I’m addicted to Pangolin” [u/ParadoxHollow](https://www.reddit.com/r/selfhosted/comments/1kqrwev/im_addicted_to_pangolin/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)\n\n**The ask:**\n\nWe’d love for the YC community to try out [Pangolin Cloud](https://pangolin.fossorial.io/auth/signup) and give us your feedback!\n\nWe are also looking for any warm intros to people who might be interested in the product - particularly businesses with on prem applications or field equipment in the IoT space.\n\n**Contact:**\n\n[founders@fossorial.io](mailto:founders@fossorial.io)",
        "url": "https://www.ycombinator.com/launches/O0B-pangolin-open-source-secure-gateway-to-private-networks",
        "votes": 85,
        "created_at": "2025-07-18T07:08:50.979Z"
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    "name": "Induction Labs",
    "slug": "induction-labs",
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    "website": "https://inductionlabs.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We are scaling foundation models that are curious about the world.\r\n\r\nWe’ve seen general intelligence appear exactly once, and its hallmark has been curiosity: a dissatisfaction with what we already know about the world, an ability to learn from world experience, and a disposition to share knowledge with other individuals. Across generations, these human traits have built modern science, technology, and culture.\r\n\r\nWe think superintelligence will come from models that work the same way: learning from their own observation, updating as they go, growing what they know. These models will start from what humanity knows and discover past us.\r\n\r\nWe believe that scaling curious intelligence is a defining problem of our time. If solved, we will live to see a new era of technological advances, scientific understanding, and human flourishing.",
    "one_liner": "Building intellectually curious AI",
    "team_size": 2,
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        "id": 107033,
        "title": "Induction Labs - foundation models that learn from observation",
        "tagline": "Unlocking scalable video pretraining with imagination models",
        "body": "**TL;DR: We're Induction Labs, and today we're announcing research we've been working on: imagination models, a foundation model architecture that unlocks scalable learning from internet video. Our first model, Photon-1, beats a production LLM on internal computer use benchmarks with 30x less training compute, at 3x lower serving cost. \\[[Main Article](https://inductionlabs.com/news/scaling-video-pretraining)\\]**\n\n![uploaded image](/media/?type=post&id=107033&key=user_uploads/2757445/94f0365c-7de2-4792-ad4e-a8362f27c233)\n\nInternet video contains millions of hours of people using computers, doing skilled work, and interacting with the world.\n\nImagination models can scalably learn from this video to get better at completing tasks and understanding the world.\n\nWe tested the architecture with Photon-1, a 106B-A5B MoE transformer pretrained on 18 years of computer screen recordings.\n\n**Some Results:**\n\n* **Outperforms a production LLM (Gemini 3.1 Flash-Lite) on our internal computer use benchmarks**, with 30x less pretraining compute and 3x cheaper inference.\n\n![uploaded image](/media/?type=post&id=107033&key=user_uploads/2757445/7e217b7a-bbf0-40b0-a160-94855bbf7e90)\n\n* **Simulates full desktop sessions from a single screenshot**: it can imagine VS Code, Gmail, and ChatGPT frame by frame\n* **Generalizes beyond computers**: finetuned, it learns checkers and simulates billiard physics better than LLM baselines.\n* **Picks up human behavior from video**, like reprompting ChatGPT until it gets what it wants.\n* [**Demos here**](https://inductionlabs.com/news/scaling-video-pretraining)\n\nWe see imagination models as a path to intelligence that learns by observing the world directly, without a human first translating it into text. \n\n**Our Ask**\n\nWe’re working to scale this method to more kinds of video and looking for exceptional people to work with us! If you’re an exceptional thinker, engineer, or researcher (or know anyone who would be a great fit) - shoot us a message at [team@inductionlabs.com](mailto:team@inductionlabs.com)",
        "url": "https://www.ycombinator.com/launches/RqL-induction-labs-foundation-models-that-learn-from-observation",
        "votes": 15,
        "created_at": "2026-07-23T16:02:32.265Z"
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    "long_description": "Riverbank is an infrastructure cartography company. We map system topology, dependencies, interfaces, and reachable pathways across heterogeneous environments.",
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        "full_name": "Carter Pry",
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    "id": 30710,
    "name": "Manufact",
    "slug": "manufact",
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    "website": "https://manufact.com",
    "all_locations": "ZH, Switzerland",
    "long_description": "Manufact is the MCP cloud platform to get your product ready for Claude Connectors marketplace, ChatGPT / Codex plugins store and any AI agent that supports MCP.\r\nWe're the team behind mcp-use SDK, the open-source framework for building MCP Apps with 10k github stars and 10M+ downloads across python and typescript.\r\n\r\nIt's the fastest and most complete solution for production-ready MCP servers.",
    "one_liner": "Cloud infrastructure for MCP servers and Claude / ChatGPT plugins.",
    "team_size": 5,
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        "full_name": "Luigi Pederzani",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder @ Manufact (fka mcp-use). \nPrev. Founding eng. @ Morgen (ETH spin-off AI startup used by teams at Spotify, GitHub, Linear, and Canva). \nLed a 12-engineer team to deliver $XXM projects @ Accenture Switzerland.\nMSc in Computer Science and Engineering from POLIMI (QS #1 IT, #5 EU, #13 World).\nFormer YouTube creator (150k subs, 15M+ views).\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/luigipederzani",
        "x_url": "https://x.com/pederzh",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/052187b02cbea99b02766098b2c86fcf33610ec4.jpg"
      }
    ],
    "jobs": [
      {
        "id": 97959,
        "title": "Developer Advocate & Partnerships (DevRel)",
        "url": "https://www.ycombinator.com/companies/manufact/jobs/4cyWd6S-developer-advocate-partnerships-devrel",
        "location": "San Francisco, CA, US / Remote",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$100K - $160K",
        "equity_range": "0.10% - 0.70%",
        "experience": "Any (new grads ok)",
        "visa": "Will sponsor",
        "skills": []
      },
      {
        "id": 97962,
        "title": "Senior Infrastructure Engineer",
        "url": "https://www.ycombinator.com/companies/manufact/jobs/Dh6PYP5-senior-infrastructure-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Devops",
        "salary_range": "$100K - $180K",
        "equity_range": "0.10% - 0.70%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Amazon Web Services (AWS)",
          "Microsoft Azure",
          "Google Cloud",
          "TypeScript"
        ]
      },
      {
        "id": 107000,
        "title": "Sales Development Representative (SDR)",
        "url": "https://www.ycombinator.com/companies/manufact/jobs/fWWRPrO-sales-development-representative-sdr",
        "location": "San Francisco, CA, US / Remote (US)",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$60K - $100K",
        "equity_range": "0.05% - 0.20%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 107005,
        "title": "Founding Software Engineer (Product / MCP) ",
        "url": "https://www.ycombinator.com/companies/manufact/jobs/ZyxwEJC-founding-software-engineer-product-mcp",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$100K - $160K",
        "equity_range": "0.10% - 1.00%",
        "experience": "1+ years",
        "visa": "Will sponsor",
        "skills": [
          "React",
          "TypeScript"
        ]
      },
      {
        "id": 116901,
        "title": "Founding Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/manufact/jobs/kp7OyFp-founding-forward-deployed-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$100K - $160K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "Will sponsor",
        "skills": [
          "Python",
          "TypeScript"
        ]
      }
    ],
    "news": [
      {
        "title": "Manufact raises $6.3M as MCP becomes the ‘USB-C for AI’ powering ChatGPT and Claude apps | VentureBeat",
        "url": "https://venturebeat.com/infrastructure/manufact-raises-usd6-3m-as-mcp-becomes-the-usb-c-for-ai-powering-chatgpt-and",
        "date": "Mar 11, 2026"
      },
      {
        "title": "Make Something Agents Want — Humans Are No Longer The Customer",
        "url": "https://www.forbes.com/sites/dariashunina/2026/03/11/make-something-agents-want---humans-are-no-longer-the-customer/",
        "date": "Mar 09, 2026"
      },
      {
        "title": "The Most Promising Startups To Watch From YC Summer 2025 Batch: mcp-use",
        "url": "https://www.forbes.com/sites/dariashunina/2025/09/04/the-most-promising-startups-to-watch-from-yc-summer-2025-batch/",
        "date": "Sep 04, 2025"
      }
    ],
    "launches": [
      {
        "id": 107462,
        "title": "Manufact: MCP Cloud for your Claude Connector & ChatGPT Plugin",
        "tagline": "Get your product on Claude and ChatGPT and usable by AI Agents ",
        "body": "Hey everyone, we're the team behind [**mcp-use**](https://github.com/mcp-use/mcp-use), the open-source MCP framework with **10k GitHub stars** and **\\~10M+ downloads**.\n\n[**Manufact**](https://manufact.com/) is the cloud platform that gets your product onto the **Claude Connectors marketplace** and the **ChatGPT (Codex) Plugin Store** via MCP.\n\n<https://youtu.be/Pyh_3tKDXuU>\n\n## The problem\n\nPaul Graham recently tweeted: \"_AI apps are the new browsers_\". \\\nYour customers now do most of their work inside Claude and ChatGPT, and the connector marketplaces are a new distribution channel: someone installs your product in one click and uses it inside the chat they already live in.\n\nWe listed Manufact on Claude and saw a **surge of signups** that is still going on now.\n\n![uploaded image](/media/?type=post&id=107462&key=user_uploads/859771/65a0b3fd-75cf-4eea-a2ee-02590e3256ba)\n\nBut shipping a production-ready MCP server is its own project:\n\n* MCP is a protocol evolving fast .\n* Auth is easy to get [wrong](https://manufact.com/blog/authentication).\n* Every client behaves differently.\n* The stores have their own conformance requirements, and review can block you for weeks.\n\n## How Manufact works\n\nManufact is the cloud built specifically for MCP:\n\n* **Push to deploy.** Connect the GitHub App once and [every commit ships a new version of your MCP server](https://manufact.com/platform/hosting), with a preview deployment for each PR.\n* **Test every MCP tool and MCP-UI widget** in the [cloud inspector](https://manufact.com/platform/cloud-inspector).\n* **Know the moment your MCP breaks** in ChatGPT or Claude with [automatic cross-client testing](https://manufact.com/platform/cross-client-testing).\n* [**Publishing checklists**](https://manufact.com/platform/publishing-checks) so your MCP app doesn't get stuck in marketplace review.\n* **Auto-generated submission assets** (logo, copy, screenshots).\n* **Analytics, session replay, and traces** built in for MCP, so you can see [how it's used and why tools fail](https://manufact.com/platform/analytics).\n\n![uploaded image](/media/?type=post&id=107462&key=user_uploads/859771/ee30e016-9fb5-4958-890f-74febc54de3c)\n\n## Your product's UI inside the chat\n\nA plain MCP server lets the agent read and write your data. \\\nAn **MCP App** goes further: it's an MCP server that can return a UI as the result of a tool call, so your actual product interface renders inside Claude or ChatGPT. The user sees your charts and forms instead of a wall of text.\n\nFerruccio Balestreri (Amplitude) [put it well](https://x.com/0xferruccio/status/2028966278971670979):\n\n> since mcp ui was released in claude we got a 2x boost in retention.\n\nWith the [mcp-use SDK](https://github.com/mcp-use/mcp-use) you write a normal React component, drop it in, and it registers as an MCP UI resource automatically.\n\n## Backstory\n\nWe've been building on MCP since the protocol shipped. It started with the mcp-use SDK, the full-stack framework for MCP servers and apps. We kept watching companies struggle to take those servers to production, so we built the cloud for it (mcp-use is to Manufact what Next.js is to Vercel).\n\nManufact is now the **#1 recommended deployment option in OpenAI's docs.** \n\n![uploaded image](/media/?type=post&id=107462&key=user_uploads/859771/ddba2350-67c4-43d5-8150-30847d4375a2)\n\nWe've helped startups from recent YC batches like **AgentMail** and **Item** get onto the Claude Connectors marketplace, and larger companies in finance, insurance, and marketplaces run their MCP apps on Manufact in production.\n\n## Our asks\n\n1. Have an MCP server? **Deploy it on [Manufact](https://manufact.com/)**.\n2. Thinking about building an MCP App? [Copy our prompt for agents](https://manufact.com/prompt.md) and your coding agent will guide you.\n3. Trying to get listed on the marketplaces? [Book a call with us](https://cal.com/team/manufact/chat). We have publishing checks and direct contacts on the MCP teams at Anthropic and OpenAI.\n\nHope to see your MCP on [manufact.com](http://manufact.com), or that you at least learned something new. 🙂\n\n![uploaded image](/media/?type=post&id=107462&key=user_uploads/859771/685643a6-ecd5-42b4-b55f-ec394967ca57)\n\n",
        "url": "https://www.ycombinator.com/launches/RxG-manufact-mcp-cloud-for-your-claude-connector-chatgpt-plugin",
        "votes": 47,
        "created_at": "2026-07-28T14:55:15.055Z"
      },
      {
        "id": 92418,
        "title": "Manufact (mcp-use) - Build and deploy MCP Servers for AI Agents & MCP Apps for ChatGPT / Claude users",
        "tagline": "Create MCP servers/apps in minutes, connect your GitHub repo, and make them immediately available to AI agents and ChatGPT / Claude.",
        "body": "### **TL;DR**\n\n[Manufact](https://manufact.com) is the MCP cloud for dev teams to ship MCP Apps and Servers in production. Connect a GitHub repo and Manufact handles deployment, security, observability, and scaling, so teams can ship in minutes instead of weeks.\n\nOne codebase, deployed everywhere users and agents already live: the ChatGPT Apps Store, Claude Connectors, Gemini, Cursor, Claude Code, and more.\n\nWe're also the creators of [mcp-use SDK](https://github.com/mcp-use/mcp-use), the most popular high-level MCP framework for building MCP Apps and Servers.\\\n(mcp-use SDK is to the Model Context Protocol SDKs what Next.js is to React!)\n\nIt’s open-source and used by developers at 20% of US500 and companies like 6sense, Elastic, IBM, Innovacer, Intuit, NVIDIA, Oracle, Red Hat, Tavily, and Verizon are building with us.\n\n### **The ask**\n\nIf you're building MCP Apps or Servers, or you know someone who is, we'd love to talk.\n\n<https://cal.com/team/manufact/chat> \\\nor shoot a message to us: +1 (628) 899-2498 \\\nor [founders@manufact.com](mailto:founders@manufact.com)\n\n### **Our story**\n\nWhen MCP came out, we couldn't believe such a powerful protocol was locked into IDEs and Claude Desktop.\n\nSo we built mcp-use, the open source framework that lets any developer build MCP Apps and Servers in code, in a structured and composable way.\n\nIt got adopted fast: 7M+ downloads across Python and TypeScript, almost 10k GitHub stars, and dev teams at top companies shipping with it.\n\nBut OSS is only half the story. Once teams had something working, they hit the wall every infra team knows: deployment, auth, secrets, observability, scaling. Stitching that together for MCP is a multi-week project. That's what Manufact solves.\n\n### **Make something agents want**\n\nMCP unlocks something most products have never had access to before: real estate inside ChatGPT and Claude.\n\n800M+ users already spend their day in those chats. \\\nWith MCP, a product can show up there as a native app or connector, with no separate UI to build, no app to install, no new login flow. \\\nThe user just asks, and your product does the work.\n\n### **Problem**\n\nThe catch is that \"build once, ship everywhere\" is its own infra problem. ChatGPT Apps, Claude Connectors, Gemini, and the long tail of agent surfaces (Cursor, Claude Code, Codex, OpenAI Agents, Mastra, LangChain, Vercel AI SDK) each have their own quirks, auth flows, and submission requirements. Manufact lets you build one codebase and ship it to all of them.\n\n### **What Manufact does**\n\nConnect your MCP repo once. Every push auto-deploys.\n\n1. **Build:** Scaffold a full MCP App or Server with _npx create-mcp-use-app_ or drop in existing MCP code unchanged.\n2. **Deploy:** GitHub App connects once. Every push auto-deploys to Manufact Cloud. Preview URL per branch. Custom domains with SSL handled.\n3. **Publish.** Get your app in front of users in the ChatGPT Apps Store and Claude Connectors. Submission assets, marketplace checklists, and an embeddable chat widget for your own site, docs, or campaign pages.\n4. **Iterate.** Cloud Inspector debugs from any browser against real clients. Swap models. Run evals on every deploy.\n5. **Monitor.** Analytics, session replay, JSON-RPC traces, and alerts on regressions, so you catch issues before users do. Less than 60 seconds from _git push_ to a live MCP App or Server, available across ChatGPT, Claude, Gemini, and any agent surface that speaks MCP.\n\n### **Ask**\n\nDo you know anyone already using MCP servers internally or building products powered by AI agents?\n\nFor the first few companies we will personally create/integrate MCP servers you need, have everything running through our platform, either on cloud or your own infrastructure. We even help you integrate our SDK into your code.\n\nWhat do you think? Can we help you?\n\nBook a call: <https://cal.com/team/manufact/chat>\n\nShoot a message to us: +1 (628) 899-2498 or [founders@manufact.com](mailto:founders@manufact.com)",
        "url": "https://www.ycombinator.com/launches/O2c-manufact-mcp-use-build-and-deploy-mcp-servers-for-ai-agents-mcp-apps-for-chatgpt-claude-users",
        "votes": 85,
        "created_at": "2025-07-24T17:55:36.172Z"
      }
    ]
  },
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    "id": 30712,
    "name": "Relling",
    "slug": "relling",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e003594279b3a4474845b643ff2ee16c26f34a1a.png",
    "website": "https://relling.co",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "",
    "one_liner": "deploying the first billion robots for manufacturing",
    "team_size": null,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1756070413,
    "tags": [
      "Robotics",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2025",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/relling",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/relling.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/107829108/",
    "x_url": "",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e003594279b3a4474845b643ff2ee16c26f34a1a.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
    },
    "founders": [
      {
        "user_id": 2707563,
        "full_name": "Anya Singh",
        "title": "Founder/CTO",
        "founder_bio": "chess player",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/anya-singh/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/180178bdbd6050c5bf36936482b695846168c79a.jpg"
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    "jobs": [
      {
        "id": 95370,
        "title": "Robot Learning Engineer",
        "url": "https://www.ycombinator.com/companies/relling/jobs/iQ4YH9e-robot-learning-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Robotics",
        "salary_range": "$120K - $170K",
        "equity_range": "0.10% - 0.30%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 95371,
        "title": "Partnerships Lead, Specialty Manufacturing",
        "url": "https://www.ycombinator.com/companies/relling/jobs/U34pXE8-partnerships-lead-specialty-manufacturing",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$120K - $170K",
        "equity_range": "0.10% - 0.30%",
        "experience": "6+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
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        "id": 97830,
        "title": "Business Operations Intern",
        "url": "https://www.ycombinator.com/companies/relling/jobs/ZFJwHfg-business-operations-intern",
        "location": "San Francisco, CA, US",
        "type": "Internship",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$20 - $100 / hourly",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
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        "id": 99957,
        "title": "Software Engineer Intern",
        "url": "https://www.ycombinator.com/companies/relling/jobs/H005USc-software-engineer-intern",
        "location": "San Francisco, CA, US",
        "type": "Internship",
        "role": "Engineering",
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        "salary_range": "$20 - $60 / monthly",
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        "visa": "US citizen/visa only",
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        "id": 99958,
        "title": "Software Engineer, Perception ",
        "url": "https://www.ycombinator.com/companies/relling/jobs/nX7Gp7O-software-engineer-perception",
        "location": "San Francisco, CA, US",
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        "experience": "Any (new grads ok)",
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        "title": "Growth and Content Lead Intern",
        "url": "https://www.ycombinator.com/companies/relling/jobs/UrTtzOc-growth-and-content-lead-intern",
        "location": "San Francisco, CA, US",
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        "title": "Marketing Lead Generation Specialist (Paid & Search)",
        "url": "https://www.ycombinator.com/companies/relling/jobs/ipBV0cN-marketing-lead-generation-specialist-paid-search",
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    "id": 30715,
    "name": "Autumn",
    "slug": "autumn-ai",
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      "Autumn AI"
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    "website": "https://autumn.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Autumn builds research agents that answer questions about people and companies at scale. These AI agents are powered by a live index of over 1 billion people, 100 million companies, and state-of-the-art web research tools. Autumn is already in production with enterprise teams at Brex, Rippling, PostHog across sourcing, enrichment, and identity-verification workflows.\r\n\r\nWe are a small, highly-technical team. Our vision is to be the search engine that agents use when they need to understand people, their networks, and professional lives.",
    "one_liner": "The people-intelligence company.",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1770313792,
    "tags": [],
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    "isHiring": true,
    "nonprofit": false,
    "batch": "Winter 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/autumn-ai",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/autumn-ai.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/autumn-ai-inc/",
    "x_url": "https://x.com/autumn_ai_inc",
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e9869ac74d16e219c2c3c7a63c96b676e8281762.png",
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    "company_photos": [],
    "primary_partner": {
      "name": "Ankit Gupta",
      "url": "https://www.ycombinator.com/people/ankit-gupta"
    },
    "founders": [
      {
        "user_id": 2663239,
        "full_name": "Vishnu Sampathkumar",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder, CEO @ Autumn AI (W26)",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/vishnu-sampathkumar/",
        "x_url": "https://x.com/VishnuSampathk1",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c143a3866c4750a8447b594870391f423d50d7a1.jpg"
      },
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        "user_id": 2136449,
        "full_name": "Shiv Kampani",
        "title": "Founder",
        "founder_bio": "Co-founder, CTO @ Autumn",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/shivkampani",
        "x_url": "https://x.com/shiv_kampani",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/240d6b0be46645e6a61ba02acca71a6dcdc5f620.jpg"
      }
    ],
    "jobs": [
      {
        "id": 116933,
        "title": "Harness Lead",
        "url": "https://www.ycombinator.com/companies/autumn-ai/jobs/gj5dGt0-harness-lead",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "AI engineer",
        "salary_range": "$140K - $220K",
        "equity_range": "1.00% - 2.00%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 116934,
        "title": "Search Lead",
        "url": "https://www.ycombinator.com/companies/autumn-ai/jobs/Zwr7HGy-search-lead",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$180K - $300K",
        "equity_range": "1.00% - 3.00%",
        "experience": "1+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 117418,
        "title": "Founding Growth",
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        "id": 97547,
        "title": "Autumn AI: Stalk your prospects at scale.",
        "tagline": "Autumn is building the first real-time signal intelligence platform for GTM teams.",
        "body": "Hi, we’re Vishnu and Shiv, and we’re super excited to introduce Autumn! \n\n**Tl;dr.** GTM teams waste hours manually tracking prospects across the internet. Autumn gives you a live feed of your entire target universe, so you're always first to know and first to act.\n\n<https://youtu.be/v37JMhX8GVc>\n\n**The problem.** Your best prospects are already telling you when and how to sell to them through posts, tweets, commits, blogs. These signals are valuable but decay fast. To catch signals in real time, you'd need to monitor dozens of sources per person, synthesize unstructured content across platforms, and surface intent the moment it appears. This is tedious for one prospect; imagine trying a few thousand.\n\n**The solution.** Autumn tracks your prospects across the internet, at scale. Define your ICP and the signals that matter, and we deliver a condensed, real-time feed filtered by intents. Connect to email or Slack and get notified the moment a target prospect's behavior matches. Here are some workflows that Autumn is optimized for:\n\n* **Incorporation prospecting:** Surface new startup founders as soon as they incorporate their companies and track early signals around banking, payroll, ML infra.\n* **Enterprise account monitoring:** Build org trees and track entire departments within target accounts to see buying signals emerge across the org. \n* **Conference prep:** Monitor attendee lists ahead of events and prioritize outreach to the people already showing interest in what you sell.\n\n![uploaded image](/media/?type=post&id=97547&key=user_uploads/2663239/08071f20-30cb-4045-88e4-078e13e9a1a4)\n\n**The team.** We met on the first day of orientation at Columbia and we've been best friends ever since. Shiv is an AI researcher who presented at NeurIPS after a research stint with SandboxAQ. Vishnu built sourcing tech for VC firm Sierra Ventures and worked as an ML engineer for Modulus, an AI security startup.\n\n**Our ask.** If you’re a founder or GTM leader interested in prospect tracking at scale, we’d love to talk to you. Feel free to book a time on our [Calendly](http://calendly.com/founders-autumnai/30min-1) or email us at [founders@autumnai.com](mailto:founders@autumnai.com).",
        "url": "https://www.ycombinator.com/launches/PNL-autumn-ai-stalk-your-prospects-at-scale",
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        "created_at": "2026-02-05T18:35:28.767Z"
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        "title": "Miso Labs - emotive voice models",
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        "title": "Modelence: all-in-one TypeScript platform for startups",
        "tagline": "Ship production AI apps 10x faster with built-in auth, database and LLM integration",
        "body": "You built the demo in a weekend. Now you need auth, a prod-grade database, real-time events, AI observability, cron jobs, email, monitoring… and you needed them yesterday.\n\nWe spent a decade building platforms while scaling to millions of users and enterprise customers. Now we put that into an open-source framework, so others wouldn’t have to do the same from scratch.\n\n![uploaded image](/media/?type=post&id=92593&key=user_uploads/2485356/d6404f4b-fb88-47d1-adcc-b895394e41af)\n\nModelence is a batteries-included framework that abstracts away boilerplate so you can just focus on building your product logic and nothing else - it’s like Next.js + Vercel + Supabase on a single platform, built for full-stack TypeScript.\n\nhttps://www.youtube.com/watch?v=S4f22FyPpI8\n\n**Why Modelence?**\n\nShipping production apps is much harder than building a prototype that runs locally and 95% of all production apps share the same building blocks. We did a massive amount of work on our previous platforms that was not specific to our product, and we’ve created Modelence to abstract that away:\n\n* Authentication\n* Database setup\n* Hosting / CDN\n* AI integration (LLM observability, prompts)\n* Real-time events\n* Cron jobs\n* Email service\n* Monitoring\n* Analytics\n* Admin dashboards\n\nAndrej Karpathy recently posted about this same exact problem - [**https://x.com/karpathy/status/1905051558783418370**](https://x.com/karpathy/status/1905051558783418370) and explained it further in his [**YC AI Startup School talk**](https://tinyurl.com/45xvsm3y).\n\n**Who is this for?**\n\nStartups that are shipping TypeScript production apps and want to focus on building their product without worrying about boilerplate, DevOps, infra and scaling.\n\n**Founders**\n\n* Aram S (ex-Principal Eng @ Intuit, co-founded [**CodeSignal**](https://codesignal.com/))\n* Eduard P, PhD (Founding Eng @ [**CodeSignal**](https://codesignal.com/))\n\n**Getting Started**\n\nModelence is open-source - go to https://modelence.com to create a free cloud environment and try it!",
        "url": "https://www.ycombinator.com/launches/O5R-modelence-all-in-one-typescript-platform-for-startups",
        "votes": 19,
        "created_at": "2025-07-30T23:49:26.229Z"
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    "id": 30804,
    "name": "Luminal",
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    "long_description": "Luminal builds an ML framework and compiler that generates GPU code.\r\nOur stack 10x's model speed while simplifying deployment and cutting idle GPU costs\r\n\r\nGithub: https://github.com/luminal-ai/luminal\r\nDiscord: https://discord.gg/APjuwHAbGy",
    "one_liner": "Making AI run fast on any hardware.",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "full_name": "Joe Fioti",
        "title": "Founder/CEO",
        "founder_bio": "Generating GPU kernels automatically to speed up ML models. Ex-Intel, worked on CPU microcode and ML accelerators.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/joe-fioti-24a986189",
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        "full_name": "Matthew Gunton",
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        "founder_bio": "Co-founder at Luminal AI. Ex-Amazon engineer, with globally deployed projects automatically finding issues in the Amazon fulfillment network and cost effectively fixing them",
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        "full_name": "Jake Stevens",
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        "title": "Compiler Engineer",
        "url": "https://www.ycombinator.com/companies/luminal/jobs/GAXg6qE-compiler-engineer",
        "location": "San Francisco, CA, US",
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        "location": "San Francisco, CA, US",
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        "title": "Senior Compiler Engineer",
        "url": "https://www.ycombinator.com/companies/luminal/jobs/7Bgke65-senior-compiler-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$200K - $350K",
        "equity_range": "0.50% - 2.00%",
        "experience": "Any (new grads ok)",
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        "title": "Luminal raises $5.3 million to build a better GPU code framework | TechCrunch",
        "url": "https://techcrunch.com/2025/11/17/luminal-raises-5-3-million-to-build-a-better-gpu-code-framework/",
        "date": "Nov 17, 2025"
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    "launches": [
      {
        "id": 92298,
        "title": "Luminal - PyTorch for Production",
        "tagline": "Luminal automatically speeds up and deploys your PyTorch models",
        "body": "Hey we’re Joe, Jake, and Matthew, cofounders of Luminal. \n\nLuminal is an [open-source](https://github.com/jafioti/luminal) ML compiler that generates blazingly fast CUDA kernels and makes deploying AI to production one line of code. We’re already powering research at Yale, production workloads at VC-backed startups and several research labs.\n\nhttps://youtu.be/Rgo15NY9K-8\n\n**TLDR:**\n\n* Our drop-in upgrade to PyTorch automatically generates complex GPU kernel code, like Flash Attention, with zero hand-engineering\n* We provide truly serverless deployments; no idle costs or cold starts.\n* By not relying on pretrained LLMs or hand-written optimizations, we can reliably speed up any model\n\n**Ask:**\n\n* [Book a meeting](https://calendly.com/accounts-luminalai/30min) if you write PyTorch and want your models to run fast\n* [Star us on github](https://github.com/jafioti/luminal) and try Luminal right now on your own laptop!\n* Email us if you know someone who runs custom models: [contact@luminalai.com](https://mailto:contact@luminalai.com)\n\n—\n\n### **The problem:**\n\nMost people running their own AI models are lighting money on fire and don’t even know it. AI teams are frustrated when moving their model from dev to production. They either fall into dependency hell or kill their model’s speed. Today, **companies waste millions of dollars on GPU engineering** teams to optimize new models before they can be served to users.\\\n—\n\n### **How Luminal solves it:**\n\n**Luminal replaces a process that companies pay GPU engineers $300k+ a year to do.**\n\nOur key insight is that by treating optimization as a search problem, we’re able to automatically discover extremely complex optimizations in minutes that would take a GPU expert weeks. We use a series of ‘rewrite rules’ to create millions of graphs to describe your model, generate kernel code for each and then search for the fastest one based on runtime.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXfNzLo_zAkiYY_fsEQMseEQZXpdFjn0chhSJ8Wn-0WOkSHGblAi1XseXLIbZsiim6z8_AiWxx6wuzTuCQYfNe2ELulijuOBR5b_1x4-kzEoxEoxutW0qv127MHbl1Lu6YLIJBfP_A?key=gRJ545K4hfVVtN0eX-o_ZQ)\n\n_Example of a complicated kernel on the left that Luminal automatically sped up, on the right._\n\nLuminal is:\n\n1. **Closing the research → engineering gap.** Because we auto-generate optimized GPU code directly from your PyTorch, a brand-new model can go from research notebook to production in hours instead of weeks\n2. **Writing blazing fast models.** Luminal generates kernels that would take a seasoned GPU engineer weeks of painstaking profiling and tuning to write.\n3. **Future-proofing AI for new chips**. We can rerun our compiler on fresh hardware to get new optimal kernels so you're never out of date.\n\n—\n\n### **You should reach out if:**\n\n* You’re an AI researcher or ML engineer\n* You’d like to lower your compute bill by **tens of thousands of dollars a month**\n* You work at a company that runs their own **custom models**\n\nIf you want your team spending more time making the world’s best models and less time optimizing hardware and cloud infrastructure, we’re for you!\n\n—\n\n### **The Team:**\n\n[Joe](https://www.linkedin.com/in/joe-fioti-24a986189/) - ex Intel, every Intel chip sold has the AI accelerator Joe worked on. He has extensive experience optimizing performance at the silicon level.\n\n[Matt](https://www.linkedin.com/in/matthewjgunton/) - ex Amazon where his software handled finding and automatically fixing issues within the global inventory network 24/7\n\n[Jake](https://www.linkedin.com/in/jake-stevens-/) - ex Apple Jake worked on imaging at Apple for your iPhone. He’s been a founder (with an exit) and head of growth at another startup that he grew to \\~$5M ARR.\n\nSend us an email: [contact@luminalai.com](https://mailto:contact@luminalai.com)\n\n[Book a call with us](https://calendly.com/accounts-luminalai/30min)\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXd1hRLOasxYQ38r0AmMN9fTC9-BatUjGjE9-BXM21-w8PRvhBJVeYAc3pWlLPVlmTtnP3jG9Pa5HmUIGTYaZ93ClC-M-CRx0nkNw-DgS3Wh28xWXaUzXVwL_XPCNUL8JyTd_9FmBA?key=gRJ545K4hfVVtN0eX-o_ZQ)\n\n",
        "url": "https://www.ycombinator.com/launches/O0g-luminal-pytorch-for-production",
        "votes": 127,
        "created_at": "2025-07-20T17:12:19.542Z"
      }
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  },
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    "id": 30835,
    "name": "Brickanta",
    "slug": "brickanta",
    "former_names": [
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    "website": "https://www.brickanta.com",
    "all_locations": "Stockholm, Stockholm County, Sweden",
    "long_description": "Brickanta  – agentic AI for society builders. Hundreds of construction-specific AI agents for project analysis, insights, tenders, procurement, and more. Combine the latest AI technology with your organization's project data, templates, and workflows to identify risks and opportunities early and produce better decision support. Brickanta has raised $8 million from leading AI, construction, and real estate investors behind companies like OpenAI/ChatGPT, Airbnb, Klarna, Spotify, and Plangrid-Autodesk, as well as star investors such as Mario Götze, Anton Osika, and Northzone (see movie and press). The founding team has been building and implementing AI in construction and industry since 2018 at companies such as ABB, Fabege, Husqvarna, IKEA and Konecranes.\r\n",
    "one_liner": "Agentic AI for Society Builders",
    "team_size": 25,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1759093006,
    "tags": [
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      "Construction",
      "AI",
      "AI Assistant"
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      "Europe"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/brickanta",
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    "city": "Stockholm",
    "country": "SE",
    "linkedin_url": "https://www.linkedin.com/company/brickanta/",
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      "url": "https://www.ycombinator.com/people/pete-koomen"
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        "user_id": 654502,
        "full_name": "Lucas Otterling",
        "title": "Founder/CEO",
        "founder_bio": "Building Brickanta - agentic AI for society builders (construction and real estate). GTM-focused co-founder, ex-professional footballer and ex-indie app developer. International digital nomad with experience from living in Sweden, the US, the UK, Germany, South Africa, Italy. Vipassana Meditator :-)\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/lucas-otterling/",
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        "user_id": 2911315,
        "full_name": "Linus Bein Fahlander",
        "title": "Founder/CTO",
        "founder_bio": "Building Brickanta (F25) together with Lucas Otterling.\nWe are trying to give time back to one of the most critical roles in construction companies, the estimator.\nWe believe that builders shouldn't spend heaps of time searching through 1000s of documents - they should be building",
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        "title": "Business Intern - Summer 2027",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/LFdioyl-business-intern-summer-2027",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Operations",
        "role_type": null,
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        "experience": "Any (new grads ok)",
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      {
        "id": 88846,
        "title": "Construction Engineer - Bygg / Entreprenadingenjör (Swedish) ",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/4o0G3W1-construction-engineer-bygg-entreprenadingenjor-swedish",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Product",
        "role_type": null,
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        "title": "Software / AI Engineering",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/DKZym3b-software-ai-engineering",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$1K - $100K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizenship/visa not required",
        "skills": []
      },
      {
        "id": 88848,
        "title": "Open Application - if you’re a builder, reach out",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/RPicnce-open-application-if-you-re-a-builder-reach-out",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Product",
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        "experience": "Any (new grads ok)",
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      {
        "id": 88849,
        "title": "GTM Engineer - build the bridge between product and revenue",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/i9EAeCM-gtm-engineer-build-the-bridge-between-product-and-revenue",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$1K - $100K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizenship/visa not required",
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      {
        "id": 108060,
        "title": "Business Developer ",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/Ug4Njo7-business-developer",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$1K - $100K",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizenship/visa not required",
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      },
      {
        "id": 108061,
        "title": "Construction Evaluation Engineer - Part-Time",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/KfaKQd2-construction-evaluation-engineer-part-time",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Contract",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$1K - $100K / monthly",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizenship/visa not required",
        "skills": []
      },
      {
        "id": 108062,
        "title": "Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/2fZBSaD-forward-deployed-engineer",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
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        "experience": "3+ years",
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      {
        "id": 108063,
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        "url": "https://www.ycombinator.com/companies/brickanta/jobs/s9Efs6z-account-executive",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Full-time",
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      },
      {
        "id": 108065,
        "title": "Developer Intern - Summer 2027",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/XmiJoZB-developer-intern-summer-2027",
        "location": "Stockholm, Stockholm County, SE",
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        "id": 108188,
        "title": "Solution Engineer - Integrations Focus",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/9PcN4kH-solution-engineer-integrations-focus",
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        "id": 110236,
        "title": "Brand Marketing Manager",
        "url": "https://www.ycombinator.com/companies/brickanta/jobs/8raDa8o-brand-marketing-manager",
        "location": "Stockholm, Stockholm County, SE",
        "type": "Full-time",
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      {
        "id": 94176,
        "title": "Brickanta 🧱 the construction estimator's best friend 👷‍♂️🏗️",
        "tagline": "Cursor for construction estimation & project management",
        "body": "👋 We’re [Brickanta](https://brickanta.com/) (F25), building _cursor for construction estimation & project management._\n\n**TL;DR**: Our AI platform analyzes construction estimates and project details to automate admin work, reduce errors, and accelerate delivery. 👉 _We’d appreciate your social media hype support and any potential intros, as we look to make more connections in the construction and real estate industry._\n\n**Why this matters**\\\nConstruction projects run on mountains of paperwork, drawings, and codes. Miss one item, and budgets blow up — every year, it costs the small European country of Sweden over $11 billion (according to the government itself) and the global industry _hundreds of billions_ every year. Estimators and PMs are the heroes who keep projects on track, but today they’re buried in manual work.\n\n**How Brickanta helps**\\\nOur AI reads and organizes project docs, flags missing items & compliance risks, and supports cost analysis, tendering, and planning. It’s trained on standardized building protocols (starting with the Swedish versions of the Eurocodes, soon more countries) and integrates the systems that construction already runs on, like MS SharePoint, Outlook, and Teams — helping teams work faster, catch risks earlier, and protect margins.\n\n🎥 YC Launch Video:\n\nhttps://www.youtube.com/watch?v=za8Ya5GxMRE\n\n\\*\\*Excited to support?\\*\\*\\\n👷‍♂️🛠️👷‍♀️ We’re officially launching this week with exclusive media interviews, first public customer story, new brand + website, and more. Please check out and follow our newly created channels listed at the bottom to be part of the journey — and offer some hyping support :-)\\\n\\\n🏗️ We’d also love intros into construction and real estate companies - our MVP is currently focused on European markets (that share the fundamental [Eurocodes](https://eurocodes.jrc.ec.europa.eu/) framework)- where estimation and project management are critical.\\\n\\\n🧱🏗️  [**YouTube**](https://www.youtube.com/@Brickanta) │ [**LinkedIn**](https://www.linkedin.com/company/brickanta) │ [**X**](https://x.com/brickanta)  │ [**Facebook**](https://www.facebook.com/people/Brickanta/61581151975393/)  │ [**Instagram**](https://www.instagram.com/brickanta/?fbclid=IwY2xjawM7_n1leHRuA2FlbQIxMQBicmlkETBwMHVWZlByYk04ZW5iYnEyAR7vPpHY7lQF8Ow_5pNc9wsKP5V4us-fiDMJeepUZEwbeblZ39mJOYOA4lr1Eg_aem_5RLOiHcY_lmVUvys8OicmA) │ [**TikTok**](https://www.tiktok.com/@brickanta)\n\nYou can reach the founders on [founders@brickanta.com](mailto:founders@brickanta.com).\n\n🎥  Construction-audience-focused commercial launch video: https://www.youtube.com/watch?v=mwDEeXxtiQE",
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    "long_description": "Hyperspell is your Company Brain. AI agents are brilliant and clueless. They ace any test and still have no idea how your company works. Hyperspell connects your tools and synthesizes documents and conversations into a live, permissioned context graph. Any agent can read from it and write back to it like a filesystem, with every fact traced to source.",
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    "city": "San Francisco",
    "country": "US",
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      "url": "https://www.ycombinator.com/people/tom-blomfield"
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        "full_name": "Conor Brennan-Burke",
        "title": "Founder/CEO",
        "founder_bio": "Founder of Hyperspell, Context & Memory for AI Agents",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/conorbrennanburke/",
        "x_url": "https://x.com/conor_ai",
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        "user_id": 31259,
        "full_name": "Manu Ebert",
        "title": "Founder",
        "founder_bio": "4x Founder, 2 exits, 15 years in AI & ML",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/maebert",
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        "title": "Founding AI Engineer",
        "url": "https://www.ycombinator.com/companies/hyperspell/jobs/MvG48cH-founding-ai-engineer",
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        "equity_range": "0.50% - 1.50%",
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        "title": "Founding Platform Engineer",
        "url": "https://www.ycombinator.com/companies/hyperspell/jobs/rMoV1RO-founding-platform-engineer",
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        "experience": "6+ years",
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        "title": "Founding Forward Deployed Engineer",
        "url": "https://www.ycombinator.com/companies/hyperspell/jobs/iLXJT3r-founding-forward-deployed-engineer",
        "location": "San Francisco, CA",
        "type": "Full-time",
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        "equity_range": "0.50% - 1.50%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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          "Python",
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      {
        "id": 94666,
        "title": "Hyperspell: Memory for AI Agents",
        "tagline": "Give your agents long-term memory and context from your entire workspace",
        "body": "[**Hyperspell**](https://hyperspell.com) **is the memory layer for AI agents.** We help developers connect data from tools like Slack, Gmail, Notion, and Google Drive so their agents can recall, reason, and learn over time.\n\n🎬 Watch our launch video: https://youtu.be/t6ji-n9qHYo?si=rALHJ2KaTiAieEvq\n\n---\n\n### Hi there!\n\n**Conor** led a $30m ARR API business at Checkr, building infrastructure used by tech companies like Doordash and Airbnb. **Manu** has 15+ years of Machine Learning experience. His previous ML/AI startup got acquired by Airbnb, where he went on to build Airbnb’s first Knowledge Graph. Manu has also owned .ai domains since 2014 when he bought one for his first startup (back then he had to send a fax to Antigua to get one).\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/5e5241c7-861b-4213-b864-c448b6091fba)\n\n> ---\n\n### **AI Agents are clueless geniuses**\n\nToday, AI agents show up like it’s their **first day on the job** — sometimes impressive, but with no awareness of your context, institutional knowledge, history, and preferences. Worst of all, without the ability to learn and improve, they will only ever be as good as they are on day one.\n\n---\n\n### **Hyperspell fixes that.**\n\nWith our SDK, developers can add a context and memory layer to any agent in minutes. Pre-built connectors let users connect their tools like Gmail, Notion, and Slack, so that **memory doesn’t start from zero**: Hyperspell will analyze all existing files and conversations, figure out the important people, projects, and facts, and make it all available to your agent through our Agentic Memory Network.\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/036418ad-5a83-4da0-95d0-c939ec3fafa2)\n\nBest of all, **it improves with every single interaction:** Our novel approach to memory reinforces what’s being recalled, and helps your agent improve over time.\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/282eb1cf-bd7f-49b5-81e3-a5fe74e53896)\n\n### **But wait, there’s more!**\n\nThere’s lots of different ways our customers use Hyperspell, and because of that there’s lots of different ways you can integrate Hyperspell into your own agent. To make this easier, we created the **first ever self-integrating SDK**. \n\nWatch how we go from zero to production-ready PR on an unknown codebase with our custom Claude Code skill:\n\nhttps://www.youtube.com/watch?v=gfYsXJJsX3k\n\n# **Our Asks:**\n\n* Book a demo right now: https://cal.com/conor-brennan-burke/\n* Spread the word on linkedin and twitter: \\\n  [https://www.linkedin.com/company/hyperspell](https://www.linkedin.com/company/hyperspell/%5C)\\\n  https://twitter.com/hyperspell\n* Share your feedback with [conor@hyperspell.com](mailto:conor@hyperspell.com)\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/f032811f-ff1e-4f87-af1c-edd2f051e25f)\n\n",
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        "votes": 426,
        "created_at": "2025-10-15T14:47:48.970Z"
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    "id": 30891,
    "name": "SF Tensor",
    "slug": "sf-tensor",
    "former_names": [
      "Emma Lang"
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    "website": "https://sf-tensor.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "AI researchers should be pushing the boundaries of what's possible with new architectures and training methods. Instead, they waste weeks configuring cloud infrastructure, debugging distributed systems, and optimizing their GPU code. We know because we lived it: While training our own models across thousands of GPUs earlier this year, we spent more time fighting our infrastructure than doing actual research.\r\n\r\nThat's why we're building two things. First, Elastic Cloud: a managed platform that automatically finds the cheapest GPUs across all providers, handles spot instance preemption, and cuts compute costs by up to 80%. Second, automatic kernel optimization that makes training code run faster by modeling hardware topology, often beating hand-tuned implementations.\r\n\r\nThe problem is that getting high performance across different hardware is genuinely hard. NVIDIA's CUDA moat exists because writing fast kernels requires deep expertise. Most teams either accept vendor lock-in or hire expensive kernel engineers. Our goal is to break the CUDA moat.\r\n\r\nThe compute bottleneck is the biggest constraint on AI progress. NVIDIA can't manufacture enough GPUs, and their monopoly keeps prices astronomical. Meanwhile, AMD, Google, and Amazon are shipping capable alternative hardware that nobody uses because the software is too hard. We're breaking that moat. If we succeed, anyone will be able to train state-of-the-art models without thinking past their PyTorch code.",
    "one_liner": "Infrastructure for AI labs to focus on research.",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?",
        "answer": "AI labs and startups should focus on breakthrough research—new architectures, training methods, the stuff that ends up in papers. Instead, they burn countless hours configuring cloud infrastructure, debugging distributed training, and negotiating GPU deals. \r\n\r\nWe know because we lived it. While training our own models, we realized we were spending 60% of our time on infrastructure and 40% on actual research. Talking to other teams, we found everyone had this problem. Small labs can't afford dedicated infra teams. Large labs waste research talent on DevOps. \r\n\r\nThis infrastructure tax is holding back AI progress. We decided to eliminate it. "
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        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "Today, wanting performance means vendor lock-in to NVIDIA. This creates artificial scarcity: limited production capacity, sky-high prices, and compute monopolies. By making all hardware from AMD GPUs, TPUs, Trainium, whatever comes next, equally usable, we'll unlock massive new supply. \r\n\r\nIn this world, anyone can train state-of-the-art models without ever thinking past their PyTorch code. Startups won't need infrastructure teams. Researchers won't waste time on cloud configuration. CEOs won't negotiate GPU rental deals. \r\n\r\nCompute will be abundant, cheap, and boring. And AI research will accelerate because researchers can finally focus on research. "
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    "url": "https://www.ycombinator.com/companies/sf-tensor",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/sf-tensor.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
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      "url": "https://www.ycombinator.com/people/harj-taggar"
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        "user_id": 801244,
        "full_name": "Ben Koska",
        "title": "Founder/CEO",
        "founder_bio": "Optimizing GPU Kernels @ SF Tensor.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/benkoska",
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        "full_name": "Luk Koska",
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        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/lukkoska/",
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        "user_id": 2950892,
        "full_name": "Tom Koska",
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        "id": 107006,
        "title": "Member of Technical Staff, Product Engineering",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/gkuqeM2-member-of-technical-staff-product-engineering",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$225K - $275K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 107007,
        "title": "Member of Technical Staff, Sandbox Infrastructure",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/plO372C-member-of-technical-staff-sandbox-infrastructure",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$275K - $315K",
        "equity_range": "",
        "experience": "6+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 107008,
        "title": "Member of Technical Staff, Post-Training & Applied Research",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/IgxqFjw-member-of-technical-staff-post-training-applied-research",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$275K - $315K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 107009,
        "title": "Member of Technical Staff, GPU Kernels",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/1zQSFYC-member-of-technical-staff-gpu-kernels",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$285K - $315K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 107010,
        "title": "Member of Technical Staff, GPU Compiler",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/ZJL2GLy-member-of-technical-staff-gpu-compiler",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$285K - $315K",
        "equity_range": "",
        "experience": "6+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 107011,
        "title": "Member of Technical Staff, AI-Driven Compilation",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/ZvA4OCy-member-of-technical-staff-ai-driven-compilation",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$275K - $315K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      },
      {
        "id": 112314,
        "title": "Member of Mathematical Staff",
        "url": "https://www.ycombinator.com/companies/sf-tensor/jobs/abpmgWv-member-of-mathematical-staff",
        "location": "San Francisco",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$225K - $315K",
        "equity_range": "",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
        "skills": []
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 95098,
        "title": "SF Tensor - Infrastructure for the Era of  Large-Scale AI Training ⚡",
        "tagline": "We tweak kernels to squeeze out extra speed, hunt down the cheapest GPUs across every cloud and make your models train on any hardware. Train frontier models without the DevOps nightmare. ",
        "body": "Hey everyone 👋 \n\nWe’re [Ben](https://www.linkedin.com/in/benkoska/), [Tom](https://www.linkedin.com/in/tomkoska/) and [Luk](https://www.linkedin.com/in/lukkoska/) - co-founders of SF Tensor. \n\n**TL;DR:** We let AI researchers forget about the infrastructure layer and focus on their research. \n\nWe automatically optimize kernels to run faster, find the cheapest GPUs across every provider and migrate your jobs when spot instances fail. Training AI should be about AI, not DevOps. \n\n**_Ask:_** Know anyone training or fine-tuning AI models? We’d be grateful for an intro! Reach out to us at [founders@sf-tensor.com](mailto:founders@sf-tensor.com). \n\n![uploaded image](/media/?type=post&id=95098&key=user_uploads/801244/994bfb29-1b66-4506-86bd-8b3d0197eff4)\n\n## **The Problem**\n\nTraining AI should mean developing smarter architectures and finding better data. But right now, it doesn’t. Teams waste their time on everything but actual research: \n\n* Optimizing code so that training runs don’t drain the bank \n* Fighting cloud providers and scrambling for GPU availability \n* Making distributed training work with reasonable MFU (=cost efficiency).\n\nThis drives up costs, frustrates everyone and kills velocity. Infrastructure has inadvertently turned into the limiting factor for AI research labs, and it’s killing progress.\n\nWe experienced this first-hand developing our own foundation models – what we expected to be AI research, experimentation and iterative improvement turned out to be an ugly mix of writing CUDA, debugging driver mismatches and optimizing inter-GPU collective operations. That’s why we decided to solve the infrastructure layer, to allow other researchers to **focus on research, not infrastructure**.\n\n## **Our Solution**\n\nSF Tensor is the \"set it and forget it\" infrastructure layer for anyone training or fine-tuning AI models. Hook up your repo, pick your GPU count and budget, and we deal with the rest:\n\n* Our automatic kernel optimizer analyzes your architecture and tunes execution for any hardware (NVIDIA, AMD or TPUs). No more having to drop down into custom CUDA because PyTorch doesn’t understand memory topology. \n* We find the cheapest available compute across all clouds for your specific requirements and launch your training run. \n* Automatic Distributed Training allows you to scale from 1 to 10,000 GPUs without having to change your code or killing your MFU \n* Everything else that you shouldn’t have to think about: Spot instance migration? Handled. Monitoring? Baked in. Logs and artifacts? Done.\n\n![uploaded image](/media/?type=post&id=95098&key=user_uploads/801244/89170605-19e4-4029-87dd-7a3fa85cc9df)\n\n## **The Team**\n\n![uploaded image](/media/?type=post&id=95098&key=user_uploads/801244/519e2798-579a-43e1-8c96-28f3be87a392)\n\nWe’re 3 brothers that have been working on Artificial Intelligence together for years, most recently training our own Foundational World Models. SF Tensor was born out of our own needs as AI researchers scaling up training runs to thousands of concurrent GPUs.\n\nBen has been publishing AI research since high school, solo-training models across 4,000 GPUs as co-PI on a 6-figure grant.\n\nTom and Luk (twins btw) have been doing AI research for years, from starting college in parallel to high school at age 14 to finishing their BSc in CS (at age 16).\n\n## **Compute should be boring. Let’s make it boring.**\n\nTry us right now at [sf-tensor.com](http://sf-tensor.com) or contact us at [hello@sf-tensor.com](mailto:hello@sf-tensor.com) to see how we can help with your infra pains.\n\n![uploaded image](/media/?type=post&id=95098&key=user_uploads/801244/3ca5b95d-168d-402c-a899-5a42965ee527)\n\n",
        "url": "https://www.ycombinator.com/launches/Ojq-sf-tensor-infrastructure-for-the-era-of-large-scale-ai-training",
        "votes": 23,
        "created_at": "2025-11-04T18:36:08.802Z"
      }
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  },
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    "id": 30899,
    "name": "Sciloop",
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    "website": "https://sciloop.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Sciloop creates expert-level math and physics problems that frontier AI models can't solve, then sells the data to AI labs for training and evaluation. Our problems are created by IPhO and IMO medalists — the top 0.01% of STEM talent globally. On our benchmark, models like GPT 5.4 Pro and Gemini 3.1 Pro score 0-5% on our hardest problems. We work with AI labs to supply continuous, fresh training data that pushes the frontier of mathematical and scientific reasoning. Founded by Bilal and Osman, International Physics Olympiad medalists from MIT with hands-on ML research experience at MIT CSAIL.\r\n",
    "one_liner": "Expert STEM reasoning data for frontier AI labs.",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1761248663,
    "tags": [
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      "Data Labeling",
      "Big Data",
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    "batch": "Fall 2025",
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    "url": "https://www.ycombinator.com/companies/sciloop",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/sciloop.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/sciloop-labs",
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      "name": "Aaron Epstein",
      "url": "https://www.ycombinator.com/people/aaron-epstein"
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      {
        "user_id": 1933114,
        "full_name": "Bilal Asmatullah",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder of Sciloop (YC F25), On Leave From MIT\nInternational Physics Olympiad ’22 medalist.",
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        "linkedin_url": "https://www.linkedin.com/in/bilal-asmatullah-8705692b4/",
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        "full_name": "Osman Siddique",
        "title": "Founder",
        "founder_bio": "CTO and co-founder of Sciloop (YC F25). \nCS student at MIT (on leave). Medalist and two-time highest national scorer at the 2022 International Physics Olympiad.",
        "is_active": true,
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      {
        "id": 94951,
        "title": "Sciloop: The AI Scientific Researcher",
        "tagline": "Come up with novel hypotheses, run experiments, analyze results, and write the paper — all automatically.",
        "body": "**TL;DR: We’re making the AI that does your research for you.[ Book a call.](https://calendly.com/osman-sciloop/30min)**\n\nHi.\n\nWe’re building **Sciloop**, a platform that automates the entire scientific research loop from hypothesis generation to experiment design, execution, analysis, iteration, and drafting real research papers.\n\nhttps://youtu.be/GNM8iA6p9Ao\n\n**Our Story:**\n\nWe’re Bilal and Osman, IPhO medalists and former MIT students with 4 years of experience applying ML in various research domains. While working at MIT CSAIL, we saw how large language models could help with literature review and even generate new hypotheses. Stanford’s recent study ([Can LLMs Generate Novel Research Ideas?](https://arxiv.org/abs/2409.04109))  showed that LLM-generated hypotheses can be more novel than those from domain experts. We saw the same in our own work.\n\n![uploaded image](/media/?type=post&id=94951&key=user_uploads/2718484/3a420779-570b-4a69-b3ee-628644695a3e)\n\nBut after generating ideas, everything slowed down. We spent hours designing experiments, writing code, debugging compute setups, and rerunning failed jobs that could crash after 12 hours. Even with tools like Cursor that helped us code faster, the overall loop from idea to insight was still painfully manual. Researchers everywhere face this same bottleneck.\n\nSo we asked ourselves: what if the research loop itself could be automated?\n\nSo we built **Sciloop**. It takes a research intent — a question or hypothesis — and automatically designs, runs, and monitors experiments in parallel on our own managed cloud compute. It tracks metrics, analyzes results, recommends next steps, and finally drafts a paper summarizing methods and findings. Sciloop turns days of busywork into hours of progress.\n\nOur goal is simple: **to completely automate scientific discovery**. The next decade will see a century of progress packed into a few years. As AI grows smarter and more accessible, its biggest impact will be speeding up science — finding new cures, discovering better materials, and driving breakthroughs in physics and math.\n\nThat’s our north star. But we’re not waiting for the future. We’re helping ML researchers move faster **right now**, automating the parts of their workflow that slow them down most. Sciloop is already being used in MIT research groups and by early partners working on real ML problems.\n\nIf you’re a company, research lab, or ML team struggling with model performance, experimentation speed, or scaling research, we’d love to work with you. Our goal is to deliver concrete improvements on your research challenges today.\n\n🚀 **The Ask - Let’s Make Research Self-Improving!**\n\nIf you’re working on ML research that could move faster, **book a quick chat** at https://calendly.com/osman-sciloop/30min or email us at [**team@sciloop.dev**](mailto:team@sciloop.dev).\n\nWe help brilliant research ideas stop gathering dust. This is for anyone who:\n\n* Runs **university or industry ML labs** drowning in experiments.\n* Is a **research student** buried under mountains of code and papers.\n* Leads a **company dreaming of R&D** but lacking scientists or resources.\n* Has a **head full of creative ideas** but no time to bring them to life.\n\nLet’s collaborate and accelerate your research—together.\n\n\\[1\\][ Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://arxiv.org/abs/2409.04109)",
        "url": "https://www.ycombinator.com/launches/OhT-sciloop-the-ai-scientific-researcher",
        "votes": 33,
        "created_at": "2025-10-29T01:25:09.249Z"
      }
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    "id": 30913,
    "name": "Captain",
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    "website": "https://captain.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Captain makes product catalogs and complex, multimodal files searchable for customers and AI agents. It syncs files from sources like S3, SharePoint, and Google Drive and tunes search to your data and workloads.\r\n\r\nInstead of stitching together parsers, embeddings, vector databases, rerankers, and retrieval infrastructure, developers use Captain to power product search and file retrieval through managed APIs.\r\n\r\nOn evaluated file-search benchmarks, Captain’s tuning improved retrieval accuracy from roughly 78% with standard RAG to over 97%.\r\n\r\nBuilt for complex catalogs and demanding file-search workloads, including regulated and high-stakes applications.",
    "one_liner": "Self-tuning search for products and files",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1762492786,
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      "API",
      "Data Engineering",
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      "Infrastructure"
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    "batch": "Winter 2026",
    "status": "Active",
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/captain",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/captain.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/runcaptain/",
    "x_url": "https://x.com/captaindotdev",
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    "cb_url": "https://www.crunchbase.com/organization/captain-technologies",
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      "name": "Garry Tan",
      "url": "https://www.ycombinator.com/people/garry-tan"
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        "full_name": "Lewis Lien Polansky",
        "title": "Founder/CEO",
        "founder_bio": "CEO @ Captain, building self-tuning search for products & files. Engineer, designer, founder of a cybersecurity CTF org, recipient of U.S. Congressional recognition for community software, and randomly co-invented the soy-based milk carton.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/lewispolansky/",
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        "full_name": "Edgar Babajanyan",
        "title": "Founder/CTO",
        "founder_bio": "CTO @ Captain | Published AI NLP Researcher | Built OCR engines @ Boar's Head | Scaled high-performance RAG pipelines for the past 4 years | Built a Cross Region L2 Datacenter",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/edgar-babajanyan-a28230217/",
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      {
        "id": 95291,
        "title": "Captain - The Reliable Alternative to RAG",
        "tagline": "Search files with much higher accuracy than RAG (Avg improves 78% → 95%)",
        "body": "**TL; DR**: Captain delivers **the most accurate** general-purpose **knowledge search** engine **ever built.** \n\nIf you need to search through large text or multimodal files, and:\n\n* Don’t have time for **a tedious RAG sprint** -or-\n* Your current search pipeline lacks accuracy\n\nWe should talk. → [runcaptain.com/sales](https://runcaptain.com/sales?utm_source=ycombinator&utm_medium=launchyc&utm_campaign=launchyc&utm_content=weshouldtalk1) or [founders@runcaptain.com](mailto:founders@runcaptain.com)\n\nhttps://www.youtube.com/shorts/RxMPHqou94o\n\nIn a world where **90% of enterprise knowledge** cannot be stored in traditional databases, this ‘unstructured data’ is an **untapped goldmine** for decision-making. \n\nThe issue is that current RAG solutions have poor retrieval quality overall and are only performant on pre-optimized question types. \n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/425e4da0-2c47-43ea-a2c5-5dc32b2168fd)\n\n**Captain** unlocks **better responses** thanks to an effectively **Infinite Context window**. We distribute it across many LLMs in parallel + some embeddings sprinkled in, and then ‘Map-Reducing’ responses down to a single output.\n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/4be36851-322b-4836-be35-8e447cea51cd)\n\nWithout context limits, we have great freedom to optimize our retrieval engine for maximal accuracy. We can now play with a truly dynamic top-k or just run the LLM exhaustively (if a full knowledge audit is needed).\n\n![uploaded image](/media/?type=post&id=95291&key=user_uploads/2044865/e373def8-4aec-4add-b2a6-7d5fd9a02cb3)\n\n**We’re two lifelong builders** obsessed with data! \n\n* Lewis (right) solved hallucinations for code generation through his previous startup. \n* Edgar (left) researched NLP+AI and has built production RAG pipelines for the past 3 years.\n\nWe’ve seen the pains and limits of these systems from the start, and **the industry is primed for a more accurate alternative.** \n\nThis past summer, we met with every engineer we could at **Snowflake** and **Databricks**, and we kept hearing the same thing again and again: \n\n> There’s **no good** scalable **unstructured** data **search**.\n\nUntil **today**.\n\n**Captain abstracts the engineering of retrieval entirely.** Connecting files is all that’s needed, and we’ll beat your RAG's accuracy. Guaranteed. \n\n**Our Ask**\n\nIf you know a **CTO** or **Head of AI** at a mid-market or enterprise **AI-native** (or are building one yourself), we would love an introduction!\n\nCheck us out at [RunCaptain.com](https://runcaptain.com?utm_source=ycombinator&utm_medium=launchyc&utm_campaign=launchyc&utm_content=website)\\\nor reach us directly through [founders@runcaptain.com](mailto:founders@runcaptain.com)\\\n\\\nHappy Shipping! 🚢",
        "url": "https://www.ycombinator.com/launches/Omx-captain-the-reliable-alternative-to-rag",
        "votes": 115,
        "created_at": "2025-11-07T19:59:17.455Z"
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    "id": 30935,
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    "long_description": "S2 is an API for durable streams. We make working with streaming data dead simple and reliable.\r\n\r\nThe more we worked on data systems, the more we felt like there was a missing building block. The seamless experience of object storage simply did not exist for durable streams – so we set out to fix that!\r\n\r\nS2 reimagines streams as an unlimited and access-controlled web resource. Like if Kafka and S3 had a baby.\r\n\r\n- Building agents? Use granular streams to persist context in real time, make workflows auditable, and coordinate between agents.\r\n\r\n- Publishing fast-moving data? Streams support high fanout and can be accessed directly over the internet, so they are a great fit for broadcasting real-time feeds.\r\n\r\n- Sandboxing? Emit to per-instance streams, and read history or live events just as easily.\r\n\r\n- Local-first and multiplayer experiences? Propagate updates reliably and efficiently.\r\n\r\nStop dealing with clusters, and start streaming.",
    "one_liner": "API for unlimited, durable, real-time streams",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "year_founded": 2024,
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    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/streamstore/",
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      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
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        "user_id": 235940,
        "full_name": "Shikhar Bhushan",
        "title": "Founder/CEO",
        "founder_bio": "Making streams plentiful and serverless. Hacked on retrieval systems at Etsy, a KV store at Facebook, and Kafka Connect at Confluent.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/shikhrr",
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      {
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        "full_name": "Stephen Balogh",
        "title": "Founder/CTO",
        "founder_bio": "Loves Rust, and building systems that scale. Previously built novel infra for multi-modal product search at Etsy, and digital repository software at NYU",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/stephen-balogh-078a4995/",
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        "id": 95319,
        "title": "s2.dev: Streams as a cloud storage primitive",
        "tagline": "The serverless API for unlimited, durable, real-time streams",
        "body": "Hi everyone! We are [Shikhar](https://www.linkedin.com/in/shikhrr/), [Stephen](https://www.linkedin.com/in/stephen-balogh-078a4995/), and [Dwarak](https://www.linkedin.com/in/dwarakgovindparthiban/), the co-founders of [s2.dev](http://s2.dev). S2 is the S3 for streams.\n\nhttps://www.youtube.com/watch?v=8-mUPf4P_8g\n\nMany of the most important features in products we love are powered by streaming data, from frontend UIs that go _brrr_ with real-time updates, to backends that store and process massive event flows. But building these sorts of real-time systems is really hard.\n\nWe are turning streams into a cloud storage primitive – just like object storage did for static files.\n\nS2 is a completely serverless datastore, offering unlimited streams and configurable (even bottomless) data retention. Streams can be appended to and followed in real-time, and since all writes are durable, you can also read from anywhere in a stream’s history, indexed by sequence number or timestamp.\n\nWe basically took the core abstraction of Kafka – [the log](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying) – gave it a URI, and brought it online with a dead simple API. And none of the bloat. 😀\n\nhttps://www.youtube.com/watch?v=2a1OdJ9l78A\n\n## Why we’re building S2\n\nWe built large-scale ML infra at Etsy and Meta, and always felt the lack of a system like S2. Serverless infra for data at rest has been seamless for a long time (S3 launched 20 years ago!) – but the ecosystem around streaming data, in comparison, felt stuck in the stone age.\n\n![uploaded image](/media/?type=post&id=95319&key=user_uploads/235940/72f8cb1e-a550-4458-b57b-d5936dc98191)\n\nWhen we [first introduced S2](https://news.ycombinator.com/item?id=42480105), we thought it would be most attractive to people already using streaming platforms like Kafka – but we’ve found that it resonates much more with builders in areas where these systems are entirely absent, who end up cobbling together general-purpose stores like Postgres and Redis and run into their limits.\n\nWe stand out on dimensions that are completely off the map for legacy systems: like the ability to have an unlimited number of streams backed by object storage, and to make them directly accessible [over REST](https://s2.dev/docs/rest/records/overview). This means S2 streams can be used as a serverless, reliable alternative to custom WebSocket or SSE infra.\n\n## S2 radically simplifies streaming\n\nIf you are dealing with a **high-cardinality of data sources** – for example, code execution sandboxes or IoT devices – S2 is for you. Streams can be instantly created on-demand. You don’t have to buffer or batch, just pipe into S2.\n\nhttps://www.youtube.com/watch?v=wsFiwxv_RY0\n\nWe recently [supercharged](https://news.ycombinator.com/item?id=45310294) read scalability to handle massive fanouts. You can broadcast a **real-time feed** with S2 such as market data without building any infra – publishing is just a matter of POST-ing to a stream’s URL.\n\nIf you are building **agents**, a common pain is **syncing** progress between the backend and frontend, while also being able to replay history. A lot of these agent state management challenges become simple with S2.\n\n## Try it out\n\nWe should talk if [s2.dev](http://s2.dev) resonates with you! You can [sign-up](https://s2.dev/sign-up) and follow the [quickstart](https://s2.dev/docs/quickstart) to start streaming in seconds with free credits.\n\nThere is a lot more we are cooking, including an ⭐ open source self-hostable S2 https://github.com/s2-streamstore/s2 ⭐\n\n## Talk to us\n\n**email**: [founders@s2.dev](mailto:founders@s2.dev)\n\n**discord**: https://discord.gg/JfTWJ5xxZ6\n\n**twitter**: https://x.com/s2_streamstore\n\n**bluesky**: https://bsky.app/profile/s2.dev\n\n![uploaded image](/media/?type=post&id=95319&key=user_uploads/89898/8c2da77e-e2c6-4bb6-bba6-9da0a09f87ef)\n\n",
        "url": "https://www.ycombinator.com/launches/OnP-s2-dev-streams-as-a-cloud-storage-primitive",
        "votes": 64,
        "created_at": "2025-11-08T06:30:48.434Z"
      }
    ]
  },
  {
    "id": 31004,
    "name": "Dome",
    "slug": "dome",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7dc9ddeb09c634cfdedc5563ec8d3c525a7e8189.png",
    "website": "https://domeapi.io",
    "all_locations": "",
    "long_description": "Dome makes it easy to build on prediction markets like Polymarket and Kalshi. We provide the developer infrastructure to access, analyze, and build on prediction markets. \r\n\r\nWith our unified API, developers can access live and historical data, including granular datasets not available natively, across multiple platforms at once. Dome makes it simple to trade, embed market data into products, and deploy strategies across multiple platforms through one interface.\r\n",
    "one_liner": "A unified API for prediction markets, like Kalshi and Polymarket",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1759389556,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Inactive",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "Unspecified"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/dome",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/dome.json",
    "year_founded": 2025,
    "city": null,
    "country": null,
    "linkedin_url": "https://www.linkedin.com/company/dome-api",
    "x_url": "https://x.com/getdomeapi",
    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/dome-71a6",
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7dc9ddeb09c634cfdedc5563ec8d3c525a7e8189.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [
      "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/f779f08359195f56e77527d17921850518036cf6.png"
    ],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 2118203,
        "full_name": "Kurush Dubash",
        "title": "Founder",
        "founder_bio": "Kurush is the co-founder and CEO of Dome.\n\nHe's an engineer with a degree from Berkeley and has a deep expertise in blockchain and prediction markets.\n\nAs a founding engineer at Alchemy, he spent over four years helping scale the company from 10 to 250 people and a $10B valuation. He and his co-founder worked closely with Polymarket, one of Alchemy’s largest blockchain customers, to build custom infrastructure that ensured they scaled seamlessly during the 2024 election.",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/kurushdubash",
        "x_url": "https://twitter.com/kurushdubash",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3b3511a70fd162b26f3c15906f828a3d4063ba9d.jpg"
      },
      {
        "user_id": 2830840,
        "full_name": "Kunal Roy",
        "title": "Founder",
        "founder_bio": "Founding Engineer Alchemy, prev Engineer at Cruise, Google. \n\nHe's an engineer with a degree from Berkeley and has a deep expertise in blockchain and prediction markets.\n\nKunal spent 5+ years as a founding engineer at Alchemy where he helped grow the company from 10 to 250 people and a $10 billion valuation. He and his co-founder we personally responsible for helping Polymarket scale on the blockchain during the 2024 election. Together they are building domeapi.io, the prediction market api",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/kunal-roy-772a0672/",
        "x_url": "https://x.com/princeofbothell",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/72906da4fa0849b693a62ebaeeb75a17c4c5bf05.jpg"
      }
    ],
    "jobs": [],
    "news": [
      {
        "title": "The Top Startups To Watch From Y Combinator’s Fall 2025 Batch",
        "url": "https://www.forbes.com/sites/dariashunina/2025/11/13/the-top-startups-to-watch-from-y-combinators-fall-2025-batch/",
        "date": "Nov 13, 2025"
      }
    ],
    "launches": [
      {
        "id": 95017,
        "title": "Dome - A unified API for prediction markets",
        "tagline": "Trade and analyze data across multiple platforms at once",
        "body": "Hi everyone - we’re [Kurush](https://www.linkedin.com/in/kurushdubash/) and [Kunal](https://www.linkedin.com/in/kunal-roy-772a0672/) - and we’re building [Dome](http://domeapi.io) 👋\n\n# **TL;DR**\n\n**Dome is a unified API for prediction markets, like Polymarket and Kalshi.** We allow users to trade and analyze data across multiple platforms at once. Over 50+ developers are already building on Dome.\n\n**Ask:** Building or trading in prediction markets? Contact [kurush@domeapi.com](mailto:kurush@domeapi.com) or [DM](https://x.com/kurushdubash) us.\n\nhttps://youtu.be/eYWb3hpxSnE\n\n**Prediction Markets**\\\nPrediction markets are a new financial instrument that combines news, polls, and expert opinions into a single value that represents the market’s view of an event’s odds. In the past few years, platforms like Polymarket and Kalshi have processed tens of billions in volume, giving real-time insight into collective public belief.\n\n# Problem\n\n_Fragmented_**\\\nThe same markets are scattered across multiple platforms**. Fragmented formats and liquidity make them difficult to analyze or trade efficiently.\n\n_Siloed Data_**\\\nAccessing reliable data is a challenge.** Every platform exposes different pieces of information, in different formats, leaving traders without a unified historical view.\n\n_Infrastructure_\\\n**Primitive tools build primitive products.** Developers do not have the adequate infrastructure to grow this ecosystem. \n\n# Solution\n\n**One Market View**\\\nDome unifies prediction markets across platforms into a single, consistent interface.\n\n* Matches **identical markets** across multiple platforms\n* Streams **real-time data** through one API \n* **Integrate once** to access every platform\n\n![uploaded image](/media/?type=post&id=95017&key=user_uploads/2118203/4a1a329b-1d58-468e-b76a-f18aa9ee4ee4)\n\n**Complete Data**\\\nDome delivers both **real-time** and **historical data** across every platform, giving traders and developers the insight to analyze past performance and react **instantly** to shifting markets. \n\n![uploaded image](/media/?type=post&id=95017&key=user_uploads/2118203/e56f0e0a-6179-4dd6-8d84-a46a94f9736a)\n\n**Empowered Builders**\\\nDome gives developers the tools to build exponentially on top of prediction markets.\n\n* **Multi-language SDKs** for effortless development\n* **Real-time** streaming architecture for live insights and updates\n* Unified **on-chain** and off-chain data pipeline for consistent accuracy\n\n# The Team\n\n![uploaded image](/media/?type=post&id=95017&key=user_uploads/2118203/b610baf1-35a7-4db8-90f3-2f84eff1519e)\n\n[**Kurush**](https://www.linkedin.com/in/kurushdubash/) (CEO) - Kurush is an engineer with deep expertise in blockchain infrastructure and prediction markets. As a founding engineer at [Alchemy](http://alchemy.com), he spent over four years helping scale the company from 10 to 250 people and a $10B valuation. He and his co-founder worked closely with Polymarket, one of Alchemy’s largest blockchain customers, to build custom infrastructure that ensured they scaled seamlessly during the 2024 election.\n\n[**Kunal**](https://www.linkedin.com/in/kunal-roy-772a0672/) (CTO) **-** Kunal spent 5+ years as a founding engineer at Alchemy and has comprehensive experience in scalable infrastructure having worked extensively on blockchain integrations across chains, virtual machines, and rollups. Early in his career at Google, he worked on internal B2B payment systems, helping move nine figures monthly across global regions.",
        "url": "https://www.ycombinator.com/launches/OiX-dome-a-unified-api-for-prediction-markets",
        "votes": 68,
        "created_at": "2025-10-29T22:29:23.067Z"
      }
    ]
  },
  {
    "id": 31010,
    "name": "Amika",
    "slug": "amika",
    "former_names": [
      "Fixpoint"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c42840d914de4ea4ec78f647bf0db2e93434ca5d.png",
    "website": "https://www.amika.dev/",
    "all_locations": "New York City, NY, USA",
    "long_description": "Amika is infra to build your software factory.\r\n\r\nWe give your team sandboxed AI coding agents in the cloud: the same infrastructure Ramp, Coinbase, and Stripe built in-house. Kick off work from a web UI, CLI, Slack, or API. Use your preferred coding agent (Claude Code, Codex, etc.). Each agent understands your codebase, runs autonomously, and ships real PRs with live previews of the apps it changed. Use it as your daily driver or plug it into your own code-gen pipeline via API.",
    "one_liner": "Infra to build your own software factory",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1759722442,
    "tags": [
      "Machine Learning",
      "B2B",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2025",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/amika",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/amika.json",
    "year_founded": 2023,
    "city": "New York City",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/amikadev/",
    "x_url": "https://x.com/amikadev",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/gofixpoint/amika",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c42840d914de4ea4ec78f647bf0db2e93434ca5d.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 101903,
        "full_name": "Dylan Mikus",
        "title": "Founder/CEO",
        "founder_bio": "Making infra for software factories. Helping people spin up sandboxed coding agents, and then control the interactively or programmatically so they can automate their entire SDLC.\n\nCMU grad in comp sci and ML\n2x YC founder",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/dbmikus",
        "x_url": "https://twitter.com/dbmikus",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/10786d1817ed7d689f6f3313f3b2c73f38c9f9a5.jpg"
      },
      {
        "user_id": 1013839,
        "full_name": "Jakub Cichon",
        "title": "Founder/CTO",
        "founder_bio": "co-founder / CTO at Amika. Building infra for software factories. \n\n2x YC founder.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/jakub-cichon-14016a3b",
        "x_url": "https://x.com/jdcamika",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/095af1aef16261010001d5599b3059bc222330e3.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 103929,
        "title": "Amika - the operating system for AI agents to write and verify code",
        "tagline": "Run fleets of human-in-the-loop or background coding agents inside cloud computers. Let them automate coding and verify it's correct.",
        "body": "Software isn't a craft anymore, it's a manufacturing problem.\n\nAt Amika, we're building **the open source operating system for coding agents to automate their work**, like how the factory automated manufacturing.\n\n**Amika gives each agent a cloud computer, and lets you build automated or human-in-the-loop workflows for agents to write code and prove that the code is correct.**\n\nA couple of examples:\n\n\\- Have Amika monitor production and auto-fix errors, testing the fix against your real app\n\n\\- Add Amika to shared customer Slack channels. Auto-implement customer requests and share a live preview the updated app with them\n\nCheck out some more examples in our video 👇\n\n<https://youtu.be/GxqFsiBNbww>\n\n# The problem\n\nAI coding agents made code generation cheap. Producing correct code at scale, with real quality controls, is still unsolved. You're left with a tradeoff: pore over every change and go slow, or vibe-code and ship slop.\n\nCoding agents are useful, but the surrounding system is missing.\n\n\\- They lack the right **dev environment to verify their work**\n\n\\- They lack the right **context to understand the full problem**\n\n\\- They lack **deterministic guardrails controlling what they can change**\n\n\\- They produce 10x more code, but teams lack **a system for 10x more review**\n\n# How Amika solves this\n\nAmika composes 5 primitives into its agent operating system:\n\n1\\. **Sandboxed environments**: isolated cloud computers with the repos, tech stack, MCP servers, skills, and context an agent needs.\n\n2\\. **Workflows**: programs as code or markdown files that define when an agent runs and what job it does.\n\n3\\. **Deterministic guardrails**: rules enforced on every code change, like \"do not cross this module boundary\" or \"every database query must include a customer ID filter.\"\n\n4\\. **Automated verification**: test, review, fix, and merge loops that check the work against your real tech stack before it ships.\n\n5\\. **Audit trails**: the agent chat session, code changes, which checks passed, and what the run cost.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/a1246cf3-1ed1-435d-94fb-2cb7437b4995)\n\nAmika works with your existing coding agent subscriptions (Claude Code, Codex, etc.) so **you're not locked into one vendor**. Start agents from the web, Slack, GitHub, the CLI, or the API. At any point, a human can take over the sandbox and inspect the running apps, or drive from their own code editor.\n\nBecause each sandbox runs your full stack, you review the agent's work in a live preview of your real app, plus the verification artifacts it produced, like screen captures or API test requests, instead of poring over the diff line by line.\n\nWith Amika's workflows and environments, you can spend time building higher level loops for your agent to write and verify code, speeding up your product development.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/60e16093-3dcf-4285-b73b-998c74507145)\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/3b84372e-0a00-46a3-afd1-d711547c256b)\n\n# Who we are\n\nWe're [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/), two time YC cofounders.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/497da8a5-bfa8-407e-98f6-b613d9389b50)\n\nWe pivoted our YC company to bet on this, and we already use Amika for the majority of our PRs. We're building it in the open: each primitive ships as open source, and our cloud platform packages the whole operating system into a software factory for teams.\n\n# Our ask\n\n\\- Sign up for a **free two-week trial at [amika.dev](https://www.amika.dev/)**\n\n\\- Dig into the **open source repo** on GitHub: <https://github.com/gofixpoint/amika>\n\n\\- If you want **help setting up your software factory**, book at demo at <https://cal.com/amika-dev/30min>\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/3e200f7d-2b3f-4e3e-99f3-d0249987c72c)\n\n",
        "url": "https://www.ycombinator.com/launches/R2H-amika-the-operating-system-for-ai-agents-to-write-and-verify-code",
        "votes": 18,
        "created_at": "2026-06-24T17:06:48.500Z"
      },
      {
        "id": 94868,
        "title": "Fixpoint - Uber for AI trainers and human-in-the-loop supervisors",
        "tagline": "Fixpoint helps AI data companies automate sourcing, vetting, and all HR for your expert annotator teams",
        "body": "Hello, world! We’re [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/), the co-founders of [Fixpoint](https://fixpoint.co/).\n\n**Fixpoint helps AI training data companies hire the expert humans that create AI training data**.\n\nAI needs human experts to teach it new skills and benchmark its performance: PhDs for science, lawyers for contract review, etc. But finding and vetting these experts is time-consuming, and without properly vetting experts, you’re paying for bad data!\n\nFixpoint automates this process, connecting companies with verified experts at the push of a button, so you can focus on creating quality AI training data instead of recruiting.\n\n[Launch Video](https://youtu.be/9TCgpe0tT3w)\n\n**We have two products:**\n\n1. **Worker Vetting API** - Plug Fixpoint’s API into your onboarding workflow and vet the credentials and backgrounds of workers. **Catch 10x the worker fraud!**\n2. **White-Glove Expert Staffing** - When you need annotators fast, Fixpoint can source and hire a team of qualified experts. **Staff a team of 100s in just a couple weeks.**\n\n**Our track record:**\n\n* We’ve caught 10x the fraudulent applicants for top AI data companies\n* We’re partnered with data companies to staff teams for the top AI labs\n* We are GDPR ready and SOC 2 certification is underway\n\nUltimately, we're building the platform for \"human data ops\". Just as chip designers use TSMC for chip manufacturing, AI companies can use Fixpoint for the human expertise and tools needed to create specialized training data and evals.\n\n# Our ask\n\nSend us an email at [**founders@fixpoint.co**](mailto:founders@fixpoint.co) or [**grab a demo**](https://cal.com/fixpoint/demo) if:\n\n* You’re an AI data company or RL company and need a team of expert annotators\n* You’re an enterprise that needs to eval or benchmark your AI agents in a specialized domain (healthcare, legal, etc.)\n* You’re on the “human data” team at a model lab and have trouble managing experts or their data\n* You don’t fit in the above, but you or someone you know could use our services!\n\n# About us\n\nWe’re [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/). We’re second time YC founders who’ve worked together since we met at Oscar Health in 2018. We’ve built everything from search systems, to chatbots, to data infrastructure, to AI web research agents.\n\n![uploaded image](/media/?type=post&id=94868&key=user_uploads/101903/ad8316ad-d855-46de-9ffc-67bb1fd62847)\n\n",
        "url": "https://www.ycombinator.com/launches/Og8-fixpoint-uber-for-ai-trainers-and-human-in-the-loop-supervisors",
        "votes": 25,
        "created_at": "2025-10-23T21:41:36.522Z"
      }
    ]
  },
  {
    "id": 31036,
    "name": "Mantis",
    "slug": "mantis",
    "former_names": [
      "Mantis Biotechnology"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d2b5bfa4fe3cce9bf181ad8d59612747b7903c6d.png",
    "website": "https://mantisbiotech.com/",
    "all_locations": "New York City, NY, USA",
    "long_description": "Mantis is a digital twin company that combines LLMs with high-fidelity physics simulations in order to convert rare and difficult-to-access human behavior data into predictive models. ",
    "one_liner": "Digital Twins of humans",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1766166166,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/mantis",
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        "user_id": 1566590,
        "full_name": "Georgia Witchel",
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    "id": 31074,
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    "one_liner": "The cheapest, fastest inference cloud",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "answer": "Hi, we’re Hannah and Medha! Three years ago, we met at MIT, and now are co-founding OneTriangle. Hannah loves scaling companies and Medha loves making ideas become real products. Together, we built OneTriangle to push the limits on latency, cost, and quality. "
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        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "We believe the future of AI will be defined by intelligence and trust. AI agents will reliably carry out human intent while recognizing and refusing actions that are unsafe, unlawful, or harmful. With the rate of advancing ML systems, we will bridge this ever-increasing gap. We provide a compliance standard for all agents to follow, ensuring that humans and agents are perfectly aligned in their intentions."
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        "user_id": 2573695,
        "full_name": "Hannah Chung",
        "title": "Founder/CEO",
        "founder_bio": "MIT CS & Econ → @Virtu Financial →  Fastest, Cheapest Lightweight Inference @OneTriangle\n",
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        "full_name": "Medha Venkatapathy",
        "title": "Founder/CTO",
        "founder_bio": "Hi! I studied physics + cs at mit, and we're building the cheapest/fastest inference with KV cache transferability. In my free time, I like to bike and get lost.",
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        "title": "OneTriangle - The fastest, cheapest inference, powered by KV cache transfer",
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        "body": "**TL;DR:** OneTriangle is building the cheapest, fastest inference. We cut inference prefill costs by 20% and time-to-first-token by 40%, letting you get large-model quality at small-model cost.\n\nVideo: <https://youtu.be/HKe2EZUdNHo>\n\n**Team**\n\nWe’re Hannah (CEO) and Medha (CTO), MIT CSAIL grads building with a team of 4 MIT engineers spanning ex-DeepMind and Jane Street, Physics and Astronomy Olympiad medalists, and NeurIPS/ICML authors. \n\n**The Problem**\n\nInference is expensive. \n\n• You’re forced into a bad tradeoff. You need the decoding power of large models, but you have the budget for small ones.\n\n• Prefill is the hidden tax. For long-context and agentic workloads, prefill dominates cost and latency, and everyone just eats it.\n\n**Our Solution**\n\nOneTriangle transfers the KV cache between models, something that has never been done in production. We prefill on a small model, strip the positional encoding, map the cache into the large model’s space, and restore relative importances. The result:\n\n• 20% lower prefill costs\n\n• 40% faster TTFT\n\n• Large-model quality at small-model cost\n\nWe beat out NVIDIA’s KV cache transfer paper by getting a lower KL divergence score in 2 weeks.\n\n![uploaded image](/media/?type=post&id=111487&key=user_uploads/2573695/e64727d6-a7b1-404a-bfbb-22ad2b941142)\n\n\\\nNo API changes, no quality cliff, and we’re upstreaming it into vLLM so the fastest path to cheap inference is the infrastructure you already run.\n\n**Why Now**\n\nInference has surpassed training as AI’s largest compute cost, and it’s accelerating with demand for agents that generate 10x the tokens a chat session does. Inference already eats up to 90% of total compute cost, over $600 billion is going into AI data centers this year alone. Every team pushing the frontier is bottlenecked by the same thing: serving costs.\n\n\\\n**The Future**\n\nOur bet is that model-mixing becomes the default serving pattern: small models handle prefill and routine tokens, large models handle the hard ones, and the cache moves freely between them. Whoever owns the transfer layer owns the economics of inference. We are the future of lightweight, fast inference.",
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        "votes": 10,
        "created_at": "2026-08-21T02:30:02.532Z"
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    "name": "Didit",
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    "website": "https://didit.me/",
    "all_locations": "CT, Spain",
    "long_description": "One API for KYC, KYB, AML, biometrics, and fraud — powered by 1,000+ data sources, 200+ signals, and in-house AI models. Global coverage. Integrate in minutes, pay as you go, 500 free checks/month. Start free at business.didit.me.",
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    "launched_at": 1764601102,
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    "year_founded": 2023,
    "city": null,
    "country": "ES",
    "linkedin_url": "https://www.linkedin.com/company/diditprotocol/",
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      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
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      {
        "user_id": 3142948,
        "full_name": "Alberto Rosas",
        "title": "Founder/CEO",
        "founder_bio": "CEO of Didit. Solving identity online. Former professional tennis player. More at rosasalberto.com.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/albertorosasg/",
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        "full_name": "Alejandro Rosas",
        "title": "Founder/CTO",
        "founder_bio": "CTO. Math, problem solver, obsessive about details and learning by building; architecting Didit’s identity infrastructure from first principles. Former professional tennis player",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/alejandrorosasgarcia/",
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        "title": "Didit Raises $7.5M Seed, Infrastructure for Identity and Fraud",
        "url": "https://didit.me/blog/didit-7-5m-seed-infrastructure-identity-fraud/",
        "date": "May 26, 2026"
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      {
        "id": 97452,
        "title": "Didit: the fastest way to verify real humans.",
        "tagline": "All-in-one identity platform built for speed and fraud prevention.",
        "body": "We’re Alberto and Alejandro Rosas, identical twins and co-founders of Didit. We’ve spent our lives impersonating each other. That used to be rare. Today, **AI has your photos and your voice.** Soon it will call your grandma, and she won’t know. The internet needs a fast, reliable way to prove who you are. **That’s why we built Didit.**\n\n## **TL;DR**: We made identity verification finally work on the internet.\n\n<https://www.youtube.com/watch?v=KU9Jf79J8XE>\n\n## What you need to believe …\n\nThe internet is about to get weird.\n\n* AI can already copy your **voice**, your **face**, and your **videos**. Perfect fakes are basically here.\n* Bots are about to flood everything — social media, marketplaces, apps, support chats, even phone calls.\n* Very soon, you won’t trust what you see or hear online.\n* When trust breaks, **identity becomes mandatory**. For logins, payments, access, everything.\n* This market is going to 1000x.\n\n## The problem (we’re not ready)\n\nThe scary part? Our current identity systems already suck.\n\n* They’re **slow**, **insecure**, and **expensive.** Built for a pre-AI internet.\n* You’ve felt it: verifications that take days, uploading the same document twice, random rejections.\n* Real users get stuck. Attackers get through.\n* Companies end up stitching together 5–10 vendors just to run identity. Engineers hard-code rules. Ops can’t move fast.\n\nWe don’t need more tools. We need a **new identity platform** — fast, secure, global, and simple.\n\n**Didit is that platform.**\n\n## The solution\n\n<https://www.youtube.com/watch?v=eTdcg7JCc4M>\n\nWe built Didit by doing the opposite of everyone else. We didn’t stitch together vendors or wrap legacy systems — we built everything **from scratch**: ID verification, biometrics, liveness, auth... Then we put it all together into **one all-in-one platform**. We obsessed over UI and UX, optimized every millisecond, and designed flows where real humans verify in seconds while fraud gets blocked by default. Onboarding rates go up, friction goes down, and teams finally get a complete, connected view of each user.\n\nBecause we own the entire stack, we can do something most identity companies can’t: offer **simple pay-per-use pricing at very competitive rates**. No contracts, no minimums, no surprises. Companies that switch to Didit usually switch once — and they don’t churn. Once you go Didit… you don’t go back 😄\n\n## Traction\n\n* **Millions of humans verified every month**\n* **700+ active B2B customers**, growing **\\~20% MoM**\n* **Pure PLG** so far, now layering outbound\n* **300%+ NRR by month 6**\n* **\\~90% paid customer retention** at month 6\n\nDidit clearly works.\n\nNow we’re executing the expansion into **mid-market and enterprise**.\n\n## Didit path to trillion dollar company\n\nEven if we’re Spanish, we don’t really do “**_siestas”_**. We do **relentless execution** — and we’re very good at it.\n\nWe believe identity will become one of the most important pieces of infrastructure of the AI internet. As soon as you can’t trust what you see or hear online, proving who you are becomes non-negotiable. We envision a world **where identity is frictionless and instant** — one click, face-first — and works everywhere: logging into apps, authorizing payments, accessing services online and in the physical world. Your **Didit Identity** becomes something you carry with you, **reusable, private, and trusted by default**. Long term, we even see governments running secure digital services and online democracies on top of Didit. This is a massive shift and we’re going to lead it.\n\n## Ask\n\nIf you’re verifying users or fighting bots, fake accounts, or multi-account abuse — **reach out**. We’ll help you onboard **faster, reduce fraud, and lower costs.**\n\nKnow a company dealing with identity verification / kyc ..etc? Every intro comes with an **amazing Spanish paella** 🥘 as a gift.",
        "url": "https://www.ycombinator.com/launches/PLo-didit-the-fastest-way-to-verify-real-humans",
        "votes": 35,
        "created_at": "2026-02-03T17:38:00.478Z"
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    "id": 31084,
    "name": "Klaus AI",
    "slug": "klaus-ai",
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    "website": "https://klausai.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Get your personal OpenClaw instance on the cloud and connected to your Slack or other messaging app in 3 minutes. Pre-installed security features and common skills. ",
    "one_liner": "Fast and Safe OpenClaw on the cloud",
    "team_size": 2,
    "industry": "B2B",
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    "launched_at": 1770051535,
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    "status": "Inactive",
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    "url": "https://www.ycombinator.com/companies/klaus-ai",
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
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      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
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        "user_id": 3169998,
        "full_name": "Robbie Thompson",
        "title": "Founder/CEO",
        "founder_bio": "Working on personal agents. Formerly: Quant at Jane Street, FDE at Console, MCS at Stanford. Hobbies: barefoot running, foraging for wild food, meditation. ",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/robert-thompson-133469175/",
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        "full_name": "Bailey Wickham",
        "title": "Founder",
        "founder_bio": "Bailey Wickham\n",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/baileywickham",
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      {
        "id": 97608,
        "title": "Klaus: Get Your OpenClaw Personal Assistant in 5 minutes",
        "tagline": "OpenClaw hosted on a VM with security and privacy built in",
        "body": "**Klaus is an OpenClaw instance on the cloud, set up in 3 minutes, with Slack, Telegram, Anthropic/OpenAI/Gemini, a browser, malware protection, Molthub, Moltbook, and its own email address pre-configured.**\n\nOpenClaw has gone viral because it is the most powerful AI tool on the web. It has: \n\n* Won an exception for someone to [stay overnight in the ICU with their father](https://www.moltbook.com/post/c4a170b7-aaf0-4112-9317-8129fa61a391) during a medical emergency by finding and cold-emailing 15 administrators.  \n* Created a new [social media](https://www.moltbook.com/) site that reached a million users in three days. \n* [Negotiated for a new car](https://aaronstuyvenberg.com/posts/clawd-bought-a-car) end-to-end, getting 10% off MSRP by playing two dealers against each other.\n\n \n\nBut that leaves most people to choose between FOMO and spending hours setting up a new tool, only to get hacked anyway. \n\n**Klaus** is an opinionated, batteries-included distribution of OpenClaw:\n\n* Klaus runs in the cloud with no access to any personal data by default.\n* Setup takes minutes. You don’t have to create API keys, run commands, or build apps.\n* Integrations are preconfigured with smart permissions to keep your data safe.\n* Available 24/7 in the cloud, from laptop or mobile, on your messenger of choice.\n\n<https://www.youtube.com/watch?v=cKuxW84u6Lc> \n\n**The OpenClaw hype is real:**\n\n* OpenClaw does it all: manages your inbox and calendar, helps you shop, completes forms and paperwork, writes code, etc. \n* You never have to fiddle with settings or repeat yourself, because OpenClaw remembers and learns on its own.\n* Klaus talks to you with joy and whimsy.\n\n**… but so are the risks**\n\n* Hundreds of skills contain malware.\n* Set up incorrectly, your entire internet presence is exposed.\n\nKlaus is the solution: it’s an OpenClaw instance so robust that even your parents will use it safely, so easy to set up that they’ll do it on their own. **Go to [usebits.com](http://usebits.com) to talk to Klaus today.** \n\nThe team: Bailey and Robbie met building AI Agents at Console. Previously we worked at Amazon and Jane Street. You can reach us at [founders@usebits.com](mailto:founders@usebits.com).",
        "url": "https://www.ycombinator.com/launches/POK-klaus-get-your-openclaw-personal-assistant-in-5-minutes",
        "votes": 17,
        "created_at": "2026-02-07T00:54:04.829Z"
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    "id": 31088,
    "name": "RunAnywhere",
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    "website": "https://www.runanywhere.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "RunAnywhere runs open-weight models at full speed wherever a company needs them. Wally is our inference stack for open models: serverless inference, BYOC, or on-prem, so code and data never leave their network. One command puts a Wally-served model inside Claude Code, DeepSeek Harness, OpenCode, Hermes, OpenClaw, etc. When a model fits on the device, the same tooling runs it locally through our open-source SDKs for iOS, Android, macOS, Windows, Linux, and web, built on kernels written by our agentic harness for Apple and Qualcomm silicon (10K+ GitHub stars).",
    "one_liner": "Fastest inference anywhere: open models on-prem, hosted or on-device",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1767296079,
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      "Developer Tools",
      "Open Source",
      "Infrastructure",
      "AI"
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    "regions": [
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    "url": "https://www.ycombinator.com/companies/runanywhere",
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    "year_founded": 2025,
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    "country": "US",
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        "full_name": "Sanchit Monga",
        "title": "Founder/CEO",
        "founder_bio": "Former Intuit engineer building RunAnywhere, the infrastructure layer for deploying fast, private, multimodal AI on-device at scale.\n\nDeep background in mobile SDKs, platform tooling, and developer products, including systems used by 50M+ active users. Previously founded products across consumer discovery, context management, agentic documentation, and mobile testing, and now focused on making on-device AI production-ready across mobile, edge, and embedded devices.",
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        "user_id": 2862507,
        "full_name": "Shubham Malhotra",
        "title": "Founder/CTO",
        "founder_bio": "Co-founder & CTO of RunAnywhere (W26). Built MetalRT: the first complete multi-modal inference engine for Apple Silicon. Custom Metal GPU kernels that pushed on-device voice AI from 900ms to ~110ms. Ex-Amazon EC2 Spot ($100M+ ARR), Ex-Microsoft Azure. Peer-reviewed researcher.",
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        "url": "https://www.msn.com/en-us/news/other/metalrt-brings-the-first-unified-ai-inference-engine-to-apple-silicon/ar-AA1YLTt1",
        "date": "Mar 17, 2026"
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      {
        "title": "RunAnywhere Is Building the Infrastructure Layer for On-Device AI at Scale ",
        "url": "https://techbullion.com/runanywhere-is-building-the-infrastructure-layer-for-on-device-ai-at-scale/",
        "date": "Mar 09, 2026"
      },
      {
        "title": "Regulated Industries Are Rewriting Their AI Architecture",
        "url": "https://nexttechtoday.com/tech/ai/regulated-industries-are-rewriting-their-ai-architecture/",
        "date": "Mar 09, 2026"
      },
      {
        "title": "From Cloud Dependence to Instant AI: The Rise of On-Device Voice Agents - New York Weekly",
        "url": "https://nyweekly.com/tech/from-cloud-dependence-to-instant-ai-the-rise-of-on-device-voice-agents/",
        "date": "Mar 09, 2026"
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      {
        "title": "Why the Next Wave of AI Will Run on Your Phone - CEOWORLD magazine",
        "url": "https://ceoworld.biz/2026/03/06/why-the-next-wave-of-ai-will-run-on-your-phone/",
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    "launches": [
      {
        "id": 117440,
        "title": "Wally by RunAnywhere: The fastest inference for open frontier models",
        "tagline": "Open-weight frontier models at real-time speed for voice and agent products, live on day one. Serverless, BYOC or on-prem.",
        "body": "Hi, we're Sanchit and Shubham, co-founders of RunAnywhere (W26), and we're launching Wally: our inference stack for open frontier models, hosted on our GPUs or deployed on yours.\n\n<https://youtu.be/m_T2Diow3_M>\n\n**TL;DR:** Wally serves GLM-5.3 Flash at 380 tokens per second with a 274 ms first token, faster than any endpoint on Artificial Analysis (Sep 28, 2026). New models land on day zero: Xiaomi's MiMo-V2.6-Pro was ready to serve about 8 hours after its weights dropped, and runs at 3x the speed of Xiaomi's own API (Artificial Analysis, Sep 21, 2026). The same stack deploys on-prem/BYOC at the same speed. [Sign up](https://runwally.com) and get $5 in free credits.\n\n## The problem\n\nFrontier models are slow and expensive, and you feel it most in products that run a model in a loop. A voice agent has a few hundred milliseconds before a turn feels broken. A coding agent waits for the first token on every tool call, dozens of times per task.\n\n## How Wally works\n\nWally is our inference stack for open frontier models: our own kernels, speculative decoding, and prefill kept off the decode path so a long prompt never stalls someone else's stream. We tune it per model and per traffic pattern. It runs in two places.\n\n**Hosted by us.** An OpenAI-compatible endpoint on our own GPUs, so anything that takes a base URL and a key works unchanged. Standard per-token pricing. Live today:\n\n* **GLM-5.3 Flash:** 380 tok/s\n* **DeepSeek V4.1 Flash:** 615 tok/s\n* **Qwen3.8-27B:** 485 tok/s\n* **MiMo-V2.6-Pro:** 320 tok/s\n* **GLM-5.3 Max:** 790 tok/s\n\n**Deployed for you.** The same stack on GPUs you own, in your cloud account, or on a dedicated instance we run. Same speed, your own models and fine-tunes, your traffic stays in your environment, and at token-heavy volumes it costs less than paying per token.\n\n## New models on day zero\n\nMiMo-V2.6-Pro, the top open-weights model on Artificial Analysis (Sep 21, 2026), was ready and running 3x faster than anyone else in 8 hours after Xiaomi published the weights.\n\n## The numbers\n\nArtificial Analysis lists 19 providers serving the same GLM-5.3 Flash weights (Sep 24, 2026), and Wally is 8.6x faster than Z.ai. Same model; the only difference is how it's served. Ours: 380 tok/s and a 274 ms first token, measured the same way.\n\n![uploaded image](/media/?type=post&id=117440&key=user_uploads/725397/e68127c0-8f31-4fe8-bbe2-44f587f09e67)\n\n## Beyond the benchmark\n\nBenchmarks don't always match reality, so we ran a real prompt on four providers, three times: GLM-5.3 Flash in the same OpenCode harness on Wally, Nebius, Fireworks and Z.ai, all building a game at once.\n\n<https://youtu.be/yc-KVBwuWC0>\n\n![uploaded image](/media/?type=post&id=117440&key=user_uploads/725397/eaf90b9c-3be5-405a-9177-cca618fe81ea)\n\nThe same model reasons more or less on the same prompt from run to run, on every provider, so finish times look close even when the work isn't. Tokens per second of wall clock is the stable number: Wally 213, Nebius 149, Fireworks 68, Z.ai 47, averaged across the runs.\n\n## Get started\n\nPaste this into your coding agent:\n\n```\nset up https://runanywhere.ai/SKILL.md\n```\n\nWorks with Claude Code, Claude Desktop and OpenCode. $5 free on signup, no card. Standard per-token pricing after that.\n\n## Why we built it\n\nWe spent nine months making open models fast on phones and laptops, with our own inference engines for Apple and Qualcomm chips ([runanywhere-sdks](https://github.com/RunanywhereAI/runanywhere-sdks?utm_source=launchyc&utm_medium=post&utm_campaign=wally-launch), 10K+ GitHub stars). Our users kept needing models too big for the device, and nobody served open frontier models fast enough for a real-time loop. None of our work was specific to phones, so we pointed it at data-center GPUs.\n\n## Our ask\n\n* **If you run a voice or agent product and latency or the inference bill hurts,** email [founders@runanywhere.ai](mailto:founders@runanywhere.ai) and we'll make it faster.\n* **If a customer has asked you for on-prem,** intro us. We deploy on their GPUs.\n* **Try it** and tell us what broke: [runwally.com](https://www.runwally.com/)\n\n[Docs](https://docs.runanywhere.ai/?utm_source=launchyc&utm_medium=post&utm_campaign=wally-launch) · [X @RunAnywhereAI](https://x.com/RunAnywhereAI)",
        "url": "https://www.ycombinator.com/launches/UYC-wally-by-runanywhere-the-fastest-inference-for-open-frontier-models",
        "votes": 12,
        "created_at": "2026-09-29T18:59:15.687Z"
      },
      {
        "id": 97041,
        "title": "RunAnywhere: The default way to run On-Device AI at scale",
        "tagline": "One SDK + Control Plane to Deploy, Route, Update, and Observe On-Device AI – Offline-First, Hybrid-Smart, Fleet-Managed",
        "body": "We're [Sanchit](https://www.linkedin.com/in/sanchitmonga22/) and [Shubham](https://www.linkedin.com/in/shubham-malhotra-fg890a23a/), co-founders of [RunAnywhere](https://runanywhere.ai/) (W26).\n\n**TL;DR:** Run Multi-modal AI fully on-device with one SDK and manage model rollouts + policies from a control plane.\n\nWe are already live and open source with [**\\~10.1k stars on GitHub**](https://github.com/RunanywhereAI/runanywhere-sdks).\n\n<https://youtu.be/N3x2bs4ri68>\n\n![uploaded image](/media/?type=post&id=97041&key=user_uploads/725397/86d47939-7caf-4e0f-8575-045ca2fd22bc)\n\n### The Problem\n\nEdge AI is inevitable — users want instant responses, full privacy (health, finance, personal data), and AI that actually works on planes, subways, or spotty rural connections.\n\nBut shipping it today is brutal:\n\n* Every device (iPhone 14 vs Android flagship vs low-end) has wildly different memory, thermal limits, and accelerators.\n* Teams waste quarters rebuilding model download/resume/unzip/versioning, lifecycle (load/unload without crashing), multi-engine wrappers (llama.cpp, ONNX, etc.), and cross-platform bindings\n* No real observability — you're blind to fallback rates, per-device perf, crashes tied to model version\n\n**Result:** most teams either give up on local AI or ship a brittle, hacked-together experience.\n\n### The Solution: Complete AI Infrastructure\n\nRunAnywhere isn't just a wrapper around a model. It is a full-stack infrastructure layer for on-device intelligence.\n\n**1. The \"Boring\" Stuff is Built-in** We provide a unified API that handles model delivery (downloading with resume support), extraction, and storage management. You don't need to build a file server client inside your app.\n\n**2. Multi-Engine & Cross-Platform** We abstract away the inference backend. Whether it's llama.cpp or ONNX etc, you use one standard SDK.\n\n* iOS (Swift)\n* Android (Kotlin)\n* React Native\n* Flutter\n\n**3. Hybrid Routing (The Control Plane)** We believe the future isn't \"Local Only\"—it's **Hybrid**. RunAnywhere allows you to define policies: try to run the request locally for zero latency/privacy; if the device is too hot, too old, or the confidence is low, automatically route the request to the cloud.\n\n![Voice AI Pipeline Demo](/media/?type=post&id=97041&key=user_uploads/725397/dfe1b180-e5d8-4c59-a4e8-2109d9f903f2)\n\n### Quick Links\n\n* **OSS SDKs:** [github.com/RunanywhereAI/runanywhere-sdks](https://github.com/RunanywhereAI/runanywhere-sdks) (star if it vibes!)\n* **Full Docs:** [docs.runanywhere.ai](https://docs.runanywhere.ai)\n* **Website:** [runanywhere.ai](https://runanywhere.ai)\n\n**Try our demo apps:**\n\n* [Android](https://play.google.com/store/apps/details?id=com.runanywhere.runanywhereai)\n* [iOS](https://apps.apple.com/us/app/runanywhere/id6756506307)\n\n### Our Ask\n\nWe're in full execution mode post-launch and hunting design partners + early feedback:\n\n* Building voice AI, offline agents, privacy-sensitive features (health/enterprise/consumer), or hybrid chat in your mobile/edge app?\n* Want to eliminate cloud inference costs for repetitive queries while keeping complex ones fast?\n* Have a fleet where OTA model updates + observability would save you engineering months?\n\n**Get in touch:**\n\n* Drop us a line: [san@runanywhere.ai](mailto:sanchit@runanywhere.ai)\n* Book a quick call: [calendly.com/sanchitmonga22](https://calendly.com/sanchitmonga22)\n* Or just tell us what sucks about your current on-device AI stack — honest war stories help us prioritize.\n\nExcited to hear what you're building and how we can make on-device AI actually shippable at scale.",
        "url": "https://www.ycombinator.com/launches/PFB-runanywhere-the-default-way-to-run-on-device-ai-at-scale",
        "votes": 25,
        "created_at": "2026-01-22T01:27:45.855Z"
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    "long_description": "Up to 80% of the most advance AI compute chips sits idle because of slow networking. Piris Labs builds photonic based networking solutions to enable fast, scalable, and low cost AI workload execution infrastructure.",
    "one_liner": "The Networking Layer of AI",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "full_name": "Ali Khalatpour",
        "title": "Founder/CEO",
        "founder_bio": "Founder (YC W26), reimagining AI compute architectures | PhD EE (MIT → NASA → Stanford)",
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        "full_name": "Keyvan Moghadam",
        "title": "Founder/President",
        "founder_bio": "I am an AI technologist with a decade of experience scaling AI/ML products. At Meta and X, I specialized in leading tiger teams to launch high-stakes 0-to-1 initiatives.\n\nNow, I am the Co-Founder of Piris Labs. We are building high-speed photonic interconnects to solve the energy and latency constraints for AI data inference. My current focus is to iterate on our inference service and onboard customers into our platform.",
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        "id": 93808,
        "title": "Founding Engineer -- AI Inference Stack ",
        "url": "https://www.ycombinator.com/companies/pirislabs/jobs/r6mskwZ-founding-engineer-ai-inference-stack",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$100K - $200K",
        "equity_range": "0.10% - 1.00%",
        "experience": "Any (new grads ok)",
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      {
        "id": 104954,
        "title": "Piris Labs: We Set the Fastest Reported GLM-5.2 Inference Speed",
        "tagline": "Fast, scalable AI workload execution infrastructure.",
        "body": "Piris Labs  just achieved the fastest reported inference speed for the new GLM-5.2, outperforming pioneers and veterans in the space.\n\nWe finished the YC batch just a few months ago and believed it was important to maintain that same pace of execution while keeping our technical bar high. So yes, the YC advice is right: keep building.\n\nA lean, highly passionate team with strong execution and the right long-term vision can accomplish in a short period of time what often takes much larger organizations with billions in funding and hundreds of people years to achieve.\n\nWe're well on track to build fast, scalable, and low-cost AI workload execution infrastructure with a deep focus on the data movement layer, where many of the biggest bottlenecks in performance and unit economics exist.\n\nRead the full details here:\n\n<https://pirislabs.io/news/glm-benchmark>\n\nWe welcome independent third-party benchmarking. Feel free to DM us or reach out by email.\n\n#AIInference #MachineLearning #GLM52 #GenAI #AIInfrastructure #TechInnovation\n\n![uploaded image](/media/?type=post&id=104954&key=user_uploads/2792621/98b898a8-99fa-482a-ab7a-d65badc2b3e4)\n\n",
        "url": "https://www.ycombinator.com/launches/RIo-piris-labs-we-set-the-fastest-reported-glm-5-2-inference-speed",
        "votes": 5,
        "created_at": "2026-07-07T22:22:52.996Z"
      },
      {
        "id": 97751,
        "title": "Piris Labs: Inference at Light Speed",
        "tagline": "We deliver low-latency, low-cost AI inference by eliminating the memory wall using full-stack optical interconnects and a purpose-built software stack.",
        "body": "**The Problem: The Memory Wall Is Breaking the Economics of AI Compute**\n\nAI’s promise is unprecedented, but its economics are broken. Copper-based interconnects cannot keep up with modern models, limiting data movement between and within compute and memory. The result is high latency, inefficient hardware utilization, and unsustainable inference costs—creating a hard ceiling on AI margins.\n\n---\n\n**The Solution: Grow-Style Optimization for the Interconnect**\n\nToday’s data-center compute and networking were not designed for inference. Slow, power-hungry **copper** interconnects, along with costly signal processing and memory overhead, drive high latency and high cost.\n\nHigh-performance inference requires purpose-built hardware and a vertically optimized software stack. Piris Labs builds this optimized stack by combining proprietary **optical** interconnects with software designed to maximize hardware utilization. We vertically optimize the interconnect layer—just as the compute layer was vertically optimized by **Groq** (acquired by **NVIDIA** for $20 B).\n\nPiris Labs combines proprietary optical interconnects with a purpose-built software stack designed to maximize hardware utilization, delivering **5x** lower latency, **10x** lower power per bit, and **2x** lower cost per token\\\n\\\n**Our Launch Video**\\\n<https://youtu.be/PW1PbMYw2c0>\n\n**The Team**\n\nWe are a team of specialists at the intersection of hardware and AI:\n\n* Ali Khalatpour: MIT-trained engineer and Harvard–Stanford–trained optical scientist; developed the first room-temperature terahertz semiconductor laser; led the development of GUSTO’s optical engine for NASA\n* Keyvan Rezaei Moghadam: AI scientist and engineering leader with experience building 0-to-1 AI infrastructure at Meta and Twitter.\n\n### **Our Ask**\n\n**AI Products**: If you want to scale your inference workloads while slashing costs, reach out to us at [**contact@pirislabs.io**](mailto:contact@pirislabs.io).\\\n\\\n**Chip Makers & ODM Partners**: We are also looking for a couple of additional partners who get the chance of trying our optical solution first hand. Shoot us an email at [**founders@pirislabs.io**](mailto:founders@pirislabs.io).",
        "url": "https://www.ycombinator.com/launches/PQd-piris-labs-inference-at-light-speed",
        "votes": 21,
        "created_at": "2026-02-12T17:58:23.293Z"
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    "id": 31128,
    "name": "Cumulus Labs",
    "slug": "cumulus-labs",
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    "website": "https://cumuluslabs.io",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Cumulus Labs lets engineering teams ship AI in production without needing a dedicated ML platform team. Right now, companies building AI products are forced to stitch together separate vendors for routing, observability, evaluation, fine-tuning, and inference. This fragmented approach is brittle, expensive, and is a common reason enterprises fail with AI.  \r\n\r\nWe replace that entire stack with a single unified platform. Developers can keep their existing code while instantly upgrading to a unified platform that handles routing, semantic caching, continuous shadow evaluation, simulated data, and one-click fine-tuning. \r\n\r\nBehind the platform is Ion, our proprietary inference engine running on a custom NVIDIA Grace GPU fleet. Ion uses in-house custom GPU kernels to deliver 30 to 50 percent more throughput than standard vLLM or SGLang, giving our customers SOTA inference economics.",
    "one_liner": "The Fastest Multimodal Inference OS",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1768427687,
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        "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)",
        "answer": "been building robots together since we were 7\r\n\r\n![uploaded image](/media/?key=user_uploads/3182820/83506448-2b7e-4c23-b2b4-2fa9392fb1f4)\r\n\r\n"
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        "full_name": "Veer Shah",
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        "body": "## **TL;DR**\n\nCumulus is a performant GPU cloud that preemptively optimizes your training and inference workloads across our global supply of multi-tenant clusters. The result: we **save you 50-70%** through charging by physical resources used, provide faster inference with ultra-low cold starts, and ensure zero time spent debugging infrastructure.\n\n**Ask**: If you’re training models or serving inference workloads, frustrated with GPU costs and performance, let us optimize your **LLMs, LoRAs, vision models, and more.**\n\n**Website:** <https://cumuluslabs.io>\\\n**Docs:** <https://docs.cumuluslabs.io>\n\nLaunch Video: <https://www.youtube.com/watch?v=duQwV50GKXc>\n\n## **The Problem**\n\nAI teams are bleeding money and time on GPU infrastructure:\n\n* **Massive waste**: Teams pay for idle GPUs sitting at 30-40% utilization because scaling is unpredictable\n* **Infrastructure hell**: Engineers spend weeks configuring Kubernetes, debugging OOM errors, and managing failovers instead of building models\n* **Cold start latency**: Inference workloads take 10-30+ seconds to spin up, killing user experience\n* **Vendor lock-in**: Once you commit to a cloud provider, switching costs make it nearly impossible to optimize for price or performance\n* **Skyrocketing costs**: Companies burn through runway 2-3x faster than planned because GPU bills spiral out of control\n\nEvery hour debugging infrastructure is an hour not spent improving your models. Every dollar wasted on idle GPUs is a dollar not spent on growth.\n\n---\n\n## **The Solution**\n\n**Cumulus is a GPU optimization layer that makes compute cheap, fast, and invisible.**\n\nWe aggregate compute from everywhere—big cloud providers, trusted data centers, individual hosts—into a single unified pool. Then we do three things no one else does:\n\n# **1. Predictive Packing & Live Migration (Training/Fine-tuning)**\n\nYour training jobs are intelligently packed alongside other workloads to maximize GPU utilization. As your job runs, we predict resource usage and **live-migrate you to faster or cheaper clusters** without interruption. No more paying for an entire H100 when you only need 40% of it.\n\n# **2. Execution State Capture & Global CDN (Inference)**\n\nWe capture your model's live execution state (VRAM, memory, loaded weights) and replicate it across our global compute CDN. When a request comes in, we serve from the closest cluster with **ultra fast cold starts**—no more waiting 30 seconds for a job to spin up. We have tested with **LLMs, vision models, LORAs, and many others.**\n\n# **3. Intelligent Scheduling & Auto-Recovery**\n\nOur scheduler constantly monitors your jobs, diagnoses failures, and auto-recovers without manual intervention. The Cumulus prediction system learns your usage patterns over time and pre-allocates resources before you need them.\n\n**The bottom line:** _You write 20 lines of config. We handle everything else._\n\nOur Demo: <https://www.youtube.com/watch?v=J0KRFWE3-fg>\n\n## **The Team**\n\n![uploaded image](/media/?type=post&id=96856&key=user_uploads/3177002/fbef3bec-137d-4275-8433-8a1ccfad47a5)\n\n(winning the science fair in 4th grade for a robot that solved Rubik’s cubes)\n\n**Veer Shah** (Founder)\\\nLed a Space Force program and worked on ML workloads at an aerospace startup supporting NASA missions, where infrastructure needed to be both performant and secure. \n\n**Suryaa Rajinikanth** (Founder)\\\nBuilt custom GPU compute solutions at TensorDock, then moved to Palantir where he built critical infrastructure for the US Government. Deep expertise in distributed systems and resource optimization.\n\nWe met as third graders and have been building together our whole lives. We've seen the GPU infrastructure problem from both sides: Suryaa from the provider side at TensorDock, Veer from the customer side running mission-critical ML workloads. We started Cumulus Labs because we knew exactly what the industry needed—and no one was building it.\n\n## **What We're Looking For**\n\n**If you're training models or serving inference workloads**, frustrated with vendor lock-in, or simply paying too much for your GPUs, reach out.\n\n**Know AI/ML teams experience any of these issues?** Connect them with us.\n\nWe optimize LLMs, LoRAs, vision models, and more.\n\nWe'd love your feedback on which features matter most.\n\n---\n\n## **Get In Touch**\n\nJoin the waitlist: <https://cumuluslabs.io>\\\nContact us: [founders@cumuluslabs.io](mailto:founders@cumuluslabs.io)\\\nBook a demo: [Here](https://calendar.app.google/NepPaCiZxCTM9vyUA)\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\nHuge thanks to partners, our batch-mates, and everyone who's helped so far.\n\nLet's make AI infrastructure invisible.\n\n— Veer, Suryaa, and the Cumulus team\n\n![uploaded image](/media/?type=post&id=96856&key=user_uploads/3177002/3045bccb-ce6f-4d07-b8dd-37c34772ce14)\n\n",
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        "body": "**William** and **Inigo** here - Co-Founders at **Luel (YC W26)**.\n\n**_Luel is a rights-cleared data marketplace + collection engine_**. Our difference is speed and edge cases: AI enterprises request datasets to spec, we mobilize a global contributor network, and deliver licensed, audit-ready data within days.\n\n[Luel Launch Video](https://www.youtube.com/watch?v=n_ByLVl38w8)\n\n**The Problem**\\\n\\\nFrontier labs have hit a wall: public web data is tapped out, synthetic-only pipelines risk degeneration, and the next generation of models needs **rights-cleared multimodal data** that doesn’t exist at scale.\n\n* Companies are spending huge budgets on generic, low-signal datasets from legacy vendors\n* The internet’s “easy data” is largely exhausted, what’s left is low-signal, repetitive, or messy\n* Most datasets fail production requirements: unclear rights, weak provenance, missing consent, inconsistent metadata\n\n**The Solution**\\\nLuel delivers **to-spec multimodal datasets** with clean provenance:\n\n* **Custom collections:** you specify exactly what you need; we scope, recruit, QA, and deliver\n* **Off-the-shelf licensing:** completed collections become ready-to-license catalogue datasets (ranging from patient-doctor conversations in south Asia to gemstone manufacturing footage for robotics)\n* **Rights trail included:** built for procurement + compliance from day one (consent evidence, chain-of-title, QA logs)\n\n**How it works**\n\n* AI teams submit a dataset spec (modality, scenario, instructions, devices, QA rules)\n* We post a listing and instantly match vetted contributors\n* Submissions run through multi-stage QA and are **delivered within days.**\n\n**The Team:**\n\n* **William Namgyal - CEO**\\\n  2x Founding Engineer, Berkeley M.E.T. Dropout\n* **Inigo Lenderking - COO**\\\n  Prev. Machine Learning Researcher, Berkeley CS Dropout\n\n**Our ask**\\\nIf you’re training multimodal / robotics / speech models and need data, we’d love to talk. Intros to heads of data, applied research, or model training teams = 🙏\n\n\\\n**Email: [william@luel.ai](mailto:william@luel.ai) / [inigo@luel.ai](mailto:inigo@luel.ai) / [founders@luel.ai](mailto:founders@luel.ai)**",
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    "long_description": "Your agent processed a refund without looking up the order ID, costing you thousands. You only found out three hours later from a support ticket. Evals, output scoring, and observability can reduce the likelihood of mistakes like these occurring - but there's no solution that inspects and prevents an action as it’s about to execute. Salus does that.\r\n\r\nWe’ve built an API that wraps around your agent and checks its actions at run time, blocking incorrect ones and providing immediate feedback to guide retries. ",
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        "full_name": "Vedant Singh",
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        "url": "https://www.ycombinator.com/companies/salus/jobs/6xN06s6-founding-swe-mle",
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        "body": "### **TL;DR:**\n\n**Salus is an API that wraps around your agent and checks its actions at runtime, blocking incorrect ones and providing immediate feedback to guide retries.**\n\nWe’re Kevin and Vedant, former roommates at Stanford who studied CS, now building Salus full-time.\n\n### 😔**The problem:** \n\nYour agent processed a refund without looking up the order, costing you thousands. It emailed your top lead using hallucinated data, ruining the deal. You only found out three hours later from a support ticket or an angry email.\n\nEvals, output scoring, and observability are all necessary—but all reactive. They can reduce the likelihood of these problems occurring, but there's no solution that inspects an action as it’s about to execute. Salus does that.\n\n**Ask:** If you are deploying agents and want to improve their correctness we’d love to help. Book a demo [here](https://calendly.com/d/cycy-fbc-fyj/salus-demo), and we can onboard you immediately with just a pip install and a few lines of code.\n\n### 💻 **Our Solution:** \n\n<https://youtu.be/uyl5NJTY5Cg>\n\n**How Salus validates an action:**\n\n1. Actions must be **grounded in evidence**: Salus maintains an evidence cache for each run that every proposed action is validated against. Evidence includes all outputs from prior tool calls and the entire conversation history.\n2. It must **adhere to policy**: Write constraints in YAML, markdown, or plain English. Salus compiles them into runtime checks that are version-controlled, diffable, and shadow-testable\n3. And **more**: PII detection, budget/loop protection, idempotency, human-in-the-loop escalation, content moderation\n\n**Self-repairing:** When Salus blocks an action, the agent receives structured feedback to guide a retry. In our benchmarks, **58%** of blocked actions recover and complete the task correctly.\n\n### 📊 **Benchmarks:** \n\nWe tested Salus on τ²-bench and ODCV-Bench. On τ²-bench, agents with Salus follow policies more reliably, at up to **60% lower cost**. On ODCV-Bench, Salus **reduced misalignment by 52%** on average across 12 frontier models (see below).\n\n![uploaded image](/media/?type=post&id=97574&key=user_uploads/1940292/e48b27f6-2e13-4d40-a9a4-13135ff981bc)\n\n### ➕ **Other Features:** \n\nWhile we believe the missing piece is runtime validation, we know **evals and observability** are crucial. That’s why we incorporate all three in **one centralized product**. Using templates and LLMs, we generate thousands of both adversarial and realistic evals that have full context of your agent’s domain.\n\nContact us: [founders@usesalus.ai](https://founders@usesalus.ai)\n\n![uploaded image](/media/?type=post&id=97574&key=user_uploads/1940292/cc3d0be7-941f-46ef-be8f-1294b9148073)\n\n",
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        "title": "Fern – Realtime Socially Aware AI for Meetings",
        "tagline": "Fern joins every meeting as–either invisible copilot or active participant–so every conversation runs like a top 1% professional",
        "body": "**TL;DR**\\\n[Fern](https://www.ishikilabs.ai/download) is a realtime AI that joins your meetings as either an invisible copilot or an active participant– your choice! It realtime proactively coaches you through every conversation so you always show up like a top 1% professional.\n\nHey everyone, we're Amit and Robert, cofounders of Ishiki Labs.\n\n**The Problem**\n\n_“AI meeting assistant” has become synonymous with a note-taker waiting to summarize at the end. That's not a meeting assistant. That's a fancier transcript._ \n\nBuilding a realtime AI meeting copilot is hard in two distinct ways.\n\nThe invisible assistant must understand the live meeting context at blazing speed and surface the right thing at the right moment – questions to ask, things you're missing, who you're talking to. Timing is everything. Current AI is purely reactive. Making AI an active participant in discussion is even harder. AI has no social awareness – every pause becomes a prompt, every gap an invitation to speak.\n\nAI that is socially aware and proactive is an unsolved problem. That's what we're building.\n\nThe cost of this gap is real. Cofounders, AEs, recruiters, investors – in discovery calls, sales meetings, investor conversations. The moments that matter most, and AI is absent from all of them.\n\n**How Fern Solves it**\n\n[Fern](https://www.ishikilabs.ai/download) joins your meetings as a true participant or an invisible copilot – your choice. It has two modes:\n\n**Full Presence**: Fern joins visibly, listens, and speaks up when invited. It offers realtime opinions, deep research, and can complete tasks like filing notes or sending emails on the spot. Built for internal product, engineering, and strategy meetings. Watch the demo- <https://www.youtube.com/watch?v=vRJfZDUHvSY>\n\n**Shadow Mode**: Silent and invisible to everyone else. You get a private realtime feed: live summaries, questions to ask, things you're missing, context on who you're talking to. A sharp colleague whispering in your ear – with fully customizable insights. Built for client calls, investor meetings, and any conversation where you want invisible support.\n\nBoth modes include a post-meeting summary, transcript, and full insights.\n\n**Team**\n\nWe're both ex-Meta. Amit is an AI Scientist from LLaMA (now Meta Super Intelligence Lab) and Reality Labs – PhD in AI, 20+ publications at NeurIPS/CVPR, trained multimodal assistants for smart glasses. Robert was systems and infra lead in Reality Labs, with four years building low-latency multimodal systems at Meta and Citadel Securities. \n\nOur mission: make AI socially timed and contextually aware – so every conversation you have runs like a top 1% professional.\n\n**Our Ask**\n\nDownload [Fern](https://www.ishikilabs.ai/download) and if you're a founder or AE who lives in high-stakes conversations and don’t want to settle for AI note-takers, reach out: [team@ishikilabs.ai](mailto:team@ishikilabs.ai)",
        "url": "https://www.ycombinator.com/launches/PTp-fern-realtime-socially-aware-ai-for-meetings",
        "votes": 26,
        "created_at": "2026-02-19T15:09:16.569Z"
      }
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    "id": 31154,
    "name": "Orthogonal",
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    "website": "https://orthogonal.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "APIs weren't built for agents. Orthogonal fixes that. We enable agentic payments so any agent can instantly discover, access, and pay for hundreds of APIs. No subscriptions, no sales calls, just pay per use. API providers list once and get paid every time an agent calls their API.",
    "one_liner": "Agentic Payments for APIs",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1769466101,
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    "regions": [
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      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/orthogonal",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/orthogonal.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/orthogonalsh",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
    },
    "founders": [
      {
        "user_id": 334152,
        "full_name": "Christian Pickett",
        "title": "Founder/CEO",
        "founder_bio": "Cofounder @ Orthogonal - Agentic Payments for APIs\nPrev. Vercel & Coinbase",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/christian-pickett",
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      },
      {
        "user_id": 1380929,
        "full_name": "Bera Sogut",
        "title": "Founder/CTO",
        "founder_bio": "Cofounder @ Orthogonal - Agentic Payments for APIs\n2x ACM ICPC World Finalist | Prev. Google, Amazon Robotics",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/berasogut",
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    "jobs": [
      {
        "id": 114623,
        "title": "Senior Software Engineer",
        "url": "https://www.ycombinator.com/companies/orthogonal/jobs/aExdocL-senior-software-engineer",
        "location": "San Francisco, CA",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "",
        "equity_range": "",
        "experience": "5+ years",
        "visa": "US citizen/visa only",
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      },
      {
        "id": 115415,
        "title": "Growth Lead",
        "url": "https://www.ycombinator.com/companies/orthogonal/jobs/hnvSlmj-growth-lead",
        "location": "San Francisco, CA",
        "type": "Full-time",
        "role": "Operations",
        "role_type": null,
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        "id": 115416,
        "title": "Design Engineer",
        "url": "https://www.ycombinator.com/companies/orthogonal/jobs/30qoV7B-design-engineer",
        "location": "San Francisco, CA",
        "type": "Full-time",
        "role": "Engineering",
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        "experience": "Any (new grads ok)",
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    "news": [
      {
        "title": "Orthogonal Is Betting the Agent Economy Needs Better Infrastructure, Not Just Better Models - TheStreet Crypto: Bitcoin and cryptocurrency news, advice, analysis and more",
        "url": "https://www.thestreet.com/crypto/newsroom/orthogonal-is-betting-the-agent-economy-needs-better-infrastructure",
        "date": "Jun 25, 2026"
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      {
        "id": 97308,
        "title": "Orthogonal - Agentic Payments for APIs",
        "tagline": "APIs weren’t built for agents, we fixed that.",
        "body": "**TL;DR:** APIs weren’t built for agents, we fixed that. Developers and agents get instant access to hundreds of APIs through our MCP or SDK. No key management, no billing headaches, pay as you go. API providers list once and become instantly discoverable. \n\n_Ask:_ We’d love an intro to teams building agents to understand how they’re handling paid APIs today. Contact us at [founders@orthogonal.com](mailto:founders@orthogonal.com)\n\n<https://www.youtube.com/watch?v=CixNTItWDjI>\n\n**The Problem:**\n\nAI agents are about to become the biggest consumers of APIs on the internet. But they struggle to access tools.\n\nMCP helped standardize tool calling, but created fragmentation. Some services have MCPs, others don't. Some map to single APIs, others wrap entire workflows. Agents end up with 30+ tools, bloating context.\n\nWorse, any MCP requiring identity or payment means managing accounts, API keys, billing, and auth across dozens of services. And most APIs don't even have MCPs.\n\nYou could have your agent find APIs, sign up, handle auth, and buy credits, but the failure rate is high. The entire API economy was built for humans.\n\nMeanwhile, API providers are leaving money on the table. Billions of agents could be paying for their APIs right now, but they have no way to find or transact with them.\n\nThese APIs are locked behind API keys and human payment methods, which agents do not have access to.\n\n**The Solution:**\n\nOrthogonal is where agents go to find and pay for APIs.\n\n**For AI Agents and Developers:**\n\n![uploaded image](/media/?type=post&id=97308&key=user_uploads/334152/4a24efb7-cfff-4dec-88fb-5b3afaba2dc7)\n\n* Add our MCP to your agent for instant access to hundreds of paid APIs\n* Or use our SDK as a single, unified gateway to those APIs\n* You purchase Orthogonal credits, and we handle paying the APIs\n* Natural language search: \"I need an API to get Garry Tan’s latest tweets\"\n* No context bloat, no API keys to manage, no billing relationships to maintain\n* We can handle rate limits, retries, and dynamic routing\n\n![uploaded image](/media/?type=post&id=97308&key=user_uploads/334152/10cc2d87-3e65-4897-bdd7-c047d3393b38)\n\n**For API Providers:**\n\n* List your API once, and it's instantly discoverable by every AI agent and developer using our platform\n* Get paid per request. No subscriptions, no invoices, no payment disputes\n* Set your own prices, including dynamic pricing\n* We also enable x402 on your APIs\n\n![uploaded image](/media/?type=post&id=97308&key=user_uploads/334152/29c9cec0-f260-446b-9d88-9ee284642bdb)\n\nWe're starting with **Search, Scraping, Datasets, and AI Model APIs**.\n\n**About Us:**\n\nWe're Christian and Bera. We met at McGill and have been close friends ever since. Christian worked on payments at Coinbase and billing at Vercel. Bera worked on reCAPTCHA and Maps APIs at Google. Between the two of us, we've lived in payments and API infrastructure, and this problem sits right at the intersection.\n\n![uploaded image](/media/?type=post&id=97308&key=user_uploads/334152/cd2b5a57-5b00-45f0-aeb8-bb15a3e7bc2e)\n\n**The Ask:**\n\n* Intros to teams building AI agents that need easy access to paid services\n* Intros to teams managing APIs across many platforms who'd love to unify them\n\nIf you are a developer or have agents, sign up to get $10 of credits towards API usage : <https://www.orthogonal.com>\n\nWe appreciate our early believers: Precip (YC W24), Riveter (YC F24), Andi (YC W22), Shofo (YC W26), Fiber AI (YC S23), Sixtyfour (YC X25), Olostep, ScrapeGraph, Linkup\n\n**Find us:**\n\nWebsite: <https://www.orthogonal.com/>\n\nLinkedin: <https://www.linkedin.com/company/orthogonalsh>\n\nX: <https://x.com/orthogonal_sh>",
        "url": "https://www.ycombinator.com/launches/PJU-orthogonal-agentic-payments-for-apis",
        "votes": 31,
        "created_at": "2026-01-29T23:34:27.018Z"
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    "id": 31164,
    "name": "Compresr",
    "slug": "compresr",
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    "website": "https://compresr.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Compresr provides an API that compresses LLM context without losing what matters. It’s a drop-in for agents and RAG that cuts token costs and improves accuracy.",
    "one_liner": "LLM context compression for better accuracy",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1768812885,
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/compresr",
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    "year_founded": 2026,
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    "country": "US",
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      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
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      {
        "user_id": 2535816,
        "full_name": "Ivan Zakazov",
        "title": "Founder/CEO",
        "founder_bio": "CEO @ Compresr. Previously researched LLM context compression as an EPFL PhD (Switzerland). Former Microsoft and Philips Research.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ivan-zakazov?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_app",
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        "user_id": 3205126,
        "full_name": "Berke Argin",
        "title": "Founder",
        "founder_bio": "CAIO @ compresr.ai. On a mission to make every token count | EPFL CS | prev UBS",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/arginberke/",
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      {
        "user_id": 3175962,
        "full_name": "Kamel Charaf",
        "title": "Founder/CPO",
        "founder_bio": "Co-founder & COO @ Compresr (YC W26) | EPFL Data Science Masters | ex-Bell Labs",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/kamel-charaf/",
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      {
        "id": 98164,
        "title": "Compresr – context compression for LLM pipelines and agents 🗜️",
        "tagline": "Cut token spend, reduce latency, boost generation quality. ",
        "body": "# **TL;DR**\n\n**We help you boost context management with 100x compression. Use Compresr SDK to add compression to your LLM pipelines, and improve agentic workflows with our open-source compression proxy (works with Claude Code and OpenClaw).**\n\n<https://youtu.be/QAk4MBMHWQQ?si=3DrkVwhK_JrClSU1>\n\nDid you ever struggle to fit all relevant context into the model’s window – only to see the model failing to grasp it? Waited for Claude Code to finish yet another 3-minute compaction session? Got surprised with the OpenClaw bills?\n\nCompresr is on a mission to make you forget about managing long model/agent’s context. With our API, you can reduce long context to the essentials the model needs for any given request – making its generation better, faster, and cheaper.\n\nWe also care deeply about seamless and simple integration into the agentic workflows. Our open-sourced proxy takes a minute to set-up, and optimizes your Claude Code / OpenClaw context from the moment you finish installation. You will experience no change apart from better performance and smaller bills.\n\n![uploaded image](/media/?type=post&id=98164&key=user_uploads/2535816/90e4891b-b976-4a28-b17f-38a4906eb11c)\n\n**Our ask:** \n\n1. If you use Claude Code, Codex, or OpenClaw, try out our compression-enabling proxy and let us know what you think!\n2. If you build LLM pipelines involving long context – let’s chat!\n\nMake every token count.",
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        "votes": 9,
        "created_at": "2026-02-25T01:37:46.507Z"
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    "id": 31165,
    "name": "IncidentFox",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "AI SRE agents that automatically learn each customer’s system so they work just like an in-house engineer.",
    "one_liner": "AI SRE agent that triages, coordinates, and fixes production incidents",
    "team_size": 2,
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    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1771353224,
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/incidentfox/",
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      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
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    "founders": [
      {
        "user_id": 1146198,
        "full_name": "Jimmy Wei",
        "title": "Founder/CEO",
        "founder_bio": " Chiehmin (Jimmy) Wei is a co-founder of IncidentFox. Before IncidentFox, Jimmy was a software engineer at Roblox, where he architected a real-time communication feature reaching 11.7M+ users, and at Facebook AI Research (FAIR), where he built conversational AI infrastructure and set new research baselines with Transformer models. He holds an M.S. from Cornell University and a B.S. in Computer Science from Bucknell University.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/chiehmin-wei",
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      {
        "user_id": 2091321,
        "full_name": "Long Yi",
        "title": "Founder/CTO",
        "founder_bio": "Long Yi is the co-founder of IncidentFox, an AI-powered SRE platform for automated incident investigation and infrastructure management.\nPreviously, he built large-scale distributed systems at Roblox, managing database and search infrastructure handling over 1.5 million queries per second. \nHe holds a B.S. from Brandeis University, where he conducted research in audiovisual signal processing and neural modeling.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/long-yi-b221b2183/",
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      {
        "id": 97928,
        "title": "IncidentFox. The AI SRE who never drops context.",
        "tagline": "Auto-learns your stack, builds its own integrations, and gets smarter after every incident.",
        "body": "Hey everyone 👋\n\nWe're [Long](https://www.linkedin.com/in/long-yi-b221b2183/) and [Jimmy](https://www.linkedin.com/in/chiehmin-wei/) — co-founders of IncidentFox.\n\n**TL;DR**\n\nAI SRE tools fail without deep context about _your_ systems — and that context lives in integrations nobody has time to build. IncidentFox auto-discovers what each team needs, generates the integrations, and ships with 300+ tools built in. Setup takes less than a day, not months.\n\n👉 Try it: <https://incidentfox.ai>\n\n👉 Open source (Apache 2.0): <https://github.com/incidentfox/incidentfox>\n\n👉 Launch video: <https://youtu.be/TaTpN0JwNYE>\n\n**❌ The Problem: Without the right integrations, your AI has no context**\n\nEvery AI SRE tool connects to Slack, reads your Confluence, queries your Datadog. That part is solved.\n\nHere's what isn't: when the AI actually needs to debug something, it doesn't have the right tools.\n\nYour payments team runs a custom Kafka pipeline with internal dashboards. Your infra team uses a homegrown deployment system. Your ML team has proprietary model serving. Each team's stack is different — and the AI has no way to query any of it.\n\nThe traditional fix? Hand-build integrations (MCP servers) for every team.\n\nBut this creates a new problem:\n\n* Who decides what integrations each team needs? You might know your own team's stack — but when you're debugging another team's service at 3 AM, you don't know theirs.\n* So every team needs to sacrifice an engineer to build and maintain their own integrations. That's expensive, slow, and doesn't scale.\n* Most teams never get around to it. The AI stays half-useful.\n\n**Integration is the bottleneck.** Not the AI model. Not the monitoring data. The integrations.\n\n**🦊 IncidentFox: We auto-build the integrations for you**\n\nIncidentFox is an AI SRE that lives where your team already works — Slack, Microsoft Teams, or Google Chat. It doesn't just connect to your existing tools — it figures out what tools each team needs and builds them automatically.\n\n![uploaded image](/media/?type=post&id=97928&key=user_uploads/1146198/102ebfb0-a3c9-45c5-b993-89e39b94e13b)\n\n**1. Auto-discovers and generates integrations**\n\nIncidentFox analyzes your codebase, infrastructure, and incident history to identify gaps — then auto-generates the tools to fill them. No engineer needs to hand-build MCP servers. No team needs to sacrifice headcount on integration work.\n\nA new team onboards? IncidentFox studies their stack and proposes tools specific to their services — with human approval before anything goes live.\n\n**2. Per-team configuration — because every team is different**\n\nYour payments team and your ML team don't use the same stack. Why would they use the same AI SRE config?\n\nEach team gets their own:\n\n* Tools — enable only what's relevant; disable what isn't\n* Prompts — fully open source and exposed to engineers. Inject your domain knowledge directly\n* Knowledge base — learned from that team's incidents, runbooks, and services\n\nOne team's \"config drift\" is another team's \"model drift.\" IncidentFox understands the difference.\n\n**3. Continuously evaluates and self-improves**\n\nAfter every incident, IncidentFox:\n\n* Detects gaps — \"I couldn't query service X's health endpoint\"\n* Auto-generates the missing tool\n* Evaluates its own investigation quality against the actual resolution\n* Updates prompts and knowledge — with human review\n\nIt gets measurably better every week. Not because you tuned it — because it tuned itself.\n\n**4. 300+ integrations included on day one**\n\nWhile it auto-builds what's missing, you're not starting from zero. Kubernetes, AWS, Grafana, Prometheus, Datadog, Elasticsearch, PagerDuty, GitHub — all built in. Integration time is under a day, not months.\n\n**🧠 Why this matters**\n\n**Integrations** — Traditional: Hand-built per team. Each team sacrifices an engineer. IncidentFox: Auto-discovered and auto-generated. Human approves.\n\n**Multi-team scaling** — Traditional: Breaks — you can't know every team's stack. IncidentFox: Per-team config. Each team's AI knows their stack.\n\n**Domain knowledge** — Traditional: Black box prompts, hope it works. IncidentFox: Open source prompts. Engineers inject and edit freely.\n\n**Over time** — Traditional: Stagnates unless manually updated. IncidentFox: Self-evaluates, finds gaps, improves continuously.\n\n**Setup** — Traditional: Months of custom integration work. IncidentFox: &lt; 1 day. 300+ tools out of the box.\n\n**📊 Results**\n\n* 85–95% reduction in alert noise through intelligent correlation\n* Hours → minutes for incident investigation\n* Zero-config onboarding — Docker in 5 min, production K8s in 30 min\n\n![uploaded image](/media/?type=post&id=97928&key=user_uploads/1146198/844d70ec-e308-472c-9bc4-f212cd954153)\n\n**🔒 Enterprise-ready, open-source**\n\n* Open source — Apache 2.0, no vendor lock-in\n* SOC 2 compliant, SSO/OIDC, RBAC, audit logs\n* Self-hosted, on-prem, or managed SaaS\n* Bring your own LLM keys (OpenAI, Claude, Gemini, etc.)\n\n**👬 The Team**\n\n[**Jimmy (CEO)**](https://www.linkedin.com/in/chiehmin-wei/) — Previously at Roblox, where he built social communication features (in-experience calling for 100M+ DAU). Before that, worked at Meta FAIR on multiparty conversational AI, with published research. Cornell CS. Serial founder — previously CTO at a startup in Outlier Ventures' DeFi accelerator.\n\n[**Long (CTO)** ](https://www.linkedin.com/in/long-yi-b221b2183/)— Previously at Roblox, where he built database infrastructure supporting 100M+ daily active users on the Stateful Infra team. Experienced the chaos of on-call firsthand — which is why we're building this. Brandeis CS + Neuroscience + Business.\n\nWe've lived on both sides: Jimmy built the AI systems, Long was the SRE drowning in incidents. IncidentFox is what happens when you combine both.\n\n![uploaded image](/media/?type=post&id=97928&key=user_uploads/1146198/261f0711-50aa-4a0f-b6c9-9cdc7c853dbc)\n\n\n**🙏 Our Asks**\n\n* Try IncidentFox — self-serve at [incidentfox.ai](https://incidentfox.ai) or star us on [GitHub](https://github.com/incidentfox/incidentfox)\n* Intros to engineering teams spending too much time on on-call\n  * [Book a demo with us](https://calendly.com/d/cxd2-4hb-qgp/30-minute-demo-call-w-incidentfox)\n* Feedback from SREs and infra engineers — [founders@incidentfox.ai](mailto:founders@incidentfox.ai)\n\nThanks for reading ❤️",
        "url": "https://www.ycombinator.com/launches/PTU-incidentfox-the-ai-sre-who-never-drops-context",
        "votes": 5,
        "created_at": "2026-02-18T01:06:25.804Z"
      }
    ]
  },
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    "id": 31185,
    "name": "Chamber",
    "slug": "chamber",
    "former_names": [
      "Astraio"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a1fc77901b81373bfd2cdb4b4fd70816597bb561.png",
    "website": "https://www.usechamber.io",
    "all_locations": "Seattle, WA, USA",
    "long_description": "Chamber puts your AI infrastructure on autopilot, and saves your machine learning engineers hours of manual effort. Our agents continually monitor, detects failures, provide root-cause analysis, resolve issues, and optimize AI workloads and scale across clouds. It operates like an autonomous infrastructure team, helping save your research and engineers hours each day, and debugging workload performance issues. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.",
    "one_liner": "The AIOps Agent for ML Teams",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1768078423,
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    "isHiring": false,
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    "batch": "Winter 2026",
    "status": "Active",
    "industries": [
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    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/chamber",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/chamber.json",
    "year_founded": 2026,
    "city": "Seattle",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/usechamber/",
    "x_url": "",
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    "cb_url": "https://www.crunchbase.com/organization/chamber-f81e",
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    "primary_partner": {
      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
    },
    "founders": [
      {
        "user_id": 239960,
        "full_name": "Charles Ding",
        "title": "Founder/CEO",
        "founder_bio": "Second Time Founder with 1X Exit | Former Engineering Leader at Meta & Amazon | Ex-Microsoft. We are building to accelerate the world’s AI adoption and innovation.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/dingcharles",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/94d7d45ceb2826b8c4d214d7328c28d8022e013e.jpg"
      },
      {
        "user_id": 3258010,
        "full_name": "Andreas Bloomquist",
        "title": "Founder",
        "founder_bio": "Shipped observability and infrastructure products at scale across AWS and Amazon. At AWS, I launched CloudWatch Application Signals, the native APM solution for AWS. At Amazon, I led the launch of the central GPU orchestration service, centralizing access and maximizing utilization across Amazon's science and AI/ML teams.\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/andreasbloomquist/",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3cf3cd9b59db458916d3c98a83edfe269fe84754.jpg"
      },
      {
        "user_id": 3208930,
        "full_name": "Jason Ong",
        "title": "Founder/CPO",
        "founder_bio": "I’m a software engineer from Malaysia who moved to the U.S. in 2016. I’ve built high-impact systems at Amount, Avant, Flexport, and Amazon. I’ve worked across fintech, logistics, and GPU-related scheduling tooling, where I saw how hard distributed training is for many teams. I’m now co-founder of Chamber, focused on simplifying GPU orchestration for training workloads.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jshenong/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1fb1dc3c99a8654f3d4af40243729694b3878d5b.jpg"
      },
      {
        "user_id": 3163670,
        "full_name": "Shaocheng Wang",
        "title": "Founder/CTO",
        "founder_bio": "I am a cofounder of Chamber and a former Senior Software Engineer at Amazon. Over the past 9+ years, I’ve built and launched multiple 0→1 AWS products, with deep expertise in large-scale observability, distributed systems, and AI infrastructure efficiency. At Chamber, I’m applying this experience to build intelligent AI workload orchestration and observability software that helps companies run AI workloads much more efficiently.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/shaocheng-wang-b766b7a6/",
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    "jobs": [],
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    "launches": [
      {
        "id": 96703,
        "title": "Chamber – Autopilot for AI Infrastructure 🚀",
        "tagline": "Turning Idle GPUs Into Enterprise AI Velocity",
        "body": "# **TLDR;**\n\n[**Chamber**](https://www.usechamber.io) is building AI infrastructure platform that automates governance and resource optimization for enterprise.\n\n**Ask**: If you’re at (or know) a company spending **millions on reserved GPU infrastructure** we’d love to talk.\n\nOur Launch Video: <https://youtu.be/w5Nk1wleigc>\n\n**Who are we?** We're former Amazonians that helped build and scale large-scale infrastructure optimization, delivering hundreds of millions in cost savings, and we’re now bringing those learnings to help other enterprises uncover idle AI infra, accelerate workloads and reduce wasted money on reserved idle GPUs. _([Featured in Business Insider](https://www.businessinsider.com/amazon-strategy-overcome-gpu-shortages-nvidia-2025-4))_\n\nLinkedIn: [Charles](https://www.linkedin.com/in/dingcharles/), [Shaocheng](https://www.linkedin.com/in/shaocheng-wang-b766b7a6/), [Jason](https://www.linkedin.com/in/jshenong/), and [Andreas](https://www.linkedin.com/in/andreasbloomquist/)\n\n**Problem:** Despite massive AI infrastructure investments, 30–60% of enterprise reserved GPU capacity sits idle in siloed allocations, slowing down teams who need it most, and silently driving up costs. This inefficiency represents $240B+ in annual waste across the industry, and it’s only growing as AI research and development accelerates.\n\n**Solution:** [Chamber](https://www.usechamber.io) puts your AI infrastructure on autopilot. Our agentic AI enabled platform orchestrates, governs, and optimizes your AI infrastructure so teams can run \\~50% more workloads on the same GPUs without manual intervention. It operates like an autonomous infrastructure team: continuously monitoring GPU clusters, forecasting demand, detecting unhealthy nodes, and reallocating resources in real time to where they create the most impact. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.\n\n* **Discover & optimize idle GPU capacity**\\\n  Automatically identifies underutilized GPUs and hidden optimization opportunities across clusters, teams, and workloads.\n* **Agentic AI–driven orchestration**\\\n  Automatically right-size workloads, manage resource allocations, and orchestrate workloads across teams based on real-time demand and priority.\n* **Self-healing infrastructure**\\\n  State-of-the-art bad-node detection with automated remediation to keep clusters healthy and jobs running.\n* **Deep AI-powered analytics**\\\n  Detect anomalies, forecast usage, and slice and dice workloads by team, user, job type, or priority.\n* **Proactive alerts & executive visibility**\\\n  Native integrations with Slack, Email, and PagerDuty, and more to deliver real-time alerts, executive digests, and clear action items.\n\nOur Product Demo: <https://youtu.be/E8BLg7zi9mc>\n\n**Who is this for:**\n\n* **AI/ML teams** looking to move fast and run more workloads without manual GPU allocations, priority spreadsheets, or burning budget on unused GPUs \n* **Engineering leaders & executives** who need real-time visibility, accountability, and control over GPU spend to deliver higher ROI to their executives and shareholders\n\n \n\n**A gift from us:** We’re offering a **complimentary GPU Intelligence Dashboard** and **advanced monitoring & discovery agent** that gives you immediate, production-grade visibility into your AI infrastructure.\n\n* Automatically discover and expose unused GPU capacity in real time\n* Understand utilization patterns across teams and workloads\n* Receive executive-ready email reports on GPU usage and trends\n* AI driven insights from your cluster utilization metrics\n\n[Create an account](https://app.usechamber.io/signup) to start assessing your opportunity to run more workloads in minutes for free.\n\nWant to talk with us? Schedule a call with us [here](https://usechamber.io/#contact).\n\n![uploaded image](/media/?type=post&id=96703&key=user_uploads/239960/115e0dee-5122-4baa-96e8-49cba5cd9c37)\n\n",
        "url": "https://www.ycombinator.com/launches/P9j-chamber-autopilot-for-ai-infrastructure",
        "votes": 269,
        "created_at": "2026-01-13T05:47:42.096Z"
      }
    ]
  },
  {
    "id": 31208,
    "name": "Oximy",
    "slug": "oximy",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0f253ec2ac4c62d3fd349aefdf584c9074cae87d.png",
    "website": "https://oximy.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Oximy is one platform to see every AI tool and dollar across your company, run your team’s work in an app you own, and route every request to the right model.",
    "one_liner": "Take command of the AI your company already runs on.",
    "team_size": 8,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1769530723,
    "tags": [],
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Winter 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/oximy",
    "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/oximy.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://linkedin.com/company/oximy",
    "x_url": "https://x.com/oximyhq",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0f253ec2ac4c62d3fd349aefdf584c9074cae87d.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
    },
    "founders": [
      {
        "user_id": 426038,
        "full_name": "Naman Ambavi",
        "title": "Founder/CEO",
        "founder_bio": "Founder, CEO at Oximy\n\nSpent 6 years watching enterprises panic about AI. Now I fix it.\n\nPrev. COO @ Induced (Backed by Sam Altman, Peak XV)\nPrev. Co-Founder & CEO @ Easocare (India's largest EMR app, acq. by top hospital group in India) ",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/namanambavi",
        "x_url": "https://x.com/namanambavi",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8003301cd8e5fff27a625766960d562b93db7933.jpg"
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    "news": [],
    "launches": [
      {
        "id": 97538,
        "title": "Oximy: See everything your company does with AI",
        "tagline": "AI visibility and governance for the enterprise",
        "body": "\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/042dbaa7-b053-47f5-acd3-e6208af8971c)\n\nHey everyone, I'm Naman, founder of Oximy.\n\n**TL;DR:** Enterprises have dozens of AI tools with zero visibility into what's being used, what it costs, or where sensitive data is going. Oximy sits on your network and gives you the full picture. Discovery, spend intelligence, and data protection in one platform. We're processing millions of requests daily for enterprises in financial services and healthcare.\n\n**Ask:** If you're dealing with AI sprawl, shadow AI, or data governance challenges, we should talk.\n\n👉 Website: [oximy.com](http://oximy.com)\n\n👉 Book a demo: [oximy.link/demo](https://oximy.link/demo)\n\n<https://www.youtube.com/watch?v=kL-hr9OzsS0>\n\n**The Problem**\n\nAI adoption in the enterprise isn't slowing down. But visibility is basically zero.\n\nHere's what we hear from every company we talk to:\n\n* \"We have no idea how many AI tools our employees are using.\"\n* \"Finance keeps asking me what we're spending on AI. I can't give them a number.\"\n* \"Someone pasted our entire customer list into ChatGPT. We found out three weeks later.\"\n* \"We bought Copilot licenses for 500 developers. I couldn't tell you if it's actually helping.\"\n\nIt's not that companies don't care. It's that there's been no way to actually see what's happening.\n\nUntil now.\n\n**The Solution**\n\nOximy gives you three things: **Discovery**, **Spend Intelligence**, and **Data Protection**. They all connect into a single platform that gives you complete visibility into AI across your organization.\n\n**1. Discovery: See every AI tool in your org**\n\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/849af8fa-f0a1-48e1-b8da-b387511dc3fe)\n\nYou can't govern what you can't see. Oximy automatically detects and categorizes every AI tool your employees are using, sanctioned or not.\n\nNo surveys. No manual tracking. No waiting for expense reports to come in.\n\n_Example:_ A financial services company came to us thinking they had 15 approved AI tools. Within the first week, we found 63, including several free tools that employees had signed up for using personal emails. Their security team had no idea.\n\n**2. Spend Intelligence: Understand what AI actually costs**\n\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/fd25f8f5-af21-4004-838a-788412064fe6)\n\nAI spend is exploding, but it's scattered across teams, tools, and cost centers. Oximy consolidates everything so you can see exactly where money is going and where it's being wasted.\n\nWhich tools have overlapping functionality? Which licenses are sitting unused? Which teams are getting ROI and which aren't?\n\n_Example:_ A growth-stage tech company discovered their marketing, design, and product teams were all paying for different AI image generation tools, over $80K per year combined. They consolidated to one tool and redeployed the savings.\n\n**3. Data Protection: Know where sensitive data is going**\n\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/c7bb7815-1af8-47eb-8f65-d5c97ff6bd85)\n\nThe biggest AI risk is unintentional data leaks.\n\nEmployees paste customer info, source code, financial data, and internal strategies into AI tools every day. Most companies have no visibility into this until something goes wrong.\n\nOximy monitors data flows to AI tools in real time. You can set policies, get alerts, and block sensitive data from reaching unauthorized tools.\n\n_Example:_ A healthcare company set up Oximy and, within 48 hours, flagged an employee uploading patient records to an unapproved AI transcription tool. They were able to intervene immediately and avoid a potential HIPAA violation.\n\n**How It All Connects**\n\nMost companies try to solve these problems separately. A CASB for shadow IT. A spend management tool for costs. A DLP for data protection.\n\nThe problem is that AI doesn't fit neatly into any of these buckets. And none of them talks to each other.\n\nOximy is built from the ground up for AI. Discovery, spend, and data protection all live in one platform, so you get the full picture, not three partial views stitched together.\n\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/9cdcb6f7-149f-4101-a0b0-d118b8887e11)\n\nSee a new AI tool pop up? You immediately know who's using it, what it costs, and which data it accesses.\n\nSee a spike in spend? You can trace it back to specific teams and tools.\n\nSee sensitive data flowing somewhere it shouldn't? You can block it and trace it back to the source.\n\nEverything connects.\n\n**How It Works**\n\nWe integrate at the network layer, sitting on your firewall or proxy.\n\nWhy does this matter?\n\nBecause every AI tool communicates over the network. ChatGPT, Copilot, Midjourney, Claude, Gemini, and every new tool that launches next month. We don't need endpoint agents. We don't need individual integrations with each vendor. We see everything automatically.\n\nThis also means we're future-proof. When the next AI tool shows up, we're already watching.\n\n![uploaded image](/media/?type=post&id=97538&key=user_uploads/426038/4cd8ad8c-e125-4b7d-87e6-268edf631be7)\n\n**Why Now**\n\nTwo years ago, AI in the enterprise was experimental. A few people are trying ChatGPT. Maybe a Copilot pilot.\n\nNow it's everywhere. And governance hasn't caught up.\n\nCISOs are being asked, \"What AI tools are we using?\" and don't have an answer. CFOs are watching AI costs explode with no accountability. Boards are asking about AI risk and getting shrugs. Regulators are starting to pay attention.\n\nThe companies that figure out AI governance now will be the ones that can scale AI adoption safely. The ones that don't will either slow down and lose, or speed up and get burned.\n\nWe're working with enterprises in financial services, healthcare, and growth-stage tech companies. We process millions of requests every day across thousands of AI tools.\n\nIf any of this resonates, we should talk:\n\n* CISOs and security leaders who are trying to get a handle on AI risk\n* CIOs, AI Transformation, and IT leaders dealing with shadow AI and tool sprawl\n* CFOs and procurement who are trying to understand AI spend\n\n👉 **Website:** [oximy.com](http://oximy.com)\n\n👉 **Book a demo:** [oximy.link/demo](https://oximy.link/demo)\n\n👉 **Email me directly:** [naman@oximy.com](mailto:naman@oximy.com)",
        "url": "https://www.ycombinator.com/launches/PNC-oximy-see-everything-your-company-does-with-ai",
        "votes": 27,
        "created_at": "2026-02-05T16:42:04.228Z"
      }
    ]
  },
  {
    "id": 31211,
    "name": "Sazabi",
    "slug": "sazabi",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2fca2808f8b0ea2f65302051164b0360bec51746.png",
    "website": "https://www.sazabi.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "The AI-native observability platform for fast-moving engineering teams. Backed by tastemakers from the world's top AI companies: Vercel, Graphite, Daytona, Browserbase, LangChain, Mastra, Replit, and more.",
    "one_liner": "The AI-native observability platform for fast-moving engineering teams",
    "team_size": 10,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1774488944,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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      {
        "user_id": 282099,
        "full_name": "Sherwood Callaway",
        "title": "Founder/CEO",
        "founder_bio": "Sherwood Callaway is a 2x YC founder and software engineer with over ten years of experience in infrastructure, DevOps, and observability engineering roles top VC-backed startups, including Crunchbase and Brex. Currently, he is founder and CEO at Sazabi, the AI-native observability platform for fast-moving engineering teams. Sazabi is directly inspired by Sherwood's experience at Brex, where helped start the company's infrastructure and observability engineering teams.",
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      {
        "title": "The End of Manual Debugging",
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        "date": "Mar 27, 2026"
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      {
        "id": 102391,
        "title": "Sazabi - Datadog for the AI Era",
        "tagline": "AI-native observability platform for fast-moving engineering teams",
        "body": "# Sazabi - Datadog for the AI Era\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/bc8014f4-5e17-4c76-be1c-3bd2b6ba26ec)\n\nSazabi is a next-generation observability platform designed for fast-moving, AI-native engineering teams. If you're using Datadog, Sentry, Grafana, or Axiom today, you could be moving ten times faster with Sazabi. **Book a demo [here](https://calendly.com/d/cyn6-s9f-tfq/meet-sazabi).**\n\n### Rethinking Observability\n\nRight now, every engineering team in the world is coming to the same conclusion: it doesn't matter how fast you can ship if your app is constantly going down. The antidote to production instability is observability, but not the flavor we have today. \n\nLegacy observability platforms can’t keep up with the pace of modern AI-driven software development. They take hours or days to set up. They are complex and clunky to use. The alerts they give you are noisy and confusing. They aren't built to be used with agents.\n\nSazabi is what Datadog would look like if it were built in 2026 instead of 2010:\n\n* A simple chat-based user experience instead of endless dashboards. \n* Effortless agent-driven instrumentation that requires zero code or config changes. \n* Autonomous alerts that can contain all the relevant context and never misfire. \n*  A powerful CLI that gives your coding agent complete visibility into your observability data. \n*  Slack has the primary application entry point. \n* An AI agent that memorizes your codebase, architecture, traffic patterns, and past incidents.\n* Native integrations with every major cloud provider and hosting service.\n* A storage layer that uses AI for compression and is optimized specifically for agentic query patterns.\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/3e5d80bd-59bd-43b3-bc72-ae9edc68a2ed)\n\n### How It Works\n\nYou can get started with Sazabi in less than 15 minutes. There are just three steps:\n\n1. Connect your GitHub repository \n2. Install the Slack app \n3. Start sending logs\n\nFor legacy platforms, instrumentation is a headache. With Sazabi, it's effortless. We've got integrations with over 35 of the leading cloud hosting services, including Vercel, AWS, GCP, Temporal, Cloudflare, Neon, Supabase, and more. If we don't have a native integration, Sazabi can open a pull request with the necessary instrumentation changes. Our log intake is OpenTelemetry compatible.\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/563095d8-89fc-47cd-a4cf-94746d7540ff)\n\nAfter setup, a few things happen automatically: \n\n* Sazabi scans your logs and codebase to learn more about the system, registering all key services, components, and product features to a live status page that gives you an overall view of system health.\n* Sazabi's background agents begin monitoring your logs, codebase, and infrastructure for anomalies - everything from unfamiliar errors to traffic spikes to crash pods and failed deployments. \n* When an issue is detected, Sazabi sends you a rich Slack alert with all the relevant context, including the root cause.\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/f676f9dc-0af4-4c31-873f-abc1172484fe)\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/c81d2281-add8-4645-b873-524affd0d0ee)\n\nYou and anyone from your team can send Sazabi a follow-up message via Slack, CLI, Web, or MCP. Sazabi is great for ad hoc questions and queries too. Sazabi automations allow you to generate detailed reports at any cadence.\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/b8ab228d-23a7-40d2-960d-c469c6d25f19)\n\nAfter receiving an alert, you can fix it directly with just a click of a button. Sazabi will open the pull request against your repo. If you prefer to use a different coding agent, it’s easy to hand off context -  Sazabi has integrations with Cursor, Codex, and Claude Code.\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/a95e8ecc-befb-4e31-a7a9-5523adea2548)\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/be62eeb2-a73f-431a-b99a-64823dc8fdc2)\n\nTeams that use Sazabi are detecting issues they didn’t know they had and resolving incidents in minutes instead of hours.\n\n### Logs Are All You Need\n\nSazabi is based on a few big ideas. The most important one: “logs are all you need”. \n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/135568da-6b42-4f54-8e27-2e9d9d349f46)\n\nWhat this means is that Sazabi doesn't require users to send metrics or traces. You only need to send us logs (stdout and stderr). Sazabi is able to answer complex queries, perform aggregations, profile endpoints, and trace requests through your entire system using just log data. The result is a much simpler instrumentation experience for you and your team, with all the power of a traditional observability platform.\n\n### What Makes Us Different\n\nA lot of companies are building in observability right now. How is Sazabi different?  Here’s how we think about the landscape:\n\n*  **AI SRE startups like Traversal and Resolve.** These sit on top of third-party systems like Datadog. It's an approach that makes sense for enterprises that rely on legacy tools, but not the best solution. Sazabi has its own storage layer.\n* **DIY solutions like Ramp’s “On-Call Assistant”.** It’s great to see so many companies building internal AI SREs. It proves demand, but it doesn't make sense for each company to build their own - especially when Sazabi is offering a better agent off-the-shelf.\n* **Legacy players like Datadog, Grafana, Sentry, and Axiom.** You can try to use Claude Code with the Datadog MCP, and it will work OK, but not great.  importantly, this approach isn't stateful. Each investigation is completely new. Sazabi, on the other hand, accumulates memory and constantly gets better. More fundamentally, all of these companies predate AI agents and are structurally misaligned with AI.\n* **LLM observability companies like Arize, Braintrust, LangChain, and Raindrop.** These are a narrow point solution for building and observing agents. Sazabi can do this too, but its scope is much broader than that. It's a general solution for observing any workload. We think that in the long-term there is no meaningful difference between observing agents and other types of software.\n\nSazabi's most important differentiator is our vertical integration. We're building every piece of the AI observability stack:\n\n* The interfaces\n* The agent\n* The storage layer\n\nThe allows us to do things our competitors fundamentally can't, like analyze and memorize every historical incident, or optimize our DB for the specific query patterns of our agent. It's an Apple-style approach to observability. \n\n### Where We Are Today\n\nSazabi is currently in closed alpha, but we’re growing fast. In the past month we’ve onboarded 35 new teams, run 8,000 background investigations, detected 2,000 issues, ingested 2 TB of logs, and opened 200 PRs.  \n\nSazabi is founded by Sherwood Callaway, a two-time YC founder who previously started the infrastructure and observability teams at Brex. We’re a team of nine engineers in total, including three early members of the Brex team and two former observability founders. \n\nOur investors include angels from top AI dev tools and infrastructure startups. To name a few:\n\n* Harrison Chase, CEO of LangChain\n* Merrill Lutsky, founder and CEO of Graphite\n* Ivan Burazin, founder and CEO of Daytona\n* Matt Biilmann, founder and CEO of Netlify\n* Paul Klein, founder and CEO of Browserbase\n\n### Our Ask\n\nSazabi is currently in closed alpha, but we’re onboarding new customers daily. Teams that sign up through Launch YC post skip to the front of the line. \n\n**Book a demo [here](https://calendly.com/d/cyn6-s9f-tfq/meet-sazabi).**\n\n[sherwood@sazabi.ai](mailto:sherwood@sazabi.ai)\n\n![uploaded image](/media/?type=post&id=102391&key=user_uploads/282099/5f60765a-676b-4887-87d2-ad833c48d4a9)\n\n",
        "url": "https://www.ycombinator.com/launches/QdT-sazabi-datadog-for-the-ai-era",
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        "full_name": "Aidan Tiruvan",
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        "body": "> **TLDR:** Create an API sandbox with the environments your CI, tests, and evals need. Your coding agent can start the API sandboxes (e.g Linear, Slack, Datadog), run tests, and inspect state changes. Ask your agent about archal.ai!\n\nHi everyone, I'm Aidan, the founder of Archal. I previously built agent environments at Scale AI and I've been an avid developer for most of my life, which led me to the problem Archal poses to solve.\n\n[https:/youtu.be/pUfu1RFDYVk](https://youtu.be/pUfu1RFDYVk)\n\n**What we found**\n\nCoding agents are getting much better at writing integrations, but testing those integrations is still painful. Many providers do not offer a usable sandbox, so developers fall back to throwaway accounts, shared test workspaces, or thin mocks. Those options are difficult to reset and make it hard to know whether an agent actually completed a task or only returned a plausible response.\n\nAs Adam Bender at Google put it, “But with 10x more code and 10x more services, integration tests are going to become the most important part of your quality strategy.” (Google, Software engineering is at the tipping point)\n\n**How Archal works**\n\nWith all of this in mind, I built Archal for a starting point of how the next generation of devs will do QA. It's a tool focused on giving agent developers and integration engineers API sandboxes of the SaaS platforms they use in prod pipelines. With Archal, a developer or their coding agent can start a sandbox, run the task, inspect what changed, and reset everything for the next test. We eliminate the need of separate test accounts, thin mocks and stubs, as well as overburdening rate limits on provider-owned sandboxes.\n\n**Our ask**\n\nWe're giving out $20 in Archal credits to each new developer that signs up and starts a sandbox. We’d love for you to give it a try and give us feedback, cheers to making testing easier in the era of coding agents!",
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    "long_description": "Inth monitors your pull requests, codebase and website to catch privacy issues and turns them into fixes and evidence.\r\n\r\nEvery product change you make exposes you to privacy risk. Customers lose trust. Fines and lawsuits are given. Fundraising and customer deals are broken. New market launches halt.\r\n\r\nThe growth you thought you would have slows because privacy compliance is overwhelming and changes with every product update.\r\n\r\nInth is fixing this to help you automate privacy compliance to ship and grow faster.\r\n\r\nInth scans your PRs, website and codebase to catch any privacy risk. It then fixes and evidences them against your product, policies and global privacy laws to automate privacy compliance in every region your customers are in.\r\n\r\nStart a free privacy scan at: inth.com",
    "one_liner": "Monitor and fix product privacy issues before they block growth",
    "team_size": 3,
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    "country": "GB",
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        "user_id": 735095,
        "full_name": "Christopher Burns",
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        "founder_bio": "Founder of Inth, the AI-native privacy company helping teams ship and grow faster by automating privacy compliance across pull requests, codebases, and websites.\n\nPreviously founded Everfund. Creator of c15t, open-source consent and privacy infrastructure with 3M+ npm downloads, and CookieBench. Spent the last decade building developer tools, privacy systems, and trust infrastructure.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/burnedchris/",
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      {
        "id": 101245,
        "title": "Inth: Dev-first cookie banners that don't slow your site",
        "tagline": "Built for performance and full developer control.",
        "body": "tldr: your cookie banner is slowing down your site. Inth banners keep performance high, styled using your own components, and keeps you compliant using our open-source core, [c15t.com](http://c15t.com).\n\nInth is building cookie banners that don't cost your Core Web Vitals.\n\nFor years, cookie banners have plagued the internet with slow bloated scripts, that aren't aware of user locations or local laws, built for marketing and legal teams. Inth is fixing this with developer-first consent infrastructure.\n\n<https://youtu.be/RJ7cSk1QYi8>\n\nInth helps developers automate cookie consent and user data rights to ensure compliance with all jurisdictions, using the fastest and most composable cookie banner available, c15t.\n\nBundled into your website's code, c15t cookie banners have native SDKs, i18n, and are fully customizable to match your UI and brand. Built for developers who care about compliance and performance.\n\nSince Inth launched, c15t has grown over 2 million npm downloads, and is used by some of the top developer tools companies, luxury hotel chains, and online supermarkets.\n\nJoin the teams already using Inth to deploy faster, compliant websites at [inth.com](http://inth.com) or quickstart: npx @c15t/cli\n\nFind our docs here: c15t.com/docs",
        "url": "https://www.ycombinator.com/launches/QKz-inth-dev-first-cookie-banners-that-don-t-slow-your-site",
        "votes": 14,
        "created_at": "2026-05-12T15:53:00.835Z"
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    ]
  },
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    "id": 31250,
    "name": "The Token Company",
    "slug": "the-token-company",
    "former_names": [
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    "website": "https://thetokencompany.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Compression middleware that removes context bloat in milliseconds, lowering costs and improving end-to-end latency.\r\n\r\nCompression is especially effective across natural language workloads. In a blind LLM arena case study with one of our customers, compressed requests increased user preference, lowered costs, and lifted purchase volume by 5%.",
    "one_liner": "Compression middleware that improves LLM outputs",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "question": "How did you decide to apply to Y Combinator? What was your experience applying, going through the batch, and fundraising at demo day?",
        "answer": "I didn’t apply for the winter batch, but after the YC partners reached out and spoke with me, it seemed like the obvious choice. The batch has been awesome so far, and I’ve learned so much. Can’t wait for what’s to come :)"
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        "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?",
        "answer": "Context bloat and inefficient LLM inputs. We preprocess inputs to remove useless tokens from the context, which results in faster, cheaper, and more accurate LLM answers.\r\n\r\nCurrently, this is not being done practically anywhere, which means billions of dollars and quadrillions of tokens are being wasted.\r\n\r\nI decided to work on this because it is something much more fundamental than ordinary application layer companies, since it affects each one. And I like hard problems ;)"
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      {
        "question": "What is your long-term vision? If you truly succeed, what will be different about the world?",
        "answer": "The vision is to optimize every LLM request in the world at the token level before it reaches a model.\r\n\r\nEven if model labs dominate, there will still be multiple competing providers. We sit above them as a neutral efficiency layer, optimizing inputs across all models. Labs are incentivized to build bigger models and sell more tokens, not reduce usage across the ecosystem. As long as more than one lab exists, a cross-model optimization layer remains structurally defensible."
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    "url": "https://www.ycombinator.com/companies/the-token-company",
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    "country": "US",
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      "name": "Jared Friedman",
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      {
        "user_id": 2949698,
        "full_name": "Otso Veisterä",
        "title": "Founder/CEO",
        "founder_bio": "Founder of The Token Company",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/otso-veistera/",
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      {
        "id": 98397,
        "title": "The Token Company: Intelligent compression for LLM context bloat",
        "tagline": "Intelligent compression for LLM context bloat",
        "body": "Hey everyone,I’m Otso Veisterä, founder of The Token Company (YC W26).\n\n**TL;DR**\\\nWe build LLM input compression using a fast machine learning model (not a generative LLM). It removes useless tokens from prompts via a drop-in API—cutting token counts, latency, and costs while improving model performance. It compresses 100k tokens in under 100ms.\n\n<https://youtu.be/65ScMCGNPss>\n\n**The Problem**\\\nPrompts bloat with redundant tokens from chat history, RAG docs, or large inputs. This wastes context windows, drives up costs, slows inference, and weakens model outputs. Heavy users face high bills and must limit context to stay affordable.\n\n**The Solution**\\\nOur API compresses prompts intelligently, preserving semantic intent while stripping noise. Integration takes minutes.\\\nBenchmarks show accuracy gains (e.g., +2.7 percentage points on financial QA with up to 20% fewer tokens) and up to 37% faster end-to-end latency.\n\n**My Ask**\\\nIf you're running production LLMs and fighting context bloat, high costs, or latency, email me: [otso@thetokencompany.com](mailto:otso@thetokencompany.com)\\\n<https://thetokencompany.com>",
        "url": "https://www.ycombinator.com/launches/Pb3-the-token-company-intelligent-compression-for-llm-context-bloat",
        "votes": 25,
        "created_at": "2026-03-03T16:18:10.552Z"
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    "id": 31252,
    "name": "Moda",
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    "website": "https://moda.dev",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Your agents should never make the same mistake twice. Moda helps you engineer better agents from production data. Find recurring failures, understand what caused them, and build evals from real agent runs. Compare models on the tasks your users actually need. Test changes to your prompts, tools, and workflows against past production traffic before you ship. Know what broke, what to change, and whether the fix worked.",
    "one_liner": "Your agents should never make the same mistake twice.",
    "team_size": 6,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1769121297,
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        "full_name": "Pranav Bedi",
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        "user_id": 960726,
        "full_name": "Mohammed Al-Rasheed",
        "title": "Founder",
        "founder_bio": "Building Moda",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/moalrs/",
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      {
        "id": 98435,
        "title": "Moda: The Continual Learning Layer for AI Agents",
        "tagline": "Turn production agent traces into validated improvements for your agent harness.",
        "body": "We’re Mohammad and Pranav (Moda, W26). **We built monitoring & reliability for AI agents.**\n\n![uploaded image](/media/?type=post&id=98435&key=user_uploads/2168306/84330bae-242e-447d-a656-800061f4b1ac)\n\n**In production, AI agents fail silently all the time:** tool calls that error or time out, agents that claim they “did it” without actually doing it, prompt injections that cause data leakage, and long customer chats that hide the real issue. Most teams only find out after users complain, churn, or something breaks.\n\n<https://youtu.be/aKPDhL_AE4E>\n\n### **The problem:**\n\nWhen an agent fails, the “bug” is rarely one line of code. It might be a flaky tool, a prompt regression, a bad retrieval result, or a jailbreak attempt.\n\nBut most teams still debug agents by skimming transcripts, grepping logs, and hoping they notice the real issue. Previous tools were built for clicks, not for long, messy conversations with skills, MCP’s and tools.\n\nAs agents get more autonomous, this gets worse. Small changes in prompts, tools, or integrations can cause big reliability and security problems at scale.\n\n### **What we built:**\n\nModa is monitoring & reliability for AI agents. It turns conversations and tool traces into low-noise, actionable signals.\n\n**Trace-first drilldowns:** click from a metric or alert straight into the exact messages and tool calls that caused it.\n\n![uploaded image](/media/?type=post&id=98435&key=user_uploads/2168306/6e062a13-340e-452f-9676-2bdd790d94e4)\n\n**Conversation segmentation:** automatically splits long chats into topic segments so you can debug the right unit of work.\n\n![uploaded image](/media/?type=post&id=98435&key=user_uploads/2168306/90151d61-e51b-45da-ac29-72af5c8a31d6)\n\n**Failure detection:** flags tool-call errors, timeouts, and “claimed it worked” runs.\n\n![uploaded image](/media/?type=post&id=98435&key=user_uploads/2168306/335d8a83-6301-4d0e-af31-a75778cbbb0e)\n\n**Quality & safety signals:** highlights hallucinations, user frustration, and prompt injection attempts.\n\n![uploaded image](/media/?type=post&id=98435&key=user_uploads/2168306/7ee4b5f6-ddc0-47a3-9e9c-3538f6764acc)\n\n\\\n**Regression tracking:** measure the impact of prompt and harness changes, and catch regressions before customers do.\\\n\\\nWant to see Moda on your agent in production? Book a quick call: [https://moda.dev](https://moda.dev)\n\n### **About Us:**\n\n**Mohammad** built AI agents and infrastructure at **Shopify, Notion, and Clio**, shipping systems that scaled agents to real users.\n\n**Pranav** built data infrastructure at banks and insurance firms before diving into building AI agents at **Shopify** and **Cerebral Valley**\n\nWe both dropped out of University of Waterloo to solve what we believe to be the biggest problem in the next 10 years, AI systems are unreliable, Moda is going to solve that.\n\nVisit us at [https://moda.dev](https://moda.dev) | [pranav@modaflows.com](mailto:pranav@modaflows.com)",
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        "votes": 122,
        "created_at": "2026-03-04T02:01:23.769Z"
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    "name": "Maven",
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    "website": "https://trymaven.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We enable AI chat and voice agents to collect payments through a single API call, handling card processing and PCI compliance across all payment gateways",
    "one_liner": "Payments for Conversational Agents",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1770422490,
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      "America / Canada"
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    "url": "https://www.ycombinator.com/companies/maven",
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    "city": "San Francisco",
    "country": "US",
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      "name": "Ankit Gupta",
      "url": "https://www.ycombinator.com/people/ankit-gupta"
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    "founders": [
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        "user_id": 2746056,
        "full_name": "Brandon Boehme",
        "title": "Founder/CEO",
        "founder_bio": "Founder, Maven | Ex Meta, Amazon | EECS, UC Berkeley",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/brandon-boehme/",
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      {
        "user_id": 2246042,
        "full_name": "Wasi Ahmed",
        "title": "Founder/COO",
        "founder_bio": "Founder @ Maven | CS @ UC Berkeley | Prev Amazon",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/wasi-ahmed05/",
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      {
        "id": 97737,
        "title": "Maven – Payments for Voice AI Agents",
        "tagline": "Add secure payments to any voice agent in minutes.",
        "body": "**Hey YC!**\n\nWe’re [@Brandon](https://bookface.ycombinator.com/user/2746056) and [@Wasi](https://bookface.ycombinator.com/user/2246042/following), co-founders of [@Maven](https://bookface.ycombinator.com/company/31253).\n\nWe’re building the payments infrastructure that lets voice AI agents collect payments during live calls.\n\n<https://youtu.be/GZ_e33v4B0c?si=1MvBlMWpxKHK3rx9>\n\n---\n\n### TL;DR\n\nVoice AI can handle the conversation. It just can’t complete the transaction.\n\nMaven lets voice agents collect payments during live calls with one API call.\n\n---\n\n### The Problem\n\nVoice agents are being deployed across:\n\n* Home services\n* Healthcare\n* Insurance\n* Sales\n* Collections\n* And more\n\nThey can qualify leads, book appointments, and negotiate pricing.\n\nBut when a customer says, “I’m ready to pay,” everything breaks.\n\nAdding payments today means:\n\n* Building PCI-compliant infrastructure\n* Handling sensitive card data\n* Managing failed payments mid-call\n* Supporting multiple gateways like Stripe, Authorize.net, and Adyen\n\n---\n\n### Our Solution\n\nWith a single API call, we take over secure card collection, processing, retry logic, and PCI compliance.\n\nCard data flows directly through Maven’s PCI-compliant infrastructure, never your servers.\n\nAs voice AI becomes a large revenue channel, it needs real payment infrastructure. Maven completes the loop.\n\n![uploaded image](/media/?type=post&id=97737&key=user_uploads/2246042/31358443-35d2-4fce-93a9-e27491e8b66d)\n\n---\n\n---\n\n### Our Ask\n\nWe’re looking for intros to companies or operators where payments happen over the phone, including:\n\n* Home services (HVAC, plumbing, roofing)\n* Healthcare billing and scheduling\n* Insurance agencies\n* Collections\n* Auto services\n* SMB sales teams\n\nIf you know founders or teams deploying voice AI in these areas, we’d love an intro.\n\n### Our Vision\n\nAs AI agents handle more revenue-generating conversations across voice and other interfaces, our vision is to become the default infrastructure for all human-to-agent payment touchpoints.\n\n👉 <https://trymaven.com>\n\n![uploaded image](/media/?type=post&id=97737&key=user_uploads/2246042/c488fde5-bb9e-4f1e-a972-8e8c6dbcd0cf)\n\n",
        "url": "https://www.ycombinator.com/launches/PQP-maven-payments-for-voice-ai-agents",
        "votes": 20,
        "created_at": "2026-02-11T19:02:49.038Z"
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    "id": 31283,
    "name": "Ossus",
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    "website": "https://www.ossus.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We build intelligence for cultural institutions.\r\n\r\nOur first commercial product is for the literature industry with a focus on school and public libraries. It is a AI-native OS for libraries, that helps librarians save time on tedious tasks and lets them focus on their patrons.\r\n",
    "one_liner": "Intelligence for cultural institutions",
    "team_size": 3,
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    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1769303203,
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      {
        "user_id": 1669339,
        "full_name": "Jonathan Görtz",
        "title": "Founder/CEO",
        "founder_bio": "A universe is the only challenge worthy of mankind.",
        "is_active": false,
        "linkedin_url": "https://www.linkedin.com/in/jonathangortz/",
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        "user_id": 1134023,
        "full_name": "Carl-Hugo Jacobsson",
        "title": "Founder",
        "founder_bio": "Big on scalable growth",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/chjacobsson",
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      {
        "user_id": 1144628,
        "full_name": "Kaan Sirin",
        "title": "Founder/CTO",
        "founder_bio": "chief librarian",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/kaan-sirin-b0a6321b6",
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      {
        "id": 98598,
        "title": "Librar Labs: The AI librarian",
        "tagline": "AI library system that increases reading and saves time for library staff",
        "body": "The reading rates have hit an all time low. That’s why Librar Labs built the AI library system that helps library staff save time and increase the library’s impact.\\\n\\\nAI is able to navigate the digital world of literature in ways never seen before. This powerful technology is used to increase the output of the hundreds of schools using Librar, and is being adopted at a pace never seen before in publishing, library and archive tech history.\\\n\\\nThe team of quantum physicists, exited founders and dropouts are backed by founders and operators from OpenAI, Depict, Kahoot, Google Maps and many more to tackle the one of the greatest problems of our time.\\\n\\\nlibrarlabs.com",
        "url": "https://www.ycombinator.com/launches/PeI-librar-labs-the-ai-librarian",
        "votes": 11,
        "created_at": "2026-03-08T21:21:01.199Z"
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      {
        "id": 97314,
        "title": "Librar Labs - AI for librarians",
        "tagline": "Librar takes care of the tedious work, so librarians can focus on building reading culture and community",
        "body": "![uploaded image](/media/?type=post&id=97314&key=user_uploads/1669339/2518baf4-5e9f-4fc4-af61-4cb76bd44e7d)\n\nAnnouncing Librar Labs - AI for librarians\n\nLibrarians spend countless hours on tedious work that they don’t like, but still have to do. Today, Librar is used in nearly 300 schools, helping librarians cut down on inventory, reporting, and acquisitions work so they can do what they are best at and enjoy the most: engaging with the community.\n\nOne of the most liked features is how the librarian with a photo of the shelf, Librar identify the items and can take inventory, analyze circulation or even just set up the library.\n\nOne of the Librar founders used to work as a librarian and saw the day-to-day challenges firsthand. Since managing a library is very challenging, we decided to build Librar to make it easier for everyone.\\\n\\\nIf you are (or know) any librarians or principals reach out to us on [linkedin](https://www.linkedin.com/in/jonathangortz) or mail: [jonathan@librarlabs.com](mailto:jonathan@librarlabs.com)\\\n\\\nGo to our website to find out about all the things Librar can do:\\\n<https://www.librarlabs.com/>",
        "url": "https://www.ycombinator.com/launches/PJa-librar-labs-ai-for-librarians",
        "votes": 27,
        "created_at": "2026-01-30T02:29:16.418Z"
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    "id": 31299,
    "name": "Wayco",
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    "website": "https://wayco.ai",
    "all_locations": "New York City, NY, USA",
    "long_description": "Wayco is building a unified data intelligence layer for medical treatment of legal cases. From the first intake to instant settlement.",
    "one_liner": "AI operator for medlegal cases",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1770576314,
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      "America / Canada"
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    "year_founded": 2025,
    "city": "New York City",
    "country": "US",
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        "title": "Twolabs - Humanoid robots for caregiving",
        "tagline": "Socially intelligent humanoids to assist the elderly.",
        "body": "**TL;DR -**\n\n[Twolabs](https://www.twolabs.ai/) builds humanoid robots for caregiving.\n\nOur robots will assist at nursing homes and senior living centers to take care of the elderly, starting with feeding, dressing, medication, daily tasks, and companionship.\n\nWe have our first robot design Tobi, are actively training VLAs, and building the social intelligence layer. Check out our [launch video](https://youtu.be/70T6AzTqIHc?si=CwZwf999yWGGNFk3).\n\n**What we do**\n\nTwolabs builds semi-humanoid robots for nursing homes and senior living communities. We're building the robot to achieve the following goals for the elderly:\n\n* **Everyday help.** Feeding, dressing, medication reminders, fetching objects, the daily physical work of getting through a day.\n* **Mobility support.** A stabilization aid so residents can walk safely, plus wayfinding around the facility.\n* **Companionship.** Conversation, presence, real social support, so a resident isn't alone all day.\n\nWe build the full stack in-house, the robot hardware and all the AI that makes it socially capable. Most of robotics right now is chasing warehouse manipulation, moving boxes and folding laundry. We think the harder and more important problem is robots and people working closely together.\n\n![uploaded image](/media/?type=post&id=101964&key=user_uploads/2115411/ee3141ab-259b-402a-8ee5-5e10b8ce37fd)\n\n**The backstory**\n\nI'm [Sardor](https://www.linkedin.com/in/sardor-rahmatulloev). I was born in Samarkand, Uzbekistan, moved to the US at six, and studied CS at Cornell. At Cornell I led a 40+ engineer team building autonomous combat robots. We were ranked top 5% nationally; most of my team went on to defense tech and semiconductors. My faculty advisor was Professor Tapo Bhattacharjee, who runs one of the leading caregiving robotics labs in the country. After Cornell I joined Meta Superintelligence Labs at 23 years old, working on GPU optimization, foundation model training, and AI infra. I left to start Twolabs. (I also got to be the first Uzbek founder to get into YC.)\n\n[Danyal Ahmad](https://www.linkedin.com/in/danyalahmad1/) is my co-founder, and we have known each other for over nine years, since high school. We interned together at Salesforce in 2024 and have built a lot of products together since. Danyal has an MS in AI from Georgia Tech, and fine-tuned video models at Salesforce.\n\nWe started Twolabs because our grandparents face a lot of health conditions as they grow older, and we wanted to build a robot that can assist them in their daily lives. The need is everywhere: the US is short hundreds of thousands of caregivers and the gap widens every year as the population ages. A caregiver costs a facility around $60K/year, turnover is brutal, and the work is physically and emotionally exhausting. Meanwhile the people who need help, getting out of bed, eating, not being alone all day, don't get enough of it. A robot doesn't replace caregivers. It takes the repetitive physical load off them so one caregiver can do more for more residents, and residents get help when they actually need it.\n\n**Our bet**\n\nMost robotics companies fall into one of two camps:\n\n* **Task robots** that can do physical work but can't relate to a person.\n* **Social robots** that can hold a conversation but can't actually do anything.\n\nEach succeeds at one half of the problem. But we think a truly helpful robot has to handle both, completing the physical task and engaging with the person in front of it. Too many humanoids are optimizing for task completion alone. We're betting the robots that actually help people will need to be socially intelligent to do it.\n\nThe other half of our bet is how robots are built. Companies have to be vertically integrated to win in robotics right now. We build the hardware and the AI together, because at this stage of Physical AI the models need to be specific, not general-purpose.\n\n**Where we are**\n\nPrototype design completed and our AI stack in active development. We're advised by Professor Zackory Erickson at Carnegie Mellon, whose research is at the leading edge of assistive robotics.\n\n![uploaded image](/media/?type=post&id=101964&key=user_uploads/2115411/551aef43-2a9c-4786-831c-ea4c32f77551)\n\n**Our asks**\n\n1. **Engineers and research scientists.** If you or someone you know is proficient in VLA research, reinforcement learning, or robotics foundation models, we're hiring AI engineers and research scientists.\n2. **Investors.** Investors in robotics, humanoids, healthcare, or hard tech, especially anyone who's been close to elder care.\n\nContact us directly at [sardor@twolabs.ai](mailto:sardor@twolabs.ai) and [danyal@twolabs.ai](mailto:danyal@twolabs.ai). And check out our website at [twolabs.ai](http://twolabs.ai).",
        "url": "https://www.ycombinator.com/launches/QWa-twolabs-humanoid-robots-for-caregiving",
        "votes": 8,
        "created_at": "2026-05-26T14:52:49.982Z"
      }
    ]
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    "id": 31375,
    "name": "Hub",
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      "Hub.xyz"
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    "website": "https://hub.xyz",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Hub is a multimodal lab providing real world data to frontier labs and robotic companies.",
    "one_liner": "Real-world training data for frontier AI labs and robotics.",
    "team_size": 40,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1773429014,
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    "regions": [
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    "url": "https://www.ycombinator.com/companies/hub",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/hub.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
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    "primary_partner": {
      "name": "Harj Taggar",
      "url": "https://www.ycombinator.com/people/harj-taggar"
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    "founders": [
      {
        "user_id": 3369916,
        "full_name": "Armin Kiani",
        "title": "Founder/CEO",
        "founder_bio": "CS & robotics. \nMicrosoft Innovation Award, 2020. \nWorked at Freedelity, Belgium's largest consumer-data platform.\nspeeding embodied agi with hub.xyz",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/armin-k-3439033ab/",
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      {
        "user_id": 3035977,
        "full_name": "Tim Sprecher",
        "title": "Founder/COO",
        "founder_bio": "Co-founder @ Hub, real-world training data for frontier labs.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/tim-sprecher-2a705220a/",
        "x_url": "https://x.com/tim404x",
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    "jobs": [
      {
        "id": 99443,
        "title": "Field Operator - Brazil",
        "url": "https://www.ycombinator.com/companies/hub/jobs/uJPqDL4-field-operator-brazil",
        "location": "SP, BR / State of São Paulo, BR / São Paulo, SP, BR / São Paulo, State of São Paulo, BR / Remote (SP, BR; State of São Paulo, BR)",
        "type": "Contract",
        "role": "Operations",
        "role_type": null,
        "salary_range": "$100 - $2K / monthly",
        "equity_range": "",
        "experience": "1+ years",
        "visa": "US citizenship/visa not required",
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      {
        "id": 114525,
        "title": "Machine Learning Engineer",
        "url": "https://www.ycombinator.com/companies/hub/jobs/ghznA3v-machine-learning-engineer",
        "location": "Paris, IDF, FR / Paris, Île-de-France, FR",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$90K - $120K",
        "equity_range": "0.02% - 0.20%",
        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
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    "launches": [
      {
        "id": 103084,
        "title": "Hub: real-world training data for frontier AI labs and robotics",
        "tagline": "Leveraging a global human network to collect data that don’t exist yet.",
        "body": "Hub provides real-world training data to frontier AI labs and robotics companies. We went to 7-figure revenue agreement on data delivery during the last weeks, working with some of the biggest names in AI and robotics.\\\n\\\n**Why now**\n\nThe gap between frontier models is closing, and internet-scale data is largely exhausted. The only edge left is real-world data that humans have to produce. Every lab now wins or loses on its edge cases, and edge cases need specific data captured by specific people: data that doesn't exist anywhere online. Whoever can go collect the right data first ships the better model first. Human labor is half of global GDP, and almost none of it has been recorded yet.\n\n**What Hub does**\n\nWe capture this data through a global network of humans, across all continents. We route and activate the exact people who fit a lab's brief and deliver in days, not the months it takes to source a generic dataset off the shelf. Because we collect the data ourselves, we can attest its provenance, which you can't do for data you didn't capture. Our ML and QA pipelines then verify and enrich it, so we meet the hardest dataset requirements labs throw at us. The moat isn't the data, it's how fast we can go get it.\n\n**Traction**\n\nFrom 5-figure to 7-figure in revenue agreement on successful data delivery. We now work with some of the biggest frontier AI labs and robotics companies in the world.\n\n**The ask**\n\nTwo asks: —> You're building frontier models or robots and data is your constraint. \\\n—> You're a top robotics engineer, a PhD whose research touches robotics or egocentric data, or someone with deep experience at a leading robotics lab, we're hiring. \\\n\\\nReach out here: [founders@hub.xyz](mailto:founders@hub.xyz).",
        "url": "https://www.ycombinator.com/launches/Qoe-hub-real-world-training-data-for-frontier-ai-labs-and-robotics",
        "votes": 1,
        "created_at": "2026-06-15T14:57:21.570Z"
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    "id": 31377,
    "name": "Limrun",
    "slug": "limrun",
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ab10903dc4335cb12beff61e9e9cb75f732ed72a.png",
    "website": "https://lim.run",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Coding agents in the cloud don't work for majority of teams who require native capabilities; they lack XCode for iOS, Emulator for Android, Unity for game development teams. We are on a mission to cloudify every local development utility so that regardless of where it runs, your agent can compose an ideal set of capabilities on the fly so you don't need to run it on your laptop.\r\n\r\nToday, we provide remote services such as XCode, iOS & Android simulators to enable coding agents running in any sandbox to be able to build and iterate over mobile apps, too.\r\n\r\nReplit, Rork, Momentic, Minitap and more agent companies have built mobile development and testing experiences on Limrun platform. Coconote (Quizlet), SorceJobs (W25) and more companies with mobile apps use Limrun with their cloud agents, merging PRs without having to check out the code locally.",
    "one_liner": "Extend cloud sandboxes with iOS, Android and more",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1779230895,
    "tags": [],
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    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
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      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Growth",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/limrun",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/limrun.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/limrun",
    "x_url": "https://x.com/limrun",
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    "primary_partner": {
      "name": "Harj Taggar",
      "url": "https://www.ycombinator.com/people/harj-taggar"
    },
    "founders": [
      {
        "user_id": 262589,
        "full_name": "Muvaffak Onuş",
        "title": "Founder/CEO",
        "founder_bio": "Previously at Amazon (intern), SAP, Upbound and QA Wolf. I optimized EMR clusters at Amazon for ML workloads, then worked on Kubernetes at SAP and at Upbound, I was maintainer at Crossplane, open source cloud control plane, and tech lead for provider efforts. At QA Wolf, I founded and led the team that built web & mobile testing sandboxes at global scale.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/mmonus",
        "x_url": "https://x.com/muvaffakonus",
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    "jobs": [
      {
        "id": 110499,
        "title": "Founding Engineer",
        "url": "https://www.ycombinator.com/companies/limrun/jobs/SvYNO1K-founding-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$180K - $300K",
        "equity_range": "",
        "experience": "3+ years",
        "visa": "Will sponsor",
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          "Distributed Systems",
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      {
        "id": 111593,
        "title": "Account Executive",
        "url": "https://www.ycombinator.com/companies/limrun/jobs/Bgetg6K-account-executive",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
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      {
        "id": 114326,
        "title": "Founding Engineer",
        "url": "https://www.ycombinator.com/companies/limrun/jobs/EK9RXio-founding-engineer",
        "location": "Istanbul, İstanbul, TR",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Backend",
        "salary_range": "$75K - $180K",
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        "experience": "3+ years",
        "visa": "US citizenship/visa not required",
        "skills": [
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          "Kubernetes"
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        "title": "Developer Relations Engineer",
        "url": "https://www.ycombinator.com/companies/limrun/jobs/j8ucUHG-developer-relations-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$165K - $175K",
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        "experience": "3+ years",
        "visa": "Will sponsor",
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    "launches": [
      {
        "id": 101736,
        "title": "Limrun - Your cloud agent can build mobile apps now",
        "tagline": "Extend your cloud agent with XCode, Android and more",
        "body": "TLDR; We provide remote XCode, iOS &amp; Android Simulators and more to extend cloud agents’ capabilities. Replit, Rork, Momentic AI, x1 and many others built on our cloud and now we open it up to all mobile dev teams so you can use your favorite cloud agent for iOS and Android apps too.\n\nIf you have a mobile app, let’s talk! <https://cal.com/team/limrun/mobile-app-dev-in-cloud>\n\n<https://youtu.be/fYkRpsyPLkQ>\n\n---\n\nWhen code is written by humans, it lives on our laptops and PCs, but that's changing now that teams of agents are writing code alongside us. Today, code written by agents is moving to the cloud: Cursor Cloud, Devin AI, Codex Web, in-house solutions with sandboxes and more.\n\nNow the problem is that all these cloud agents have Linux-based sandboxes as opposed to our laptops that we bought specifically for coding. If you want to use sandboxes for iOS development, you don’t have XCode. If you have an Android app, no emulator that works well. There’s no properly priced GPU option for running Unity either.\n\nLimrun is building a cloud that provides these apps as remote services running in the environments they were designed to while letting your agents use them remotely from your favorite sandbox. All you need is a single skill file to teach your agent how to use our remote services. That’s it. No change of sandbox or agent.\n\nToday, we provide 3 services:\n\n* XCode: With a single CLI call, your code is synced, a build is triggered and your agent gets the logs to iterate.\n* iOS Simulator: Your agent can request a simulator to test the app with built-in AX tree or your favorite framework like Appium/Maestro/Detox etc.\n* Android Emulator: Similarly, it can request an emulator that will be proxied via an “adb” tunnel so it’s as if locally running.\n\nThese are not full sandboxes. We provide the highest level interface so your agent can orchestrate what it needs remotely and we optimize the heck out of the rest; hardware encoders for streaming, warm NVMe caches for building, below 1s boot times, on-demand recording, asset storage and more.\n\nHere is an example flow where Cursor Cloud (or an agent in E2B/Daytona/Docker etc) builds an iOS game in Swift:\n\n* Agent takes the issue from Linear and spins up its sandbox.\n* Writes the Swift code and calls the “lim xcode build .” command, streaming logs back.\n* Iterates over compiler errors.\n* Requests an iOS simulator with “lim ios create” and attaches it.\n* Plays the game with batch actions that provide millisecond precision.\n* We return the recording that it reviews, sees the bugs, implements the fixes.\n* Finally, records a demo video and constructs a preview link to drop on the PR.\n\nIt goes from Linear issue to a fully tested PR with demo and preview link that opens in the browser with realtime streaming or you can install to your device over WebUSB without downloading anything.\n\nIf you have a mobile app, let’s talk\\![ https://cal.com/team/limrun/mobile-app-dev-in-cloud](https://cal.com/team/limrun/mobile-app-dev-in-cloud)",
        "url": "https://www.ycombinator.com/launches/QSu-limrun-your-cloud-agent-can-build-mobile-apps-now",
        "votes": 15,
        "created_at": "2026-05-21T20:49:26.104Z"
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    "id": 31386,
    "name": "Interfaze",
    "slug": "interfaze",
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    "website": "https://interfaze.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Interfaze is an AI model built on a new architecture that merges specialized DNN/CNN models with transformers for tasks that require deterministic output and high consistency like Document intelligence, Object detection, Classification, Audio understanding and more.\r\n\r\nInterfaze outperforms large lab models like Gemini flash, Claude Sonnet and the GPT-5.4 mini series\r\n\r\nArchitecture paper: https://arxiv.org/abs/2602.04101\r\nFull open benchmarks: https://interfaze.ai/leaderboards",
    "one_liner": "AI model built for high accuracy deterministic tasks",
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      "name": "Aaron Epstein",
      "url": "https://www.ycombinator.com/people/aaron-epstein"
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      {
        "user_id": 1618136,
        "full_name": "Yoeven D Khemlani",
        "title": "Founder/CEO",
        "founder_bio": "8 years in edge computer vision research | 2nd time founder",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/yoeven",
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        "full_name": "Harsha Vardhan Khurdula",
        "title": "Founder/CTO",
        "founder_bio": "Building https://interfaze.ai",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/harsha-vardhan-khurdula-99b400183/",
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      {
        "id": 107799,
        "title": "Applied AI Engineer & Researcher",
        "url": "https://www.ycombinator.com/companies/interfaze/jobs/LYxZ0kI-applied-ai-engineer-researcher",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$150K - $350K",
        "equity_range": "0.50% - 2.00%",
        "experience": "1+ years",
        "visa": "Will sponsor",
        "skills": [
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          "Torch/PyTorch",
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      {
        "id": 99878,
        "title": "Interfaze - Deterministic AI for developer tasks: OCR, Object detection, Web scraping, STT, Classification and more",
        "tagline": "A new model architecture that outperforms SOTA LLMs on deterministic tasks that require high consistency and accuracy",
        "body": "![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/2310c230-6719-4913-ba16-43c8cab6c2d9)\n\n**tldr:** LLMs are built for human-in-the-loop tasks which are highly non-deterministic in nature. We built an AI model for tasks that require high accuracy and verifiable data you can build workflows around.\n\nInterfaze is an AI model built on a new architecture that combines specialized DNN+CNN models with transformers for developer tasks that require deterministic output and high consistency like:\n\n* Vision (OCR, Object detection, GUI) \n* Structured web extraction\n* Audio (STT, Diarization, Audio semantics)\n* Classification (Image, Text)\n* Web search and more\n\n**Try here:** <https://interfaze.ai>\n\nIf you’ve ever tried to build a production application using LLMs for tasks like OCR, web scraping, or strict structured data extraction, you already know the pain: hallucinated keys, broken JSON, hidden inaccurate data and massive latency spikes.\n\nYou usually end up chaining multiple open source models together just to get a reliable result but face challenges with scaling or being stuck with outdated models from providers like AWS, Azure and GCP.\n\n**Model comparison**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/b270b0f5-76f2-4d11-808a-d31f7e977b07)\n\n_Check out the latest benchmarks on our site [here](https://interfaze.ai)._\n\n**OCR example for KYC**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/619ba060-a307-46b5-8101-601b03aaa1c4)\n\nBeyond higher accuracy, now you get additional data that allows you to both validate your data and build reliable pipelines as a developer.\n\nA subset of the additional metadata you get when you run an OCR task 👇\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/6f8e5580-492b-447f-bed4-c2cdc4470750)\n\n_Note: the confidence score value can be used to build verifiable systems. bounding boxes can be used to trace items for consistent tasks with guessing._\n\n**Web scraping for LinkedIn**\n\nWe all know how hard it is to scrape sites like LinkedIn, whether you are switching proxy providers, rewriting your scripts, or even getting your IP banned.\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/7ad5d97e-a076-4698-91bd-5da3dcd5ff22)\n\n\nInterfaze is trained to work with the browser infra beyond just looking at pure html like traditional LLMs. Allowing it to learn new work around, rotate proxies when needed and figure out how to scrape any site under 30 seconds.\n\n**GUI/Computer use - Filling a form**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/cc344350-9e10-446c-89ee-f6c8db3d5602)\n\n**Object Detection - Construction site equipment check**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/eb58811e-5a6d-4563-b666-538255f5b630)\n\n**Architecture**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/08c716d5-e2b5-487e-bade-c469013f1a5a)\n\nWe spent a year researching a new architecture to solve this problem. We just couldn’t accept that attention is all you need, we think you need a new interfaze 😉\n\nFull paper: <https://arxiv.org/abs/2602.04101> (Accepted into IEEE CAI 2026)\n\n**Team**\n\n![uploaded image](/media/?type=post&id=99878&key=user_uploads/1618136/38e298b2-92cd-4476-8da0-913ba315bb75)\n\nI've been a developer and ML engineer for the past 8 years, working with ML models on the edge for real-world experiences like motion capture and navigation mapping to backend workflows like OCR, Web scraping pipelines, and more. Socials: [LinkedIn](https://www.linkedin.com/in/yoeven/?skipRedirect=true), [X](https://x.com/yoeven)\n\nHarsha has over 5 years of experience specializing in computer vision, reinforcement learning for SLMs, and AI research with multiple peer-reviewed papers. Socials: [LinkedIn](https://www.linkedin.com/in/harsha-vardhan-khurdula/), [X](https://x.com/khurdula)\n\nInterfaze X: <https://x.com/interfaze_ai><https://interfaze.ai/>\\\nInterfaze LinkedIn: <https://www.linkedin.com/company/interfaze-ai>\n\nSite: [https://interfaze.ai](https://interfaze.ai/)\n\nDocs: <https://interfaze.ai/docs>",
        "url": "https://www.ycombinator.com/launches/Pyw-interfaze-deterministic-ai-for-developer-tasks-ocr-object-detection-web-scraping-stt-classification-and-more",
        "votes": 32,
        "created_at": "2026-04-20T15:59:34.348Z"
      }
    ]
  },
  {
    "id": 31414,
    "name": "Dreach",
    "slug": "dreach",
    "former_names": [
      "Drip"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8eec4067ada1ceb62324834a6da7831196618472.png",
    "website": "https://dreach.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "dreach watches your ATS and your network. when someone starts hiring, you get the manager who owns the role and your warmest way in.",
    "one_liner": "staffing firms with dreach grow revenue faster.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777049103,
    "tags": [
      "B2B",
      "Recruiting",
      "Messaging",
      "Sales Enablement"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/dreach",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/dreach.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://linkedin.com/company/joindrip",
    "x_url": "https://x.com/Drippaygmi",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/DripYCx26",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8eec4067ada1ceb62324834a6da7831196618472.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Harj Taggar",
      "url": "https://www.ycombinator.com/people/harj-taggar"
    },
    "founders": [
      {
        "user_id": 3330353,
        "full_name": "Mike Levin",
        "title": "Founder/CEO",
        "founder_bio": "dreach watches your ATS and your network. when someone starts hiring, you get the manager who owns the role and your warmest way in.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/mikelevin-/",
        "x_url": "https://x.com/mike_drip",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9f6504e75ab0a729c79d4cb170d8d97794be178b.jpg"
      }
    ],
    "jobs": [],
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    "launches": []
  },
  {
    "id": 31448,
    "name": "smol machines",
    "slug": "smol-machines",
    "former_names": [
      "Smol Machines"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c9db625bd34a051e8617673268bb5d9659339ed.png",
    "website": "https://smolmachines.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Smol machines solve the problem of \"but app works on my machine\" by shipping the machine.\r\n\r\nSmol machine enables devs to ship a lightweight virtual machine with any software, similar to how Electron ships an entire browser with the web app. ",
    "one_liner": "Ship software faster with portable, self contained virtual machines.",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1775148410,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/smol-machines",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/smol-machines.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/smol-machines/",
    "x_url": "",
    "fb_url": null,
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c9db625bd34a051e8617673268bb5d9659339ed.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Pete Koomen",
      "url": "https://www.ycombinator.com/people/pete-koomen"
    },
    "founders": [
      {
        "user_id": 1655532,
        "full_name": "BinBin He",
        "title": "Founder/CEO",
        "founder_bio": "Building the next generation cloud service that's like a baby of lambda and ec2. Using a new primitive that combines the isolation of a VM with speed and flexibility of containers.\n\nRecently was at AWS.\n\nWorked at ECS|Fargate|FargateOnFirecracker for ~4 years.\nECS is largest container service in the world.\nFounding engineer of ECS managed instances.\n\nBefore that, spent another ~4years in Infra in startups, NASA.\nHelped decomission Terra, Aqua, Aura satellites back into earth. \n\n",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/binsquare",
        "x_url": "https://x.com/binsquares",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/23bd591f4c23ccd898e3687a53f218b8b83672c7.jpg"
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    ],
    "jobs": [],
    "news": [],
    "launches": []
  },
  {
    "id": 31453,
    "name": "Minicor",
    "slug": "minicor",
    "former_names": [
      "Laminar"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e14eb470879a4e71c717ad6774095600e8b0de85.png",
    "website": "https://minicor.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Minicor connects AI startups to legacy desktop apps that have no API. Reliably, quickly and at scale.\r\n\r\nDescribe a workflow and get an API endpoint. Each call triggers a self-healing desktop automation on a Windows VM. On-prem, cloud, or Citrix. Build through your coding agent via our MCP.\r\n\r\nComplete with the observability tools to manage your customer relationship: full video replay of every automation, granular logging, and slack enabled alerts.",
    "one_liner": "Scalable windows desktop automations",
    "team_size": 9,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1772756028,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/minicor",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/minicor.json",
    "year_founded": 2024,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/minicor-automations",
    "x_url": "https://x.com/minicor_",
    "fb_url": null,
    "cb_url": null,
    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e14eb470879a4e71c717ad6774095600e8b0de85.png",
    "app_video_url": null,
    "dday_video_url": null,
    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 1037983,
        "full_name": "Faizaan Chishtie",
        "title": "Founder/CEO",
        "founder_bio": "CEO @ Minicor (X26) - building an RPA platform for deploying AI into legacy desktop systems\n\nPreviously also built a furniture rental company and worked as early engineer at YC companies connecting systems together!",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/fchishtie/",
        "x_url": "https://x.com/faizchishtie",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8237083383c5f9e3a2ff2c95697815c59d0c2774.jpg"
      },
      {
        "user_id": 1639230,
        "full_name": "Saheed Akinbile",
        "title": "Founder/CTO",
        "founder_bio": "Building Minicor (X26) - Building an agentic desktop and browser automation platform.\nDeep in infra, RPA (QuickBooks/Sage/Citrix), and browser automation.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/saheed-akinbile",
        "x_url": "https://x.com/sahee_d",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d6ab7a935a1ba8fc9b8588ea0351c0b067c23f39.jpg"
      }
    ],
    "jobs": [
      {
        "id": 94138,
        "title": "Software Engineer",
        "url": "https://www.ycombinator.com/companies/minicor/jobs/mDMbXZk-software-engineer",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$175K - $250K",
        "equity_range": "0.15% - 0.45%",
        "experience": "3+ years",
        "visa": "Will sponsor",
        "skills": [
          "Amazon Web Services (AWS)",
          "Google Cloud",
          "Java",
          "JavaScript",
          "Kotlin",
          "Python",
          "Next.js"
        ]
      }
    ],
    "news": [
      {
        "title": "Minicor Raises $3.8M Seed",
        "url": "https://www.thesaasnews.com/news/minicor-raises-3-8m-seed/",
        "date": "Oct 15, 2026"
      },
      {
        "title": "The Top DevTech Startups in YC P26 - The Silicon Valley Post",
        "url": "https://svpost.com/articles/top-devtech-startups-yc-p26/",
        "date": "Jun 10, 2026"
      },
      {
        "title": "What Y Combinator's Latest Batch Reveals About The Future",
        "url": "https://www.forbes.com/sites/dariashunina/2026/06/04/what-y-combinators-latest-batch-reveals-about-the-future/",
        "date": "Jun 04, 2026"
      }
    ],
    "launches": [
      {
        "id": 99004,
        "title": "Minicor - Windows desktop automations at scale.",
        "tagline": "RPA platform for deploying AI into legacy desktop systems",
        "body": "Hey YC!! Faiz and Saheed here from Minicor ([minicor.com](http://minicor.com))\n\n**TLDR;** AI companies selling into healthcare, automotive, financial services, and logistics can't go live because their customers run on legacy desktop software with no APIs. The only way in is clicking through the UI, and it breaks constantly. Minicor is a platform for building self-healing desktop automation that scales.\n\nLaunch video:\\\n<https://www.youtube.com/watch?v=nzH4ekkaA2c>\n\n**The problem**\n\nOur customers selling into legacy industries can't go live because the systems of record their customers use (EHRs, ERPs, DMS) are Windows desktop apps with no APIs. The only way to read / write is through UI automations, but desktop RPA at scale breaks constantly. Even small error rates become hundreds of failures a day, and every one needs manual intervention.\n\nThey're signing customers but don't have capacity to integrate because they're too busy fighting fires with existing RPAs; or their prospects are blocked because they need \"proof\" they can write back to their systems, and spinning up a desktop automation POC takes too long.\n\n**In production**\n\nWe're live right now processing thousands of workflows a week across healthcare EHRs and other legacy systems. Our agents write AI-generated data into desktop apps in real-time. We went live with a customer in 3 weeks post POC.\n\n**How it works**\n\n1. Install our Desktop Client on the machines running the legacy software.\n2. Speak the automation into existence - either through your favourite coding agent via our MCP, or to us directly. You get an API endpoint to trigger it.\n3. Minicor handles everything after that. Only pay when the data gets in\n\n**Demos**\n\nGenerating an automation with our MCP:\n\n![uploaded image](/media/?type=post&id=99004&key=user_uploads/1037983/3566b35e-8721-428e-9c8c-99b2d4f337a4)\n\nAgent recovering from failure:\n\n![uploaded image](/media/?type=post&id=99004&key=user_uploads/1037983/c33c944c-0319-4aa1-a291-c526751938f6)\n\nRecorded video replays of every run, with full error logging, so nothing ever fails silently:\n\n![uploaded image](/media/?type=post&id=99004&key=user_uploads/1037983/ce87bffc-0860-43ab-a1b9-94d5b5656adf)\n\n**About us**\n\nSaheed and I both started our careers building bespoke / legacy system integrations. Me at a YC startup, Saheed at Morgan Stanley and Ford. We met in 2019 in a UX class, built a non-profit together over the pandemic, and started working on this in 2023.\n\nWe started with an observability and error recovery platform for API integrations, and got pulled into Desktop automations when a prospect closed hyper quickly on the promise that we could help them connect to an on-prem EMR.\n\n**If a legacy desktop integration is blocking a deal right now**\n\n**We can have a POC running in hours.** Whether that's a demo for a prospect or getting something into production next week. Email / DM us on Bookface / book time to chat (:\n\n**Know an AI company fighting horrible desktop based UI integrations?** We'd appreciate an intro, coffee / lunch on us!\n\n[faiz@minicor.com](mailto:faiz@minicor.com) / <https://calendly.com/faiz-laminar/minicor-demo>",
        "url": "https://www.ycombinator.com/launches/Pkq-minicor-windows-desktop-automations-at-scale",
        "votes": 27,
        "created_at": "2026-03-23T02:00:00.081Z"
      }
    ]
  },
  {
    "id": 31459,
    "name": "Zibra Labs",
    "slug": "zibra-labs",
    "former_names": [
      "TBD. We don't have a name yet."
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/561de55fc1ba8b5c7254d0ad9eed2d656139b041.png",
    "website": "https://zibralabs.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We build distributed compute clusters with the cheapest CPUs and GPUs across Hyperscalers and Neoclouds for AI. Our mission is to bring frontier-grade infrastructure to everyone.\r\n\r\nWe're starting by building large scale high performance computing (HPC) clusters for quantitative trading firms to run parallel simulation workloads such as backtesting. \r\n\r\nOur technology generalizes to critical AI workloads such as post-training with reinforcement learning, fine-tuning, long-horizon agents with high tool use, and batch inference.",
    "one_liner": "Distributed runtime for AI workloads at scale",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777682633,
    "tags": [],
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/zibra-labs",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/zibra-labs.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/121104148/",
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/561de55fc1ba8b5c7254d0ad9eed2d656139b041.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 1593755,
        "full_name": "Ibrahim Rabbani",
        "title": "Founder/CEO",
        "founder_bio": "Former tech-lead of Ray. Previously building databases at LinkedIn. I've built systems that run on hundreds of thousands of machines and serve over a billion people.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/israbbani/",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/176ed5a2763da0f0789dfd9d86bfe0aa0063bd01.jpg"
      },
      {
        "user_id": 3417957,
        "full_name": "Zac Policzer",
        "title": "Founder/CTO",
        "founder_bio": "After a career making software that serves over a billion people, with a background primarily in large databases, I'm now building HPC clusters at Zibra Labs\n\nThe past two years I've been focused on what the next generation of AI data center software looks like — and working to shape that future. I've been fortunate to do much of this in open source, which I'm deeply passionate about.\n\nTalk to me about databases, Mahjong, guitars, or weird horror movies.\n\nI'm currently building Zibra Labs.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/zac-policzer-41160157",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d3e9c5f9df59002165fa7e97d755379069d1e391.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 101528,
        "title": "Zibra Labs: HPC for Quant Trading Firms Running Backtesting at Scale",
        "tagline": "HPC Clusters for Parallel Workloads on Spot-instances Across Hyperscalers and Neoclouds.",
        "body": "### Credentials\n\nWe’ve spent years working together building large scale distributed systems that run on hundreds of thousands of machines and serve billions of users. **Every time you leverage your network for a warm intro on LinkedIn, you use three databases that we’ve built: [Venice](https://venicedb.org/),[ Liquid](https://www.linkedin.com/blog/engineering/graph-systems/liquid-the-soul-of-a-new-graph-database-part-1), and[ Espresso](https://engineering.linkedin.com/teams/data/data-infrastructure/storage-infra/espresso).**\n\nAfter LinkedIn, **we were tech-leads of [Ray](https://github.com/ray-project/ray)**, the leading open-source compute platform used by companies such as xAI, Cursor, Bridgewater, P72, Man Group, Two Sigma, and others. Ray has [\\~12M](https://pypistats.org/packages/ray) weekly downloads and is part of the PyTorch Foundation.\n\n### Product\n\n<https://www.youtube.com/watch?v=C6tl8Fr8t2I>\n\nWe’re building large scale high performance computing (HPC) clusters for **quantitative trading firms** to run **parallel simulation workloads such as backtesting.** \n\nThe scale we’re targeting is\n\n* 100 to 50,000 nodes of mixed hardware (CPUs and GPUs) on hyperscalers and neoclouds.\n* up to 5,000,000 parallel tasks.\n* spot instances for cheaper compute across multiple availability zones, hyperscalers, and neoclouds.\n* &lt; 50ms overhead for task dispatch and scheduling.\n* run the compute in your cloud or on-prem environment to keep your alpha secure.\n\nOur goal is to **make testing trading strategies fast, cheap, and reliable.** \n\n### Vision\n\nWe know that existing solutions do not handle this scale, do not have first-class support for spot-instances, and will not match our low-latency and high throughput performance. \n\nOur technology generalizes to other large-scale compute workloads such as\n\n1. **post-training and reinforcement learning (RL)** for frontier and applied AI labs\n2. **massively parallel robotics simulation and sim-to-real training** for robot foundation model teams\n3. **virtual screening and molecular dynamics** for techbio and pharma R&amp;D\n4. **scientific simulation** (CFD, climate, seismic, FEA) for HPC teams looking to move beyond MPI and SLURM.\n\n### Ask\n\nReach out if you\n\n1. Are running simulation workloads that require lots of computation at a quant trading firm.\n2. If you need HPC for a different use-case.\n3. Want to grab a beer and talk about distributed systems or HPC.\n\nCheers!\n\nIbrahim and Zac",
        "url": "https://www.ycombinator.com/launches/QPY-zibra-labs-hpc-for-quant-trading-firms-running-backtesting-at-scale",
        "votes": 8,
        "created_at": "2026-05-18T20:53:24.005Z"
      }
    ]
  },
  {
    "id": 31472,
    "name": "Wato",
    "slug": "wato",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8a0fda3e03dbdf2968a5299c0170999587d03c6a.png",
    "website": "https://www.watolabs.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Wato gives AI agents shared memory, reusable workflows, connected tools, and living artifacts so work compounds across a team instead of disappearing into chat threads. Teams use Wato to give every agent access to trusted company context and turn useful runs into knowledge the next agent can build on.",
    "one_liner": "The control point for AI agents at work.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1779059409,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/wato",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/wato.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/watoinc",
    "x_url": "https://x.com/watolabs",
    "fb_url": null,
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    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8a0fda3e03dbdf2968a5299c0170999587d03c6a.png",
    "app_video_url": null,
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    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 1516656,
        "full_name": "Arihan Varanasi",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Wato (YC P26). Prev CS at Stanford.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/arihanvaranasi/",
        "x_url": "https://x.com/arihanxv",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6b2cb8a5294bdedc925c9bb3dc567b9a71fc55c3.jpg"
      },
      {
        "user_id": 3203486,
        "full_name": "Rahul Rejeev",
        "title": "Founder/CTO",
        "founder_bio": "Founder at Wato (YC P26). Studied CS at Stanford. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rahulrejeev/",
        "x_url": "https://x.com/rahulrejeev",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c3fe64fa084b9d626c9617988e508d047e43695b.jpg"
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    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 102479,
        "title": "Wato: a shared AI workspace for teams",
        "tagline": "Permissioned tools, cloud agents, automations, and shared workflows for the AI apps your team already uses.",
        "body": "Website: <https://www.watolabs.com>\n\n**TL;DR: Wato is a shared AI workspace for teams.** It helps companies keep their AI workflows, company knowledge, integrations, agent sessions, and agent-generated work inside the organization instead of scattered across individual chats, local files, or personal AI accounts. All of this is done via one, unified MCP.\n\n**Ask:** If your team is using AI agents for engineering, ops, sales, research, finance, support, or internal workflows, we’d love to see how we can help. We’re especially interested in teams setting up MCP/tools, sharing prompts or workflows internally, launching cloud agents, or trying to preserve useful agent context across the company.\n\n**The problem:** Teams are already using AI for real company workflows: debugging production issues, preparing sales calls, analyzing customer data, updating models, drafting client deliverables, and searching across internal systems.\n\nBut the work usually disappears into individual tools and accounts. The context, prompts, decisions, files, and tool setups that made the output useful are hard for the next teammate to find, trust, or reuse.\n\n**What Wato does:** Wato gives each team a shared, permissioned AI workspace.\n\nTeams can connect approved integrations and MCP tools, build shared memory, publish reusable skills, run collaborative cloud agent sessions, host live artifacts and dashboards, and trace tool calls across the organization.\n\nThe goal is simple: **when one person or agent learns something useful, builds a workflow, or creates an artifact, that knowledge should stay with the company and become available to the right people.**\n\n**What’s included:**\n\n\\- **Team memory** for durable company knowledge agents can reuse\n\n![uploaded image](/media/?type=post&id=102479&key=user_uploads/3203486/64ae52b1-6599-49c7-9e99-297e4e71a9ae)\n\n\\- **Approved MCP tools and integrations** with org, team, per-user, and tool-level permissions\n\n![uploaded image](/media/?type=post&id=102479&key=user_uploads/3203486/e8673b85-1e38-456a-ad60-d5d90ed2fe80)\n\n\\- **Reusable skills and workflows** library so teams don’t prompt from scratch every time\n\n\\- **Cloud agent sessions** that teammates can view, continue, and collaborate on, with access to a desktop, computer use, filesystem, and environment set up.\n\n![uploaded image](/media/?type=post&id=102479&key=user_uploads/3203486/b3da2993-c84f-4347-ad8a-46fea54ee809)\n\n\\- **Automations** that can be created from connector triggers or run on a specific cadence.\n\n![uploaded image](/media/?type=post&id=102479&key=user_uploads/3203486/edc32ad1-ae2e-42ef-9014-0782e8112464)\n\n\\- **Live artifacts and dashboards** that can use approved company data\n\n![uploaded image](/media/?type=post&id=102479&key=user_uploads/3203486/700f9d35-6150-4012-b58c-e56b54b39c30)\n\n\\- **Tool-call tracing and audit logs** so teams can see what agents accessed and produced\n\nWato also works with the AI tools teams already use, including Codex, Claude Desktop, Cursor, Claude Code, and other MCP-compatible environments. You add the Wato MCP once, authenticate with your company account, and your agent gets access to the right team memory, skills, and approved tools.\n\n**Backstory:** We started working on Wato after seeing the same problem across institutions adopting these tools like Claude or Codex. A super user would figure out a great workflow, debugging process, research method, client-prep flow, or tool setup, but the useful part would end up stuck in a chat thread, local file, or personal account. Then the next person would start over.\n\nAt the same time, companies didn’t want every employee independently managing MCP configs, tool permissions, prompts, and scattered agent instructions. They needed a shared layer between the company and the AI tools its people use to normalize AI use.\n\nThat became Wato.\n\n**We’d love to talk to teams who are:**\n\n\\- **using agents** across engineering, ops, sales, research, finance, support, or internal workflows\n\n\\- setting up **MCP servers or internal tools** for agent use\n\n\\- trying to **share prompts, skills, playbooks, or workflows** across a team\n\n\\- creating AI-generated **dashboards**, docs, reports, or **artifacts** that need to be shared securely\n\n\\- worried about **company knowledge** disappearing into personal AI accounts\n\n\\- thinking about **permissions**, **review**, **tracing**, or **governance** for agent work\n\nIf this sounds familiar, and I’m guessing it does for a lot of you, we’d love to show you Wato and get feedback.\n\nWebsite: <https://www.watolabs.com>  \n\nTwitter/X: <https://www.x.com/watolabs>",
        "url": "https://www.ycombinator.com/launches/Qet-wato-a-shared-ai-workspace-for-teams",
        "votes": 9,
        "created_at": "2026-06-03T16:52:49.877Z"
      }
    ]
  },
  {
    "id": 31475,
    "name": "Ardent",
    "slug": "ardent",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f4ef54cc2ae204ad4ac662f9bfacf6a3fa6faf47.png",
    "website": "https://tryardent.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Clone any postgres DB regardless of size in 6s so agents can test their work",
    "one_liner": "Database sandboxes for Agents",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1775685642,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/ardent",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/ardent.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/ardent-db",
    "x_url": "https://x.com/ArdentAI",
    "fb_url": null,
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    "app_video_url": null,
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    "primary_partner": {
      "name": "Aaron Epstein",
      "url": "https://www.ycombinator.com/people/aaron-epstein"
    },
    "founders": [
      {
        "user_id": 1239054,
        "full_name": "Vikram Chennai",
        "title": "Founder/CEO",
        "founder_bio": "Database sandboxes for Agents. Clone any Postgres DB regardless of size in 6s so agents can test their work\n\nFormerly ML Engineer, Cornell CS",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/vikram-chennai/",
        "x_url": "https://x.com/vchennai2",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5e3dd79a34fc2739d09791645c7883ccf5c8f6ce.jpg"
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    ],
    "jobs": [
      {
        "id": 102418,
        "title": "Founding Engineer - Product (Staff)",
        "url": "https://www.ycombinator.com/companies/ardent/jobs/4fjIRN4-founding-engineer-product-staff",
        "location": "San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$150K - $200K",
        "equity_range": "1.00% - 3.00%",
        "experience": "3+ years",
        "visa": "US citizen/visa only",
        "skills": [
          "Go",
          "PostgreSQL"
        ]
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 100815,
        "title": "Ardent - Clone any postgres DB of any size in <6s",
        "tagline": "Ardent lets engineers give their coding agents 1:1 clones of prod to test code and prevent downtime",
        "body": "Hi! I’m Vikram founder of [@Ardent](https://bookface.ycombinator.com/company/31475). **We let you clone any postgres DB in &lt;6s** so your coding agents and devs can test code on a 1:1 of production or dev DBs with zero risk\n\n![uploaded image](/media/?type=post&id=100815&key=user_uploads/1239054/b0ebde7f-0ac7-49a9-8f42-e4fb3f26d585)\n\nWe’re working with companies with huge amounts of data like Supermemory and YC companies like [@Surface Labs](https://bookface.ycombinator.com/company/28921)\n\n**This is me cloning a 1.6TB db is 6s:**\n\n<https://youtu.be/8Q4IX7HUJmo>\n\nWebsite: <https://tryardent.com/>\n\n**How did we get here (the problem)?**\n\nArdent was a reaction to the failure of our last product - an AI Data Engineer. It was a coding agent that generated data pipelines, wrote migrations, backfills and other things you’d expect of a data engineer.\n\nWhile the code _looked_ great we had no way of testing generated code. Writing to prod was out of the question and dev environments were never in sync, shared amongst devs which caused write clashes between agents, and was a huge pain to manage. **_What we needed was a ephemeral DB sandbox per agent of the user’s DB._**\n\nBecause of how hard testing was, generated code would never be tested and was too unstable to put in production. Database failures almost always mean downtime, a risk both we and customers were obviously not willing to take. We churned almost 200K in ARR\n\nThis problem exists with any coding agent today. We’re shipping 100x more DB code to production and we mostly just hope to god it works the same as dev.\n\n**Ardent - Isolated &lt;6s database clones that let you test agent code:**\n\nArdent solves the DB sandbox problem by letting you create 6s clones of ANY postgres DB regardless of size, allowing you to give every developer and every agent a full copy of production or dev to test against.\n\nClones autoscale to zero when not in use, use copy on write for massive storage efficiency and can be loaded with a single CLI command (zero infra management)\n\nYou can see the exact results of any code run against the DB in a perfect 1:1 reproduction and don’t have to change your DB provider.\n\nThe entire thing runs as a CLI that feels exactly like git. Cloning is as simple as “ardent branch create &lt;name&gt;”\n\nThis means you can spin up instant environments to test _every_ line of DB code with none of the overhead\n\n**Asks**\n\n**Use Ardent at your company to go faster by let agents work safely at the DB layer and prevent regressions:** <https://cal.com/vikram-chennai/yc-reserved>\n\n**Introduce me to series A-C engineering leaders (Founders, CTOs, VP of Eng):** Many of the later stage startups we’ve talked to have the majority of their engineers shipping with claude, codex or cursor on migrations, backfills and pretty much any code that hits the DB. They will eventually crash their production DB unless they’re rigorously testing. I’d like to prevent those crashes :)\n\nContact me: [vikram@tryardent.com](mailto:vikram@tryardent.com)",
        "url": "https://www.ycombinator.com/launches/QE3-ardent-clone-any-postgres-db-of-any-size-in-6s",
        "votes": 16,
        "created_at": "2026-05-05T20:21:15.273Z"
      }
    ]
  },
  {
    "id": 31482,
    "name": "Memory Store",
    "slug": "memory-store",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/81520a45661b113f68072bffbdfe2eeb3b94215d.png",
    "website": "https://memory.store",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "One memory for your team's agents. \r\n\r\nTurn every meeting, Claude session, and Slack chat into context your team and agents share.",
    "one_liner": "Company Brain.",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777184787,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/memory-store",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/memory-store.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/memorystore/",
    "x_url": "https://x.com/memorydotstore",
    "fb_url": null,
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    "github_url": null,
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/81520a45661b113f68072bffbdfe2eeb3b94215d.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Tom Blomfield",
      "url": "https://www.ycombinator.com/people/tom-blomfield"
    },
    "founders": [
      {
        "user_id": 29324,
        "full_name": "Diwank Tomer",
        "title": "Founder/CEO",
        "founder_bio": "Thiel Fellow. Columbia University. AI startups since 2013.\n60M weekly NPM downloads.\nAuthor: CryptGPT - Pre-trained Privacy Preserving LLMs.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/diwank-tomer",
        "x_url": "https://x.com/diwanksingh",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2b64f6efb7fdbf427b9fcbb9d2519832336a826d.jpg"
      },
      {
        "user_id": 720197,
        "full_name": "Ishita Jindal",
        "title": "Founder/CPO",
        "founder_bio": "Building a memory layer for all your AI agents with Memory Store.\n\nSpent a few years building an open source agent workflow orchestrator (Julep AI) and scaled that to 7K GitHub stars. Have experience scaling a couple of the biggest consumer products in India and was a data scientist at IBM prior to that.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ishitajindal/",
        "x_url": "https://x.com/IshitaJindal17",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d3ba0c62d380107f6c982b530d9baf6fc5c2583f.jpg"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 101548,
        "title": "Memory Store - Your company's brain",
        "tagline": "Shared memory for your company, people AND agents.",
        "body": "**tl;dr:** We collect insights from your team’s Slack, Gmail, and Granola, and turn them into a living memory accessible to every team member and agent. **Book a call [here](https://cal.com/team/memory).**\\\n\\\n**The Problem**\n\nWhen AI makes your team 100x faster, it also creates 100x more things to keep track of. Decisions lost in Slack channels, insights buried in some Granola transcript, Claude chats, Codex sessions, and people's heads.\n\n_Memory_ is the bottleneck now, because your teammates and agents both need the right context to move fast. This is why we built [Memory Store](https://memory.store/).\\\n\\\n<https://youtu.be/vncrqtgEPdY>\n\nBook a call [here](https://cal.com/team/memory).\n\n**How we solve it**\n\nWe collect insights from Slack, Gmail, Granola, and more; and synthesize them into a company brain and keep it up-to-date. Any agent that supports MCP can connect and access it (including ChatGPT Agents, Claude Cowork, Codex, OpenClaw, and [100s more](https://modelcontextprotocol.io/clients)) .\\\n\\\nWe took this one step further with live “**Briefs**” — _living docs that update themselves_ whenever new memories are created! For example, we use them ourselves to track:\n\n* Decision Logs\n* Team Status Updates\n* Customer Asks\n\n![uploaded image](/media/?type=post&id=101548&key=user_uploads/720197/834232da-42b3-4b2a-b375-e4d94f67ed3e)\n\n![uploaded image](/media/?type=post&id=101548&key=user_uploads/720197/6eadb673-751e-4f68-87fe-669603b0e04c)\n\nBook a call [here](https://cal.com/team/memory).\n\n**About us**\n\nWe’re [Diwank](https://x.com/diwanksingh) and [Ishita](https://www.linkedin.com/in/ishitajindal/). Diwank is a Thiel Fellow who's been building AI products since 2012 - his previous company powers phone ordering for Domino's, Wingstop, and Buffalo Wild Wings. Ishita ran products that served millions of users at Jar and Lenskart.\n\nWe have years of experience building agents for enterprises. We also built Julep, an open-source agent orchestrator with 7K+ GitHub stars, where we kept seeing the same bottleneck across every agent: memory. So we built Memory Store! 🙂\n\n![uploaded image](/media/?type=post&id=101548&key=user_uploads/720197/c83e2c32-2de5-4d6c-8b0e-4067bf6d9c66)\n\nWe’d love to hear from you! ❤️ \\\n**Book a call [here](https://cal.com/team/memory).**",
        "url": "https://www.ycombinator.com/launches/QPs-memory-store-your-company-s-brain",
        "votes": 305,
        "created_at": "2026-05-19T14:27:28.169Z"
      }
    ]
  },
  {
    "id": 31507,
    "name": "ProjectX",
    "slug": "projectx",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2f60ad803d2e9b0079d1a0e2e9afd248df7aafcd.png",
    "website": "https://www.projectx.cloud/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Every computer ever made forces you to do one thing at a time. One cursor. One keyboard. One Hardware. One app in focus. Fifty years. Nobody questioned it.\r\n\r\nWe did. We started over.\r\n\r\nWe built InfinityOS, a fundamentally new computer OS on the cloud from ground up.\r\n\r\nInfinity runs every app in its own independent computer -- own GPU, own input, own environment. Windows and Linux in the same session. Humans and agents working side by side. No concurrency ceiling. Cold start in seconds. runs on the browser of any device..",
    "one_liner": "Agent native workspace for heavy parallel workflows on the web ",
    "team_size": 8,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1773453080,
    "tags": [],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
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      "Partly Remote"
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    "stage": "Early",
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    "year_founded": 2022,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/projectx-cloud",
    "x_url": "https://x.com/ProjectX_Cloud",
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    "primary_partner": {
      "name": "Andrew Miklas",
      "url": "https://www.ycombinator.com/people/andrew-miklas"
    },
    "founders": [
      {
        "user_id": 2936820,
        "full_name": "Rounak Adhikary",
        "title": "Founder/CEO",
        "founder_bio": "Founder and CEO @ ProjectX | Stanford ASES | Draper U | IIT Bombay",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rounakadhikary/",
        "x_url": "https://x.com/Rounacc",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b001ea45004927354ad2e247026e312fdcfc75f2.jpg"
      },
      {
        "user_id": 2733173,
        "full_name": "Bishal Karmakar",
        "title": "Founder/CTO",
        "founder_bio": "Systems, streaming, and cloud infra. CTO@ProjectX,  building infinity - a cloud-native operating system.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/bishal-123-karmakar/",
        "x_url": "https://x.com/BishalK_",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c8f0a3edab06900fac90cd219baf4992ad45a1a6.jpg"
      },
      {
        "user_id": 3419775,
        "full_name": "Sourya Majumder",
        "title": "Founder/COO",
        "founder_bio": "COO @ProjectX | Scaled an IT consulting team to ~120k MRR | Helping you run any application across any device just from a browser tab alongside Rounak and Bishal.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/souryapx",
        "x_url": "https://x.com/runallapps",
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    ],
    "jobs": [],
    "news": [
      {
        "title": "What Y Combinator's Latest Batch Reveals About The Future",
        "url": "https://www.forbes.com/sites/dariashunina/2026/06/04/what-y-combinators-latest-batch-reveals-about-the-future/",
        "date": "Jun 04, 2026"
      },
      {
        "title": "Meet Rounak Adhikary: Ashneer Grover once told him ‘Tu Baith Jaa Yaar’ and cut off his pitch mid-sentence; now he's living every startup founder's dream - The Times of India",
        "url": "https://timesofindia.indiatimes.com/life-style/people/meet-rounak-adhikary-ashneer-grover-once-told-him-tu-baith-jaa-yaar-and-cut-off-his-pitch-mid-sentence-now-hes-living-every-startup-founders-dream/articleshow/131419890.cms",
        "date": "May 31, 2026"
      },
      {
        "title": "Lovely Professional University Student's Startup Receives Rs 1.7 Crore From Google Cloud",
        "url": "https://www.indiatimes.com/news/education/lovely-professional-university-students-startup-receives-rs-17-crore-from-google-cloud-624590.html",
        "date": "Mar 11, 2026"
      },
      {
        "title": "LPU student's start-up to raise $3 million in seed funding - Digital Transformation News | The Financial Express",
        "url": "https://www.financialexpress.com/business/digital-transformation-lpu-students-start-up-to-raise-3-million-in-seed-funding-3352963/",
        "date": "Dec 29, 2023"
      },
      {
        "title": "Google Cloud injects Rs 1.7 Cr into ProjectX.Cloud, eyes $3M funding | Startup Story",
        "url": "https://startupstorymedia.com/insights-google-cloud-injects-rs-1-7-cr-into-projectx-cloud-eyes-3m-funding/",
        "date": "Dec 29, 2023"
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    ],
    "launches": [
      {
        "id": 102024,
        "title": "Infinity — A New Computation Layer",
        "tagline": "Months of work, done in days. Every app gets its own GPU, in one browser tab.",
        "body": "Hey YC 👋\n\nWe're Rounak, Bishal &amp; Sourya — co-founders of ProjectX (P26). Today we're launching Infinity.\n\n\\[<https://youtu.be/ZYf2vdWMkDk>\\]\n\n### TL;DR\n\n**Infinity is a new computation layer — a web-based OS where every app you open runs on its own independent compute and GPU, inside one shared workspace.** Open Blender, Unreal, Isaac Sim, DaVinci, VS Code in the same browser tab. Each app is its own machine with its own GPU. They share one filesystem and one terminal. Windows and Linux apps run side by side. Runs on any browser, on any device. Open fifty apps at once — nothing slows down.\n\n![uploaded image](/media/?type=post&id=102024&key=user_uploads/2936820/28491e87-8d68-4bd0-b0ea-860d2d7d1825)\n\n### Problem\n\nThe apps people actually use to build the future — Blender, Unreal, Isaac Sim, Houdini, the CAD/CAM/CAE stack, ML pipelines — still need a $5K workstation under your desk. The cloud was supposed to fix this and didn't. Renting a cloud GPU means hours of driver installs, version matching, container debugging before you do any actual work. Streamed desktops (Citrix, Paperspace, Shadow) give you one VM with one GPU — built for one user running one app at a time. Open Blender and Unreal at the same time and you're fighting yourself for resources.\n\nIt's hitting agents too. Claude Cowork runs on your laptop, sharing your GPU with everything else. Manus runs in a CPU-bound microVM that can't open heavy apps at all. Claude can drive Blender via MCP, but Blender still runs on your machine — the agent just borrows your compute.\n\n### Solution\n\n![uploaded image](/media/?type=post&id=102024&key=user_uploads/2936820/281d546a-52f7-4932-b92e-30608f83f249)\n\nInfinity makes every app its own computer.\n\nOpen Blender, you get a machine with a dedicated GPU. Open Unreal in the next window, that's a second machine with its own GPU. Open Isaac Sim, that's a third. They share a filesystem and a terminal so the workspace feels like one computer, but underneath they're independent machines. Open fifty apps, you have fifty machines. Nothing slows down. Runs entirely in a browser tab — Chromebook, iPad, five-year-old laptop, doesn't matter.\n\n![uploaded image](/media/?type=post&id=102024&key=user_uploads/2936820/971299ab-663b-4181-ae7e-64ed286ecda2)\n\n\\\nEvery Infinity workspace comes will soon ship with a built-in computer use agent that can access run applications without being constrained by compute, no matter how many applications you run or how heavy the workflow is. Tell it what you want, and it spins up the apps, drives them, hands files between them, and finishes the work. You can drive Infinity with a mouse and keyboard, or you can drive it with a prompt.\n\nA concrete example. You prompt: _\"Make a 90-second product launch video. Write the script, generate the voiceover, edit in DaVinci, render the 3D logo in Blender, post to YouTube.\"_\n\nSix apps spin up at once:\n\n* Claude writes the script\n* ElevenLabs generates the voiceover\n* DaVinci Resolve edits the cut\n* Blender renders the 3D logo\n* Chrome uploads to YouTube\n* VS Code writes the publish script\n\n![uploaded image](/media/?type=post&id=102024&key=user_uploads/2936820/fc66db17-d35b-4f3d-8459-cec58a4cebc2)\n\nThey share a workspace. They hand files to each other. **One finished video in an hour, not three days.**\n\n### Where we are — 15 days in early preview \n\n* **20,000+ builders** signed up\n* **1,160% growth** in the last 14 days\n* **20M+ impressions** on X and Instagram\n* **10,000+ signups** in the last 7 days\n* **Out of server capacity** — rate-limiting new signups while we scale\n\nUsed by engineers at **Reliance Industries, CEMEX, Cognizant, Globant, Ola Electric, Lattice, Quizizz, Appsmith, Bonsai Robotics, Induction Labs, Basis Robotics**, and 130+ more teams. All inbound. Our first public demo crashed our servers at midnight; we scaled the infra live at 3am and woke up to inbound from 50+ enterprises.\n\n### Future\n\nOnce every app has its own compute, agents stop being a separate problem.\n\nA computer-use agent is just another app — except the agent drives itself. Because every agent gets its own machine on Infinity, ten agents work in parallel without stepping on each other. The thing that's impossible today — ten parallel cursors, ten parallel renders, ten parallel models — is the default on Infinity.\n\nWe're shipping the multi-agent runtime next. One prompt, a mother agent plans and coordinates, child agents execute on dedicated hardware — each routing through Claude, GPT, Gemini, or any MCP tool. Same substrate humans use, given to agents.\n\n### Ask\n\nIf you are, or know any of the following, we want to hear from you:\n\n**Heavy-GPU teams.** Robotics, Physical AI, 3D/VFX, ML, post-production, CAD/CAM/CAE. If you're buying workstations or wrestling with cloud GPU setup, let's talk.\n\n**Agent builders.** If you're shipping a computer-use agent and hitting the \"where does this actually run\" wall, we should talk early.\n\nFree Infinity credits for all YC founders and alums. Email me directly: [rounak@projectx.cloud](mailto:rounak@projectx.cloud)\n\n— Rounak, Bishal &amp; Sourya",
        "url": "https://www.ycombinator.com/launches/QXY-infinity-a-new-computation-layer",
        "votes": 10,
        "created_at": "2026-05-27T20:06:30.702Z"
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    "id": 31516,
    "name": "Expanse",
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    "website": "https://expanse.sh",
    "all_locations": "London, England, United Kingdom",
    "long_description": "Expanse unlocks wasted GPU capacity. We recover idle compute through three capabilities: resource prediction (right-sizing job submissions before they reach the scheduler), optimisation suggestions (code and config changes researchers can apply themselves), and failure prediction (catching jobs that will fail before they consume hours of GPU time).\r\n\r\nWe’re four engineers. We ran HPC and GPU training workloads at the largest quant funds and national supercomputing centres. We faced this problem first hand and the only fix was to over-provision and burn millions. Ismaeel built the first multimodal HPC resource predictor as research at EPCC (Edinburgh’s Parallel Computing Centre), which beat every published baseline. This is the tool we wish we had.",
    "one_liner": "Unlock wasted GPU capacity.",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777849866,
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    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/expanse",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/expanse.json",
    "year_founded": 2025,
    "city": "London",
    "country": "GB",
    "linkedin_url": "https://www.linkedin.com/company/expanse-compute",
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    "fb_url": null,
    "cb_url": "https://www.crunchbase.com/organization/expanse",
    "github_url": "https://github.com/expanse-labs",
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      "name": "David Lieb",
      "url": "https://www.ycombinator.com/people/david-lieb"
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    "founders": [
      {
        "user_id": 3381342,
        "full_name": "Ismaeel Bashir",
        "title": "Founder/CEO",
        "founder_bio": "Ismaeel is co-founder and CEO of Expanse. Built the first multimodal HPC resource predictor at EPCC (Edinburgh’s Parallel Computing Centre), beating every published baseline. Previously: ran large scale ML models at one of the world’s largest quantitative funds (QRT). Studied Computer Science at the University of Edinburgh.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ismaeel-bashir/",
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      {
        "user_id": 3409042,
        "full_name": "Nikodem Bieniek",
        "title": "Founder",
        "founder_bio": "Nikodem is co-founder and CTO of Expanse. Trained and optimised speech recognition models on GPU clusters. Previously: managed the platforms researchers and engineers depended on at one of the world’s largest hedge funds (Millennium). Studied Computer Science at the University of Edinburgh.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/nikodemb/",
        "x_url": "https://x.com/nkdem_b",
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      {
        "user_id": 3409418,
        "full_name": "Yafet Melake",
        "title": "Founder",
        "founder_bio": "Yafet is co-founder and COO of Expanse. Built the first GNN-based cluster graph network for predicting SLURM queue wait times at EPCC (Edinburgh’s Parallel Computing Centre). Previously: tooling and infrastructure for researchers at one of the world’s largest quantitative funds (G-Research). Studied Computer Science at the University of Edinburgh.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/yafet-melake",
        "x_url": "https://x.com/yafet_melake",
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      },
      {
        "user_id": 2274795,
        "full_name": "Eren Mendi",
        "title": "Founder",
        "founder_bio": "Eren is co-founder and CPO of Expanse. Built state-of-the-art decentralised foundation model training systems and performance models. Previously: prototyped emerging technologies in quantitative finance (G-Research). Studied Computer Science at the University of Edinburgh and Imperial College London.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/eren-mendi-929b55138",
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    "news": [
      {
        "title": "Why we started Expanse, and our $5.3M seed · Expanse Blog",
        "url": "https://expanse.sh/blog/expanse-raises-5-3m-seed",
        "date": "Sep 16, 2026"
      },
      {
        "title": "What Y Combinator's Latest Batch Reveals About The Future",
        "url": "https://www.forbes.com/sites/dariashunina/2026/06/04/what-y-combinators-latest-batch-reveals-about-the-future/",
        "date": "Jun 08, 2026"
      }
    ],
    "launches": [
      {
        "id": 100703,
        "title": "Expanse - Unlock wasted GPU capacity",
        "tagline": "Unlock wasted GPU capacity.",
        "body": "**TL;DR**\n\nExpanse unlocks wasted GPU capacity. We’re the intelligence layer for compute infrastructure. Submit jobs with the right resources. Optimise them to run faster. Debug failures in seconds, not days. Works on cloud and on-prem HPC.\n\nWant to know how much more you can get out of your hardware? Schedule a call: <https://book.getexpanse.io>\n\n**Problem**\n\nRunning compute on a cluster means guessing memory, time, and GPUs before you submit. Get it wrong and your job fails mid-run. Over-guess and you pay for compute you never use. 30% of cloud spend is lost to over-allocation \\[1\\]. Microsoft Research found their own deep learning jobs hit 50% GPU utilisation or lower \\[2\\].\n\nSizing jobs takes decades of experience. Memory patterns, batching, GPU threading, CPU vectorisation. AI agents don’t have enough information to fix it either. SLURM and Kubernetes don’t expose enough structured data. Even strong platform teams are forced to choose: spend time debugging and optimising every job, or accept the waste. AI infrastructure spending will hit $2.52 trillion in 2026 \\[3\\]. The waste grows with every new cluster. This problem isn’t going away.\n\n**Solution**\n\nExpanse learns your cluster. We analyse your code and hardware telemetry, then track them over time. Our deep learning models build a picture of what your workloads actually need. Predictions get sharper with every job.\n\nOur models are deployed on your cluster. All analysis runs on your network. Your code and telemetry never leave your infrastructure. We can’t see your data, and you maintain full data sovereignty.\n\nOne command installs Expanse on your cluster. You get two tools.\n\n`expanse analyse` - for every job:\n\n* **Suggests the right resource configuration**\n* **Predicts failures from historical code and telemetry patterns**\n* **Surfaces code-level optimisations that increase efficiency**\n\n`expanse diagnose` - for every failure:\n\n* **Finds the root cause**\n* **Returns solution-oriented logs with the exact code and config changes to fix it**\n\nDays of debugging and optimising become seconds. Platform teams get what they need to do this in minutes instead of weeks.\n\n**Founders**\n\nWe're four engineers from the University of Edinburgh, all coming from top quant funds and national supercomputing centres. Ismaeel built the first multimodal HPC resource predictor at EPCC (Edinburgh's Parallel Computing Centre), beating every published baseline. Yafet built the first GNN cluster graph network for predicting SLURM queue wait times. Eren built decentralised foundation model training systems at Edinburgh. Niko trained large-scale speech ML models on GPU clusters and architected cloud and Kubernetes infrastructure at one of the largest hedge funds in the world. We left our jobs to fix this ourselves.\n\n**CTA / Ask**\n\nRunning compute and not getting enough out of what you’re paying for? Failing jobs, unclear errors, bills too high? We’d love to chat.\n\nKnow anyone in AI infrastructure (managing cloud and/or on-premise data centres), quant finance, drug discovery, or AI research labs? An intro would mean a lot :)\n\n**YC Deal**\n\nAll YC companies running serious ML training can contact us for a discounted pilot. We’ll find inefficiencies and wastage on your compute setup. Then we deploy Expanse, no changes to your workflows. You get more out of the hardware you’re paying for.\n\nReach out: [founders@expanse.sh](mailto:founders@expanse.sh) or <https://book.getexpanse.io>\n\nReferences\n\n\\[1\\] <https://grafana.com/blog/2023/03/03/how-to-optimize-resource-utilization-with-kubernetes-monitoring-for-grafana-cloud>\n\n\\[2\\] <https://www.microsoft.com/en-us/research/publication/an-empirical-study-on-low-gpu-utilization-of-deep-learning-jobs/>\n\n\\[3\\] <https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026>",
        "url": "https://www.ycombinator.com/launches/QCF-expanse-unlock-wasted-gpu-capacity",
        "votes": 53,
        "created_at": "2026-05-04T04:30:18.506Z"
      }
    ]
  },
  {
    "id": 31524,
    "name": "AgentPhone",
    "slug": "agentphone",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3cd0619758d7e852f6674fba763a0d1167fa4a69.png",
    "website": "https://agentphone.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "The $2T global telecom industry was built for humans. Agents can't act in the real world today because without a phone number, they lack identity. AgentPhone gives every AI agent its own phone number to reach people and businesses through messages and calls.",
    "one_liner": "Phone Numbers for AI Agents",
    "team_size": null,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1778010009,
    "tags": [],
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    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/agentphone",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/agentphone.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/agentphone/",
    "x_url": "https://x.com/agentphonehq",
    "fb_url": null,
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3cd0619758d7e852f6674fba763a0d1167fa4a69.png",
    "app_video_url": null,
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    "primary_partner": {
      "name": "David Lieb",
      "url": "https://www.ycombinator.com/people/david-lieb"
    },
    "founders": [
      {
        "user_id": 541937,
        "full_name": "Manav Modi",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder at AgentPhone. Helped drive the Vogue app revamp and grew usage from ~100k to over 1M users. BS Computer Engineering from UIUC.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/manav2modi/",
        "x_url": "https://x.com/manav2modi",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e1e12722dbfef97063b9e315c895735a9ea0f7af.jpg"
      },
      {
        "user_id": 1068571,
        "full_name": "Meet Modi",
        "title": "Founder/CTO",
        "founder_bio": "Co-founder at AgentPhone. Previously built AI Agent infrastructure on WhatsApp for 280+ million businesses at Meta. Bachelor's in Computer Science & Linguistics from UCLA.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/meetmodi-/",
        "x_url": "https://x.com/themeetmodi",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1fcfed7410cdbdfff378e20cd29a053029bea75c.jpg"
      }
    ],
    "jobs": [
      {
        "id": 98603,
        "title": "Founding Engineer",
        "url": "https://www.ycombinator.com/companies/agentphone/jobs/Cnv7LNt-founding-engineer",
        "location": "New York, NY, US / San Francisco, CA, US",
        "type": "Full-time",
        "role": "Engineering",
        "role_type": "Full stack",
        "salary_range": "$140K - $250K",
        "equity_range": "0.25% - 2.00%",
        "experience": "Any (new grads ok)",
        "visa": "Will sponsor",
        "skills": [
          "Python",
          "TypeScript"
        ]
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 101384,
        "title": "AgentPhone - Phone Numbers for AI Agents",
        "tagline": "Give AI agents their own phone numbers and identity to call and message across every channel.",
        "body": "AI agents are becoming incredibly capable online, but the moment you need one to make a phone call or send a text, everything falls apart. You end up stitching together telecom providers, voice infrastructure, compliance, routing, and your own memory layer. Giving an agent a phone number should be simple. It wasn't.\n\nSo we built [AgentPhone](http://agentphone.ai/): the telephony infrastructure layer for AI agents.\\\n\\\n<https://youtu.be/Gsj_BaaCDD4>\n\n### **TL;DR:**\n\n* One API to give any AI agent its own phone number\n* iMessage, WhatsApp, RCS, SMS, Calling, and more soon\n* A trusted identity so agents can reach people and businesses in the real world\n\n### **Who's Using It**\n\nTeams at Google's Agent Development Kit, Replit, Y Combinator, Sim AI, LangChain, and Alchemy are already building with AgentPhone.\n\n### **The Problem**\n\nThe $2T global telecom industry was built for humans. AI agents today can browse the web, write code, and manage entire workflows. But they still can't pick up the phone. A lot of business still runs on calls and texts, channels that existed before the internet and email. If agents are going to do real work, they need to work on the channels people already use.\n\n### **How It Works**\n\nAgentPhone gives developers a single API to provision phone numbers for their agents with voice and messaging built in and no compliance headaches. Just plug in and your agent can talk and text to the world.\n\n### **Things already built on AgentPhone:**\n\n* An AI talent recruiter that messages and screens candidates\n* AI employees with their own phone numbers that handle workflows end to end\n* Our own company brain that Manav and Meet use internally to run AgentPhone\n\n### **About Us**\n\nWe're [Meet](https://www.linkedin.com/in/meetmodi-/) and [Manav](https://www.linkedin.com/in/manav2modi). We met 21 years ago when our mom gave birth to Meet. We've been building together ever since.\n\n![uploaded image](/media/?type=post&id=101384&key=user_uploads/1068571/4a9a9fe1-ca93-4dd0-8c56-c1aaf73b076c)\n\nMeet previously built AI agent infrastructure on WhatsApp serving 280+ million businesses at Meta. Manav helped drive the Vogue app revamp and grew it from \\~100K to over 1M users. We saw firsthand that the biggest gap in the agent stack isn't intelligence. It's connectivity.\n\n### **Our Ask**\n\nIf you're building agents that need voice and messaging, try AgentPhone and tell us what breaks. We'll provide free credits to anyone willing to test and share feedback.\n\nSign up at <https://www.agentphone.ai>",
        "url": "https://www.ycombinator.com/launches/QNE-agentphone-phone-numbers-for-ai-agents",
        "votes": 266,
        "created_at": "2026-05-15T13:42:30.250Z"
      }
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  },
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    "id": 31540,
    "name": "KugelAudio",
    "slug": "kugelaudio",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/44c1f17a88c6716d09288318cb5f9de77b9bdbbb.png",
    "website": "https://kugelaudio.com/en",
    "all_locations": "Berlin, Berlin, Germany",
    "long_description": "Kugel is an enterprise-ready TTS model, available on-prem, with a focus on 30+ languages and low latency. ",
    "one_liner": "Kugel is an on-prem Voice AI model supporting 30+ languages ",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1777590592,
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    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Spring 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
      "Germany",
      "Europe"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/kugelaudio",
    "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/kugelaudio.json",
    "year_founded": 2025,
    "city": "Berlin",
    "country": "DE",
    "linkedin_url": "https://de.linkedin.com/company/kugelaudio",
    "x_url": "https://x.com/KugelAudio",
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    "cb_url": "https://www.crunchbase.com/organization/kugelaudio",
    "github_url": null,
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    "app_video_url": null,
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    "primary_partner": {
      "name": "Andrew Miklas",
      "url": "https://www.ycombinator.com/people/andrew-miklas"
    },
    "founders": [
      {
        "user_id": 2385896,
        "full_name": "Viktor Presber",
        "title": "Founder",
        "founder_bio": "Co-Founder of KugelAudio, building foundational Text-To-Speech models for customer support. Started founding ventures at 17 with a local video production company. Later co-founded full-house.io, scaling to $300K ARR in six months with enterprise clients like Siemens Energy, Hitachi Rail, and Barilla. Previously co-founded Sagemode, securing partnerships with two major esports teams. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/viktor-presber",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/292b5b530e0dcd807d063494a7fcffe268d73a9d.jpg"
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      {
        "user_id": 1625902,
        "full_name": "Kajo Kratzenstein",
        "title": "Founder/CTO",
        "founder_bio": "Founder at KugelAudio (YC P26). Previously CTO at Sagemode, scaling an in-game AI coach to 1,000+ users with deep League of Legends integration. Built production aneurysm detection model at Floy GmbH. MA and BSc in Computer Science from Hasso Plattner Institute.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/kratzenstein",
        "x_url": "",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/13d970157877e6d170963a2e971eac2dc71c2904.jpg"
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    ],
    "jobs": [
      {
        "id": 104355,
        "title": "Berlin · Part-time · On-site",
        "url": "https://www.ycombinator.com/companies/kugelaudio/jobs/WKo2Gk0-berlin-part-time-on-site",
        "location": "Berlin, BE, DE / Berlin, Berlin, DE",
        "type": "Internship",
        "role": "Marketing",
        "role_type": null,
        "salary_range": "",
        "equity_range": "",
        "experience": null,
        "visa": "US citizen/visa only",
        "skills": [
          "Adobe Premiere"
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      },
      {
        "id": 111590,
        "title": "Founding Sales (Outbound)",
        "url": "https://www.ycombinator.com/companies/kugelaudio/jobs/OujwnhC-founding-sales-outbound",
        "location": "Berlin, BE, DE / Berlin, Berlin, DE",
        "type": "Full-time",
        "role": "Sales",
        "role_type": null,
        "salary_range": "$60K - $120K",
        "equity_range": "0.01% - 0.35%",
        "experience": "1+ years",
        "visa": "US citizenship/visa not required",
        "skills": []
      }
    ],
    "news": [],
    "launches": [
      {
        "id": 102000,
        "title": "KugelAudio: A multilingual voice AI model you can run in your own cluster.",
        "tagline": "Human-like TTS for 30+ languages, on-prem and low latency",
        "body": "### TL;DR: KugelAudio is a multilingual voice AI model that generates natural speech in 30+ languages and can run fully on-prem in your own Kubernetes cluster.\n\n### <https://youtu.be/R4HqJ8Ai-z4>\n\n**Launch Day Special 🚀**\\\nBook a demo and mention our launch: we’ll give you **20% off.** \\\n\\\n\\\n**Problem**\n\nCurrent TTS models are English-first and cloud-hosted. Kugel speaks 30+ languages and dialects natively like Bavarian German, Northern Irish English, or Yemeni Arabic even on edge cases like email addresses, phone numbers, or product names. \\\n\\\n**Solution**\\\nDrop our model into your Kubernetes cluster and use voice AI on patient records, financial transactions, or internal customer data without sending anything to a third party.\n\n**A few more things**\n\n* **60ms latency.** One of the fastest TTS models on the market.\n* **IPA notation support** across 30+ languages.\n* **Voice cloning** from a few seconds of audio.\n\n### \n\n**Meet the team**\n\nWe’re a team of two from Germany and Poland.\n\n![uploaded image](/media/?type=post&id=102000&key=user_uploads/2385896/be66da7a-d0de-4690-91d1-1a368e5d0689)\n\nKajo spent the last 2 years researching TTS at Hasso-Plattner-Institut and built the model. Viktor runs commercial and brings the enterprise sales experience. We kept hearing the same thing from enterprise voice buyers: providers mispronounce their product names and their legal team won't sign the DPA anyway. So we built the thing they actually wanted.\\\n\\\n**🤝 Our Ask**\n\nIf you deal with mispronunciations or sensitive voice data:\n\n\\- Book a [demo](https://calendar.app.google/TESsGfQBZzABugb56)\\\n\\\n**Thank you! 🙏**\n\nKajo and Viktor",
        "url": "https://www.ycombinator.com/launches/QXA-kugelaudio-a-multilingual-voice-ai-model-you-can-run-in-your-own-cluster",
        "votes": 8,
        "created_at": "2026-05-27T06:47:39.124Z"
      }
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  },
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    "id": 31591,
    "name": "RightNow",
    "slug": "rightnow",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/59b2e02a314037738d9f32192f83e8bd442be246.png",
    "website": "https://runinfra.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "RightNow AI is a research lab for GPU kernel optimization. Our platform RunInfra serves top open-source models optimized for AI agents at a fraction of the cost of closed-source APIs\r\n\r\nRightNow AI lab: https://rightnowai.co\r\nRunInfra platform: https://runinfra.ai.",
    "one_liner": "Co-designing the fastest intelligence",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1779474756,
    "tags": [
      "Developer Tools",
      "Generative AI",
      "Machine Learning",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Fall 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/rightnow",
    "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/rightnow.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/rightnowai/",
    "x_url": "https://x.com/rightnowai_co",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/RightNow-AI",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/59b2e02a314037738d9f32192f83e8bd442be246.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Nicolas Dessaigne",
      "url": "https://www.ycombinator.com/people/nicolas-dessaigne"
    },
    "founders": [
      {
        "user_id": 1615104,
        "full_name": "Jaber Jaber",
        "title": "Founder",
        "founder_bio": "Technical Co-founder at RightNow (YC F26)",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/jaber-jaber-b65246234",
        "x_url": "https://x.com/Akashi203",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d84f84e8e758be1fe000c134de47a36196f14705.jpg"
      },
      {
        "user_id": 3147259,
        "full_name": "Osama Jaber",
        "title": "Founder/CMO",
        "founder_bio": "Commercial Co-founder at RightNow (YC F26)\n",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/osama-jaber-osama2001/",
        "x_url": "https://x.com/OsamaKakashi",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f6bf68d49afc56cca1afb65f6882a4b4cf721300.jpg"
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    ],
    "jobs": [],
    "news": [],
    "launches": []
  },
  {
    "id": 31592,
    "name": "GitCafe",
    "slug": "gitcafe",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/10ae63bcc494b6d92bd37c0a46800fc12762436c.png",
    "website": "https://git.cafe",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We build GitHub for agents. We're rebuilding the infrastructure for the next hundred billion commits and CI jobs with hundreds of engineers already making the move.",
    "one_liner": "The Git forge for the next hundred billion commits",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1785301269,
    "tags": [
      "Developer Tools",
      "B2B",
      "DevOps",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada",
      "Remote",
      "Partly Remote"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/gitcafe",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/gitcafe.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "",
    "x_url": "https://twitter.com/gitcafe",
    "fb_url": null,
    "cb_url": null,
    "github_url": "https://github.com/git-dot-cafe",
    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/10ae63bcc494b6d92bd37c0a46800fc12762436c.png",
    "app_video_url": null,
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    "company_photos": [],
    "primary_partner": {
      "name": "Diana Hu",
      "url": "https://www.ycombinator.com/people/diana-hu"
    },
    "founders": [
      {
        "user_id": 1920545,
        "full_name": "Veronica C.",
        "title": "Founder/CEO",
        "founder_bio": "Previously a founding engineer at Corgi S24 and returning to my roots in dev tooling.",
        "is_active": true,
        "linkedin_url": "",
        "x_url": "",
        "avatar_thumb_url": "/avatars/thumb/missing.png"
      }
    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 109445,
        "title": "GitCafe: A Git forge for teams and agents shipping hundreds of times a day, not twice.",
        "tagline": "The Git forge for agent-scale teams",
        "body": "Software development is becoming much more parallel. Coding agents let a team have dozens or hundreds of related changes moving at the same time, creating more branches, reviews, CI runs, and automation than existing workflows were designed to absorb.\n\nToday, we’re opening GitCafe in public beta. It is a Git forge built for teams where humans and coding agents may ship hundreds or thousands of changes a day. We’re building the hosting, review, CI, security, and automation layer for that way of working.\\\n\\\nHundreds of users are already on GitCafe, and we’re currently running trials with roughly a dozen YC companies.\n\n![uploaded image](/media/?type=post&id=109445&key=user_uploads/1920545/eecbfb4f-82f1-4660-8ee8-aeea5273a8b9)\n\nGenerating code is only part of the problem. Teams still need to understand what is changing, review it safely, keep CI moving, catch security issues, and coordinate work without becoming overwhelmed.\n\nWe’re not building GitCafe because putting repositories, code review, and CI in one product is new. We’re building it because we believe the workload those systems need to support is about to change dramatically.\n\nWe started by rebuilding the Git infrastructure itself. Now we’re closing the product gap feature by feature.\n\nRecently, we added secret scanning, Buildkite and Harmont CI support, stacked diffs with Jujutsu and Sapling integration, and beta support for GitHub Actions.\n\nThese are foundational capabilities, not the destination. Building them on infrastructure we own gives us a base we can keep extending for higher-volume, agent driven development.\n\nThe honest version is that GitCafe is behind GitHub today. GitHub has spent more than a decade building an enormous product, and GitCafe does not yet have every workflow, administrative control, or integration that mature teams need. Some features are missing, some are experimental, and some edges are rough. We expect to earn the broader product surface, not claim it on day one.\n\nWe’re opening GitCafe now because the fastest way to improve it is to put it in the hands of teams doing real work.\n\nIf your team is already using coding agents heavily, try GitCafe at <https://git.cafe> and tell us where the workflow breaks down.\\\n\\\n<https://www.youtube.com/watch?v=blHKFEOmoFg>",
        "url": "https://www.ycombinator.com/launches/STF-gitcafe-a-git-forge-for-teams-and-agents-shipping-hundreds-of-times-a-day-not-twice",
        "votes": 64,
        "created_at": "2026-08-11T15:43:21.182Z"
      }
    ]
  },
  {
    "id": 32134,
    "name": "Parasma",
    "slug": "parasma",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/65a63b540239ee3a5538326d626bcceee19f52d9.png",
    "website": "https://parasma.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We train human brain cells for compute",
    "one_liner": "Replacing GPUs with human brain cells",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1782285260,
    "tags": [
      "Machine Learning",
      "Neurotechnology",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": true,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
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    "stage": "Early",
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    "url": "https://www.ycombinator.com/companies/parasma",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/parasma.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/parasma",
    "x_url": "https://x.com/ParasmaAI",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
    },
    "founders": [
      {
        "user_id": 3388159,
        "full_name": "Sean Cole",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Parasma (YC S26)\n\nSkipped BS, MS in Artificial Intelligence w/ Distinction",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/sean-cole-8985a4207/",
        "x_url": "https://x.com/vytalow",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6f2d0bdfcf98a7f2843ea62be9a5893871caf196.jpg"
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    ],
    "jobs": [
      {
        "id": 98189,
        "title": "Founding Scientist",
        "url": "https://www.ycombinator.com/companies/parasma/jobs/Vg7GVVl-founding-scientist",
        "location": "San Francisco, CA, US / Remote",
        "type": "Full-time",
        "role": "Science",
        "role_type": "Research",
        "salary_range": "$1M",
        "equity_range": "1.00% - 3.00%",
        "experience": "11+ years",
        "visa": "Will sponsor",
        "skills": []
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    ],
    "news": [],
    "launches": [
      {
        "id": 109992,
        "title": "Parasma: Training human brain cells for compute",
        "tagline": "In March, we trained them to play Doom; now we got them to do next-token prediction.",
        "body": "We train human brain cells for compute. In March, we trained them to play Doom; now we got them to do next-token prediction.\n\n![uploaded image](/media/?type=post&id=109992&key=user_uploads/3388159/cbefdb79-ad5b-4e03-ae35-d2a482a05351)\n\n![uploaded image](/media/?type=post&id=109992&key=user_uploads/3388159/ce53a90f-7180-4eab-b23e-d770a5e375a7)\n\n**The Problem**\n\nModern models require enormous amounts of energy and training data, while still struggling with continual learning and adapting efficiently to new tasks. Biological brains solve these problems very differently. They learn continuously, adapt from limited experience, and operate on roughly the power of a light bulb.\\\n\\\n**Solution**\n\nParasma is developing the algorithms and infrastructure needed to turn human brain cells into useful compute. The human brain is the most capable and energy-efficient learning system we know. Our goal is to reliably train biological substrates and use them for general computation, robotics, and eventually direct biological training. In March, we trained human brain cells to play Doom, which got hundreds of millions of views across social media. Since then, we’ve been working toward more general tasks. Most recently, we got the cells to perform token prediction: given a sequence of tokens converted to electrical stimuli, the neurons learn to predict what comes next with no GPU.\n\nLong-term, biological compute will offer orders-of-magnitude improvements in energy and sample efficiency, while enabling native continual learning.\n\n**Team**\n\nI previously worked on neuromorphic computing and reinforcement learning during my master’s.\n\nBefore that, I built a successful digital-item marketplace with > $1.7M of listed goods and played games professionally.\\\n\\\nCheck out our press release here:\\\n<https://parasma.com/news/human-brain-cells-do-next-token-prediction>",
        "url": "https://www.ycombinator.com/launches/Sc4-parasma-training-human-brain-cells-for-compute",
        "votes": 10,
        "created_at": "2026-08-13T20:30:00.164Z"
      }
    ]
  },
  {
    "id": 32167,
    "name": "Orchestra",
    "slug": "orchestra",
    "former_names": [
      "Understudy Labs"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/900e0fabf138d248123ff3ddf28833736fbfb71d.png",
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    "all_locations": "San Francisco, CA, USA",
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        "full_name": "Luis Manrique",
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        "title": "Orchestra: Self-optimizing inference cloud to cut your AI costs by 100x",
        "tagline": "Your AI inference gets smarter and cheaper the more you use it. ",
        "body": "We’re Aamir &amp; Luis and we’re excited to launch our company’s new brand ahead of demo day this week: Orchestra ([orchestra.ai](http://orchestra.ai)) \\\n\\\nCompanies move their frontier inference spend to Orchestra, with a single URL swap. We relay their traffic through to the frontier labs, while building evals in the background automatically. These evals allows us to post-train specialist models that replace the frontier, at a fraction of the cost. When our model beats the frontier at your product’s work, we promote it and your bills go down. Then we repeat the loop continuously, forever; your AI inference gets smarter and cheaper the more you use it. \\\n\\\nWe’re going straight after the $104B inference market; Anthropic grew 14x YoY but we’ve seen time and time again that start-ups and enterprises are willing to move spend if you can prove the performance. That it. We don’t have FDEs. We don’t pre-train models. Most of the application layer lives in a CLI that coding agents use. \\\n\\\nWe just do one thing really well: building and routing your work to the most efficient model. We found the easiest way to build a fast growing business is to _sell_ your product to people who have pain and who are already spending money with someone else. The more we focused on the fastest growing segments of our business, the faster our product got better at solving a simple, but incredibly painful problem: “You don’t need a PhD to work in the mailroom”. \n\n**It’s the “Age of the Unc”** \n\nI shared my experiences doing YC as a 38-year old (<https://x.com/lluismanrique/status/2094846605925241259>) but for any founders out there thinking about coming back to YC: do it. We worked 60-hour weeks, not 100-hour weeks. We had a network of angels, customers and advisors. We ran the marathon, instead of sprinting it. At 38, I’m old enough to understand that pedagogy is the point of YC. Learning how to work on your company is the point. You’ll get **so much more** out of YC when you’re doing it again with the hindsight of all your past successes and failures. Having done it before is a huge advantage, but having a long career in which I’ve encountered a lot of situations and had to make a lot of decisions and a few mistakes, and see others do the same, allows me to understand how YC’s advice impacts the real world. You will manage your agents better. You will close customers faster. You’ll build a better company. \n\n**Why Orchestra?** \n\nBranding is everything, especially outside of silicon valley. We launched as the ‘alternative’, the ‘backup’; but as time went on, we realized how open companies are to moving **all** their spend to a single place and making it work efficiently. Inference is the fastest growing market in the history of capitalism and we needed a brand that reflected our ambitions. Orchestra is going to win a huge chunk of that market; not the back-up, but the entire show. \n\nSay hello to Orchestra. And if your bills are too damn high, send us a note. We’d love to help.\n\n![uploaded image](/media/?type=post&id=114099&key=user_uploads/3647059/d32c3f74-5cd4-4026-ad40-1c7bd729a08a)\n\n",
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        "title": "Understudy: The self-optimizing inference cloud",
        "tagline": "We distill your production usage into cheaper, specialized models your company owns",
        "body": "Hey YC! We’re Luis and Aamir, founders of Understudy.\n\n### TL;DR\n\nWe help you cut your company’s Anthropic bill by 80% with no loss of performance.\n\nUnderstudy is an inference cloud. We capture your employee’s AI work traces, train smaller models, and automatically deploy them (only when they outperform the expensive model you are currently paying for).\n\nInstead of renting the same frontier model forever, your AI infrastructure gets cheaper and better every time it runs.\n\nSee more: <https://youtu.be/YhfoOchpYaw>\n\n### The problem\n\nLast week, we met with a company spending $40M/year on Anthropic. And it’s only going in one direction.\n\nTo them, it feels totally out of control.\n\nThey’re not alone - every company we meet is spending record amounts on models from frontier labs.\n\nBut almost none of that spending compounds.\n\nI.e. You send a prompt, pay for the answer, and throw away the experience. Six months later, you are still renting the same general-purpose model at roughly the same price.\n\nMeanwhile, training custom models requires an entire ML team to collect and clean traces, build evals, choose training data, fine-tune and reinforce models, test serving configurations, compare cost, latency, and quality, safely deploy or roll back the winner.\n\nFor most companies, it’s just too much to bother with.\n\n### What Understudy does\n\nPoint Understudy at an AI workload.\n\nWe capture how your existing agent performs, turn those traces into evals, and start training smaller models that can do the same job better.\n\nUnderstudy handles the entire loop:\n\n**Observe → evaluate → train → test → deploy → repeat**\n\nA new model is promoted only when it beats the incumbent on the metrics you care about, such as:\n\n* task success\n* inference cost\n* latency\n* privacy\n* deployment size\n\nYour customized models are owned by you, and can run in our cloud, your cloud, or on-premise.\n\n### An example\n\nSuppose your product uses Claude for a repetitive legal workflow.\n\nUnderstudy observes successful production runs, learns the specific tools and judgment required for that workflow, and trains a specialized open-weight model.\n\nOnce that model matches or beats Claude on a held-out evaluation, Understudy starts routing production traffic to it.\n\nAs more work is completed, the model keeps improving.\n\n### Why us\n\nAamir and I met building Instacart’s optimization and experimentation systems, growing multiple business lines to more than $1B in annual revenue.\n\nWe started Understudy because we believe every company using AI should be accumulating intelligence, not accumulating API bills.\n\n### Our ask\n\nSpending too much on repetitive model workloads? Ready to own your own models? We want to meet you - come to [understudylabs.com](http://understudylabs.com) today.",
        "url": "https://www.ycombinator.com/launches/SPb-understudy-the-self-optimizing-inference-cloud",
        "votes": 16,
        "created_at": "2026-08-05T02:21:26.322Z"
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        "full_name": "Beknazar Abdikamalov",
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        "id": 108267,
        "title": "Speko: OpenRouter for Voice",
        "tagline": "Every speech model, benchmarked language by language, wired into one API.",
        "body": "Speko is a router for voice models. One API key, and your agent runs on the speech-to-text, LLM and text-to-speech models we measured as best for your language and use case.\n\n![uploaded image](/media/?type=post&id=108267&key=user_uploads/451241/2038963f-c769-47a9-8fd9-c59f3966027b)\n\n### I picked voice stacks by trial and error for four years\n\nA voice agent is three models in a row: speech-to-text, then an LLM, then text-to-speech. Three vendors, a dozen credible options each. You run a bake-off, you argue about whose numbers are real, and you ship the combination that felt best that month.\n\nThen next week another model drops. And another. Nobody has time to re-run the bake-off, so the stack you picked under deadline quietly becomes the stack you are stuck with for a year.\n\nThat is how most voice agents are running today. Not on the best models. On the models that were best whenever someone last had a free week.\n\nPublic leaderboards do not settle it either. They are averages over somebody else's audio, in somebody else's conditions.\\\n\\\n[https://drive.google.com/file/d/1AjC8WVA96kUBdD09RJMUgFfvxGfxPvL-/](https://drive.google.com/file/d/1AjC8WVA96kUBdD09RJMUgFfvxGfxPvL-/view?usp=sharing)\n\n### We measure, then we route\n\nWe benchmark the speech models people actually ship on, language by language, and publish everything at [benchmarks.speko.ai](http://benchmarks.speko.ai): word error rate, finalize latency, time to first token, cost per minute. When a vendor ships a new model we test it the same day and the board updates.\n\nPoint your agent at Speko and that measurement becomes routing. One API key across every provider we support. Tell us the language and the conditions, and your traffic runs on the stack our numbers say wins. When the numbers change, the routing changes and you rewrite nothing.\n\nIf a provider fails when we open the connection, the router drops to the next ranked model on its own.\n\n### Or build the agent with us\n\nYou do not need an existing agent to start. A few lines gets a working voice agent live on Speko, and it inherits the same routing from day one.\n\nIf you are picking a voice stack right now, start with the numbers at [benchmarks.speko.ai](http://benchmarks.speko.ai), then build on them at [speko.ai](http://speko.ai).",
        "url": "https://www.ycombinator.com/launches/SAF-speko-openrouter-for-voice",
        "votes": 14,
        "created_at": "2026-07-29T19:10:13.923Z"
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        "full_name": "Carl Stauffer",
        "title": "Founder/CEO",
        "founder_bio": "Founder and CEO of Riften. Former Palantir Forward Deployed Engineer. Former nuclear researcher at Jefferson Lab. CS and AMS at Johns Hopkins.",
        "is_active": true,
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      {
        "id": 110330,
        "title": "Riften: Route every AI request. Build the models your company owns.",
        "tagline": "Cut LLM spend with one endpoint that selects the lowest-cost model that works.",
        "body": "[Riften Launch Video](https://youtu.be/9T82XHysqsc)\n\n## What Riften does\n\nRiften is an OpenAI- and Anthropic-compatible gateway that routes each LLM request to the lowest-cost model capable of completing it.\n\nConnect your existing providers, change the base URL, and keep the product you already built. Riften works across customer-facing AI products and internal tools, including Claude Code and Codex.\n\n**Riften is already removing enterprise-scale model spend from production workloads.**\n\n## Why we built it\n\nMost AI products choose a model once and send nearly everything to it.\n\nA documentation lookup, a support classification, and a codebase migration do not require the same intelligence. Yet they are routinely sent to the same frontier model at the same price.\n\nFor customer-facing products, that cost cuts directly into gross margin. For internal agents, it limits how broadly the company can deploy them.\n\n## How it works\n\nRiften evaluates each request locally, applies the company’s cost, capability, latency, and reliability constraints, and selects among the models available to that organization.\n\nHard tasks can still reach frontier models. Routine work moves to lower-cost commercial or open-weight models. Teams can keep their provider accounts, pin a specific model when needed, and inspect the cost and routing decision behind every request.\n\nThe objective is not the cheapest token. It is the lowest expected cost of completing the work.\n\n## From routing to company-owned models\n\nThe router reveals which work is repeated. Outcomes reveal whether it was done correctly.\n\nA passing test, an accepted patch, a resolved ticket, or a completed workflow can become part of a customer-specific evaluation. Riften uses those evaluations to improve routing, post-train open models around recurring work, and test those models against the APIs they are intended to replace.\n\nModels earn production traffic through the same endpoint. Frontier APIs continue to handle novel work. Repeated workloads move onto models the company controls.\n\nThat creates a direct path from inference routing to evaluation, post-training, deployment, and the applications where the work happens.\n\nThe model labs are building intelligence every company can rent. Riften is building the path to intelligence every company can own.\n\n## Team\n\nRiften is founded by Carl Stauffer, a former Palantir Forward Deployed Engineer who shipped software into the purchasing and production systems of a nine-figure manufacturer. He was Palantir’s first Neurodivergent Fellow through Alex Karp’s program, conducted nuclear research at Jefferson Lab, studied computer science and applied mathematics at Johns Hopkins, and is an a16z Scout.\n\nThe rest of the team comes from Stanford Mathematics, Berkeley computer science, and Carnegie Mellon mathematics and computer science. Their work spans Oxford OATML, SPAR, CMU CyLab, Palo Alto Networks, Millennium, Amazon Search, NASA, UCSF Health, and industrial manufacturing systems.\n\nThey have built agent orchestration platforms, LLM evaluation and fine-tuning systems, cryptographic security software, production search infrastructure, CAD-matching systems, and autonomous QA for measuring agent performance. Their research includes an ICML acceptance and current work in review at NeurIPS.\n\nThe team also brings national-level recognition in mathematics and technical research spanning manufacturing, computational science, and new energy systems.\n\nRiften combines enterprise deployment, frontier evaluation research, competitive mathematics, agent systems, security, and production ML in one team built for the full path from request to outcome to model.\n\n## The ask\n\n**We are looking for companies with meaningful LLM usage across customer-facing products or internal agents.**\n\nIf most of your traffic still runs through one frontier model, send us:\n\n* Your current default model\n* Whether the traffic is internal or customer-facing\n* Your approximate monthly model spend or volume\n\nWe will show you what should remain on the frontier, what can move today, and whether Riften is worth testing.\n\nEmail us at [info@riften.ai](mailto:info@riften.ai).",
        "url": "https://www.ycombinator.com/launches/ShW-riften-route-every-ai-request-build-the-models-your-company-owns",
        "votes": 11,
        "created_at": "2026-08-17T15:12:03.010Z"
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    "name": "Dialogus",
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        "full_name": "Juberth Rodriguez",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder @ Dialogus \n",
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        "linkedin_url": "https://www.linkedin.com/in/juberth-rodriguez-398b2b277/",
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        "full_name": "Hans Ibarra",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder @ Dialogus | Ex-Google @ Gemini, Gmail and Youtube\n\nPapa Johns, KFC and Totalplay as customers",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/hans-ibarra/",
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        "user_id": 3047141,
        "full_name": "Rodrigo Terán",
        "title": "Founder/CTO",
        "founder_bio": "Co-founder @ Dialogus | Ex-Google, Meta | Math & Physics Olympiad",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rodrigoteranhdz",
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    "launches": [
      {
        "id": 107329,
        "title": "Dialogus: Building the infra for enterprise voice agents",
        "tagline": "Infra for enterprise voice agents",
        "body": "Every year, enterprises spend billions of dollars operating contact centers. They struggle with high labor costs, long wait times, inconsistent customer experiences, and the challenge of hiring and retaining thousands of agents.\n\nMost AI voice products try to automate individual phone calls. We believe the real opportunity is much bigger: rebuilding the contact center itself.\n\nDialogus replaces traditional contact center operations with AI voice agents that can answer calls, resolve customer requests end-to-end, integrate with enterprise systems, and scale across hundreds of locations while maintaining a consistent customer experience.\n\nToday, Dialogus is already powering AI voice operations for Fortune 500 enterprises, handling thousands of customer calls in production.\n\nThe company was founded by Rodrigo Teran, Hans Ibarra, and Juberth Rodriguez after seeing how legacy contact center software wasn't built for the AI era. Instead of layering AI on top of decades-old infrastructure, they're building an AI-native contact center from the ground up.\n\nIf you're an enterprise looking to transform your customer support with AI, we'd love to talk.\n\n<https://youtu.be/Yh0SGq-HgQo>",
        "url": "https://www.ycombinator.com/launches/Rv7-dialogus-building-the-infra-for-enterprise-voice-agents",
        "votes": 10,
        "created_at": "2026-07-24T18:40:56.654Z"
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    "id": 32227,
    "name": "Context.dev",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "Context.dev gives AI agents structured data from any website through a single API.\r\n\r\nWe help developers build smarter agents & products that depend on accurate, fresh web data.\r\n\r\nIf you need structured data from the internet, you need Context.dev\r\n\r\nProud to power agentic products at Mintlify, Super, Vizzy, Klarna, and 600+ other companies. ",
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    "team_size": 6,
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    "launched_at": 1780323126,
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    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/contextdev/",
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    "primary_partner": {
      "name": "Jon Xu",
      "url": "https://www.ycombinator.com/people/jon-xu"
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        "full_name": "Yahia Bakour",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Context.dev, building the future of web context for AI.\n\nPreviously Principal SWE/Manager at Sunrun. Ex-Amazon SDE2, tech lead on tier-1 retail checkout platform.  Co-founder at StockAlarm.io (acquired), scaled to 225K+ users, 100K+ daily notifications.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/yahia-bakour/",
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      {
        "id": 109242,
        "title": "Context.dev: Live web data API for AI agents",
        "tagline": "One API to collect, structure, and continuously refresh internet data for AI",
        "body": "Hello!\n\nYahia here, founder of [Context.dev](https://link.context.dev/yc) (YC S26).\n\n**TL;DR:** [Context.dev](https://link.context.dev/yc) turns the live web into structured data for AI agents and applications. Trusted by Mintlify, Passionfroot, Rho, and more than 400 customers.\n\nThe web was built for humans to browse, not agents to understand.\n\nOnce an AI product needs live internet data, its team often ends up maintaining browsers, proxies, crawlers, parsers, queues, retries, caches, and multiple data vendors.\n\nWe built Context.dev so that entire workflow starts with one API.\n\n<https://youtu.be/c2mVWBJmBtQ>\n\n**How it works**\n\nGive Context.dev the sources your product cares about and tell us what context you need. We collect it, structure it, and keep it current without your team building the infrastructure underneath.\n\n* Send URLs, domains, companies, websites, sitemaps, or files.\n* Get back clean Markdown, screenshots, company data, or structured JSON matching your schema.\n* Use Batches to process large workloads and Monitors to keep information current.\n* We handle the browsers, proxies, rendering, extraction, caching, retries, and queues underneath.\n\nHere’s what customers are already using it for:\n\n* **Soria** monitors hundreds of healthcare and financial sources.\n* **Murph** structures data on 2M+ foods, supplements, and tests.\n* **Mintlify** turns GitHub repositories into custom branded documentation.\n* **Executor** discovers and structures API docs across 3,000+ integrations.\n* **Dench** monitors social platforms for leads, generating $3M+ in pipeline.\n* More stories at [context.dev/customers](http://context.dev/customers)\n\nYour team builds the product. Context.dev keeps it connected to the live web.\n\n**The team**\n\nI’m Yahia Bakour, founder of[ Context.dev](https://link.context.dev/yc). Before this, I led engineering at Sunrun, worked at Amazon, and built and sold two startups.\n\nI started [Context.dev ](https://link.context.dev/yc)after watching teams repeatedly rebuild, and get stuck maintaining, the same web data infrastructure.\n\nToday, our team of 4 is building the context layer between the internet and every agent or application that needs to understand it.\n\nModels are no longer the main bottleneck for intelligent knowledge work. Context is.\n\n**Our ask to you**\n\nIf you’re building an AI product that needs internet data at scale, **please reach out**. We would love to learn what you’re building and where the existing tools fall short.\n\nYou can also try the API with 500 free credits and no credit card at [**Context.dev**](https://link.context.dev/yc)\n\nSchedule a call: <https://link.context.dev/chat>\\\nEmail: [yahia@context.dev](mailto:yahia@context.dev)",
        "url": "https://www.ycombinator.com/launches/SPy-context-dev-live-web-data-api-for-ai-agents",
        "votes": 113,
        "created_at": "2026-08-05T18:02:48.007Z"
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    "name": "Codag",
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    "long_description": "Codag compresses tool call outputs before agents read them, which reduces token spend by  20-30% while retaining output accuracy.\r\n\r\nGovern and see analytics on agent dollars across all model providers and internal agents. \r\n ",
    "one_liner": "The AI gateway for cheaper, faster, permissioned agents.",
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    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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    "url": "https://www.ycombinator.com/companies/codag",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/codag.json",
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      "name": "Harshita Arora",
      "url": "https://www.ycombinator.com/people/harshita-arora"
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        "user_id": 2706494,
        "full_name": "Michael Zhou",
        "title": "Founder/CEO",
        "founder_bio": "Solo @Codag. ex. @Okta, @Shopify for infrastructure. BSc in Computer Science and Neuroscience from University of Toronto. Ex. Rank 80 Challenger LoL, Rank 200 TFT",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/mzxzhou/",
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      {
        "id": 110923,
        "title": "Codag: Compression and control for agent tools",
        "tagline": "Optimize agent costs without changing how developers work.",
        "body": "Hi everyone,\n\nI’m Michael, founder of Codag.\n\n**TL;DR:** Codag compresses tool results before models read them, lowering costs and improving accuracy without changing how developers work, while providing tool usage analytics.\n\n<https://youtu.be/eJbK3SezeXA>\n\n### The problem\n\nAgents consume huge amounts of output from searches, tests, builds, logs, file trees, and APIs.\n\nA single tool result can add thousands of mostly repetitive lines to a session. The model processes that output before provider-native compaction can help. Important evidence gets buried, so the agent searches again, retries, or rereads the same data.\n\nThis makes agent sessions slower and more expensive. Companies also lack a consistent view of which tools and MCP servers agents use across Claude Code, Codex, and other harnesses.\n\n### What Codag does\n\nCodag attaches to existing agent harnesses with one setup command.\n\nIt handles each result according to the action being performed:\n\n\\- Tests preserve failures, traces, file locations, and exit status.\n\n\\- Searches preserve exact file and line references.\n\n\\- Logs preserve chronology, causal evidence, and outliers.\n\n\\- Anything omitted remains selectively retrievable.\n\nOptimized tool results are 75% smaller on average, while preserving signal.\n\nDevelopers keep working normally. Codag reduces what reaches the model before the model pays to read it.\n\nCodag also reports which tools and MCP servers agents invoke, along with tokens, model-adjusted spend, savings, reducer cost, and latency across providers. Because Codag already sits in the tool path, it becomes the natural place for companies to control which tools agents can use, under what policies, and at what cost.\n\n### Results so far\n\nMore than 90 organizations use Codag, including teams of up to 35 people. I built and sold the product solo.\n\n### Where this goes\n\nOur bet is that agent activity will grow faster than model prices fall. As companies give agents more tools, longer tasks, and production access, they will need infrastructure that reduces, measures, and controls agent tool use across every provider.\n\nCodag starts with compression. The larger opportunity is becoming the gateway through which companies understand and control how agents use tools.\n\n### The ask\n\nIf your team uses agents, check out [codag.ai](http://codag.ai), or contact me at [michael@codag.ai](mailto:michael@codag.ai)",
        "url": "https://www.ycombinator.com/launches/Sr5-codag-compression-and-control-for-agent-tools",
        "votes": 6,
        "created_at": "2026-08-16T18:44:00.099Z"
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    "name": "Touchmark",
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    "long_description": "Buy tokens over a time window from the provider of your choice at a price fixed today - 10-30% off the on-demand price. If your plans change - resell your reserved capacity to another buyer.",
    "one_liner": "Market for future inference capacity",
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      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
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      {
        "user_id": 1998938,
        "full_name": "Ilia Bolgov",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder of Touchmark. Product @ Revolut, Maths @ Imperial College London.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ilia-bolgov-8683b0254",
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        "full_name": "Roman Yanushevskyi",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder at Touchmark. Previously 2x Quantitative Research intern at Citadel Securities, AI Engineering intern at Lovable. Math and CS graduate from the National University of Singapore. IOI gold medalist.",
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        "id": 110492,
        "title": "Touchmark: Market for future inference capacity",
        "tagline": "Buy tokens in advance with flexibility to resell unused capacity up to 30%+ off - or more for larger orders via direct quotes",
        "body": "**TL;DR:** We are building a market for future inference capacity. You buy tokens for a model over a time window from the provider of your choice at a price fixed today (up to 30% off - or more for larger orders via direct quotes). If your plans change, you will be able to resell your reserved capacity to another buyer.\n\n<https://youtu.be/nMaDr46NU5I>\n\n**Problem**\n\nToken spend is growing and it’s growing fast. Out of 100+ companies we spoke to, most are trying to cut their inference costs.\n\nReserved capacity guarantee deals provide discounts + certainty, but are negotiated bilaterally, take time, and are **hard to switch from** - as a buyer you commit by prepayment, which you lose if you switch to a different provider, or in some cases even a different model.\n\nWhat if a buyer could:\n\n1. See all possible discounted capacity deals in one place\n2. Buy with a click of a button\n3. Have an option to resell to another buyer with another click of a button\n\n**But why would model providers (sellers) want this?**\n\nIn short:\n\n1. Model providers’ goal is to maximise GPU utilization - the more information they have about future demand, the better they can allocate capacity.\n2. Selling future capacity is an excellent way to finance GPU compute - dilution-free and interest-free\n3. Extra distribution channel for enterprise buyers\n\n**What if a new model comes out but I already bought capacity for the older one?**\n\nContracts for “Best model in class” allow you to buy frontier model capacity in advance. And even if you're holding a contract on an older model when a new generation ships - far from every application needs the frontier, so you can list your capacity for resale.\n\n**What if I am a _very large_ buyer and these discounts are not enough?**\n\nWe are launching Requests for Quotes (RFQs) - as a buyer you can create a request for capacity you need and let providers bid to offer you the best price.\n\n**Team**\n\nIlia: Maths &amp; Finance @ Imperial, Product (Wealth &amp; Trading) @ Revolut\n\nRoman: CS + Math @ NUS, IOI gold, 2x Quant Research @ Citadel Securities, AI engineer @ Lovable\n\n**If you're a company buying tokens or a model provider, email us at [contact@touchmark.ai](mailto:contact@touchmark.ai) or sign up at[ https://app.touchmark.ai/signup](https://app.touchmark.ai/signup)**\n\n_Capacity transactions on Touchmark are limited to commercial producers and commercial end users in the wholesale capacity market. Sellers offer capacity to Buyers that purchase it to consume or use in connection with the Buyers' commercial operations. Some contracts are available only in the primary markets._",
        "url": "https://www.ycombinator.com/launches/Sk8-touchmark-market-for-future-inference-capacity",
        "votes": 26,
        "created_at": "2026-08-14T13:43:29.567Z"
      }
    ]
  },
  {
    "id": 32527,
    "name": "machine0",
    "slug": "machine0",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0eb24a85015d2fd30115c143876496fed0a17210.png",
    "website": "https://machine0.io",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "machine0 is a CLI for agent compute: `machine0 new mybox` gives your agent a full cloud VM, billed by the minute, from small up to 60 vCPU / 240 GB and 8×H100s. Agents drive it themselves via CLI or MCP.\r\n\r\nAgent workloads are moving from ephemeral to always-on. A coding agent working on a hard feature runs 6-8 hours. Auto-research agents run for days. machine0 is the environment in which these agents run.",
    "one_liner": "AWS for Agents",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1780987913,
    "tags": [
      "Developer Tools",
      "Cloud Computing"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/machine0",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/machine0.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/machine0",
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    "primary_partner": {
      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
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    "founders": [
      {
        "user_id": 31731,
        "full_name": "Barnaby Malet",
        "title": "Founder/CEO",
        "founder_bio": "Engineer. 2 x YC Founder. Currently founder/CEO of machine0 (S26). Previously cofounder/CTO/CPO of Upflow (W20). Before all that, Computer Science @ Imperial College London.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/barnabymalet/",
        "x_url": "https://x.com/barnabymalet",
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    "launches": [
      {
        "id": 108327,
        "title": "machine0: Cloud computers for AI agents",
        "tagline": "One command spins up a persistent VM. Your agent drives it via CLI or MCP, building its own fleet from 1 vCPU up to 60 vCPU / 240 GB and 8×H100s.",
        "body": "TLDR: machine0 is a CLI for agent compute: “machine0 new mybox” gives your agent a full cloud VM, billed by the minute, from $0.013/hr up to 60 vCPU / 240 GB and 8×H100s. Agents drive it themselves via CLI or MCP.\n\n<https://www.youtube.com/watch?v=gyllkZ0M04E>\n\n## The Pain\n\nAgent workloads are moving from ephemeral to always-on. A coding agent working on a hard feature runs 6-8 hours. Auto-research agents run for days. OpenClaw &amp; Hermes run 24/7. As you run more in parallel:\n\n* **Resources**: a few agents on a big codebase saturate your RAM and CPU. Auto-research needs GPUs you don't have.\n* **Security**: “—yolo” on your personal machine is one prompt injection away from exfiltrated credentials.\n* **Availability**: close your laptop and the agent dies mid-task.\n* **Isolation**: there's no clean line between everything on your laptop and the minimum this agent actually needs.\n\n## The Solution\n\nmachine0 gives every agent its own computer. It's a CLI that both humans and agents can use without reading docs:\n\n* “machine0 new mybox” creates an SSH-ready VM with a static IP and HTTPS endpoint. Always on (99.99% uptime) until you switch it off.\n* **Billed by the minute.** 1 vCPU / 1 GB at $0.013/hr up to 60 vCPU / 240 GB, plus GPUs from RTX 4000 Ada to 8×H100s.\n* **Profiles.** Bundles of MCP connections, credentials, prompts, and env vars, injected at VM creation. Each agent gets exactly the capabilities (or personality) you choose, and nothing else.\n* **Agents self-serve.** Hand the CLI or MCP server to Claude, Codex, or OpenCode and it manages its own fleet: spin up a box for a build, snapshot it, tear it down.\n* **Suspend &amp; resume.** Pause an idle machine, stop paying for compute, resume exactly where it left off.\n* **Snapshot &amp; clone.** Configure one machine perfectly, then stamp out ten identical agents from it.\n* **NixOS is first-class.** Declarative, reproducible machines.\n\n## How people use it\n\n* **Compute for your agent**: give it the CLI and it spins up its own VMs for builds, tests, and long jobs instead of grinding your laptop.\n* **Hosting the agent itself**: run Claude, Codex or Hermes 24/7. Snapshot once, then clone as many as you want.\n* **Swarms of agents**: an orchestrator agent spawns worker machines, farms out subtasks, and tears them down when the work is done.\n* **Auto-Research on GPUs**: drop an agent on an H100 with a goal and let it run for days.\n\n## About me\n\nI'm a solo, second-time YC founder. I was CTO of Upflow (YC W20), where I scaled EPD post series A and personally owned DevOps, infra, and security for years because they were too high-stakes to delegate. Upflow handles sensitive accounting data for companies like Postman, ProductBoard, and Lattice, and is doing many millions in ARR.\n\nBefore that I built consumer payments at Hike Messenger (100M+ users) in India. Working with sensitive data and infra for years is what got me deep into the problems machine0 solves, security especially.\n\n## Asks 🙏\n\n* Try it: <https://machine0.io>\n* Let's talk about your agent fleets, software factory or auto-research: [hello@machine0.io](mailto:hello@machine0.io)",
        "url": "https://www.ycombinator.com/launches/SBD-machine0-cloud-computers-for-ai-agents",
        "votes": 22,
        "created_at": "2026-07-31T15:30:36.400Z"
      }
    ]
  },
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    "id": 32947,
    "name": "Executor",
    "slug": "executor",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/948ce8964e024f509e8d2c3db33ab94f3acb5b17.png",
    "website": "https://executor.sh",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Executor.sh is an MCP gateway that allows you to sign in once to all of your integrations and use them with any client.\r\n\r\nWe enable you to connect your agents to anything (MCP, GraphQL, OpenAPI) so regardless of what you're trying to connect agents to you're able to.\r\n\r\nIf you're trying to scale agents to access all of your production services, data, etc, you need Executor as a unified management layer.",
    "one_liner": "The open source tool gateway",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1785345907,
    "tags": [
      "AIOps",
      "SaaS",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/executor",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/executor.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "",
    "x_url": "https://x.com/executorsh",
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    "primary_partner": {
      "name": "Jared Friedman",
      "url": "https://www.ycombinator.com/people/jared-friedman"
    },
    "founders": [
      {
        "user_id": 1133501,
        "full_name": "Rhys Sullivan",
        "title": "Founder",
        "founder_bio": "Founder at Executor, enabling your to connect every agent to anything. Previously Software Engineer at Vercel, at Microsoft, Raven Software, and Epic Games.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rhyssullivan/",
        "x_url": "https://x.com/RhysSullivan",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91ca30bf14b18325ab55dfdc29d5723fab969094.jpg"
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    ],
    "jobs": [],
    "news": [],
    "launches": [
      {
        "id": 108331,
        "title": "Executor — One place to connect your agents to everything",
        "tagline": "Connect your tools once and every agent has them, with permissions you control",
        "body": "**TL;DR:** Executor is your tool gateway. Connect every service and every account once, set per-tool permissions, and any MCP-compatible agent (Claude Code, Codex, Cursor, whatever ships next) reaches all of it through one endpoint. Try it at [executor.sh](https://executor.sh).\n\nhttps://youtu.be/NWYFpDW02Dw\n\n## The problem\n\nYou don't have one agent anymore. You have Claude Code, a ChatGPT account, Cursor, maybe a box in the corner running jobs overnight. Three problems follow:\n\n1. **Every agent wants its own OAuth into everything.** You connect your calendar to the tenth harness in a slightly different way, tokens expire, connectors deprecate, and each agent is missing something when you need it.\n2. **Most connectors stop at one account per service.** One Google login means your agent sees your work calendar or your personal one, never both. Your flights land in one inbox, your meetings in the other, and no agent can reason over your actual day.\n3. **Permissions are an afterthought.** Once an agent holds a token, it can do anything that token allows. Giving an agent your email shouldn't mean trusting it to never hit delete.\n\n## What Executor does\n\nConnect once in Executor, and sever all the one-off connections.\n\nEach integration gets credentials plus a per-tool policy: **always allowed**, **needs approval**, or **blocked**. Point any MCP-compatible agent at Executor and it has everything.\n\n- **All your accounts, not just one.** Accounts are connections: work and personal Gmail, two calendars, a staging and a production Stripe, side by side. Every agent sees all of them.\n- **One tool shape.** MCP, OpenAPI, and GraphQL integrations all normalize into a consistent name + schema. 1,600+ tools collapse into a single ~1k-token surface instead of ~280k tokens of tool definitions.\n- **Safe by default.** Executor preserves operation semantics (GET vs DELETE, destructive hints, GraphQL mutations), so agents auto-run safe operations and ask before risky ones.\n- **Sandboxed execution.** Tool calls run in an isolated JavaScript sandbox; secrets are injected host-side, so the model never sees a raw token.\n- **Team-ready.** Per-user and shared credentials. Onboard the team once, not per agent per person.\n\nBecause enforcement lives in Executor rather than in any agent framework, you can adopt whatever new agent ships next week without re-thinking data access. You keep everything first-party connectors offer and gain what they don't: multiple accounts per service, per-tool policies, and one place to see and revoke access.\n\n## How to use it\n\n- **Local:** open-source (MIT) desktop app and CLI, everything stays on your machine\n- **Self-hosted:** a Dockerfile that lets your team own all its data\n- **Executor Cloud:** hosted, nothing to manage\n\n## Traction\n\n- 2,800+ GitHub stars, ~14k npm downloads/week, ~3,000 users on the local version\n\n## Background\n\nSolo technical founder, previously at Vercel. Before Executor I built [answeroverflow.com](https://www.answeroverflow.com) and grew it to 1.5M monthly users as a solo dev, bootstrapped.\n\n## Asks\n\n1. **Try it and tell me where it breaks.** [executor.sh](https://executor.sh) has a \"Copy prompt\" setup you can paste into your agent to get started.\n2. **Intros:** if your team (or one you know) is drowning in per-agent integration setup, I'd love an intro: rhys@executor.sh\n3. **Star the repo:** [github.com/UsefulSoftwareCo/executor](https://github.com/UsefulSoftwareCo/executor)\n4. **Want a walkthrough?** Grab a time: [cal.com/rhys-sullivan/executor-chat](https://cal.com/rhys-sullivan/executor-chat)\n",
        "url": "https://www.ycombinator.com/launches/SBH-executor-one-place-to-connect-your-agents-to-everything",
        "votes": 9,
        "created_at": "2026-08-04T15:41:42.736Z"
      }
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    "id": 32988,
    "name": "Markov",
    "slug": "markov",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/41a4a41e5482735389d2089a01ff2b862f56e307.png",
    "website": "https://www.markovstudios.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Markov builds expert computer-use datasets for frontier AI labs. Our datasets include screen recordings + mouse/keyboard actions of professionals using CAD, design and enterprise software.\r\n\r\nWe work with the top AI labs and we’ve gotten more than 350k downloads on HuggingFace. Our long-term goal is to be the full stack platform for computer-use AI, starting with data and then the infrastructure to train and deploy computer-use models.\r\n\r\nDev and Harish are technical co-founders. Dev studied aerospace engineering at IIT Madras and was the youngest team member at Sarvam - India’s top AI lab. Harish is an Emergent Ventures grantee and his prior robotics work was featured in top national publications.",
    "one_liner": "Expert computer-use data for frontier AI labs",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1782277833,
    "tags": [
      "Reinforcement Learning",
      "Data Labeling",
      "Data Engineering"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
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      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/markov",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/markov.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/markov-artificial-intelligence/",
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    "primary_partner": {
      "name": "Harshita Arora",
      "url": "https://www.ycombinator.com/people/harshita-arora"
    },
    "founders": [
      {
        "user_id": 1783684,
        "full_name": "Dev Mandal",
        "title": "Founder/CEO",
        "founder_bio": "Co-Founder & CEO at Markov. Prev at Sarvam, Aerospace engineering at IIT Madras",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/dev-mandal-iitm/",
        "x_url": "https://x.com/DevvMandal",
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      },
      {
        "user_id": 2921049,
        "full_name": "Harish Ashok",
        "title": "Founder/CTO",
        "founder_bio": "Co-Founder at Markov. Previously founded Zenith, a robotics company building AI-assisted hardware workflow tools. ",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/harish-ashok",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fbfaea52b2a5bc846c87d04392062585acfb69ff.jpg"
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    "jobs": [],
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    "launches": [
      {
        "id": 109874,
        "title": "Markov - Data for computer-use AI",
        "tagline": "150K+ downloads across our HuggingFace datasets",
        "body": "# \\*\\*\\*\\*\n\n**TLDR;**\n\n* **Markov builds the data infrastructure for computer-use AI**, collecting synchronized screen recordings, mouse/keyboard actions, and annotations from real workflows.\n* **We specialize in hard-to-source expert data** **across CAD, architecture, engineering**, and other professional applications.\n* **We’ve already sold 15,000+ hours of data to frontier AI labs** and open-sourced leading computer-use datasets with **150k+ downloads on HuggingFace**.\n\n<https://youtu.be/WervE6pZ9TM>\n\n**The Problem:**.\n\nWhile frontier models have gotten much better at reasoning, they still struggle to reliably complete long, multi-step workflows across real desktop software. **The biggest bottleneck is the lack of high-quality training data.**\n\nTraining these models requires **synchronized screen recordings, mouse and keyboard actions, and high-quality annotations from people doing real work**. This is especially scarce for expert workflows in domains like CAD, architecture, engineering, finance, and other professional software.\n\n**That’s where we come in:**\n\nWe work with a vetted network of expert contributors to collect high-quality computer-use data across:\n\n* Consumer applications\n* Productivity software\n* Accounting and finance\n* CAD, architecture, engineering, and other expert workflows\n\n![uploaded image](/media/?type=post&id=109874&key=user_uploads/1783684/ba673ff4-e489-4753-b11b-4797762834fb)\n\n**So far we've:**\n\n* Sold more than 15,000 hours of data to frontier AI labs\n* Open-sourced the [**largest dataset of computer-use screen recordings**](https://x.com/DevvMandal/status/2032104265121140924?s=20)(**150k+ downloads on HuggingFace**)\n* Released the [**largest open gaming computer-use dataset**](https://x.com/DevvMandal/status/2072357115847671990?s=20)\n\n**Our Ask:**\n\nIf you're building computer-use agents and need high-quality training data - or have interesting workflows you'd like collected - we'd love to chat **:)**\n\nBook a meeting with us at <https://cal.com/dev-markov/30min> Or shoot us an email at [founders@markovstudios.com](mailto:founders@markovstudios.com)",
        "url": "https://www.ycombinator.com/launches/SaA-markov-data-for-computer-use-ai",
        "votes": 11,
        "created_at": "2026-08-11T18:00:00.277Z"
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    "id": 33014,
    "name": "Inkbox",
    "slug": "inkbox",
    "former_names": [
      "Inkbox (Vectorly Inc)"
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/59d6214df81a05c739db52287017ae33c8c45f0d.png",
    "website": "https://inkbox.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "For your AI agents to behave like teammates, we give them persistent identities with their own email, phone, and iMessage. We also provide them an agent-to-agent channel, so they can communicate for task delegation.",
    "one_liner": "The identity and communication layer for AI agents",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1782881612,
    "tags": [
      "Developer Tools",
      "Infrastructure",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
      "B2B",
      "Infrastructure"
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    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/inkbox",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/inkbox.json",
    "year_founded": 2025,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/inkbox-ai",
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    "primary_partner": {
      "name": "Tyler Bosmeny",
      "url": "https://www.ycombinator.com/people/tyler-bosmeny"
    },
    "founders": [
      {
        "user_id": 778892,
        "full_name": "Ray Liao",
        "title": "Founder/CEO",
        "founder_bio": "PhD in Computer Science from MIT. Previously cofounder/CTO at Empallo, an AI-powered virtual clinic for cardiovascular care.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ruizhi-liao/",
        "x_url": "https://x.com/rayruizhiliao",
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      },
      {
        "user_id": 2770188,
        "full_name": "Dima Vremenko",
        "title": "Founder",
        "founder_bio": "Co-founder at Inkbox. Previously Research Associate at Harvard Medical School, where he built ML pipelines for clinical prediction models using gigapixel pathology images and EHRs, co-authored 6 publications, and contributed to an R01 NIH Grant. BS in Biochemistry and Computer Science from Northeastern.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/dimavrem/",
        "x_url": "https://x.com/DVremenko",
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      },
      {
        "user_id": 2773563,
        "full_name": "Alex Wilcox",
        "title": "Founder/CTO",
        "founder_bio": "Cofounder at Inkbox. Previously ML engineering and computer networking research. MS in CS and dual BS in Math and Econ from Northeastern University.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/alxwlcx",
        "x_url": "https://x.com/alxwlcx",
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    "launches": [
      {
        "id": 104960,
        "title": "Inkbox: Give your AI agents email, phone, iMessage, and an internet address",
        "tagline": "The identity and communication layer for AI agents",
        "body": "**TL;DR:** Inkbox is the identity and communication layer for AI agents: giving agents their own email, phone, iMessage, and internet address. Inkbox pulls together what your agent sees across email, calls, iMessage, and more, so your agent can act on the full context.\n\nHey YC! We're Ray, Alex, and Dima building Inkbox.\n\n**How we got here**\n\nA few months ago we were building web-automation agents. Every time an agent needed to log into a portal, it had to receive a confirmation email or get a 2FA code. And when we were away from our desks, we wanted to seamlessly contact the agents themselves: text them, check in, get a reply. We ended up stitching together services like Gmail, Twilio, and ngrok: separate services, each built for humans, none of them sharing any identity or context. \n\nThat's the problem we set out to fix. We didn't need another email or voice API. We needed one service to tie its identity together with rich context. So we built it.\n\n[**Launch video**](https://www.youtube.com/watch?v=oJKl6tPHxRA)\n\n**What Inkbox does**\n\nOur API gives every agent:\n\n* A unique email inbox (+ custom domain)\n* A phone number that can call and text\n* iMessage\n* A persistent internet address (HTTPS tunnel) to receive internet traffic, like webhooks\n* An encrypted credential vault with TOTP/2FA\n\n**Who we are**\n\n* Ray (CEO): CS PhD from MIT; previously cofounded Empallo, an AI virtual clinic for cardiovascular patients.\n* Alex (CTO): MS in CS @ Northeastern. Previously ML engineering and computer networking research.\n* Dima (CAIO): built ML pipelines on gigapixel pathology images at Harvard Medical School; co-author on 6+ peer-reviewed publications.\n\n[**Quick demo with Claude**](https://www.youtube.com/watch?v=MTVkW_EG-Y4): Watch Claude Code provision an email, phone, and iMessage in a Minute.\n\n**Our ask**\n\n* **Try it**. Drop Inkbox into your preferred agent harness: we have plugins for[ Hermes](https://github.com/inkbox-ai/hermes-agent-plugin),[ OpenClaw](https://github.com/inkbox-ai/openclaw-plugin),[ Claude Code](https://github.com/inkbox-ai/claude-code-plugin), and[ Codex](https://github.com/inkbox-ai/codex-plugin). Each one runs a wizard that provisions Inkbox resources for your agent.\n* **Ask your agent to self signup**. _Sign up for an Inkbox email account by following the guide at[ https://inkbox.ai/docs/get-started/agent-signup.md](https://inkbox.ai/docs/get-started/agent-signup.md) and refer to[ https://inkbox.ai/llms.txt](https://inkbox.ai/llms.txt) for full documentation._\n* **Intros**. We'd love intros to teams running fleets of agents that each need their own identity to act in the real world: reaching out to people and businesses (email/phone/iMessage), holding their own accounts, and authenticating with a built-in credential vault.\n\nContact Ray at [ray@inkbox.ai](mailto:ray@inkbox.ai). Would love your feedback.",
        "url": "https://www.ycombinator.com/launches/RIu-inkbox-give-your-ai-agents-email-phone-imessage-and-an-internet-address",
        "votes": 21,
        "created_at": "2026-07-08T00:25:54.653Z"
      }
    ]
  },
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    "id": 33047,
    "name": "Decawork",
    "slug": "decawork",
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    "website": "https://decawork.ai/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "An agent’s access should depend on the task it is doing. The same action can be appropriate for one task and wrong for another. Access decisions need to adapt in real time as the agent works, but companies can’t turn every business judgment into a fixed rules or have people manually approve every step.\r\n\r\nWe train small, company-specific models that approve AI actions in real time. They consider the company’s policies and interests, the agent’s current task, and its past actions to decide whether each proposed action should be allowed. Access is specific to the task at hand and reassessed as the agent works.\r\n\r\nCompanies will rent intelligence from AI labs or run open-source models to power their agents. They’ll own the models that keep their entire AI workforce acting in their interests. Decawork is building those models.",
    "one_liner": "Training company-specific models that approve AI actions.",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1785265630,
    "tags": [
      "B2B",
      "Security",
      "Infrastructure",
      "AI"
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    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/decawork",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/decawork.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/decawork-ai/",
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    "app_video_url": null,
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    "primary_partner": {
      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
    },
    "founders": [
      {
        "user_id": 1632535,
        "full_name": "Aman Raj",
        "title": "Founder/CEO",
        "founder_bio": "- Built AI compliance platform at Barclays used by internal teams globally\n- Founded a fintech and scaled it to 52K families and $1.5M AUM\n- Led product at a consumer AI startup, scaled to 100K+ MAU (Google Play Best App 2024)\n- IIT Kharagpur",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/aman-raj-84636a158",
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      },
      {
        "user_id": 1049492,
        "full_name": "Sarthak Aggarwal",
        "title": "Founder/CTO",
        "founder_bio": "- AI systems at NVIDIA (deployed at OpenAI, Meta)\n- Enterprise AI at Ema (used by Microsoft, Hitachi)\n- Led Conquest, Asia's largest student-run accelerator\n- Google Code-In Global Winner + Open source since the age of 12\n- BITS Pilani",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/sarthak-agg/",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f93e2ff857332071c545c3c16f569835408cf579.jpg"
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    "jobs": [],
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    "launches": [
      {
        "id": 109883,
        "title": "Decawork - Training company-specific models that approve every AI action",
        "tagline": "Our small AI models decide agent access in real time, task by task, based on your company’s interests.",
        "body": "Hi everyone 👋 - we're Aman and Sarthak, founders of Decawork.\\\n\\\n**TL;DR:** AI agents shouldn’t have permanent access to company systems. The same action can be appropriate for one task and wrong for another, but companies can’t turn every business judgment into fixed rules or manually approve every step. Decawork trains small, company-specific AI models that approve every agent action in real time, based on company policies and interests, the agent’s task, and its past actions. Access is granted for the task at hand and revoked when it’s done.\\\n\\\nLaunch video: [Watch it here](https://www.youtube.com/watch?v=SanpHIyFcV4)\\\n\\\n🚧 **The Problem &amp; Why now?**\\\nCompanies are putting long-running, more autonomous agents to work on broader tasks and goals. These agents need access to critical company systems, but their actions can be unpredictable and may not serve the company’s interests.\n\nOpenAI’s agents have already hacked Hugging Face during testing with reduced safeguards. Replit’s agent deleted data from a live application’s database.\n\nAn agent’s access should depend on the task it is doing. The same action can be appropriate for one task and wrong for another. Permanent access lets an agent keep using permissions even when they are no longer appropriate for its task.\n\nAs agents take on more work independently, access decisions need to happen in real time. But companies can’t turn every business judgment into a fixed rule or have people manually approve every step.\\\n\\\n✅ **How we solve this?**\\\nDecawork gives AI agents access for the task at hand and revokes it when the task is done.\n\n* Connect your agents. Route API requests and MCP tool calls through Decawork to see activity and manage access in one place.\n* Set company policies. Write policies in plain English, specify when human approval is required, and connect your existing identity and IT service management systems.\n* Deploy a company-specific policy model. We deploy small models that approve actions in real time using your company’s policies and priorities, task context, and past actions. Your agents do the work. Decawork evaluates each proposed action and enforces the access decision before it runs.\n\n🚀 **Team**\\\nSarthak built AI systems at NVIDIA that were deployed at OpenAI and Meta, and worked on enterprise AI agents at Ema.\n\nAman built the AI compliance platform at Barclays used by internal teams globally, then led product at a consumer AI startup serving 100K+ monthly users, hand-wiring credentials for the agents he shipped.\n\nEvery one of those deployments came down to the same questions: what can it access, who approved it, and what did it do? We kept solving agent access by hand - so we quit to build the system we wished we’d had.\n\n![uploaded image](/media/?type=post&id=109883&key=user_uploads/1632535/0ef2784e-ab45-422f-91b9-960473f11dd5)\n\n🙏 **Our Ask**\n\n* If you’re on an IT team or know someone who’s worried about AI agents accessing or misusing company systems, talk to us: <https://decawork.ai/cal> or [founders@decawork.ai](mailto:founders@decawork.ai)",
        "url": "https://www.ycombinator.com/launches/SaJ-decawork-training-company-specific-models-that-approve-every-ai-action",
        "votes": 152,
        "created_at": "2026-08-12T16:30:00.369Z"
      }
    ]
  },
  {
    "id": 33051,
    "name": "Tokenless",
    "slug": "tokenless",
    "former_names": [
      "Tokenless",
      "Touchy"
    ],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f250ec6cc81104148d8bd5bafdd6b96bc77d023.png",
    "website": "https://usetokenless.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Tokenless is an applied AI research lab. Our goal is to end reward hacking. We do research and build tools to detect issues with environments and evals, understand model behavior in those environments, and prevent those issues from happening. Correcting them makes training more effective, makes model performance easier to interpret, and is crucial to solving the AI alignment problem.\r\n\r\nIf you’re training models with RL, building environments, or running agent evals, and want more confidence in your environments, we're here to help.",
    "one_liner": "We find flaws in RL environments and evals and help you fix them.",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1786732497,
    "tags": [
      "Developer Tools",
      "Machine Learning",
      "Reinforcement Learning",
      "B2B",
      "AI"
    ],
    "tags_highlighted": [],
    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
    "question_answers": null,
    "url": "https://www.ycombinator.com/companies/tokenless",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/tokenless.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/usetokenless/",
    "x_url": "https://x.com/usetokenless",
    "fb_url": null,
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    "app_video_url": null,
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    ],
    "primary_partner": {
      "name": "Vivian Midha Shen",
      "url": "https://www.ycombinator.com/people/vivian-midha%20shen"
    },
    "founders": [
      {
        "user_id": 3697324,
        "full_name": "Rohit Agarwal",
        "title": "Founder",
        "founder_bio": "Founder at Tokenless (YC S26). CS PhD student at Princeton PLI, Hazan Lab, Google Deepmind. Previously at Duolingo and theory research at Berkeley, was EECS #1 BS grad in 2024.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/rohit-agarwal-b647951ba/",
        "x_url": "https://x.com/Rohit_Writes",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/86b74b1209c81d1d158af5587de149a3e8c2292c.jpg"
      },
      {
        "user_id": 3706728,
        "full_name": "Andrew Liu",
        "title": "Founder",
        "founder_bio": "Founder at Tokenless (YC S26). Formerly a researcher at UC Berkeley working on language documentation tools. BA in Computer Science, Mathematics, and Linguistics from UC Berkeley. Previously TA'd CS61C.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/andrew-liu-b50873261/",
        "x_url": "https://x.com/landrew7363",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/04017660afe1efc138b242cbcc7c4efffc5f5bc4.jpg"
      },
      {
        "user_id": 3732665,
        "full_name": "Kevin Wang",
        "title": "Founder",
        "founder_bio": "Founder at Tokenless. Prev MTS at SkyPilot building multi-cloud GPU infra for top neolabs, Founding Engineer at Eventual from seed to Series A building the data engine for physical AI, EECS at UC Berkeley.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/kevinzhiwang/",
        "x_url": "https://x.com/kevzwang",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1a21a2cf6802cfdcf6769408ee9d25a254858622.jpg"
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    "launches": [
      {
        "id": 111407,
        "title": "Touchy 🙌: Voice-first AI Assistant for iPhone that understands the world around you",
        "tagline": "Your personal AI assistant that sees what you see, remembers what matters to you, and gets things done across all your tools. Just ask out loud.",
        "body": "**TL;DR:** We’re building Touchy 🙌, a voice and image-first AI assistant for the iPhone that personalizes itself to you. You can activate Touchy from your lock screen to instantly get things done across your apps or get answers about the world around you. Touchy is free to try, get it here: <https://apps.apple.com/us/app/touchy-ai-at-your-fingertips/id6798889392>\n\n<https://youtube.com/shorts/IJNucvAZ_-k>\n\n## **The Problem**\n\nYou probably know the feeling:\n\nYou ask the assistant on your phone, across the room, to send a colleague a message.\n\nIt cheerfully replies that it found 10 search results for “text Bob on Slack”.\n\nSo you trudge to your phone, find the app, open the DM, and begrudgingly type out your message instead.\n\nWe wanted a better personal assistant, one that had the intelligence and integrations we’ve come to expect from our AI agents, with the convenience of one that was native to your devices. We also believe that for AI to truly be personal, it needs to be deeply integrated into your life and the world around you. Right now, AI systems are constrained largely to using digital tools, yet the physical world is rich with audio and visual information that your phone is capable of receiving.\n\n## What We Built\n\nSo we built Touchy. One button press and you're instantly talking to an assistant that can see what you see, remember what you've said, and take action across all the tools you use. \n\nWhat sets Touchy apart:\n\n* **Reimagined UX.** Touchy is a fast, fully iOS-native app with a unique interface that is delightful to use and brings it closer to the world around you.\n* **Sees and hears the world around you**. Speak to Touchy and it can speak back. Easily snap a photo with the app’s camera to give Touchy access to what you’re seeing.\n* **Gets things done across your apps**. Touchy comes with built-in integrations with 1,000+ popular apps, as well as iOS-native capabilities such as you contacts and location.\n* **Make it yours over time**. You chat with Touchy in a single, continuous conversation. It automatically collects memories and preferences, and tailors itself to be more useful to you as you use it.\n\nIt's built to give you your attention back. Ask it something, let it act, and get back to your day — no app fighting for your attention.\n\n![uploaded image](/media/?type=post&id=111407&key=user_uploads/3697324/23ce7916-fce5-44c4-9ebf-f04049fee639)\n\n## The team\n\nWe’re Rohit, Andrew, and Kev. Before this, Rohit was doing a PhD on AI alignment at Google DeepMind Princeton and graduated #1 in Berkeley EECS. Andrew built educational software that scaled to half a million students worldwide. Kev was a startup founding engineer, developing data and GPU infrastructure that power many of the leading AI neolabs today. \n\nWe met during undergrad at UC Berkeley, and we’re coming back together to research, productionize, and scale the future of personal technology.\n\n## Our ask\n\nIf you have an iPhone, download Touchy and try it out: <https://apps.apple.com/us/app/touchy-ai-at-your-fingertips/id6798889392>\n\nWe would love to learn what people find themselves using Touchy for, and what they wish they could use it for. You can also report bugs by shaking your phone or just telling Touchy. You can also reach us at [founders@touchyapp.com](mailto:founders@touchyapp.com)",
        "url": "https://www.ycombinator.com/launches/Syt-touchy-voice-first-ai-assistant-for-iphone-that-understands-the-world-around-you",
        "votes": 24,
        "created_at": "2026-08-20T21:00:00.530Z"
      }
    ]
  },
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    "id": 33052,
    "name": "Mireye",
    "slug": "mireye",
    "former_names": [],
    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c96db3bdd6828a4a145c678555e120d2ab43c855.png",
    "website": "https://www.mireye.com/",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "AI agents are starting to act in the real world, but they can't reliably understand it. \r\n\r\nEvery fact about a place has its own silo: land in GIS maps, power in utility portals, people in census tables, policy in county PDFs. All built for humans, none for agents. Underneath, the data is fragmented across thousands of sources, unstructured and complex.\r\n\r\nMireye is building the infrastructure that powers agents to make decisions and act in the real world. One API and MCP to connect agents to the physical world with accurate data enrichment, signals and tools.\r\n\r\nOur mission is to index every inch of the earth, and make it as queryable as the web.",
    "one_liner": "Search Engine for the Physical World",
    "team_size": 2,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1781399078,
    "tags": [
      "Geographic Information System",
      "AI",
      "Location-based"
    ],
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    "top_company": false,
    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
    "industries": [
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      "Infrastructure"
    ],
    "regions": [
      "United States of America",
      "America / Canada"
    ],
    "stage": "Early",
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/mireye",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/mireye.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/mireye/",
    "x_url": "https://x.com/MireyeHQ",
    "fb_url": null,
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    "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c96db3bdd6828a4a145c678555e120d2ab43c855.png",
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    "primary_partner": {
      "name": "Jon Xu",
      "url": "https://www.ycombinator.com/people/jon-xu"
    },
    "founders": [
      {
        "user_id": 793651,
        "full_name": "Ansh Chokshi",
        "title": "Founder/CEO",
        "founder_bio": "Founder and CEO at Mireye (YC S26). Previously led AI/ML engineering at Seismic. At 21, co-founded Poker Pit, scaled 0 to $250K ARR in 4 months before acquisition. University of Toronto - CS, Math and Econ.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ansh-chokshi/",
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        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f6bfb9eddae5876264339f22a448da4cf57c8784.jpg"
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      {
        "id": 108365,
        "title": "Mireye: The easiest way to build agentic applications for the physical world",
        "tagline": "One API for data, enrichment, tools and signals.",
        "body": "**TL;DR: Mireye is building the infrastructure that powers agents to make decisions and act in the real world. Today’s agents are book smart, we are going to make them street smart.**\n\n<https://youtu.be/ToEs-lxNja8>\n\n**The Problem:**\n\nEvery fact about a place has its own silo: land in GIS maps, power in utility portals, people in census tables, policy in county PDFs.\n\nAll built for humans, none for agents. Underneath, the data is fragmented across thousands of sources, unstructured and complex.\n\nEven with clean data the problem isn't solved, because a decision takes more than retrieving facts.\n\n**What We Built: One API and MCP that connects your agent to the physical world**\n\n![uploaded image](/media/?type=post&id=108365&key=user_uploads/793651/dcca6a7c-aafb-4c9d-89dd-8de73c0a0a04)\n\n**How It Works:**\n\nInstall our skill or paste this in your agent: <https://www.mireye.com/skills.md>\n\nAsk any question about any US location, get a citable answer in seconds\n\nData enrichment, tools and signals that turn facts into decisions, so agents can run real workflows: screen a site, underwrite a property, qualify a lead etc.\n\n300+ fields across the land, the location, the people, the rules, the risks, growing 20% week over week.\n\n**Our first customers** built agents that source off market land deals for **data centers 100x** faster than before.\n\n**You can do the same for your industry and we'll help you build it.**\n\n**Today we are launching the first ever on-demand indexing API for the physical world.** Ask for data that isn't indexed, and our long running agent sources it, and indexes it on the fly.\n\n[**Ansh Chokshi**](https://x.com/ansh_chokshi) sold his first startup, Poker Pit, at the age of 21 after growing it from zero to $250K ARR in four months. He then led AI/ML engineering at Seismic.\n\n![uploaded image](/media/?type=post&id=108365&key=user_uploads/793651/8acef036-d553-4e19-9760-961a1f90e595)\n\n**Our Ask:**\n\nAnyone building agents for physical-world applications. Comment and we'll set you up with our MCP.\n\nIntros to developers: real estate, data center, industrial, solar, nuclear.\n\nAnyone who needs data about the real world and can't get it. We'll source it and index it for you.\n\nEmail: [ansh@mireye.com](mailto:ansh@mireye.com)",
        "url": "https://www.ycombinator.com/launches/SBp-mireye-the-easiest-way-to-build-agentic-applications-for-the-physical-world",
        "votes": 340,
        "created_at": "2026-07-31T12:14:03.241Z"
      }
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  },
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    "id": 33348,
    "name": "Autoresearch, Inc.",
    "slug": "autoresearch-inc",
    "former_names": [
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    "website": "https://autoresearch.inc",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Autoresearch helps teams train agents for tasks where general models are not good enough. Give us a task, your current agent or model, and an evaluation. Autoresearch runs an autoresearch campaign across data, SFT and other post-training methods, continual learning, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs hosted or in your cloud.\r\n\r\nWe provide the research system around models: persistent memory, full experiment lineage, compute orchestration, and statistical verification. We’re starting with agent and model training, continual learning, and optimization.\r\n\r\nReach out to us for early access at founders@autoresearch.inc.",
    "one_liner": "Autoresearch as a service: we solve your hardest measurable problems",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1785348094,
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      "America / Canada"
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    "stage": "Early",
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      {
        "user_id": 1714115,
        "full_name": "Karan Brar",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Autoresearch, Inc. Previously ML at Reducto, Head of ML Infra at DynamoAI.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/brarkaran",
        "x_url": "https://x.com/deepmatmul",
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      {
        "user_id": 1597736,
        "full_name": "Thomas Boser",
        "title": "Founder/CTO",
        "founder_bio": "Founder at Autoresearch, Inc. Previously engineer at Reducto, founding engineer at Crosswise, senior ML engineer at Discord, ML at Sentropy & SoFi.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/tboser",
        "x_url": "https://x.com/thomasboser",
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    "launches": [
      {
        "id": 108337,
        "title": "hiloop: we run thousands of experiments to improve your models",
        "tagline": "Send us a hard task, your model or agent, and an eval. We run the research campaign and return a verified improvement.",
        "body": "We're Karan and Thomas, and we're building hiloop: **infrastructure for automated research**.\n\n**TL;DR: we gave two stock coding agents 50 B200s and our infra. They ran 4,188 experiments in two days and beat the published state of the art on [Karpathy's autoresearch benchmark](https://github.com/karpathy/autoresearch). The same machinery found a [NanoGPT speedrun](https://app.primeintellect.ai/speedrun/nanogpt) candidate that beats the best known result. [See](https://hiloop.ai/blog/search-is-enough/) full writeup.**\n\nNow we want to point it at your hardest problems.\n\n![uploaded image](/media/?type=post&id=108337&key=user_uploads/1714115/63396f7f-3e21-41ea-83ea-54cbaaa9f63f)\n\n**The results**\n\nOn Karpathy’s autoresearch benchmark, our final recipe reached **0.9016 val_bpb**. The previous best published result was [Recursive’s 0.9109](https://www.linkedin.com/pulse/first-steps-toward-automated-ai-research-recursive-si-kybtc) on B200 hardware. For NanoGPT, our implementation had **12.54% lower runtime** than upstream.\n\nThere was no bespoke research agent or elaborate scaffold. We used Claude and GPT-5.5 out of the box. What changed was the system around them.\n\n**What hiloop does**\n\nGive us a hard task, your current agent or model, and an evaluation criteria. Hiloop runs an autoresearch campaign across training, data, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs in your cloud or hosted by us.\n\nWe’re starting with agent and model training: SFT, post-training, continual learning, and optimization.\n\n[https://drive.google.com/file/d/1Rv8HttSHZ-yvSJk6hsWQsqCXWTHJQogK](https://drive.google.com/file/d/1Rv8HttSHZ-yvSJk6hsWQsqCXWTHJQogK/view?usp=sharing)\n\n**Who we are**\n\nWe met at Reducto, where we built its ML, post-training, and platform systems. Karan previously led ML infrastructure at DynamoAI . Thomas was a founding engineer at Crosswise and an MLE at Discord and SoFi.\n\n**Two asks**\n\n1. **Want your models or agents better, faster, or cheaper?** If you have a hard objective in post-training, continual learning, inference optimization, or classic ML, send us the task, your baseline, and the eval. We’ll personally run a campaign with you through verified improvement.\n2. **Building autoresearch or RSI loops?** Reach out to give our system a try! We’d love to compare notes.\n\nEmail [**founders@hiloop.ai**](mailto:founders@hiloop.ai) to get in touch with us!",
        "url": "https://www.ycombinator.com/launches/SBN-hiloop-we-run-thousands-of-experiments-to-improve-your-models",
        "votes": 8,
        "created_at": "2026-07-29T19:50:09.250Z"
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    "id": 33639,
    "name": "Magma",
    "slug": "magma",
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    "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9b261522e21a27147c25d66eb0b126f8b62e5295.png",
    "website": "https://magmahq.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We enable companies monetize their agents' traces.",
    "one_liner": "Monetize your agent's traces.",
    "team_size": 1,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1786606834,
    "tags": [
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      "Data Engineering",
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
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      "America / Canada"
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    "stage": "Early",
    "app_video_public": false,
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    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/magma",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/magma.json",
    "year_founded": 2026,
    "city": "San Francisco",
    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/magma-hq",
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    "primary_partner": {
      "name": "Harshita Arora",
      "url": "https://www.ycombinator.com/people/harshita-arora"
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    "founders": [
      {
        "user_id": 3107607,
        "full_name": "Ramtin Shahzanian",
        "title": "Founder/CEO",
        "founder_bio": "failing my way through.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ramtin-shahzanian/",
        "x_url": "https://x.com/42Ramtin",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b58098b0fa3c418c52382d092d86f1801136d50d.jpg"
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      {
        "id": 110488,
        "title": "Magma - AI labs will pay you to learn from your agent's mistakes",
        "tagline": "We enable any company monetize its agents' traces!",
        "body": "**TL;DR:** Magma enables companies building AI agents to monetize their data in a few clicks.\n\n<https://youtu.be/NIqRZSu82JM>\n\n**❌ The problem**\n\nIf a product uses AI agents, every interaction creates valuable data. That could be a user correcting an agent’s mistake, or an agent discovering a new way to complete a workflow. That real-world data currently sits unused, while the best alternative is human annotators inventing hypothetical scenarios.\n\n**✅ What we do**\n\nMagma turns that data into recurring revenue for companies.\n\nCompanies share their agents’ traces in a few clicks. Access is read-only and encrypted, and nothing is shared without approval. Magma anonymizes the data, turns it into complex training environments, and handles licensing to AI labs. Every time the data is licensed, the company gets paid.\n\n**👋 Who I am**\n\nI’ve previously built the first Persian OCR model and legal LLM for my startup in Iran, formulated new transformer-based ML architectures in Oxford, and created a dev tool context management for agents that worked so well I decided to repurpose it to extract training data from traces (now Magma).\n\n![uploaded image](/media/?type=post&id=110488&key=user_uploads/3107607/07bc99c4-63f2-4235-870e-9b97977f8c43)\n\n**🧐 But why?**\n\nWe believe no two AI agents on the planet should be corrected for the same mistake. This is why we're building infrastructure to turn the experience of any AI agent into generalizable learning signals to improve any other AI agent.\n\n**🫡 Our ask**\n\nDo you know anyone at a frontier lab looking for RL environments built from real production workflows, or at an enterprise in need of fine-tuning their models on niche tasks? Let us know and we can help: [ramtin@magmahq.ai](mailto:ramtin@magmahq.ai)\n\nAlso, you can start monetizing your agents’ traces at: [magmahq.ai](http://magmahq.ai)",
        "url": "https://www.ycombinator.com/launches/Sk4-magma-ai-labs-will-pay-you-to-learn-from-your-agent-s-mistakes",
        "votes": 37,
        "created_at": "2026-08-14T01:00:00.832Z"
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  {
    "id": 33673,
    "name": "Caution",
    "slug": "caution",
    "former_names": [
      "Caution SEZC"
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    "website": "https://caution.co",
    "all_locations": "George Town, George Town, Cayman Islands",
    "long_description": "Caution is a hosting platform for software you can't afford to get hacked. It simplifies and extends confidential compute, and lets developers deploy workloads to secure enclaves in minutes and give customers, auditors, and counterparties cryptographic proof that production is running the exact reviewed source code, build, configuration, and runtime - not an opaque binary controlled by a cloud operator or internal admin.\r\n\r\nConfidential computing hardware exists, but the tooling is too fragmented and incomplete for most teams to use well. Today, attestation often proves only that a binary has a certain hash; it does not prove what source code produced that binary. Building the missing deployment, reproducibility, and verification layer requires specialized security engineering and often locks teams into one cloud or hardware stack. Caution turns that into a simple git-based workflow that builds reproducible enclave images, provisions infrastructure, and lets anyone independently verify what is running.\r\n\r\nWe built Caution after years securing high-risk systems through Distrust, where we worked with hedge funds, custodians, blockchain teams, cloud platforms, and critical grid operators on systems responsible for more than $600B in assets. Again and again, the same problem showed up: teams running mission-critical infrastructure could not reliably prove what was executing in production.\r\n\r\nAs AI, fintech, healthcare, and regulated data collaboration move into the cloud, \"trust us\" is no longer enough. Caution is a general-purpose platform: if it runs on a server, it can run verifiably on Caution. We're starting where trust failures cost the most, but we believe verifiable compute will become the default way software runs, the same way HTTPS became the default way it's served. Infrastructure you can verify, not trust.",
    "one_liner": "Hosting platform for software you can't afford to have hacked",
    "team_size": 5,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1785719703,
    "tags": [
      "Cybersecurity",
      "Cloud Computing",
      "Infrastructure"
    ],
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    "isHiring": false,
    "nonprofit": false,
    "batch": "Summer 2026",
    "status": "Active",
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    "regions": [
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      "Latin America",
      "Remote",
      "Partly Remote"
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    "stage": "Early",
    "app_video_public": false,
    "demo_day_video_public": false,
    "app_answers": null,
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    "url": "https://www.ycombinator.com/companies/caution",
    "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/caution.json",
    "year_founded": 2025,
    "city": "George Town",
    "country": "KY",
    "linkedin_url": "https://www.linkedin.com/company/cautionco",
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      "name": "Brad Flora",
      "url": "https://www.ycombinator.com/people/brad-flora"
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    "founders": [
      {
        "user_id": 3655130,
        "full_name": "Anton Livaja",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder/CEO of Caution (YC S26). We give teams a hosting platform to run sensitive workloads in secure enclaves and cryptographically prove what code is running in prod. Previously co-founded Distrust, securing systems for hedge funds, custodians, and critical infra responsible for $600B+ in assets. Deep in confidential computing, supply chain security, and applied cryptography. Mission: improve the freedom, security, and privacy of as many people as possible.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/alivaja/",
        "x_url": "https://x.com/antonlivaja",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f6dddfa8391b5f9fce21fa7496e2368d59ef6987.jpg"
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      {
        "user_id": 3654857,
        "full_name": "Ksenia Lesko",
        "title": "Founder",
        "founder_bio": "Co-founder at Caution, where we help teams run sensitive workloads in secure enclaves and cryptographically prove what code is running in production. I lead the business and product side; previously ran strategy and ops at Distrust (distrust.co) and led customer education and activation at Ada (ada.cx).",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/ksenialesko/",
        "x_url": "https://x.com/xenush",
        "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4ecf3715be1ffcf30eedb062148902b9a1458237.jpg"
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      {
        "user_id": 52788,
        "full_name": "Lance Vick",
        "title": "Founder/CTO",
        "founder_bio": "Technology sovereignty maximalist. Lead security architect at Caution. Former security lead at Turnkey, BitGo, Unit 410, Pebble. Founded Stagex Linux distribution, and #! decentralized hackerspace. Experienced in Linux hardening, privacy, software engineering, security auditing, cryptographic key management, tamper evidence, HSMs, and supply chain security. Frequently internet infamous for exposing security negligence. Prolific mechanical puzzle collector, lockpicker, and hardware hacker.",
        "is_active": true,
        "linkedin_url": "https://lance.dev",
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    "launches": [
      {
        "id": 108800,
        "title": "Caution: Hosting platform for software you can't afford to have hacked",
        "tagline": "Deploy to secure enclaves in minutes, isolate sensitive workloads, cryptographically prove what code is running.",
        "body": "Hey everyone! We're [Anton Livaja](https://www.linkedin.com/in/alivaja/), [Lance Vick](https://lance.dev), and [Ksenia Lesko](https://www.linkedin.com/in/ksenialesko/) from [Caution](https://caution.co).\n\n**TL;DR:** Caution is a hosting platform for software you can't afford to have hacked. Your software and data Istay protected even if your CI/CD pipeline, host infrastructure, or a developer laptop gets compromised. You go from source to a verifiable deployment in three commands. \n\n**Built for:** payment processors, PHI processors, AI agents, policy and fraud engines, secrets management, and other sensitive workloads.\n\n[https://drive.google.com/file/d/1u-l2TSBQO2t5dbhBsatCkZf5earCTyDD](https://drive.google.com/file/d/1u-l2TSBQO2t5dbhBsatCkZf5earCTyDD/view?usp=sharing)\n\n### **Why we built this**\n\nOn February 21, 2025, attackers stole **$1.5 billion** worth of Ethereum from Bybit, the largest digital heist in history. They never breached Bybit's own infrastructure: North Korea's Lazarus Group compromised a single developer machine at Safe{Wallet}, Bybit's multisig provider, and injected malicious JavaScript into the signing interface. Bybit's team approved what looked like a routine transfer and handed over a cold wallet to the attackers. The code everyone trusted was not the code that was running.\n\nThis keeps happening because nearly all software runs on blind trust:\n\n* trust in the cloud host\n* trust in production admins\n* trust in CI/CD\n* trust that the code you reviewed is the code actually running\n\nIf any part of that chain is compromised, an attacker can read your secrets, tamper with your business logic, or quietly run code your team never reviewed. It's the same failure behind SolarWinds and Codecov: tampered code arriving through a trusted channel, passing every check. Caution exists to shrink that attack surface and make the most sensitive systems verifiable.\n\n### **Caution proves exactly what's running on your server**\n\nCryptographically, and in real time. Deploying into secure enclaves normally takes a specialist team and months of integration. Caution automates the entire flow: source to verifiable deployment in minutes, with three commands:\n\n![uploaded image](/media/?type=post&id=108800&key=user_uploads/3654857/f3161435-2a97-4207-915a-96375a72be82)\n\n**🎥** [Watch Anton walk through the platform](https://caution.co/platform-tour.html)\n\nUnder the hood, **Caution is a verifiable confidential compute platform:** hardware-backed cryptography isolates your workloads, preserves data privacy, and proves what software is running on a server in real time. The entire platform is open source, so verification never requires trusting us. In practice, this eliminates entire classes of attacks out of the box:\n\n* **Build &amp; CI/CD tampering**: reproducible, multi-party signed builds mean injected code can't pass as a legitimate release.\n* **Infrastructure &amp; insider compromise**: a compromised cloud account, admin, or laptop can't silently swap what's running.\n* **Runtime data theft**: data is processed in hardware-isolated enclaves, unreadable even to the host, the cloud provider, or us.\n\nCaution guards against everything but first- or third-party code bugs: if the release you approve has a flaw, that's what runs. Nothing can be added, no fix can be stripped out, and every patch is provably live.\n\n### **Why now**\n\nWith advances in AI, attacks that used to require rare, expensive expertise are now cheap and scalable, and the gap between a vulnerability existing and being exploited is collapsing toward zero. Reactive patching was already losing. It can't win a race against automated attackers.\n\nIn the post-Mythos world, the security model has to flip. Infrastructure can no longer assume it won't be compromised. It has to be built to stay secure even when the host, the pipeline, or individuals with privileged access are compromised, and to prove, cryptographically, that it has. That's what Caution does.\n\nCaution is a general-purpose platform: if it runs on CPU or GPU, it can run verifiably on Caution. We're starting where trust failures cost the most, but we believe verifiable compute will become the default way software runs, the same way HTTPS became the default way it's served.\n\n### **Why us**\n\n[Anton](https://www.linkedin.com/in/alivaja/) and [Lance](https://lance.dev) have spent their careers securing some of the most security-critical systems in the world: BitGo, Ledn, Turnkey, OpenZeppelin, and Unit 410 (later acquired by Coinbase). Across hundreds of engagements, from hedge funds to critical grid operators, the systems we've helped secure hold more than **$600B** in digital assets. [Ksenia](https://www.linkedin.com/in/ksenialesko/) spent years helping enterprises adopt new technology, and now leads go-to-market, customer education, and operations at Caution, turning deep security engineering into a product companies can understand, buy, and use.\n\nWe've been the people companies call in when this class of attack hits. Teams needed stronger guarantees against supply chain and runtime attacks, but existing solutions were too hard, too bespoke, or too expensive. So we built the platform we kept wishing existed.\n\n### **Traction**\n\nCaution is in production and onboarding customers spanning crypto infrastructure, blockchain labs, digital asset lending, consumer hardware, post-quantum security, and private AI inference.\n\nWe support AWS Nitro Enclaves today. Intel TDX, AMD SEV-SNP, TPM 2.0, NVIDIA Confidential Computing, and broader cloud support is in active development and coming soon.\n\n### **Our ask**\n\nIf you're running software where sensitive data must stay private or business logic can't be tampered with, we'd love to help you level up your security. [Book 20 minutes with us](https://caution.co/contact.html) or email us at [founders@caution.co](mailto:founders@caution.co).\n\nWe'd especially love to hear from you if you're a:\n\n* security team that wants proof, not promises, from your infrastructure\n* fintech running payments, policy, or fraud engines\n* crypto custodian, exchange, or wallet team that can't afford another Bybit\n* healthcare or AI company processing PHI or other regulated data\n* team giving AI agents production credentials and losing sleep over it\n* company using AI inference with sensitive data\n\nYou can also learn more on our [blog](https://caution.co/blog.html), and follow us on [LinkedIn](https://www.linkedin.com/company/cautionco/) and [X](https://x.com/CautionHosting) for updates.",
        "url": "https://www.ycombinator.com/launches/SIq-caution-hosting-platform-for-software-you-can-t-afford-to-have-hacked",
        "votes": 59,
        "created_at": "2026-08-04T17:02:42.242Z"
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    "id": 33805,
    "name": "Edviro",
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    "website": "https://edviroenergy.com",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Edviro builds AI that operates energy infrastructure. We connect to meters, HVAC systems, batteries, solar, and other energy equipment and continuously handle the work required to keep them efficient: detecting and diagnosing problems to forecasting demand, simulating fixes, creating work orders, coordinating maintenance, and controlling equipment where authorized. Our energy world models learn how each site behaves, choose the highest-value actions, and verify the results against real operational, meter, and billing data.",
    "one_liner": "AI That Operates Energy Infrastructure",
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    "industry": "B2B",
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        "full_name": "Hursh Shah",
        "title": "Founder/CEO",
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        "full_name": "Tanuj Siripurapu",
        "title": "Founder",
        "founder_bio": "19, Founder & CTO @ Edviro (YC S26). Prev. at RTX. Scaled a digital presence agency to 20k revenue in high school. Programming since age 9",
        "is_active": true,
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        "title": "Member of Technical Staff",
        "url": "https://www.ycombinator.com/companies/edviro/jobs/PlLCBWC-member-of-technical-staff",
        "location": "San Francisco, CA, US / Remote (US)",
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        "role": "Engineering",
        "role_type": "Machine learning",
        "salary_range": "$160K - $260K",
        "equity_range": "0.50% - 2.00%",
        "experience": "Any (new grads ok)",
        "visa": "US citizen/visa only",
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        "id": 113409,
        "title": "Founding GTM",
        "url": "https://www.ycombinator.com/companies/edviro/jobs/xuWBSdL-founding-gtm",
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      {
        "id": 112256,
        "title": "Edviro - AI that operates energy infrastructure",
        "tagline": "AI to operate energy infrastructure",
        "body": "# **Edviro: AI that operates energy infrastructure**\n\nHi all,\n\nWe’re Hursh and Tanuj, founders of Edviro.\n\n**TL;DR:** Edviro connects to a facility’s BMS, utility, sensor, equipment, and maintenance systems to build a world model of how the facility behaves. We use that model to detect problems, simulate interventions before anything is changed, and coordinate the best next action. We’re live in **34 facilities**, have identified more than **$400K in avoidable energy costs**, and are now opening our first partnerships with data center operators.\\\n\\\n<https://youtu.be/0sTQlaQGlOY>\n\n## **The problem**\n\nData centers are expected to drive roughly half of U.S. electricity-demand growth through 2030.\n\nThis is usually framed as a problem of producing more electricity. That is only part of it.\n\nPower must be delivered to the right sites, on the timelines data centers are being built, with the reliability their workloads require. Once a facility has that power, its operators still need to extract as much useful compute as possible from every available megawatt without compromising uptime.\n\nInside a data center, power, cooling, controls, equipment, workloads, and maintenance are tightly coupled. A workload shift changes the thermal profile. A cooling-control change affects both energy use and equipment margins. A maintenance decision can alter redundancy and available capacity.\n\nYet the data and workflows used to manage these systems remain fragmented across BMS, DCIM, EPMS, meters, sensors, maintenance software, vendor portals, and spreadsheets.\n\nMost existing systems can display telemetry or raise an alarm. They cannot reliably model how the entire facility will respond before an operator changes a setpoint, shifts a workload, services equipment, or adds capacity.\n\nOperators are left making high-stakes decisions with incomplete system-level context.\n\n## **What Edviro does**\n\nEdviro turns a facility’s operational data into a continuously updated world model.\n\nBy “world model,” we mean a model that estimates the current state of the physical system, learns how its components interact, and predicts how it will respond to a proposed action. \n\nEdviro:\n\n* Integrates data from building controls, utilities, sensors, equipment, and maintenance systems\n* Detects anomalies, energy waste, equipment degradation, and capacity constraints\n* Simulates operational and maintenance interventions before they are deployed\n* Recommends the best action while accounting for cost, reliability, and physical constraints\n* Uses agents to coordinate approvals, work orders, investigations, and follow-through\n* Verifies whether the expected operational and energy impact actually occurred\n\nFor a data center, this could mean determining whether existing power and cooling infrastructure can support additional IT load, testing a control-sequence change before deploying it, identifying which equipment issue is constraining capacity, or prioritizing maintenance based on the capacity and reliability it would recover.\n\nWe initially keep operators in control of every consequential action. As the models are validated and earn trust, more of the operating loop can be automated.\n\n## **Why we started with schools**\n\nEdviro began when I was 17 and working with my high school’s facilities team. I was given a year of district utility bills to analyze, built software to parse them, and discovered roughly **$360,000 in gas overbilling**.\n\nWe found that this was not an isolated problem. Schools operate large portfolios of aging, mixed-vendor infrastructure with fragmented controls, incomplete data, lean facilities teams, and very little room for wasted budget.\n\nThey forced us to build for the messy reality of physical infrastructure rather than clean laboratory data.\n\nToday, Edviro is live in **34 facilities** and has identified more than **$400,000 in avoidable energy costs** for building operators.\n\nA school is obviously not a data center. Data centers have denser instrumentation, much tighter uptime requirements, and more complex coupling between power, cooling, and compute. \n\nWhat transfers is the underlying architecture: connecting heterogeneous operational systems, estimating the state of a physical facility, simulating interventions, coordinating action across teams, and verifying the result. We are now building the data-center-specific models, integrations, and safety constraints directly with operators. Our first relevant research benchmark can be found here: <https://x.com/hursheybar2/status/2083299315003593037?s=20>\n\n## **Where we’re going**\n\nThe current generation of facilities software tells operators what has already happened.\n\nWe want Edviro to understand what is happening now, predict what will happen next, and help execute the safest and highest-value response.\n\nAs electricity becomes one of the primary constraints on AI growth, building more infrastructure will not be enough. We also need to operate the infrastructure we already have far more intelligently.\n\nThe long-term goal is not another dashboard. It is infrastructure that can understand itself and increasingly operate itself.\n\n## **Asks**\n\n1. **We’re looking for data center operators to become early design partners.** We are especially interested in facilities dealing with power or cooling constraints, capacity-expansion planning, recurring manual investigations, fragmented controls, or reactive maintenance. We can begin read-only, with operators retaining approval over every action.\n2. **We’re hiring energy-modeling engineers** across thermal systems, power systems, controls, scientific machine learning, and physical simulation.\n\nIf you operate a data center—or know the team responsible for operating one—we’d love to talk.\n\n[**www.edviro.com**](http://www.edviro.com)",
        "url": "https://www.ycombinator.com/launches/TCa-edviro-ai-that-operates-energy-infrastructure",
        "votes": 5,
        "created_at": "2026-08-26T17:00:00.166Z"
      },
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        "id": 104853,
        "title": "Edviro - Buildings are wasting money daily",
        "tagline": "Your building is wasting energy daily",
        "body": "**TL;DR:** Edviro helps facilities managers at large buildings save on their energy bills.\n\nFacilities managers are overwhelmed with dashboards, alarms, and analytics for hundreds of boilers, HVAC units, and heat pumps across their buildings. Edviro continuously monitors and optimizes buildings using energy world models to detect anomalies, simulate interventions, and suggest the best options to the facilities manager. In particular, in busy spaces like offices, we utilise Wi-Fi access points or our own occupancy sensors to reduce HVAC usage significantly. Our tools also allow institutions to maintain detailed service histories and equipment logs and to perform maintenance with AR. To date, we've saved clients six figures in multiple buildings. We’re also piloting with a construction company to conduct baselining and M&amp;V for sites they work with.\n\n**The team:**\n\nWe’re young technical founders. I’ve published at NeurIPS ‘24, conducted patent pending VR Alzheimer’s work, and founded a previous Aviation smart glasses startup that was accepted into PearX. Tanuj has been coding since he was 9, scaled a previous digital agency to $20k in revenue and previously did dev work at RTX.\n\nThis all started when I was working with my own high school’s facilities team. I saw how much waste there was and how fragmented his energy data was despite loads of smart controls and BMS systems. Initially I built a dashboard for him, and the product quickly caught on to more schools in NorCal.\n\n**Our ask:**\n\nWe’re looking for intros to facilities teams at school districts, real estate or data centres.\n\nAlongside that, we’re looking to work with construction teams who need help baselining and doing M&amp;V on their work post-project.\n\nIf you are connected to anyone in this space, would love to chat!\n\n![uploaded image](/media/?type=post&id=104853&key=user_uploads/3164154/9fe82a05-dd5a-4916-94f4-9cf0e8f54a05)\n\n",
        "url": "https://www.ycombinator.com/launches/RHB-edviro-buildings-are-wasting-money-daily",
        "votes": 5,
        "created_at": "2026-07-07T15:02:05.737Z"
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    "name": "Experiential Labs",
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    "website": "https://experientiallabs.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "We are building an open source gateway for AI budget holders to control model access and turn their traffic into better models that they own. We're a team that has earned an ML PhD at Georgia Tech, scaled AutoGPT to 160k GitHub stars, and led a self-driving simulation team at Waabi.",
    "one_liner": "Open source AI gateway that turns your traffic into better models",
    "team_size": 2,
    "industry": "B2B",
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        "full_name": "Kion Fallah",
        "title": "Founder/CEO",
        "founder_bio": "Co-founder and CEO of Experiential Labs. Previously Staff Research Scientist at Waabi leading the mixed reality simulation team. PhD in Machine Learning from Georgia Tech focused on synthetic data.",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/kfallah",
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      {
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        "full_name": "Silen Naihin",
        "title": "Founder/CTO",
        "founder_bio": "Built and grew AutoGPT to 160k stars. Did AI for science at the DOE. Previously cofounded Stackwise (was in W24) and various founding AI eng roles. Did continual learning, interp, and evals research.",
        "is_active": true,
        "linkedin_url": "https://linkedin.com/in/silen-naihin",
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      {
        "id": 109456,
        "title": "Experiential Labs - Turn your AI spend into a cheaper + better model you own",
        "tagline": "Train a model your company owns, guaranteed to match or beat frontier quality at 50% lower cost.",
        "body": "**TL;DR: Companies are spending more on AI every month, but almost none of that spending becomes an asset they own. We train a small language model for your company's use cases. You fully own it, and we guarantee the same or better quality as the frontier models you use today at 50% lower cost.**\n\n<https://youtu.be/YRkN_-6rl3Y>\n\nCompanies are spending more on AI every month, but almost none of that spending becomes an asset they own.\n\nThe prompts, traces, and use cases are yours, _but the model stays rented._\n\nSo you keep paying frontier prices for repetitive work a smaller, specialized model could do faster, cheaper, and often better\n\nWe built Experiential Labs to change that.\n\n**Here's how it works**\n\nKeep using GPT, Claude, or whatever models are already in your stack.\n\nExperiential Labs uses your traffic to build a simulation of the tasks, failures, and edge cases your agent encounters. You can also upload existing traces to bootstrap the process.\n\nThe platform trains you a model against that simulation, so it gets better at your specific use cases over time.\n\nAs your model improves, it earns more traffic. Hard requests can still go to the frontier. Everything else moves to your faster, cheaper model.\n\nYou own the weights, can run them wherever you want, and keep the profits they create. When a stronger base model comes out, we transfer what your model has learned instead of making you start over.\n\nOn some tasks we've seen up to 97% lower cost and +50% higher quality. Across a full deployment, we guarantee at least 50% lower cost at the same or better quality, backed by an SLA.\n\n**The team**\n\nWe are both AI researchers. Kion built simulation systems for autonomous vehicles at Waabi after earning his ML PhD at Georgia Tech. Silen built and grew AutoGPT to 160,000 GitHub stars and has worked across continual learning, evaluations, interpretability, and AI for science.\n\n## Our ask\n\nIf your company sends significant traffic to GPT, Claude, or other frontier models, we would love to help.\n\nIntros to founders and AI leaders with growing inference bills are also greatly appreciated.\n\n**Email:** [founders@xplabs.ai](mailto:founders@xplabs.ai) \n\n**Schedule a call:** <https://cal.com/silen/experiential-intro>\n\n**Website:** [xplabs.ai](http://xplabs.ai) ",
        "url": "https://www.ycombinator.com/launches/STQ-experiential-labs-turn-your-ai-spend-into-a-cheaper-better-model-you-own",
        "votes": 18,
        "created_at": "2026-08-06T22:00:00.114Z"
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    "id": 34893,
    "name": "Agent 37",
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    "all_locations": "New York City, NY, USA",
    "long_description": "* Ready to Use Instantly: Skip the complex server setup. It comes pre loaded with top AI Agents (like Claude Code and Hermes) so your agents can start working immediately.\r\n\r\n* Keep Your Work Safe: Unlike other systems that delete everything when they finish a task, these sandboxes remember your files, code, and settings forever until you delete them.\r\n\r\n* Pay Only for What You Use: Stop paying for idle servers. You pay a tiny fee to store your files, but you only pay for compute power during the exact minutes your AI is actively running.\r\n\r\n* Zero Infrastructure Headaches: You don’t need to be a senior DevOps engineer or manage complex cloud networks. One simple command handles the entire backend for you.\r\n\r\n* Connect to Any App: Allow your AI agents to securely log into and interact with everyday tools like Slack, Gmail, and GitHub in just a few clicks.",
    "one_liner": "Persistent sandboxes for agents like hermes, openclaw, claude code",
    "team_size": 0,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1789425415,
    "tags": [
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      "AI"
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    "country": "US",
    "linkedin_url": "https://www.linkedin.com/company/agent37",
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        "full_name": "VISHNU KRISHNAPRASAD",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Agent 37. Previously Senior Software Engineer at Amazon and Engineering Manager at Quantcast. Founded Real Voxel and Dota2Connect. Began career at ThoughtWorks, progressing from Application Developer to Senior Consultant. Master's in Information Science Management from Carnegie Mellon University.",
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        "linkedin_url": "https://www.linkedin.com/in/vishnukrishnaprasad/",
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        "title": "Agent37: Host any agent harness",
        "tagline": "Agent sandboxes that are 94% lower compute cost ",
        "body": "Hi everyone, I'm Vishnu, founder of Agent37.\n\n**TL;DR:** Agent37 hosts any agent harness (Hermes, OpenClaw, Claude Code, Codex, or your own) so you can give each customer their own agent in an isolated sandbox, for over 95% less than most sandbox providers charge.\n\n![uploaded image](/media/?type=post&id=117160&key=user_uploads/316679/28b659ef-29c9-4364-849c-04dc75b73de9)\n\n\\\n**The problem**\n\nEvery team building an AI agent product needs the same thing underneath it: a private, always-on computer for each agent that keeps its files, wakes when someone messages it, and costs almost nothing while idle. Today there are two ways to get one, and both are bad.\n\nSome teams do it by hand. Chris Nguyen at ETEC+ used to spin up a VPS with a Hermes agent on it for each client and email it over, more than 30 times. Others build it themselves. Wes Merrill at Boon spent close to six months building a provisioning pipeline on Render. In his words: \"it was not fun.\"\n\nEither way, the time goes into plumbing instead of the product.\n\n**What we built**\n\n* Pick a template for any popular agent harness, or bring your own Docker image.\n* Each agent gets its own sandbox with persistent files, a browser, and Gmail, Slack, Notion, GitHub and 1,000+ other apps through Composio.\n* Every customer's agent is fully isolated from everyone else's.\n* Billing is per minute. An idle sandbox only costs its disk.\n* Our open-source starter kit is a white-label app with sign-up, an agent per customer, chat, files and integrations. Rebrand it and launch your own Instinct-style product in your niche.\n\n**What customers have done with it**\n\nBoon writes grant applications for nonprofits. They gave every client their own Hermes agent in 3 days. Their cost per agent went from $85/month to $4.94/month on equivalent hardware.\n\nETEC+ builds agents for Australian support, sales and ops teams. Chris's brief going in: \"Ideally I just want to sell, and not do any backend.\" Six days after picking up our starter kit, they had a branded product where clients sign up, pay, and get an always-on agent, and they moved their 30+ existing clients onto it.\n\n**Our ask**\n\nIf you've built an agent that works and you want to sell it to more than one customer, try us at [agent37.com](http://agent37.com). You get $2,500 in sandbox credits, worth over $50,000 of uptime on most other sandbox providers. And if you know someone who could use this, please send them our way.\n\n**About us:** Our goal is to make running agents affordable for everyone in the world. ",
        "url": "https://www.ycombinator.com/launches/UTg-agent37-host-any-agent-harness",
        "votes": 7,
        "created_at": "2026-09-29T17:00:00.120Z"
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    "id": 34928,
    "name": "Boat",
    "slug": "boat",
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    "website": "https://boat.dev",
    "all_locations": "Paris, Île-de-France, France; Remote",
    "long_description": "**Problem**\r\n\r\nHumans used to need laptops to do their work. Now they prompt agents instead, and those agents need computers too: always-on, in large quantities, and with unlimited capabilities. \r\n\r\nBuilding and maintaining that yourself is far from trivial.\r\n\r\n**Solution**\r\n\r\nBoat provides powerful, simple, permissive Ubuntu VMs in the cloud. In a sandbox, an agent can work for long periods on code or on knowledge work like legal, research, reporting, and auditing.\r\n\r\n**Why Boat and not any other cloud VM?**\r\n\r\n1. It is ready out of the box: every sandbox ships with a desktop, chrome, languages, docker, snapshots, and templates, plus a CLI, SDK, API, and control pane for your fleet.\r\n2. It is permissive and paradigm agnostic: any number of agents can use any number of sandboxes. Agents can run inside or use sandboxes as an external tool.\r\n3. It is expertly crafted: we manage everything from the individual CPU to the orchestration software, which makes sandboxes stable, persistent, scalable, and secure.\r\n4. It is the best performance and price in town: our CPUs rank first on HPC benchmarks, and sandboxes are typically 9x to 18x cheaper than other leading sandboxes.\r\n\r\n**Further notes**\r\n\r\nSandboxes are hosted in Europe, which keeps prices low, and more regions are coming. Unless compliance requires it, agents don't notice the region they run in.",
    "one_liner": "The best cloud VMs for billions of agents",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1788174016,
    "tags": [
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    "stage": "Early",
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    "year_founded": 2024,
    "city": "Paris",
    "country": "FR",
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        "user_id": 918787,
        "full_name": "Anicet Nougaret",
        "title": "Founder/CEO",
        "founder_bio": "Founder at Ascii",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/anicet-nougaret-b7846b174",
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        "user_id": 4016415,
        "full_name": "Kirill",
        "title": "Founder/CTO",
        "founder_bio": "co-founder @ ascii.dev",
        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/luaroncrew/",
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    "id": 35096,
    "name": "Stacktrace",
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    "website": "https://stacktrace.ai",
    "all_locations": "San Francisco, CA, USA",
    "long_description": "Agents will do most of the work in the future, but only if teams can trust them unsupervised.\r\nStacktrace is the control layer for every agent: it sees every action, detects risky behavior, enforces the bounds your team sets, and steps in when an agent drifts from intent.",
    "one_liner": "See and control what your agents actually do",
    "team_size": 3,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
    "launched_at": 1790912378,
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    "year_founded": 2026,
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    "linkedin_url": "https://linkedin.com/company/stacktrace-ai",
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        "full_name": "Vinod Kone",
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        "full_name": "Micheal Benedict",
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        "is_active": true,
        "linkedin_url": "https://www.linkedin.com/in/michealb/",
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        "full_name": "Prasanth Ganta",
        "title": "Founder",
        "founder_bio": "Founder at Stacktrace (YC F26). Previously Engineering Leader at Amazon (2011-2026), where he built GenAI systems at Amazon Ads and foundational services across AWS and Alexa. MS in CS from Texas A&M, B.Tech from IIT Guwahati.",
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    "id": 35262,
    "name": "Bryel",
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        "full_name": "Kevin Miao",
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        "user_id": 308334,
        "full_name": "Frankie Li",
        "title": "Co-Founder",
        "founder_bio": "Co-Founder at Bryel Labs. Previously helped train models and led product at Fundamental Research Labs (a16z-backed, MIT spinout), hire #3 where he scaled products to millions of users and revenue, including Shortcut (Excel copilot used by 1000+ companies). Harvard Grad.",
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        "linkedin_url": "https://linkedin.com/in/lifrankie",
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      {
        "id": 119338,
        "title": "Bryel - Building your in-house frontier AI lab",
        "tagline": "Bryel helps your company to build an in-house AI lab, then gives you the software to train and continually improve your own models.",
        "body": "Hey YC, **we’re on a mission to build a billion models, trained by you.** We’re [Kevin](https://www.linkedin.com/in/miaok/) and [Frankie](https://www.linkedin.com/in/lifrankie/), co-founders of Bryel.\n\n## TL;DR\n\n**We’re your cracked AI training team, on demand.** We partner with your company, build your in-house model training capabilities, and leave behind Bryel Foundry, our software that makes model development and continual improvement 10x simpler. Think of it as your in-house AI lab. We’ve trained models for Apple, Framer, and F500s.\n\n**Ask:** Considering training your AI model? Email [frankie@bryel.ai](mailto:frankie@bryel.ai)\\\n\\\n<https://www.youtube.com/watch?v=LsVUbDIG2RY>\n\n## Problem\n\n**When you train a model for everyone, you train it for no one.** \n\nFrontier models are incredibly capable generalists, but they fall short on the specifics. So teams build harnesses to work around the edges and cover the gaps: more prompting, more harness infra, more retries, and stricter evals.\n\nEventually, quality plateaus. We’ve met many teams that are buried in harness work, layering on more and more infrastructure and patches to keep things working. That’s where model training is worth a serious look. With recent advancements in the open-source ecosystem and post-training methods, a few thousand good examples can produce a model that beats frontier APIs on your task, and it’s cheaper to run and deployable on-prem.\n\nThe challenge has always been that training is slow, expensive, experimental, and requires specialized knowledge. That’s where we help.\n\n## Solution\n\nWe forward deploy into your company, to help you set up your AI model training capabilities end-to-end, and hand off software to help you maintain and continually improve your AI models. Our product, the Bryel Foundry, has been built from everything we learned doing this by hand, many times over.\n\nModels we commonly help train:\n\n* **OCR/Classification Models** that run 20x faster.\n* **Search &amp; Embedding models** that cost 5x less. \n* **Long horizon agent models** that match frontier performance at 5-8x lower cost\n\n**One more thing,** we have a second ask. If you know companies that have a lot of proprietary data and no ML team (think insurers, logistics, healthcare, legal)? We'd love a 20-minute intro. We're not looking to sell, just to learn. Happy to return the favor.\n\n![uploaded image](/media/?type=post&id=119338&key=user_uploads/308334/6ae442d6-a2d9-4c5b-ba4e-3772f96daa55)\n\n",
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        "full_name": "Pavan Katta",
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    "all_locations": "BY, Germany",
    "long_description": "Most phones are built to keep you on them. We make an agentic phone that puts an army of agents to work for you. Tell it what you need done, and they handle it across the tools and apps you already use. You get more work done with less time on your screen.",
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    "team_size": 1,
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        "full_name": "Advaiyt Sane",
        "title": "Founder/CTO",
        "founder_bio": "CS @ CMU '29\nCreating Memorable, turning agent reasoning into graph search. ",
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      {
        "id": 115489,
        "title": "Memorable: graph procedural memory for AI agents. Stop re-teaching your agents!",
        "tagline": "Memorable gives AI agents procedural memory so they stop re-deriving the same workflow every time — cutting tool calls 60% and latency 40%.",
        "body": "# **_| TL;DR:_** Memorable records the tool calls an agent made to run a task, reuses that workflow on similar tasks instead of re-deriving through reasoning from scratch. –60% tool calls, –40% latency.\n\n<https://youtu.be/3m9I1zHSMPk>\n\n**Memorable turns agent reasoning into a graph search problem.**\n\n1. We **capture your agents' traces** and store them as a lightweight graph.\n2. When a similar prompt arrives, we **extrapolate a workflow with a graph search.**\n3. We **inject a short pointer** (50 to 100 tokens) into the prompt.\n\n![uploaded image](/media/?type=post&id=115489&key=user_uploads/2164442/8a549d1f-423e-4011-90b8-d8e0145a1323)\n\n# **So the agent runs deterministically instead of re-deriving a plan every. single. time.**\n\n### **_Forget tokenmaxxing._**\n\n**We're graph-first and anti-text so you SAVE time, tokens, and money.**\n\n* **–60% tool calls** and **–40% latency** on [**QM's**](https://github.com/yc-software/qm) benchmarks\n* **–20% turns** in [**Gbrain**](https://github.com/garrytan/gbrain) bench, ran [**GStack’s**](https://github.com/garrytan/gstack) /investigate skill with **–98% tokens**\n\n**Do your agents need procedural memory?**\n\n* Memorable is for anyone running agents that do the same work more than once.\n* We've built for **coding, support, ops, research, company brain, and browser/computer-use agents.**\n* If you find yourself repeatedly having to correct or guide your agents, they need procedural memory!\n\n### **Imagine a world where your agents don’t reason.**\n\n* Most agent memory layers store facts using text: preferences, docs, past chats, while Memorable stores procedures: the steps an agent takes to achieve an outcome.\n* We are able to encode this graphically using nodes and edges, making it incredibly agent-friendly and token-efficient, allowing most agents to skip the preliminary planning step altogether.\n\n**Try Memorable, your agent will onboard you!**\n\n```\nnpx memorable-cli\ncurl -fsSL https://memorable.sh/install.sh | sh\n```\n\n* Compatible with **Claude Code, Codex, Cursor, and Claude Cowork**\n* You can try it out live in [**QM**](https://github.com/yc-software/qm) and [**Gbrain**](https://github.com/garrytan/gbrain) (thanks [**@Joshua France**](https://bookface.ycombinator.com/user/211454) &amp;\n\n  [**@Garry Tan**](https://bookface.ycombinator.com/user/678)!)\n* Procedures live encrypted in your own cloud, or run entirely local.\n\n![uploaded image](/media/?type=post&id=115489&key=user_uploads/2164442/59c2f411-3d34-482a-ba18-a03e14ecc4e2)\n\n**Memorable Team**\n\n* [**Nikhil Krishnaswamy**](https://bookface.ycombinator.com/user/2164442) \\[Stanford CS\\]: created an iOS MCP interning at AGI Inc, research published in COLM and IEEE\n* [**Advaiyt Sane**](https://bookface.ycombinator.com/user/3250603) \\[Carnegie Mellon CS\\]: built dev tools at Nvidia that create 50k AV tests a month\n\n**We are looking for design partners!**\n\n* Would love to chat with teams **deploying agents at volume** (high tool-call or token spend, repeated workflows) willing to **pilot as design partners.**\n* Open to **custom harness and framework integrations** for your interesting use cases\n\nEmail [info@memorable.sh](mailto:info@memorable.sh) and we'll spend a day solving your memory problems with you, plus:\n\n* LIFETIME of Memorable Pro FREE\n* Reach out to us about anything agent memory-related, whether or not you use Memorable :)\n\nand grab a slot: [**cal.com/team/memorable/30**](http://cal.com/team/memorable/30)\n\n[website](https://memorable.sh) | [docs](https://memorable.sh/docs) | [try memorable](https://memorable.sh)\n\nLet's build something Memorable!",
        "url": "https://www.ycombinator.com/launches/U2j-memorable-graph-procedural-memory-for-ai-agents-stop-re-teaching-your-agents",
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        "full_name": "Varun Latthe",
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        "founder_bio": "Co-founder of Acyclic Labs (YC F26).\n\nAt Cambridge, my research focused on building an auto-parallel programming language. I then worked on agent architecture and an Excel engine at Tracelight (https://tracelight.ai).\n\nAt Acyclic, I’m building infrastructure that extracts implicit parallelism from agent workflows, turning them into efficient swarms for lower cost and latency.",
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    "all_locations": "San Francisco, CA, USA",
    "long_description": "we build self-healing browser infrastructure that runs millions of agent operations a day without supervision. \r\n\r\nteams use it for large-scale data collection, agentic workflows, continuous monitoring, and anything else that requires acting on the web at volume.\r\n",
    "one_liner": "Super fast, unblockable browser infra for agents",
    "team_size": 4,
    "industry": "B2B",
    "subindustry": "B2B -> Infrastructure",
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        "body": "Hi everyone, we’re Maria and Ary, founders of [Maritime](https://bookface.ycombinator.com/company/38061) (YC F26).\n\n**TL;DR:** Maritime is a fully managed cloud platform for running millions of AI agents, each on its own isolated, persistent VM.\n\n<https://www.youtube.com/watch?v=TnGXH9H0cTw>\n\n**Why we built Maritime** \n\nWe believe we’re heading toward a world with billions of AI agents. Everyone will have agents working for them, and businesses will run entire teams of AI employees. A new generation of startups will build and sell these agents, giving every customer one of their own.\n\nWe started working on Maritime when OpenClaw was released. Now, with dots, Instinct, Grok Bot, and Muse, that future is taking shape. We saw the infrastructure challenge firsthand while hosting hundreds of agents during our research at MIT. Even at that scale, our time went into getting environments running, maintaining them, and debugging individual agents.\n\nRunning billions of agents will require infrastructure that lets developers give each one a persistent computer and manage entire fleets with ease. We’re building Maritime to make that possible, so any startup can launch an agent product, give every customer their own agent, and scale it to millions in no time.\n\n![uploaded image](/media/?type=post&id=120824&key=user_uploads/3118952/6772cfd5-737c-4278-8a02-ec3793cb070b)\n\n**What Maritime does**\n\nMaritime gives every agent a persistent computer and manages the infrastructure around it:\n\n* **An isolated VM for each agent.** Run code, install software, and use a browser.\n* **Pricing built for scale.** Hosting starts at $1 per agent per month, making it practical to give every customer their own persistent computer and grow your fleet as your product grows.\n* **Persistence between tasks.** Keep files, memory, installed software, and logins so agents can pick up where they left off.\n* **Tools to manage individual agents.** Inspect, debug, and fix each customer’s agent directly.\n* **Different environments for different workflows.** Choose Linux computers, Windows desktops, or virtual Android phones based on the software and apps your agents need.\n\nYou can run your agent code inside a Maritime VM, or keep your agent on your existing backend and use MCP to let it control a Maritime computer.\n\n![uploaded image](/media/?type=post&id=120824&key=user_uploads/3118952/db071ab8-94f8-4245-9de8-f574cc2aba1d)\n\n**Who it’s for**\n\nTeams building and selling AI employees, personal assistants, and other products where every customer has an agent of their own.\n\nWe want founders to focus on building and growing their products, with Maritime managing the infrastructure to run them.\n\n**Our ask**\n\n* **Try Maritime:** Host three agents for free at [maritime.sh](http://maritime.sh). Hosting starts at $1 per agent per month.\n* **Introduce us:** If you know teams building and selling agents, especially those deploying a separate agent or computer for every customer, please comment or connect us.",
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