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    "long_description": "What if you could turn your screen recording into an unattended automation, or on-screen guides for your coworkers? That is exactly what we allow you to do. We take the recording, run a compiler to analyze it, synthesize a program from that analysis, and create an automation or guide within seconds.",
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        "founder_bio": "Co-founder and CEO of Luminist Labs (Acquired by DiscernDx)\nCurrently CEO @ Curebase (S18)",
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        "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"
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    "long_description": "It's a better way to build frontend. It connects your Stories, Tokens, & Figma components. It lets you inspect designs with confidence and speed. And you can use it in Storybook.",
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    "name": "Alpha Vantage",
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    "long_description": "Alpha Vantage provides enterprise-grade financial market data through a set of powerful and developer-friendly data APIs and spreadsheets. Whether you need traditional asset classes (e.g., stocks, ETFs, commodities), economic metrics, foreign exchange rates, cryptocurrencies, fundamental data or even technical indicators, Alpha Vantage is your one-stop shop for global market data delivered through REST APIs.",
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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": "Alpha Vantage began as a passion project started by the founders while they were attending grad school together. The idea was conceived through a shared interest in the stock markets and investing as well as a fundamental desire to make something that the founders themselves would want to use. Truthfully, the founders did not originally build Alpha Vantage with the intention of creating a business out of it, but the exponential user adoption and intensity of usage made the decision to start a business natural and obvious - as well as the best way for us to serve our users (and eventually paying customers)."
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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": "A close friend and classmate in graduate school, Lee Lin, encouraged us to apply. Lee went through YC in S09 (incidentally, the same batch as Stripe!) and supported us throughout our entire YC journey by providing valuable feedback on our application, giving us practical advice on the interview, and even flying into California just to attend our Demo Day! The written application itself was very straightforward. YC makes it extremely easy with their online application. If you are working on a startup or even just an idea, you having absolutely nothing to lose by writing and submitting an application! I will admit that the interview (in-person at YC’s HQ in Mountain View, CA) was quite stressful. The nervous energy that permeated the air of the lobby and lounge area, mere steps away from the rooms in which interviews were taking place, was palpable. Naturally, the interview was the classic 10-min rapid fire format. I recall one of the first questions we were asked was quite pointed: “Many have tried to build what you two are doing and failed. How are you guys different?” Deep breathe. Do not get defensive. Answer the question succinctly and confidently. The 10 min were over before we knew it. It was stressful, but fair. Afterwards, we went to Castro street in downtown Mountain View to have dinner. In the middle of dinner, we received the call that would forever change our lives (“We would love to fund you guys!”). We immediately accepted YC’s offer of funding over the phone. We ended up having about a month from our acceptance into YC until the actual start of the batch. In the interim, there were some housekeeping items to attend to such as sending copies of our startup’s incorporation documents and other legal documents to YC’s Legal team and Finance team for review so we could receive YC’s investment! While still in the Bay Area, shortly after our interview, I remember attending a pre-batch kick-off event where Michael Seibel welcomed us to YC. I recall Michael said (paraphrasing), “So, technically the ‘batch’ has not started yet officially… but ‘YC itself’ has already started. So… I suggest you all get started! Like, right now! You can literally start booking office hours today! Start building and talking to your customers! We’ll see you this summer!”) Before long the summer came and the formal programming of the batch began. Things moved fast and we quickly settled into what would become a familiar rhythm: Write code. Talk to customers. Write more code. Talk to more customers. Write even more code. Talk to even more customers. Rinse and repeat. The weekly small group office hours and dinner events were highlights of each week that served to inspire and invigorate. The first dinner event featured the co-founder of Airbnb as a guest speaker, and subsequent dinner guests included Garry Tan (now president of YC!), Brian Armstrong (Coinbase), and even a special guest appearance from the legends themselves - Paul Graham and Jessica Livingston! Demo Day and fundraising were the culmination of our three months of hard work during the batch. The event would be split over two days and held at the venerable Computer History Museum in Mountain View, CA. Sadly, our batch (S18) would be the last batch to be hosted by the museum for Demo Day (due to the larger sizes of subsequent batches). Much like the lobby and lounge of YC’s HQ on interview day, the nervous energy in the backstage green room of the Computer History Museum auditorium was palpable with people pacing back and forth, meditating, or silently rehearsing their pitch one last time. Before long, it was our turn, and the Batch Director fitted the one of us who would be speaking (Olivier) with a microphone and sent us out on stage. The pitch exceeded our own expectations and was delivered with gravitas, poise, and even a touch of humor. The successful pitch was all thanks to the generous time and support of many partners, most notably our group partners Aaron Harris, Adora Cheung, and Yuri Sagalov, but also other partners including Dalton Caldwell. As soon as we finished our pitch, the “likes” from investors came flooding in through the Demo Day software. Fundraising is usually a tough slog and one has to usually deal with many rejections for every “yes” that they receive. The whole process can take several months to upwards of an entire year. We were very fortunate to be in the top-third of our batch in terms of ease of fundraising, with far more “yes” than “no” responses and we were ultimately able to wrap up our fundraising in less than a week. I will note that success in fundraising at Demo Day is not necessarily predictive of future success as a company - so do not get lulled into a false sense of security just because fundraising comes relatively easier to you at Demo Day. There is a quite a vast distance between the moment that one decides to start a company and the moment that one actually sees it get off the ground. Unfortunately, the reality of startup life is that the journey across this distance (and beyond) can often be emotionally brutal; rife with feelings of intense loneliness which have to be balanced against a massive faith in what you are doing – a faith that is hard to sustain alone. We can truly think of no other partner we would want more than YC in embarking on such a journey. Special thanks to our group partners Aaron Harris, Adora Cheung, and Yuri Sagalov. They did NOT read drafts of this write-up, but we can unequivocally say that we would not have made it this far if it were not for their support and wise counsel."
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        "question": "How have you kept in touch with the YC community and continued to use YC resources & programs since the batch ended?",
        "answer": "After our YC batch formally concluded in Sep 2018, we have kept in touch with the YC community through YC’s internal social network Bookface (think of it as a combination of LinkedIn, Quora, and Wikipedia). > Olivier: “I check Bookface at least once a day and it has been an invaluable source of knowledge, tips & tricks, and sometimes much needed humor/relief from the occasional ennui that comes with the territory of being a founder.”Alumni Demo Days (a dress rehearsal / practice-run of Demo Day in which the audience is made up of YC alumni / affiliates instead of professional investors) are also another excellent way for alumni to remained connected with the YC community. > Olivier: “I have attended every single Alumni Demo Day since becoming an alum. For the W19 and S19 batches, I was fortunate to attend in-person in San Francisco. These were very fun, well organized events and doubled as a kind of ‘YC mini-reunion’ of sorts - I miss them greatly. All subsequent batches (W20, S20, W21, S21, W22, S22) I have attended were done remotely through Zoom. Remote Demo Days have also been well organized (and with great music!), but naturally, they cannot compete with the experience of being in-person!”"
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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": "Alpha Vantage began as a passion project created over winter break while the co-founders were in grad school together. Our first major inflection point came less than two months after Alpha Vantage went live, when a large incumbent/competitor called Yahoo Finance abruptly pulled the plug on their market data API, resulting in a massive influx of self-proclaimed “Yahoo Finance shipwreck survivors” (users who were previously using the Yahoo Finance market data API) coming to Alpha Vantage. Seemingly overnight the number of our active users and the number of API queries processed jumped by over 10x. We have never looked back since."
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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": "High quality financial market information (e.g. stock price data of publicly traded companies) has traditionally been available only to large organizations/institutions and generally inaccessible (or very high friction to access) for individual end users. Companies such as Bloomberg and Thomson Reuters have created multi-billion dollar businesses catering to these large organizations/institutions and a specific type of user: the Wall Street trader. Traders in the modern day; however, are different. Traders today more closely resemble software developers who would prefer to access data via API as opposed to a closed-system such as a Bloomberg terminal (an expensive piece of financial hardware that one would find in virtually every financial institution). We know this for a fact because the exponential adoption and the intensity of usage of our APIs from the earliest days of our company all the way to the present have proven this time and time again."
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        "title": "Mutiny: Your AI agent to take your accounts from cold to closed",
        "tagline": "Your AI agent to take your accounts from cold to closed",
        "body": "**TL;DR:** Mutiny is your AI agent for taking accounts from cold to closed.\n\nRippling, Snowflake, and Uber are among 3,000 companies using Mutiny to automate their day to day and create beautiful customer-facing experiences that win more deals.\n\n<https://youtu.be/hRISyK2SSt4>\n\n## The problem: Generic AI like Claude are not built for GTM\n\nGeneral purpose agents are not optimized for GTM.\n\n* **Require GTM engineers.** You build everything yourself. Takes weeks of setup and GTM engineers to deliver quality.\n* **Inconsistent output.** Output such as assets is only as good as the rep's prompting. Near impossible to generate high quality output consistently.\n* **Designed for solo work.** Chat and projects are optimized for individual work. 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One agent that takes an account from cold to closed.**\n\nMutiny can now:\n\n1. **Access all your data and tools to automate workflows.** Mutiny connects to your email, calendar, CRM, call recordings, Slack, and 50+ other tools to extract context it needs and then do the work on your behalf.\n2. **Create buying experiences and track engagement.** Mutiny stores your brand and marketing content, builds assets for every deal, and shows you exactly how buyers engage.\n3. **Works out of the box for any rep, not just the AI experts.** We pre-built the highest-leverage skills and routines for reps and marketers, so everyone gets expert output on day one.\n\n![uploaded image](/media/?type=post&id=103964&key=user_uploads/220306/be9b21e6-7623-4201-9800-49e87ed1fe28)\n\nIn practice, this means Mutiny helps you:\n\n* Create deal rooms and update CRM records after every prospect call\n* Draft meeting prep and personalized decks ready for tomorrow's call\n* Map buying committees, with a personalized market report for each persona in their inbox\n* Surface top ABM accounts, with personalized sites and sequences ready to send\n\nOne person with Mutiny can now run the motion that used to take a full team.\n\n## The best GTM teams already run on it\n\nRippling, Snowflake, Uber, and GitLab are among the 3,000 companies using Mutiny to win deals.\n\n> We've always invested heavily in personalized content for our enterprise accounts, but we can't do that for every deal. Mutiny lets our commercial reps create that same caliber of content on their own. Our sales team was genuinely shocked at the quality. Turning call transcripts into something slick and deal-ready is a huge unlock for our reps.”\n>\n> \\- Hillary Carpio, VP of Marketing, Snowflake\n\n> It's been game-changing to give our sellers Mutiny's design capabilities. Right off the bat, it's reducing dependency on marketing and expediting time to publish significantly.”\n>\n> \\- Gabriel Ginorio, Senior Growth Manager, Rippling\n\n> I was blown away by the new Mutiny agent. I can create personalized content for my deals in minutes without waiting on anyone. It's a game changer for sellers.”\n>\n> \\- Celeste Cote, Account Executive, Vanta\n\n## Differentiation: Ship quality work on day one, with fewer tools\n\n1. **Great at the work customers see.** Most tools only shuffle data behind the scenes. Mutiny creates the assets that move the deal, and automates the work around them.\n2. **Pre-loaded with GTM playbooks.** Get to quality output really quickly without being a GTM or AI expert.\n3. **Full visibility.** Most of the buying process happens behind closed doors. Mutiny shows who opened your assets and what they read, so you can sell even when you're not in the room and engage new stakeholders with context.\n4. **Run by anyone on the team.** AEs, BDRs, marketers, and CSMs use Mutiny in self-serve mode, not through a marketing or design queue. That changes the volume economics of personalization.\n5. **One agent, not a stack.** Mutiny consolidates ABM, sales enablement, and AI sales agents into a single agent built to help reps and the marketers who support them.\n\n## Get started today for free\n\nSign up for free at [MutinyHQ.com](http://MutinyHQ.com). Plans are Free, Business, and Enterprise.\n\nComment \"Mutiny\" on our [X](https://x.com/jalehr/status/2070160245863899264?s=20) or [LinkedIn](https://www.linkedin.com/posts/jalehr_our-launch-today-blows-up-the-entire-gtm-ugcPost-7475925252183703552-rWe_/) post and we'll triple your credits for free.\n\nEmail us: [jaleh@mutinyhq.com](mailto:jaleh@mutinyhq.com), [nikhil@mutinyhq.com](mailto:nikhil@mutinyhq.com)\n\nIf you know AEs, sales leaders, or marketing leaders at fast-moving B2B companies, we'd love an intro.\n\n![uploaded image](/media/?type=post&id=103964&key=user_uploads/220306/8f22d528-bb55-4c6a-b4e1-001c780bc3b3)\n\n",
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        "created_at": "2026-06-25T16:18:20.467Z"
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        "title": "Mutiny: Your AI Agent for Creating Anything Customer-Facing (used by Uber & Rippling)",
        "tagline": "An agent purpose-built for sales and marketing to create beautiful, on-brand assets in minutes, and track engagement. ",
        "body": "**TL;DR:** Mutiny is the first AI agent for sales and marketing to create polished, on-brand assets that close deals.\n\n**Figma, Rippling and Uber** already use our agent to create ABM campaigns and move deals forward with tailored business cases, deal rooms, case studies and more.\n\n**Comment “Mutiny” on our [LinkedIn](https://www.linkedin.com/posts/jalehr_we-raised-72m-from-sequoiayc-and-hit-8-activity-7447310976070283264-1NRa?utm_source=share&utm_medium=member_desktop&rcm=ACoAACKHswAB8YARQchGlnITcnK7Y_raTYmZGAE) or [X post](https://x.com/jalehr/status/2041546131709292586?s=20) & we’ll triple your AI credits for free.**\n\nLaunch video: <https://youtu.be/_OfqWVrQj3s>\n\n**The Problem: Dependencies are the #1 killer of revenue.**\n\nA decade of leading GTM teams taught me that dependencies are the biggest blocker to growth. Too many great ideas die waiting for resources: design, content, ops. We started Mutiny to let sellers and marketers move at the speed of their ideas, building experiences that wow buyers and close deals.\n\nMutiny 1.0 started as a big success: $72M from tier 1 investors like Sequoia/YC, 8-figures in ARR, and beloved by the best GTM teams in the world. But it was built on a SaaS foundation, which puts the execution onus on humans. No matter how much we injected AI, we couldn’t unlock the real power of automation.\n\nSo we killed the SaaS product and rebuilt the whole thing from scratch, this time agent-first and with even bigger ambitions.\n\n**The solution: Meet the new Mutiny.**\n\nMutiny is the first AI agent that gives GTM teams full autonomy to create whatever they need to hit their goals, on brand and personalized.\n\nYou can overcome every challenge when prospecting and closing deals:\n\n* A tailored prospecting page for a senior exec at your target account.\n* A polished business case that sells for you when you're not in the room.\n* A curated customer story that creates urgency.\n* A competitor comparison that takes your rival off the table.\n* A deal room your champion is proud to share, .\n\nOne person with Mutiny can do more in an afternoon than a team in a month.\n\n**The best GTM teams love the new Mutiny already.**\n\n30,000+ assets published. Users from Rippling, Snowflake & startups say:\n\n* Speed: 4.5X faster to create assets\n* Quality: 100% said it meets or exceeds their design bar\n* Impact: 4 out of 5 reps are more likely to hit quota with Mutiny\n* Edge: 89% say it gives them an edge in competitive deals\n\n**Differentiation: Quality is what sets Mutiny apart. It’s 100% on brand.**\n\n1. **On-brand, every time.** Mutiny extracts your colors, fonts and visual style from your website to set the guardrails, so every asset looks and sounds like you.\n2. **Personalized, automatically.** Mutiny researches your prospect and pulls in your CRM data and call transcripts, so every asset is instantly tailored to the account. Their challenges, relevant metrics, case studies, industry messaging and logo are all personalized.\n3. **Full visibility.** Most of the buying process happens behind closed doors. Your champion is forwarding decks & pitching you in meetings. Mutiny shows who opened your assets & what they read, so you can sell even when you're not in the room & engage new stakeholders w/ context.\n4. **Proven GTM best practices.** Our agent has analyzed top-performing GTM assets and frameworks and built templates for every deal stage. The agent applies your brand and context so you get a polished asset in minutes.\n\n**Sign up for free:** [MutinyHQ.com](http://MutinyHQ.com)\n\n**Comment “Mutiny” on our X or LinkedIn post & we’ll triple your credits for free.**\n\nEmail us: [jaleh@mutinyhq.com](mailto:jaleh@mutinyhq.com), [nikhil@mutinyhq.com](mailto:nikhil@mutinyhq.com)\n\nIf you know AEs, sales leaders, or marketing leaders at fast-moving B2B companies, we'd love an intro.",
        "url": "https://www.ycombinator.com/launches/PtJ-mutiny-your-ai-agent-for-creating-anything-customer-facing-used-by-uber-rippling",
        "votes": 18,
        "created_at": "2026-04-15T15:33:43.131Z"
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    "name": "Synkrino Biotherapeutics",
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    "all_locations": "San Francisco, CA, USA; Oakland, CA, USA",
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    "one_liner": "Synkrino's AI, SATIS is a powerful, unbiased discovery system for…",
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    "industry": "Healthcare",
    "subindustry": "Healthcare -> Drug Discovery and Delivery",
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    "status": "Active",
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    "team_size": 4,
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        "full_name": "Ernesto Lujan",
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    "news": [
      {
        "title": "Hope for your hairline may be coming as biotech startups try to reverse balding | Fortune",
        "url": "https://fortune.com/2022/01/18/hope-for-your-hairline-may-be-coming-as-biotech-startups-try-to-reverse-balding/",
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    "id": 2017,
    "name": "Leena AI",
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