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(i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "I started my entrepreneurial journey early, building my first company in Romania at 21. Over the next decade I launched multiple products and learned how hard it is for engineering teams to explain the value they create. The idea for Waydev came from firsthand frustration while trying to understand developer output beyond gut feelings and subjective dashboards. There was no clear way to measure engineering performance using system data, so I decided to build it myself. " }, { "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 applied to YC because I believed Waydev could become a category defining product and I wanted to sharpen our execution with the best early stage accelerator in the world. It wasn’t an easy path. I applied 13 times before being accepted, and each rejection forced me to refine the idea, tighten the pitch, and improve the product. When we finally joined YC, the experience was transformative. The batch pushed us to move faster, simplify our messaging, and talk to customers every day. Demo Day helped us raise capital and accelerate our trajectory. More importantly, YC gave us a mindset of relentless iteration that still defines how we operate." }, { "question": "How have you kept in touch with the YC community and continued to use YC resources & programs since the batch ended?", "answer": "YC has remained a core part of our company. I’m active in Bookface, talking to other founders about growth, engineering metrics, and the future of AI driven development. Many of our customers and partners come through YC referrals. We share learnings with other B2B and infra founders, and we rely on YC’s internal forums, templates, and tools for fundraising, hiring, and pricing decisions. YC helped us get started, but the community is what continues to compound our progress." }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "Waydev started as a simple tool to analyze Git activity. The product evolved quickly as customers pushed us toward deeper insights and enterprise scale. Key moments included securing our first Fortune 500 customers, launching integrations with GitHub, GitLab, Bitbucket, and Azure DevOps, and building out enterprise features like SSO, RBAC, and advanced reporting.\r\n\r\nA major inflection point came when we recognized that engineering intelligence needed an AI native interface. This led to the launch of Waydev AI, our conversational layer that lets leaders ask questions and get insights instantly without dashboards. Today, we support both large enterprises and fast growing teams, and we continue to integrate AI deeply into how engineering work is analyzed and measured." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Engineering is the most expensive function in modern companies, but historically it has been the hardest to measure. Leaders rely on anecdotal feedback, siloed dashboards, or lagging indicators that fail to capture the full picture of productivity, quality, and delivery. This lack of visibility leads to poor decisions, misalignment, reactive planning, and inefficient budgets.\r\n\r\nWe solve this by giving teams a unified intelligence layer that turns system data into clear, actionable insights. The problem is huge because every company depends on software, and engineering performance directly affects speed, cost, and competitiveness. I decided to work on this because I experienced the pain myself as a founder and saw thousands of teams navigating the same challenge." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "Our long term vision is to build the intelligence engine behind every engineering organization. If we succeed, companies will understand their engineering health as clearly as they understand revenue or churn. Leaders will make better decisions, teams will work in a more predictable and balanced way, and engineering will become a transparent, measurable, and optimized function. In the future we imagine, every company will be able to ask one question, “What is happening in engineering and what should we do next?” and Waydev will deliver the answer instantly." } ], "url": "https://www.ycombinator.com/companies/waydev", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2021/waydev.json", "year_founded": 2017, "city": null, "country": "RO", "linkedin_url": "https://www.linkedin.com/company/waydev", "x_url": "https://twitter.com/waydevco", "fb_url": "https://www.facebook.com/waydevco", "cb_url": "https://www.crunchbase.com/organization/waydev", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/90694f4bdb7eb8f17b02f85faee3e3455edb365b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/6778d7987675acff0a6a787b50a403697fbc9895.png" ], "primary_partner": null, "founders": [ { "user_id": 22643, "full_name": "Alex Circei", "title": "Founder", "founder_bio": "I'm Alex Circei, co-founder and CEO of Waydev (YC W21). I started my first company in Romania at 21 and have spent 18+ years building startups. In 2017 I launched Waydev, defining the Software Engineering Intelligence category — bootstrapped to profitability, recognized by Gartner and G2, and trusted by Fortune 500 teams at American Express, Caterpillar, Dropbox, and PwC.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexcircei", "x_url": "https://twitter.com/AlexCircei", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4695778456f82cf23a8abf3d16502c8ba7eae530.jpg" } ], "jobs": [ { "id": 43959, "title": "Head of Sales", "url": "https://www.ycombinator.com/companies/waydev/jobs/BFA4rwl-head-of-sales", "location": "Remote (RO; Austin, TX, US; San Francisco, CA, US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$50K - $140K", "equity_range": "0.10% - 0.20%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 85262, "title": "Head of Growth", "url": "https://www.ycombinator.com/companies/waydev/jobs/ecnmEt7-head-of-growth", "location": "Menlo Park, CA, US / Remote (US)", "type": "Contract", "role": "Marketing", "role_type": null, "salary_range": "$50 - $250 / hourly", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Data Analytics" ] } ], "news": [ { "title": "The question AI providers hope VPs of Engineering never ask", "url": "https://thenextweb.com/news/the-question-ai-providers-hope-vps-of-engineering-never-ask", "date": "Apr 20, 2026" }, { "title": "'Tokenmaxxing' is making developers less productive than they think | TechCrunch", "url": "https://techcrunch.com/2026/04/17/tokenmaxxing-is-making-developers-less-productive-than-they-think/", "date": "Apr 17, 2026" }, { "title": "Waydev Adds Ability to Track How Much AI Code Winds Up in Production - DevOps.com", "url": "https://devops.com/waydev-adds-ability-to-track-how-much-ai-code-winds-up-in-production/", "date": "Apr 16, 2026" }, { "title": "How to Measure AI ROI on Your Engineering Team", "url": "https://waydev.co/how-to-measure-ai-roi-on-your-engineering-team/", "date": "Mar 17, 2026" }, { "title": "How to Measure Engineering Productivity When AI Writes Code", "url": "https://waydev.co/when-ai-writes-the-code-what-do-we-measure/", "date": "Feb 17, 2026" } ], "launches": [ { "id": 94663, "title": "Waydev AI: The ChatGPT for Engineering Intelligence", "tagline": "The first AI-native conversational platform that helps engineering leaders measure performance, track AI adoption, and understand how AI impacts delivery.", "body": "Hey, we’re Waydev, and we’re launching Waydev AI on YC and [ProductHunt.](https://www.producthunt.com/products/waydev?launch=waydev-ai)\n\n![uploaded image](/media/?type=post&id=94663&key=user_uploads/22643/9cf132dd-67ec-4ece-bed1-d5c53f6c5981)\n\n### https://youtu.be/0Pz5mRiLpzE?si=rIQV4WzzaGLLB31M\n\n### Problem\n\nEngineering leaders struggle to understand performance, delivery, and the real impact of AI across their teams. Traditional engineering intelligence tools require endless dashboard setup, data interpretation, and manual correlation between GitHub, Jira, and other sources.\n\nThese legacy platforms were never built for the AI era - making it hard to measure AI adoption, productivity, and team impact in a way that feels natural or conversational. Even newer tools are bolting on “AI features” rather than rethinking the experience from the ground up.\n\n### Solution\n\nWe built **Waydev AI**, the first **AI-native conversational platform for Engineering Intelligence**, designed so leaders can stop analyzing dashboards and start asking questions.\n\nWaydev AI transforms how engineering teams understand performance and delivery. Just connect your tools - GitHub, Jira, Cursor, and more—and start asking in plain English:\n\n* “What’s our AI adoption rate this quarter?”\n* “Which teams improved their cycle time the most?”\n* “How has AI impacted our delivery and bug rates?”\n\n#### Key Features\n\n💬 **Conversational Interface:** Chat directly with your data instead of digging through dashboards.\\\n🤖 **AI-Native Insights:** Instantly generate reports, visual charts, and performance breakdowns from natural-language questions.\\\n🔌 **Connect Your Stack:** Integrate GitHub, Jira, Cursor, and more in seconds.\\\n📈 **Dynamic Reports:** Save and share real-time insights that update automatically as your teams ship.\\\n🔍 **AI Adoption Metrics:** Track how generative AI tools like Copilot or Cursor are influencing developer productivity.\n\n### Our Asks\n\n🎯 Try **Waydev AI** today! Sign up at [waydev.co/ai](https://waydev.co/ai) and start asking your data.\\\n🚀 Support our [Product Hunt launch on October 15, 2025](https://www.producthunt.com/products/waydev?launch=waydev-ai)\\\n💼 We’re hiring engineers and AI researchers passionate about transforming the future of Software Engineering Intelligence.", "url": "https://www.ycombinator.com/launches/Ocp-waydev-ai-the-chatgpt-for-engineering-intelligence", "votes": 28, "created_at": "2025-10-15T07:14:08.221Z" } ] }, { "id": 23781, "name": "Openlayer", "slug": "openlayer", "former_names": [ "Unbox", "OpenLayer" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/35b3b987d05aeeeea2e5ed41eb9bc474ea3d6529.png", "website": "https://openlayer.com", "all_locations": "San Francisco, CA, USA", "long_description": "Openlayer is the governance platform for AI applications.", "one_liner": "The fastest way to ship airtight AI", "team_size": 24, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1625008531, "tags": [ "AIOps", "Developer Tools", "Generative AI", "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/openlayer", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/openlayer.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/74372366/", "x_url": "https://twitter.com/openlayerco", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/unbox-3db0", "github_url": "https://github.com/openlayer-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/35b3b987d05aeeeea2e5ed41eb9bc474ea3d6529.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 591901, "full_name": "Gabriel Bayomi Tinoco Kalejaiye", "title": "Founder", "founder_bio": "I’m a Brazilian engineer and the co-founder and CEO of Openlayer. We help enterprises test, monitor, and govern AI systems, from traditional machine learning to LLMs and agents, with controls that span development and production.\n\nBefore Openlayer, I was a Machine Learning Engineer at Apple, where I worked on Siri, combining multimodal signals to improve natural language understanding. I earned my master’s in Computer Science at Carnegie Mellon, where I worked on Amazon’s Alexa Prize project.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gbayomi/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a10fd2b5d193710537d2d513e6b743ea7dbfdcd7.jpg" }, { "user_id": 236551, "full_name": "Vikas Nair", "title": "Founder", "founder_bio": "Co-founder of Openlayer (S21). Previously, built future AI/ML-powered experiences at Apple.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vikasnair/", "x_url": "https://twitter.com/vikaaaaaaaaas", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f91b4062369f26070e4442d37b4d9ae3c3aee10f.jpg" }, { "user_id": 695361, "full_name": "Rishab Ramanathan", "title": "Founder", "founder_bio": "Co-Founder of Openlayer", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rishramanathan", "x_url": "https://twitter.com/rishramanathan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f74461698a2dbd3656bbc181b2247d678e70c186.jpg" } ], "jobs": [], "news": [ { "title": "We’ve Raised Our Series A to Build the AI Reliability Platform", "url": "https://www.openlayer.com/blog/post/series-a", "date": "May 14, 2025" } ], "launches": [ { "id": 80433, "title": "Openlayer - A Vercel-like workflow for AI evals that actually makes sense", "tagline": "Test, monitor, and optimize your AI with a single setup", "body": "**TL;DR** Openlayer provides a simpler workflow for AI evals and testing – connect your GitHub, define must-pass tests, and every commit triggers these tests automatically. Tests will also run on top of your live data and send alerts if they start failing. For AI developers, this is the ideal way to run continuous evaluations without much effort. Get started [here.](https://docs.openlayer.com/get-started)\n\n—\n\nHey guys! We’re Gabe, Rishab, and Vikas, the founders of Openlayer. We met at Apple, where we noticed that every AI/ML team inevitably ran into the same problem: their models would not perform as accurately as they thought they would in the real world. We left to solve this problem by building a best-in-class process for evaluating and improving models.\n\n![uploaded image](/media/?type=post&id=80433&key=user_uploads/236551/54d20552-4ea4-4ab2-8337-92046d1c89e5)\n\n🤯  **The problem** Most of us get how crucial AI evals are now. The thing is, almost all the eval platforms we’ve seen are clunky – there’s too much manual setup and adaptation needed, which breaks developers’ workflows.\n\n✅  **Solution** We’re releasing a radically simpler workflow.\n\nAll you need to do is connect your GitHub repo to Openlayer and define must-pass tests for your AI system. Once integrated, every commit triggers these tests automatically on Openlayer, ensuring continuous evaluation without extra effort.\n\nWe offer 100+ tests (and are always adding more), including custom tests. We’re language-agnostic, and you can customize the workflow using our CLI and REST API. We also offer template repositories around common use cases to get you started quickly.\n\nYou can leverage the same setup to monitor your live AI systems after you deploy them. It’s just a matter of setting some variables, and your Openlayer tests will run on top of your live data and send alerts if they start failing.\n\n![uploaded image](/media/?type=post&id=80433&key=user_uploads/236551/c4a17145-aa0e-455e-a04d-f0484803de6f)\n\n🙏  **Asks** We love feedback! [**Sign up for free,**](https://app.openlayer.com/) and let us know your thoughts! [Book a call](https://cal.com/vikasnair/30min?date=2024-04-16&month=2024-04&layout=mobile) with us for a demo, personalized onboarding, to give feedback, or just chat. Feel free to reach out to [founders@openlayer.com](mailto:founders@openlayer.com) for anything else.", "url": "https://www.ycombinator.com/launches/KvJ-openlayer-a-vercel-like-workflow-for-ai-evals-that-actually-makes-sense", "votes": 16, "created_at": "2024-04-25T20:13:41.755Z" } ] }, { "id": 23978, "name": "Rootly", "slug": "rootly", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/458bec513b681478458c39da4795bf6502d09edb.png", "website": "https://rootly.com", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Rootly is a modern on-call and incident management platform used by top YC companies like Dropbox, Lattice, Webflow, Faire, but also Figma, LinkedIn, NVIDIA and 100s more.", "one_liner": "AI-native on-call and incident response carefully crafted to help…", "team_size": 75, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1625761934, "tags": [ "AIOps", "Developer Tools", "SaaS", "B2B", "Security" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rootly", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/rootly.json", "year_founded": 2020, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/rootlyHQ", "x_url": "https://twitter.com/rootlyHQ", "fb_url": "https://www.facebook.com/rootlyhq", "cb_url": "https://www.crunchbase.com/organization/rootly", "github_url": "https://github.com/rootlyhq", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/458bec513b681478458c39da4795bf6502d09edb.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": 253182, "full_name": "Quentin Rousseau", "title": "Founder and CTO (YC S21)", "founder_bio": "- Early Site Reliability Engineer (SRE) at Instacart and built resilient systems now handling millions of orders.\n- 2x Co-Founder at Rootly (YC S21) and PopSQL (YC S19).\n- Originally from France, loves rock climbing, and unsurprisingly great wine! ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/quentinrousseau/", "x_url": "https://twitter.com/quentinrousseau", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/536c75bad5843062fa9c976c9275d2c92bb929bf.jpg" }, { "user_id": 642689, "full_name": "JJ Tang", "title": "Founder", "founder_bio": "Building Rootly - a modern on-call and incident management platform used by top YC companies like Dropbox, Lattice, Webflow, Faire, but also Figma, LinkedIn, and 100s more.\n\nOther than making Rootly my personality, I love cycling and my adorable dog, Nova.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jjrichardtang/", "x_url": "https://twitter.com/jjrichardtang", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eea6e0e2123efec212779e52790c7ffccb20491a.jpg" } ], "jobs": [], "news": [ { "title": "Incident response management platform Rootly secures $12M | TechCrunch", "url": "https://techcrunch.com/2023/08/10/incident-response-management-platform-rootly-secures-12m/", "date": "Aug 10, 2023" }, { "title": "Rootly nabs $3.2M seed to build SRE incident management solution inside Slack – TechCrunch", "url": "https://techcrunch.com/2021/07/08/rootly-nabs-3-2m-seed-to-build-sre-incident-management-solution-inside-slack/", "date": "Jul 08, 2021" } ], "launches": [ { "id": 66512, "title": "🚒 Rootly - AI SRE, On-Call, Status Pages", "tagline": "Top YC companies like DoorDash, Webflow, Dropbox, Replit, and 100s more all trust Rootly.", "body": "## **TL;DR**\n\nRootly is the AI-native on-call and incident management platform that helps teams detect, manage, learn from, and resolve incidents faster—without leaving Slack or MS Teams. It brings Incident Response, On-Call, AI SRE agents, Status Pages, and Retrospectives into one purpose-built, end-to-end platform.\n\n![uploaded image](/media/?type=post&id=66512&key=user_uploads/642689/d1b534a4-b1f0-444d-b767-aab1d221e1cb)\n\n## **Problem**\n\nWe met at Instacart 🥕—where Quentin was the first SRE and JJ was leading product management efforts. As Instacart grew from a $1B to a $40B company, everything had to scale with it: infrastructure, teams, and the processes that keep the business running when things go wrong. With millions of orders flowing through the platform, incidents weren’t rare edge cases—they were an inevitable part of operating at that level. Checkout issues, platform outages, weird cascading failures at the worst possible times… we saw it all.\n\nWhat surprised us wasn’t that incidents happened. It was how quickly the _process_ broke down under pressure. Our manual way of responding—jumping between Slack, PagerDuty, Datadog, and a dozen dashboards—just wasn’t enough. Every incident started with the same scramble: spinning up channels, pulling people in, figuring out who’s running point, trying to keep comms coherent, and stitching together context from different tools and threads. It worked when the team was small and the system was simpler. At scale, it turned chaotic and inconsistent.\n\nAnd the hardest part wasn’t even the debugging. It was the inconsistency. You’d rely on runbook checklists and tribal knowledge, hoping someone remembered what to do next—who to page, which workflow to follow, how to communicate internally and externally, what steps to capture for the retrospective. In the moment, that’s a lot to ask. Afterward, it meant retros were slow (or skipped), action items drifted, and the same classes of incidents kept coming back because learning didn’t keep pace with growth.\n\n## **Solution**\n\nRootly gives you an end-to-end incident lifecycle—from first page to resolution to post-incident learning—built where teams actually operate (Slack & MS Teams), and it’s all powered by AI.\n\n\n\n**Incident Response**\n\nRootly helps you declare incidents, spin up the right Slack channels and roles, coordinate responders, track tasks, and keep communication structured—while automating the operational “busywork” that normally slows teams down.\n\n**On-Call**\n\nRootly On-Call is built to notify the right person the first time, with scheduling and escalation that scales from a small team to a global org. It’s designed to be reliable and easy to use, with a beautiful mobile experience that makes getting paged somewhat enjoyable.\n\n**AI SRE Agents**\n\nRootly’s AI SRE agents help teams move faster during incidents by connecting the dots across signals like recent code changes, telemetry, and past incidents. Instead of starting from scratch every time, responders can quickly narrow in on probable causes, understand what changed, and get suggestions for fixes and next steps—along with the reasoning behind them.\n\n**Status Pages**\n\nRootly makes it easy to keep customers and stakeholders updated with public and private status pages, so incident comms don’t become another manual process during a high-stress event.\n\n**Retrospectives**\n\nRootly helps you turn incidents into learning. Create collaborative retros that teams can edit together, draft timelines and summaries faster, and track action items so the work actually gets done—not just written down.\n\n**AI everywhere responders actually feel it**\n\nDuring incidents, the biggest tax is cognitive load: listening, typing, summarizing, updating, and repeating yourself. Rootly uses AI to take that off the responder:\n\n* **AI Meeting Bot scribing:** Rootly can join your incident bridge, transcribe in real time, and capture key context so nothing gets lost.\n* **Instant catch-up:** Tag **@Rootly** in the incident channel to get a private, personalized summary of what’s happened so far, what’s been tried, and what’s next.\n* **Draft updates that match the audience:** Generate incident updates tailored to the recipient—customer-friendly language for external comms, crisp executive summaries for leadership, or detailed handoffs for engineering—so you’re not rewriting the same update five different ways.\n* **AI-generated retros:** Auto-draft retrospectives with timelines and context, then collaboratively refine and track action items to completion.\n* **Automated RCA:** Alerts, code changes, past incidents all analyzed at the speed of AI to determine probable root cause in minutes.\n\n**A full platform, or just what you need**\n\nUse Rootly as a complete incident management platform—or adopt products individually based on where you are today (Incident Response, On-Call, AI SRE, Status Pages, Retrospectives).\n\n## **Offer & Ask**\n\n* If you want to see how Rootly modernizes incident management end-to-end, book a demo and we’ll tailor it to your current stack and workflows.\n* If you’re a team with **<25 employees**, we also offer **Pay What You Can** so you can get world-class incident management without the “enterprise price tag.”", "url": "https://www.ycombinator.com/launches/HIm-rootly-ai-sre-on-call-status-pages", "votes": 86, "created_at": "2022-10-05T05:45:37.027Z" } ] }, { "id": 24476, "name": "Zensors", "slug": "zensors-inc", "former_names": [ "Zensors Inc" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/01f2ed9f6b2573e18e28f42a8c260ee15a0a777a.png", "website": "https://www.zensors.com", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "AI to understand and automates the physical world", "team_size": 12, "industry": "B2B", "subindustry": "B2B", "launched_at": 1630100572, "tags": [ "AIOps", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B" ], "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/zensors-inc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/zensors-inc.json", "year_founded": 2019, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/zensors", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/01f2ed9f6b2573e18e28f42a8c260ee15a0a777a.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 133993, "full_name": "Anuraag Jain", "title": "Founder", "founder_bio": "Add info here", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jainanuraag/", "x_url": "https://twitter.com/anuraagj", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7c58356a6152d82687c60331e2fee3b0aaa0366b.jpg" } ], "jobs": [ { "id": 46963, "title": "Product Marketing Lead - Spatial AI", "url": "https://www.ycombinator.com/companies/zensors-inc/jobs/9WxsbSF-product-marketing-lead-spatial-ai", "location": "San Francisco, CA, US / Remote (Pittsburgh, PA, US)", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$140K - $200K", "equity_range": "0.09% - 0.50%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 54313, "title": "Account Executive, GTM Strategy", "url": "https://www.ycombinator.com/companies/zensors-inc/jobs/6DpxFVM-account-executive-gtm-strategy", "location": "San Francisco, CA / Pittsburgh, PA / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 87435, "title": "AI/ML Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/zensors-inc/jobs/O6towJf-ai-ml-infrastructure-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $240K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 95716, "title": "AI Solutions Specalist", "url": "https://www.ycombinator.com/companies/zensors-inc/jobs/AEeKUE6-ai-solutions-specalist", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Product", "role_type": null, "salary_range": "$100K - $160K", "equity_range": "", "experience": "1+ years", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [] }, { "id": 25311, "name": "Lumina", "slug": "lumina", "former_names": [ "Lumina", "TazkerAI / Lumina" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/79b74bf1a8009a0f4c3b0fd036ff57515ff6760b.png", "website": "https://www.lumibot.com", "all_locations": "Singapore, Singapore; Remote", "long_description": "🚀 Lumina💧 (YC W22) is a fast growing job platform in Southeast Asia with 1.5 million workers, helping 30k companies recruit and optimize at scale.\r\n\r\n🤖 We are now expanding globally with Lumibot, a bespoke AI+human-powered automation service. We help companies automate mundane, repetitive processes with AI and human-in-the-loop catering for various use cases in data ops and complex workflows. This allows your business to focus on your core and grow faster, better and cheaper.\r\n\r\n👌Our AI-powered automation start with USD 13/hour, supplemented with human-in-the-loop at USD 2-6/hour. Our services are managed based on SLAs by dedicated service managers under a prepaid credit system without arduous contracts for optimum flexibility. No minimum commitment with fully-refundable deposit from as low as USD 500^.\r\n\r\n🛠️ We currently support the following use cases:\r\n1) operations support\r\n2) revenue operations support\r\n3) web and social media management\r\n4) email management\r\n5) content moderation\r\n6) AI training and verification\r\n7) AI text humanization\r\n8) custom process automation\r\n... with more to come ...\r\n\r\n👥️️ Lumibot (by Lumina) is backed by Y-Combinator, Monk's Hill Ventures, Alpha JWC, Goodwater Capital, SWC Global and January Capital.\r\n\r\nMore information about us:\r\n* 💬 WA: https://wa.me/message/SEMGBRM22MDYK1\r\n* 📅 CAL: https://usemotion.com/meet/aswin-andrison/global\r\n* 🌐 WEB: https://www.lumibot.com\r\n\r\n^ Reach out to us on special deals ONLY for YC companies! ", "one_liner": "AI+human powered automation on job-community platform with 1.5M…", "team_size": 12, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1643648054, "tags": [ "AIOps", "Marketplace", "SaaS", "Community", "Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Operations" ], "regions": [ "Singapore", "Southeast Asia", "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/lumina", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/lumina.json", "year_founded": 2022, "city": "Singapore", "country": "SG", "linkedin_url": "", "x_url": "", "fb_url": "https://www.facebook.com/lumina.mba/", "cb_url": "https://www.crunchbase.com/organization/lumina-6a60", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/79b74bf1a8009a0f4c3b0fd036ff57515ff6760b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 150953, "full_name": "Aswin Andrison", "title": "Founder", "founder_bio": "Aswin co-founded TazkerAI on the back of Lumina (YC W22), an autonomous AI+human-powered workflow automation for the physical industry. Prior to that, he co-founded STOQO, a pioneer fresh-produce supplier for 40,000 restaurants with 1,500-strong workforce and USD 13M raised from top investors (Accel, Monk's Hill and AlphaJWC, etc). Before startups, Aswin consulted at McKinsey, IBM and Honeywell. He graduated computer science / chemical eng. from UNSW and MBA from London Business School and MIT.", "is_active": true, "linkedin_url": "http://linkedin.com/in/aandrison", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0ce9fdf159a142e46711946b2bb326804278cfe8.jpg" } ], "jobs": [], "news": [ { "title": "Lumina nets funding from YC, Alpha JWC", "url": "https://www.techinasia.com/lumina-funding-yc-alpha-jwc-ventures", "date": "Jan 24, 2022" } ], "launches": [] }, { "id": 25561, "name": "LanceDB", "slug": "lancedb", "former_names": [ "ETO", "Eto" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b3b44ab11047bbfddb4bfe44c9d2d3c87027258.png", "website": "https://lancedb.com", "all_locations": "San Francisco, CA, USA", "long_description": "LanceDB is a new open-source vector database that can support low-latency billion-scale vector search on a single node. Built around a new columnar data format, LanceDB makes it incredibly easy to build applications for generative AI, recsys, search engines, content moderation, and more.", "one_liner": "Open-source, serverless vectordb for production-scale generative AI", "team_size": 35, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1648263864, "tags": [ "AIOps", "Machine Learning", "Open Source", "Data Engineering" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "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/lancedb", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/lancedb.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/lancedb/", "x_url": "https://twitter.com/lancedb", "fb_url": null, "cb_url": null, "github_url": "github.com/lancedb", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7b3b44ab11047bbfddb4bfe44c9d2d3c87027258.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": 858308, "full_name": "Chang She", "title": "Founder", "founder_bio": "My passion is building tools to make teams more productive working with data. I was VP of Eng at TubiTV where I designed the ML stack and the experiment platform, in addition to growing the team by more than 3x. Previously I was CTO/co-founder of DataPad and the second major contributor to the pandas library. In a former life, I was a financial quant with stints at AQR and Barclays.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/changshe", "x_url": "https://twitter.com/changhiskhan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7fac6d13a6e767ce791580c1200cbdaaa236d900.jpg" } ], "jobs": [], "news": [ { "title": "LanceDB Raises $30M Series A to Build the Multimodal Lakehouse - LanceDB Blog", "url": "https://lancedb.com/blog/series-a-funding/", "date": "Jun 24, 2025" }, { "title": "New Funding and A New Foundation for Multimodal AI Data", "url": "https://blog.lancedb.com/new-funding-and-a-new-foundation-for-multimodal-ai-data/", "date": "May 15, 2024" } ], "launches": [ { "id": 71382, "title": "LanceDB 🍢 - serverless, low-latency vector database for AI applications", "tagline": "Meet LanceDB, the easy-to-use and developer-friendly alternative to Pinecone", "body": "🙋 Are you building a generative AI app using an LLM or API? Are you working on a new recsys, a modern search engine, or a new analytical tool for unstructured data? Good news: you no longer have to struggle with Pinecone’s high cost, over the top complexity, or data privacy concerns.\n\n🚀 [LanceDB](https://lancedb.com/) is a free and open-source vector database that you can run locally or on your own server. It’s lightning fast and is easy to embed into your backend server. Check out our [github repo](https://github.com/lancedb/lancedb) or pip install lancedb to get started.\n\nFor a quick demo, you can take a look at our sample [youtube transcript search app](https://github.com/lancedb/lancedb/blob/main/notebooks/youtube_transcript_search.ipynb). We’ll be adding a lot more soon. For more details on the API, take a look at [LanceDB docs](https://lancedb.github.io/lancedb/).\n\n![](https://lancedb.com/images/eto-splash-arrow.png)\n\n### **❓ Why we built LanceDB**\n\nAs we spoke to builders of ML/AI applications, a common refrain from users was struggling to get services like Pinecone even running. After a while, we realized that the retrieve-filter-hydrate workflow was often time a big bottleneck in productivity and app latency.\n\nSo we put our heads together. I was one of the original co-authors of the pandas library. Lei was a core-contributor to HDFS and led ML infrastructure at Cruise. Using our experience building data/ML tooling, we’ve built something totally new.\n\n![uploaded image](https://bookface.ycombinator.com/media/?type=post&id=71382&key=user_uploads/858308/435bcc86-88ef-4c62-9f14-ba9d84c5c48f)\n\n### **LanceDB ❤️ builders**\n\nWe’ve reimagined vector search from the ground up for better developer productivity, scalability, and performance. LanceDB is backed by [Lance format](https://github.com/eto-ai/lance) — a modern columnar data format that is an alternative to parquet. It’s optimized for high speed random access and management of AI datasets like vectors, documents, and images.\n\nWe then added our own Rust implementations of a number of SOTA ANN-index algorithms to support low-latency vector search. These indices are SSD-based and can easily scale wayyyy beyond memory.\n\nWhat’s more, LanceDB allows you to store and filter other features along with vectors. Our users have been able to replace 3–4 different data stores with LanceDB alone and achieve a speedup at the same time.\n\n### **🛣️ Roadmap**\n\nSince our launch, the community has added LangChain support. Our integration for LlamaIndex is also under review. Our current focus is building a TypeScript implementation with a native-level experience. \n\nCurrently, we provide a python package called lancedb which is pip installable and delivers a great local workflow. Beyond what we’re working on right now, here’s what you can expect:\n\n* Ecosystem **integrations** into OpenAI plugin / AutoGPT etc\n* Richer set of **embedding functions** and **document processing** conveniences\n* **Gallery** of generative AI apps powered by LanceDB\n* Solutions for **cloud** deployment\n\n### **🙏 How to get started**\n\nTo get started with LanceDB head over to our [repo](https://github.com/lancedb/lancedb). If you have questions, feedback, or want help using LanceDB in your app, don’t hesitate to drop us a line at .", "url": "https://www.ycombinator.com/launches/IZK-lancedb-serverless-low-latency-vector-database-for-ai-applications", "votes": 22, "created_at": "2023-05-05T03:22:53.361Z" } ] }, { "id": 25610, "name": "RedBrick AI", "slug": "redbrick-ai", "former_names": [ "RedBrick AI", "RedBrick AI (Zantula", "Inc.)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/10e60623a6f9ead582ef32a7eb60d830f6544cbe.png", "website": "https://redbrickai.com", "all_locations": "Claymont, DE, USA; MH, India", "long_description": "RedBrick AI's mission is to accelerate the adoption of artificial intelligence in radiology by building world-class software infrastructure. Currently, we are focused on helping radiology AI teams prepare high-quality datasets to train their algorithms.\r\n\r\nRadiology data, such as CT and X-ray scans, is an incredibly important source of truth in healthcare delivery. In fact, over 90% of all healthcare data is medical imagery! However, the global radiology workforce is overburdened. In the UK, for example, only 2% of radiology departments are able to fulfill their reporting requirements, and this trend is reflected worldwide.\r\n\r\nThe acute state of radiology, coupled with the abundance of data, makes the use of AI in radiology a prime candidate. In 2022, $5.6 billion was invested in the development of AI in healthcare! However, a key hindrance to further adoption is the lack of sophisticated tools to build and deploy AI algorithms in clinical environments. This is the problem we’re focused on at RedBrick AI\r\n\r\nWe're a team based out of Bangalore India, and USA. We're backed by leading institutional investors like Y Combinator and Peak XV (formerly Sequoia Capital India). ", "one_liner": "Rapid medical data annotation", "team_size": 13, "industry": "B2B", "subindustry": "B2B", "launched_at": 1642587658, "tags": [ "AIOps", "Machine Learning", "Healthcare" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "India", "America / Canada", "South Asia", "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/redbrick-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/redbrick-ai.json", "year_founded": 2021, "city": "Claymont", "country": "US", "linkedin_url": "https://www.linkedin.com/company/redbrick-ai", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/redbrick-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/10e60623a6f9ead582ef32a7eb60d830f6544cbe.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 325863, "full_name": "Derek Lukacs", "title": "Founder", "founder_bio": "Derek Lukacs is the cofounder and CTO of RedBrick AI and currently spends his time leading the team on improving the RedBrick AI product capabilities and user experience.\n\nHe holds a Bachelors and Masters in Aerospace Engineering from the University of Michigan where he cofounded the SpaceX Hyperloop pod competition team alongside current Cofounder Shivam Sharma.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/derek-lukacs/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ed8ffe218eadeb092f842d5042cbb295db9fa171.jpg" } ], "jobs": [], "news": [ { "title": "Speeding Up The Use Of AI In Health With Redbrick AI’s Training Tools", "url": "https://www.forbes.com/sites/davidprosser/2022/11/23/speeding-up-the-use-of-ai-in-health-with-redbrick-ais-training-tools/?sh=2b08f84b5e8c", "date": "Dec 07, 2022" }, { "title": "Sequoia India's Surge backs health tech startup RedBrick AI in $4.6M funding • TechCrunch", "url": "https://techcrunch.com/2022/11/22/redbrick-ai-sequoia-surge-funding/", "date": "Nov 22, 2022" } ], "launches": [] }, { "id": 25715, "name": "SubscriptionFlow", "slug": "subscriptionflow", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6ea5429baf87f6ffc90d9204f3b44880844aead6.png", "website": "http://www.subscriptionflow.com", "all_locations": "London, England, United Kingdom; San Francisco, CA, USA", "long_description": "SubscriptionFlow is an AI-based customizable revenue management platform that helps SMBs optimize subscription revenue. It monitors user activity to identify customers at risk of churning and automatically alerts the sales team.\r\n\r\nFor example, eLearning/SaaS/Membership businesses require to monitor subscription usage to improve customer retention. Ecommerce/Magazine companies need to see the subscription trends to cross-sell and upsell products/services.\r\n", "one_liner": "AI based Customizable Subscription & Revenue Management Platform", "team_size": 9, "industry": "B2B", "subindustry": "B2B -> Sales", "launched_at": 1643142434, "tags": [ "AIOps", "Fintech", "SaaS" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Sales" ], "regions": [ "United Kingdom", "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/subscriptionflow", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/subscriptionflow.json", "year_founded": 2021, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/subscription-flow/", "x_url": "https://twitter.com/SubscriptionFL", "fb_url": "https://www.facebook.com/SubscriptionFlow", "cb_url": "https://www.crunchbase.com/organization/subscriptionflow", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6ea5429baf87f6ffc90d9204f3b44880844aead6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 731645, "full_name": "Kashif Saleem", "title": "Founder", "founder_bio": "Kashif Saleem has over 12 years of experience in helping SMBs with SaaS apps. Certified in Technology Entrepreneurship from London Business School (LBS).\nHe worked as a Cloud computing Research Fellow at the University of Oxford. He also worked at JP Morgan Chase as a High-Frequency programmer in London Equity Trading.\n\n\n\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kashifsaleem786/", "x_url": "https://twitter.com/KashifSaleemCEO", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/977331e8881b194baa18ebef284945971657d9c2.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 26066, "name": "Query Vary", "slug": "query-vary", "former_names": [ "Syncware Pte Ltd", "Syncware" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/cd0d06c611bd85e89ae37ce4e652e975ca7a2a46.png", "website": "https://queryvary.com", "all_locations": "San Francisco, CA, USA", "long_description": "Query Vary is a no-code tool for building LLM-powered automation. \r\n\r\nExample use cases include extracting information from a sales call transcript and piping it into a CRM, creating a Slack bot trained on product documentation, or transforming Google forms into fully formatted reports.\r\n\r\nInput Software --> LLM --> Output Software\r\n\r\nOur extensive toolset to make each workflow reliable sets Query Vary apart. \r\n\r\nBook a demo:\r\nhttps://calendar.app.google/6oxEvZA2k4kK6dBa8", "one_liner": "No-Code LLM Application / Workflow Builder", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1644001179, "tags": [ "AIOps", "Developer Tools", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/query-vary", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/query-vary.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/96061039", "x_url": "https://twitter.com/queryvary", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/cd0d06c611bd85e89ae37ce4e652e975ca7a2a46.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 867073, "full_name": "Walter Pintor", "title": "Founder", "founder_bio": "Walter is the CTO and co-founder of Query Vary, a startup specializing in prompt design, testing, and refinement tools, with a focus on reliability, latency reduction, and cost optimization. Prior to this, he founded Syncware, a warehouse optimization software tool. He similarly worked automating complex systems and products during his time at the largest research institute in Singapore. His background is in aerospace engineering and mechatronics.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/walter-pintor/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4d671c1d6eee0d94b8e6a4eb95a9505c81c1c11b.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 26666, "name": "Airtrain AI", "slug": "airtrain-ai", "former_names": [ "Sematic AI", "Sematic" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/75317513ffcf6008b389c1e9fada5072394a8511.png", "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", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Inactive", "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/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/", "x_url": "https://twitter.com/AirtrainAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/sematic-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/75317513ffcf6008b389c1e9fada5072394a8511.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": 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.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/emmanuelturlay/", "x_url": "https://twitter.com/neutralino1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/69cdd805022f44e70ed65f68b182f02ab503f7ab.jpg" } ], "jobs": [], "news": [ { "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" } ], "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" } ] }, { "id": 26750, "name": "NuMind", "slug": "numind", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a271d83ee825be78f44453963c65aea56395a341.png", "website": "https://www.numind.ai", "all_locations": "Boston, MA, USA; Cambridge, MA, USA", "long_description": "NuMind is a tool for data scientists, data analysts, but also software engineers to create custom NLP models. For example, a recruiting company uses NuMind to find which job offers best match a given resume. Etienne (CEO) was head of Machine Learning at Wolfram Research, and Samuel (CTO) co-founded Make.org (8M users). NuMind originated from our own frustration when developing NLP models. Leveraging large language models similar to GPT-3, NuMind allows to complete NLP projects at least 10x faster than before. We launched a private beta August 2 and had 9 paying customers one month later.", "one_liner": "Create custom NLP models", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1656275044, "tags": [ "AIOps", "Machine Learning", "NLP", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/numind", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/numind.json", "year_founded": 2022, "city": "Boston", "country": "US", "linkedin_url": "https://www.linkedin.com/company/numind-ai", "x_url": "https://twitter.com/numind_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/numind", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a271d83ee825be78f44453963c65aea56395a341.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/bd217a1570027d826d259c10bc1226114b16f19d.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 1000065, "full_name": "Etienne Bernard", "title": "Founder", "founder_bio": "Etienne is an expert in AI & machine learning. After a PhD (ENS) & postdoc (MIT) in statistical physics, Etienne joined Wolfram Research where he became the head of machine learning for 7 years. During this time, Etienne led the development of automatic learning tools, a user-friendly deep learning framework, and various machine learning applications. Etienne founded NuMind in 2022.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/etiennebcp/", "x_url": "https://twitter.com/etiennebcp", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/243e2ea9be95bc503ba2d1eb595aef52d47385a9.jpg" }, { "user_id": 1013117, "full_name": "Samuel Bernard", "title": "Founder", "founder_bio": "After a PhD in Distributed Systems, Samuel was an early employee of Criteo, the Technology Innovation leader at S4M, co-founder & CTO at Make.org, and an independent coach/advisor of start-ups & data teams. Over the years he refined his skills and became an expert in DevOps/DataOps and at managing tech teams.\nHe is now founder & CTO at Numind.ai.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/samuelbernardphd/", "x_url": "https://twitter.com/TaXules", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a0bad7d23931a032c19234d3534578f089ced5a3.jpg" } ], "jobs": [], "news": [ { "title": "NLP Startup NuMind Raises USD 3m in Seed Funding - Slator", "url": "https://slator.com/nlp-startup-numind-raises-usd-3m-seed-funding/", "date": "Mar 31, 2023" } ], "launches": [] }, { "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, "dday_video_url": null, "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", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5bf15bbec75b9e381a0a4c9eee0983462e4d43a4.jpg" } ], "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": 27821, "name": "PoplarML", "slug": "poplarml", "former_names": [ "Poplar" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e108e35156cecf90efeb1f504d510fca87ce7b44.png", "website": "http://poplarml.com", "all_locations": "San Francisco, CA, USA", "long_description": "PoplarML lets you deploy any machine learning model to a fleet of GPUs as a ready-to-use and scalable API endpoint with one command.", "one_liner": "Deploy Machine Learning Models with One Command", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1674681330, "tags": [ "AIOps", "Machine Learning", "B2B", "API", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/poplarml", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/poplarml.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/poplarml/", "x_url": "https://twitter.com/PoplarML", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e108e35156cecf90efeb1f504d510fca87ce7b44.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 522488, "full_name": "Danna Liu", "title": "Founder", "founder_bio": "Co-Founder of PoplarML, Previously Research @ Snap, SWE @ Stripe, Coinbase, Microsoft, PagerDuty", "is_active": true, "linkedin_url": "https://linkedin.com/in/dnaliu", "x_url": "https://twitter.com/dnaliu17", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f8031b2bc79dd8cb941d56543eeafeb8ed6f8cc6.jpg" }, { "user_id": 556433, "full_name": "Evan Chu", "title": "Founder", "founder_bio": "Co-Founder @ PoplarML. Previously SWE @ Stripe, Amazon, AWS", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/evan-j-chu/", "x_url": "https://twitter.com/evan_j_chu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/30d3a5d172cfeb2c14891b4ab942650f2de5090b.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69447, "title": "🌳 PoplarML – Deploy machine learning models with one command", "tagline": "Deploy any machine learning model to a fleet of GPUs as a ready-to-use and scalable API endpoint with one command", "body": "👋 Hey Everyone, this is Danna and Evan, and we’re super excited to introduce PoplarML!\n\n### 📖 **TL;DR**\n\nBuilding deployment infrastructure for machine learning models is difficult and time-consuming. With PoplarML, you can replace that process with one simple command. We handle all of the infrastructure to deploy and serve at scale, enabling you to focus on building your product.\n\n### ❌ **The Problem**\n\nAI-powered products have grown in popularity but many are deterred by the challenges to bring their models to life. Deploying a machine learning model to production is complex and requires a heavy engineering lift. Building the infrastructure to support ML models takes days, increasing the time to ship your product. \n\n## ⚡ **The Solution**\n\nPoplarML helps teams easily deploy custom machine-learning models to production. With one simple CLI command, you can turn your model into an API endpoint that you can integrate with the rest of your product. Our endpoints come with auto-scaling out of the box, ensuring low-latency when there are bursts of requests to your model.\n\nPoplarML can deploy any custom model, regardless of the framework used. Some examples of models that our customers have deployed on PoplarML are: \n\n* Flan-T5-XXL\n* Whisper\n* Stable-Diffusion-2\n\n### 🤖 **How it Works**\n\n1. ✏️ Define a Load and Predict function in a [main.py](http://main.py) file\n2. 💻 Use our CLI tool to pick a GPU instance and deploy your model\n3. 🚀 Use the returned API endpoint in your product\n\n![uploaded image](/media/?type=post&id=69447&key=user_uploads/522488/30758efd-4ce3-48d0-94be-e43093ad027f)\n\n### 💡 **Why PoplarML was Built**\n\nWe have personally felt the pain of deploying models to production and have previously built ML deployment infrastructure at companies such as Amazon and Snapchat. With PoplarML, we would like to provide a better and more streamlined experience for ML development. \n\n### **🙌 Our Asks**\n\n* Reach out via our [contact us](https://poplarml.com/contact.html) form or book a time directly on our [calendly](https://calendly.com/poplarml/15min) if you want to try PoplarML\n* Share this with anyone you know building AI-powered products or companies\n* Upvote and share this post!", "url": "https://www.ycombinator.com/launches/I47-poplarml-deploy-machine-learning-models-with-one-command", "votes": 297, "created_at": "2023-02-20T18:57:45.405Z" } ] }, { "id": 27919, "name": "Helicone", "slug": "helicone", "former_names": [ "Prompt Zero", "TableTalk", "Valyr" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0fd0f837e64cef0930fd804e4345c0342b9a2827.png", "website": "https://www.helicone.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Helicone.ai is creating an advanced observability platform tailored for developers working with Large Language Models (LLMs). Our goal is to simplify and enhance the operational side of deploying these models, making it easier for developers to monitor, manage, and optimize their AI applications at scale. Helicone provides a unified view of performance, cost, and user interaction metrics for various LLM providers, like OpenAI, Anthropic, and LangChain, empowering developers to make their LLM deployments more efficient, reliable, and cost-effective.\r\n\r\n### Key Features\r\n\r\n1. **Centralized Observability**: Our platform captures and visualizes detailed logs and metrics across all LLM deployments. With tools for prompt management, performance tracing, and debugging, Helicone provides real-time insights into the inner workings of your LLMs.\r\n\r\n2. **LLM Performance Optimization**: Helicone supports prompt experimentation, success rate tracking, and fine-tuning, allowing you to continuously improve response quality and efficiency. This level of insight makes it easier to deliver high-performing, cost-effective AI applications.\r\n\r\n3. **Flexible Data Management**: We understand that data privacy is critical. Helicone supports deployment options for dedicated instances, hybrid cloud integrations, or self-hosted environments, allowing clients to maintain control over their data and ensuring compliance with privacy standards.\r\n\r\n### Built for Developers and Data Scientists\r\n\r\nHelicone is designed to meet the needs of engineers and data scientists who require transparency and control over their LLMs. From chatbots to document processing systems, Helicone equips you with the insights needed to track costs, understand user interactions, and optimize outputs—all from one intuitive platform.\r\n\r\nBy combining observability with LLM-specific insights, Helicone is redefining AI monitoring, empowering developers to deploy and scale their AI models with confidence.", "one_liner": "LLM Observability for Developers", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1675832219, "tags": [ "AIOps", "Developer Tools", "Analytics", "Open Source" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Acquired", "industries": [ "B2B", "Analytics" ], "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/helicone", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/helicone.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/92878545", "x_url": "https://twitter.com/helicone_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/helicone", "github_url": "https://github.com/Helicone/helicone", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0fd0f837e64cef0930fd804e4345c0342b9a2827.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": 1178880, "full_name": "Justin Torre", "title": "Founder/CEO", "founder_bio": "Justin is the founder of Helicone, a company dedicated to improving the lives of developers using LLMs. With 5+ years of experience tinkering and hacking on various projects, Justin has honed his technical skills and understands the critical elements of good software infrastructure.\n\nBefore starting Helicone, Justin was a developer evangelist and teacher at Apple, where he developed a deep passion for supporting developers and their success. ", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/justintorre/", "x_url": "https://twitter.com/justinstorre", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/462b226661ccd47ff9b2af84b4a222ba7dbd1c36.jpg" }, { "user_id": 1203799, "full_name": "Barak Oshri", "title": "Founder", "founder_bio": "Barak is a co-founder at Helicone. Previously, he was a machine learning engineer at Sisu Data building scalable machine learning workflows for enterprise customers. Before that, he did research at the Stanford AI Lab and was a teaching assistant at Stanford, where he graduated from his MS and BS degrees. In his spare time, Barak bikes around the bay and sits for long hours at coffee shops.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/boshri/", "x_url": "https://twitter.com/barakoshri", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9a0e6414fc92986720b17bc028dc9f4ae9c055d0.jpg" }, { "user_id": 1180638, "full_name": "Scott Nguyen", "title": "Founder", "founder_bio": "https://www.scottn.dev/", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/snguy/", "x_url": "https://twitter.com/NguyenScott7", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0afb45f0cedb8142b1f050e548c5f5461895c6a7.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69629, "title": "Helicone - Open-source observability platform for generative AI", "tagline": "Log requests made to OpenAI and track costs, result quality, and latency with one line of code", "body": "**TL;DR Instead of building tools to monitor your generative AI product, use Helicone to get instant observability of your requests.**\n\nHey everyone, we are the team behind [Helicone](http://www.helicone.ai/).\n\n**Scott** brings UX and finance expertise: 4+ years across Tesla, Bain Capital, and DraftKings.\n\n**Justin** brings platform and full-stack expertise: 7+ years across Apple 🍎, Intel, and Sisu Data.\n\nWe’re on a mission to make it extremely straightforward to **observe** and **manage** the use of language models.\n\n## **❌ The Problem**\n\nYou’re using generative AI in your product and your team needs to build internal tools for it:\n\n* You want an **admin mode** to **visualize** outputs, conversations, or prompt chains\n* You don’t know the **unit economics** of your product, like the average cost of a user or conversation\n* Your usage grows and you’re quickly **running into rate limits** with your provider, but your errors are opaque\n* You don’t know **when it’s time to fine-tune** your model and when you would get cost-savings from it\n\n## 🪄 **Our Solution**\n\nHelicone logs your completions and tracks the metadata of your requests. It is an analytics interface for understanding your metrics broken down by users, models, and prompts with visual cards. It caches your completions to save on bills, and helps you overcome rate limits with intelligent retry techniques.\n\n![uploaded image](/media/?type=post&id=69629&key=user_uploads/1203799/6118a1c6-83ab-4f6d-a671-6c835bda3698)\n\n## ⚙️ How it works\n\n🎩 **Integrate Helicone with one line of code**\n\nHelicone is a proxy service that executes and logs your requests, secured by Cloudflare workers around the globe to add [less than a scratch](https://docs.helicone.ai/how-it-works/latency) to your overall latency.\n\nPlug Helicone into wherever you are calling OpenAI with a [**single line of code**](https://docs.helicone.ai/quickstart/integrate-in-one-line-of-code) by changing the base URL, and immediately get a visual experience for your requests.\n\n![uploaded image](/media/?type=post&id=69629&key=user_uploads/1203799/6230af56-0e40-4c3e-a72c-996b6cc142dd)\n\n🔖 **Customize requests with properties**\n\nAppend custom information like the user, conversation or session id to group requests, then instantly get metrics like the total latency, the users disproportionately driving your OpenAI costs, or the average cost of a user session.\n\n📥 **Setup caching and retries**\n\nEasily cache your completions so that duplicate requests don’t drive up your bill. Customize your cache for your application’s unique requirements. This removes the latency overhead when you’re experimenting to make development faster.\n\nConfigure retry rules when you run into rate limits, or even route your request to another provider when your service is down.\n\n![uploaded image](/media/?type=post&id=69629&key=user_uploads/1203799/00d73946-0303-4024-9fc3-da961f05275b)\n\n## Get started **in seconds**\n\n* Try it **for free** at [helicone.ai](http://helicone.ai)\n* Book a call with the team [on our calendly](https://calendly.com/d/4hs-9jb-b96/helicone-intro)\n* Join our [Discord community](http://discord.gg/zsSTcH2qhG)\n* Email the team at [help@helicone.ai](mailto:help@helicone.ai)\n* We’re open source! Check out and support our repository at [github.com/Helicone/helicone](http://github.com/Helicone/helicone) ⭐", "url": "https://www.ycombinator.com/launches/I73-helicone-open-source-observability-platform-for-generative-ai", "votes": 52, "created_at": "2023-02-23T23:15:47.567Z" } ] }, { "id": 28081, "name": "DAGWorks Inc.", "slug": "dagworks-inc", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/94a18b5c0cc3a340150d731fa97acee3a6a56ebd.png", "website": "https://www.dagworks.io/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "We’re on a mission to enable everyone to build reliable AI agents & AI applications. We're fully open source, and provide a unique integrated development & observability experience for those building anything in the AI space. This is the first step towards laying the foundations for Composable AI Systems; all AI systems need observability and introspection to be first class for them to be reliable.\r\n\r\nHow? We're standardizing how people write python to express data, ML, LLM, & agent workflows/pipelines/applications with lightweight frameworks. So that no matter the author, it'll be easy to collaborate, connect, and importantly in one line integrate observability and datastore needs. This speeds up time to production and reduces TCO because code remains easy to maintain and your data flywheel stays manageable. So you can increase the top line & bottom line of your business by delivering on AI that is reliable.\r\n\r\nWe've got two open source projects:\r\n- one focused on AI applications, called Burr (https://github.com/dagworks-inc/burr).\r\n- one focused on AI pipelines/workflows, called Hamilton (https://github.com/dagworks-inc/hamilton) see https://www.tryhamilton.dev\r\n\r\nBoth Hamilton & Burr come with self-hostable UIs (+ enterprise & SaaS offerings). With a one-line code change, you get versioning, lineage / tracing, cataloging, and observability out of the box with Hamilton. With Burr you get tracing, observability and persistence in a single line addition.\r\n", "one_liner": "Open source tools & services for reliable AI Agents & AI Applications", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1672773099, "tags": [ "AIOps", "Developer Tools", "Generative AI", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Acquired", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": [ { "question": "Describe what your company does in 50 characters or less.", "answer": "Collaborative ML ETL platform for data scientists" }, { "question": "What is your company going to make? Please describe your product and what it does or will do.", "answer": "An open-core platform for data science teams to develop, productionize, and maintain machine learning ETLs, using a company’s existing MLOps & data infrastructure." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/dagworks-inc", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/dagworks-inc.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/dagworks-inc/", "x_url": "https://twitter.com/dagworks", "fb_url": null, "cb_url": null, "github_url": "https://github.com/dagworks-inc/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/94a18b5c0cc3a340150d731fa97acee3a6a56ebd.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": 1208898, "full_name": "Stefan Krawczyk", "title": "Founder", "founder_bio": "A hands-on leader and Silicon Valley veteran, Stefan has spent the last 15 years working on data and machine learning systems at companies like Stitch Fix, Idibon, Nextdoor, and LinkedIn.\n\nBefore DAGWorks Inc., Stefan led the Model Lifecycle team at Stitch Fix, where its mission was to streamline the model productionization process for over 100+ data scientists. That's when he co-created https://github.com/dagworks-inc/hamilton, which he's excited to continue driving today with DAGWorks Inc.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/skrawczyk/", "x_url": "https://twitter.com/stefkrawczyk", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4846a217a87b816cef9b43c93ac9fb4bf3cdcdf6.jpg" }, { "user_id": 1209559, "full_name": "Elijah ben Izzy", "title": "Founder", "founder_bio": "Elijah has spent his career building tools for Data Scientists, Quantitative-minded engineers, and AI researchers. Prior to founding DAGWorks, Inc., Elijah built out Stitch Fix's machine learning platform, which was used and loved by over 100 data scientists. A core component of this was Hamilton (https://github.com/dagworks-inc/hamilton), an open-source framework for managing dataflows that DAGWorks's is now driving forward.", "is_active": false, "linkedin_url": "https://linkedin.com/in/elijahbenizzy", "x_url": "https://twitter.com/elijahbenizzy", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/376e0e11a4f37bad6d7e27c6baa103a0376f6dc0.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69876, "title": "DAGWorks: Avoid Machine Learning Pipeline horror stories 😱", "tagline": "From creation to maintenance, DAGWorks is building a more human capital efficient way for Data Science teams to build and own Machine Learning Pipelines.", "body": "👋 Hi, Stefan & Elijah here from [DAGWorks](https://dagworks.io).\n\n![uploaded image](/media/?type=post&id=69876&key=user_uploads/1208898/cf51f7bb-c9a2-495e-91d8-afb1ddfc0192)\n\n### **TL;DR:**\n\n(1) At DAGWorks Inc. our goal is to make Machine Learning (ML) initiatives more human capital efficient. (2) Our solution is an open source SaaS platform for Data Science teams to build and maintain model pipelines, plugging into as much of your existing MLOps and data infrastructure as you want. (3) Star our open source project [Hamilton on github](https://github.com/dagworks-inc/hamilton) and get started with it via [tryhamilton.dev](https://www.tryhamilton.dev).\n\n### **❌ The Problem:**\n\nAsk a Data Scientist whether they like inheriting someone else’s ML pipelines. 99% of the time, they’ll say no. Why? Getting ML to production is easy when you’re small, but when your team and codebase start to grow, maintaining these pipelines becomes challenging:\n\n* 😕 understanding code you didn’t write\n* 🐛 debugging production issues\n* 🏗️ migrating to new infrastructure\n* 🥼 handling personnel changes\n\nThis causes teams to spend far more time keeping existing ML pipelines afloat than innovating and driving their business forward.\n\nTo get out of/avoid such a mess, you need a high (and expensive) software engineering skill set, but for many that’s not an option. In our experience, this phenomenon is the biggest blocker to leveraging machine learning in your business as it grows.\n\n### **✨ Our Solution**\n\n(1) We leverage our open source project [Hamilton](https://github.com/dagworks-inc/hamilton), which is an opinionated way to write python code (akin to what DBT did for SQL), that naturally helps teams follow software engineering best practices.\n\n(2) Using the DAGWorks Client on top of Hamilton, we can give you full insight into how your pipelines operate on the DAGWorks Platform, including:\n\nA **catalog of functions** - feature catalog anyone?\n\n![](https://lh5.googleusercontent.com/lDX_3El7R6_OdBZg_tfBekfl--qUUmsmm10wBFcZcE59wrTn01e2_U8Ns-LC73fCfsJ1kmGirfbZ6XT_T_uqniK7SyJsPPZt6BXb8byJCq_C6HNlFhsdOLdiPdyVWAwS18x4D2bAQhN19AVX4Ouga6w)\n\n**Lineage** - see upstream and downstream dependencies:\n\n![](https://lh5.googleusercontent.com/-EwfBtPJB1OkF3JtS_JATLshDFHTnudGU5qyn9TqZJ3i29LcZGw0pU4Xed3Yb-apaRETO2HqQY_mDCD1ucOt8MQA5VH0D7Q_uCW99uh90c72nbKEeY8dvn5ppH5zhM0i-PtKJHDfxEYwaojPezU1ZlE)\n\n**Debugging insights** - e.g. know exactly what code failed given an airflow task failure:\n\n![](https://lh3.googleusercontent.com/j6gqllKW9oDbC9r8KC4_TRusN3MxSemIySmHq8PhSZnwOYGDwQ5ckwe-LIW4MFdtKtRfjpyCXlE_qTpQTT5boMbIDNfMgOz9huOG01UK0RuGQLFxTIR7YMuEAy5OfDuQJc-qDqdaGFLUy_9NR4nl3mo)\n\nAnd more like **data observability**, and **links with materialized artifacts** (like data sources, models, features, etc.).\n\n(3) The DAGWorks Platform becomes your ML Pipeline control plane. Use it to manage integrations with your existing infrastructure, as well as providing any reporting and compliance insights you need.\n\n### **⚙️ How it works**\n\nWith [Hamilton](https://github.com/dagworks-inc/hamilton), users logically express what should happen, which helps avoid vendor and infrastructure lock in. Then with the DAGWorks Platform & Client, we make your Hamilton code run on your existing infrastructure, providing out of the box connectors to common systems, and provide insights post execution. By building on a solid foundation that clearly decouples concerns, we can avoid the problems that plague traditional ML Pipelines. This enables Data Science teams to move faster and do more themselves, making ML projects more human capital efficient.\n\n**👪 Founding Team**\n\n[Stefan](https://www.linkedin.com/in/skrawczyk/) and [Elijah](https://www.linkedin.com/in/elijahbenizzy/) worked together at Stitch Fix where they built the self-service ML Platform for over 100 data scientists. They learned firsthand the troubles that occur building and maintaining ML Pipelines over a diverse set of modeling disciplines. Their work has been recognized by industry and included in books, newsletters, and was even the topic of a lecture at Stanford. Previously Stefan built data and ML systems at Nextdoor and LinkedIn and graduated from the Stanford CS Masters program with a Distinction in Research. Elijah built tools for quants at Two Sigma and studied Math and CS at Brown.\n\n### **🙏 Asks**\n\n1. Start with Hamilton open source today!\n * ⭐️ Star [Hamilton on Github](https://github.com/dagworks-inc/hamilton)! Download it from [PyPI](https://pypi.org/project/sf-hamilton/).\n * Checkout out [www.tryhamilton.dev](http://www.tryhamilton.dev) for an in browser overview of Hamilton. A common use case is to start using it for feature engineering.\n * Don’t know how to leverage Hamilton – [book time](https://calendly.com/stefan-dagworks/15-minute-intro-call-with-stefan) and we’ll walk you through it!\n2. We’re looking for more leads to larger organizations that have ML Pipeline struggles, in particular if they have compliance/auditing needs for them. Know someone, or perhaps you’re the right person? Please email us - [founders@dagworks.io](mailto:founders@dagworks.io) or book [15 minutes here](https://calendly.com/stefan-dagworks/15-minute-intro-call-with-stefan).\n3. We’re still currently in closed beta for the DAGWorks Platform, but you can sign up for early access via [www.dagworks.io](http://www.dagworks.io).\n4. Follow us on Twitter for updates:\n * [DAGWorks](https://twitter.com/dagworks)\n * [Hamilton_OS](https://twitter.com/hamilton_os)", "url": "https://www.ycombinator.com/launches/IB2-dagworks-avoid-machine-learning-pipeline-horror-stories", "votes": 91, "created_at": "2023-03-02T18:05:42.420Z" } ] }, { "id": 28409, "name": "LiteLLM", "slug": "litellm", "former_names": [ "Clerkie", "BerriAI", "LiteLLM", "BerriAI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/34f2cd4e65f065f81d234d6cfb21f911860e50cc.png", "website": "https://www.litellm.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "LiteLLM is an open-source LLM Gateway with 50K+ stars on GitHub and trusted by companies like Rocket Money, Samsara, Lemonade, and Adobe. LiteLLM provides an open source Python SDK and Python FastAPI Server that allows calling 100+ LLM APIs (Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic) in the OpenAI format\r\n\r\nWe have raised a $1.6M seed round from Y Combinator, Gravity Fund and Pioneer Fund", "one_liner": "Call every LLM API like it's OpenAI [100+ LLMs]", "team_size": 20, "industry": "B2B", "subindustry": "B2B", "launched_at": 1675814900, "tags": [ "AIOps", "Developer Tools", "Generative AI", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B" ], "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/litellm", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/litellm.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/berri-ai/", "x_url": "https://twitter.com/LiteLLM", "fb_url": null, "cb_url": null, "github_url": "https://github.com/BerriAI/litellm", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/34f2cd4e65f065f81d234d6cfb21f911860e50cc.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/70ee214cb3a6266c6700794f97a01a2995fb832f.png" ], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 551056, "full_name": "Krrish Dholakia", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/krish-d-959044116/", "x_url": "https://twitter.com/krrish_dh", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f1bfcbb699a14e3d5bfb786ec2e197310b0d5aa8.jpg" }, { "user_id": 1083928, "full_name": "Ishaan Jaffer", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/reffajnaahsi/", "x_url": "https://twitter.com/ishaan_jaff", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/40eda178611aa5f22d2a32b35e122d120451defa.jpg" } ], "jobs": [ { "id": 81269, "title": "Backend Performance Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/vopbPrG-backend-performance-engineer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150 - $200", "equity_range": "0.50% - 3.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Python", "Rust" ] }, { "id": 84038, "title": "Technical Support Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/ooYRlW4-technical-support-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "$80K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 102150, "title": "Senior Security Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/LBkjpsF-senior-security-engineer", "location": "Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$220K - $260K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102151, "title": "Senior Backend Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/qwvhKQ3-senior-backend-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K - $260K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102152, "title": "Head of Engineering", "url": "https://www.ycombinator.com/companies/litellm/jobs/pvPak4L-head-of-engineering", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$250K - $300K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102153, "title": "Rust Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/pEr48ZR-rust-engineer", "location": "San Francisco / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$220K - $280K", "equity_range": "0.10% - 0.30%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "C", "C++", "Elixir", "Rust" ] }, { "id": 102154, "title": "Senior Support Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/d7mRzGE-senior-support-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $170K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102155, "title": "Product Engineer ", "url": "https://www.ycombinator.com/companies/litellm/jobs/2gwsUNT-product-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K - $260K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 102156, "title": "Senior Support Engineer - Brazil", "url": "https://www.ycombinator.com/companies/litellm/jobs/QDeS0F3-senior-support-engineer-brazil", "location": "Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102157, "title": "Senior Support Engineer - India", "url": "https://www.ycombinator.com/companies/litellm/jobs/gxtoDSb-senior-support-engineer-india", "location": "Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102158, "title": "Security Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/ixoSdM3-security-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K - $230K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 102159, "title": "Staff Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/XVj6hFt-staff-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$240K - $270K", "equity_range": "", "experience": "8+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 102358, "title": "AI Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/Go3e4fQ-ai-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$170K - $220K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 102359, "title": "Founding Developer Relations", "url": "https://www.ycombinator.com/companies/litellm/jobs/k3JohCT-founding-developer-relations", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$150K - $220K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 103305, "title": "Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/litellm/jobs/XgKWHmI-forward-deployed-engineer", "location": "United States", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 103536, "title": "Technical Account Manager", "url": "https://www.ycombinator.com/companies/litellm/jobs/ueVWwAV-technical-account-manager", "location": "United States / San Francisco", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$150K - $220K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 104626, "title": "Forward Deployed Engineer (New Grad)", "url": "https://www.ycombinator.com/companies/litellm/jobs/y6DQJrA-forward-deployed-engineer-new-grad", "location": "United States", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $210K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 104756, "title": "Forward Deployed Engineer - India", "url": "https://www.ycombinator.com/companies/litellm/jobs/8ya5TlZ-forward-deployed-engineer-india", "location": "Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$50K - $90K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 105548, "title": "VP of Sales", "url": "https://www.ycombinator.com/companies/litellm/jobs/gRSMQEO-vp-of-sales", "location": "San Francisco", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 74316, "title": "🚅 LiteLLM - Call all LLM APIs using the OpenAI format [Llama2, Anthropic, Huggingface, etc.]", "tagline": "Add 100+ new LLMs to your application, with a drop-in replacement for the openai chat completion call.", "body": "Hello, I’m Ishaan - one of the maintainers of [LiteLLM.](https://github.com/BerriAI/litellm/)\n\n**TLDR; LiteLLM let’s you call all LLM APIs (Azure, Anthropic, Replicate, etc.) using the OpenAI format. We translate the inputs, standardize exceptions, and guarantee consistent outputs for completion() and embedding() calls**\n\n![uploaded image](/media/?type=post&id=74316&key=user_uploads/1083928/3b30d01e-433a-48e1-9a58-10a34913d45d)\n\n### **Problem ❌: Multiple LLM APIs - Hard to debug**\n\nCalling LLM APIs involved **multiple \\~100 line if/else statements** which made our **debugging problems explode.**\n\nI remember when we added Azure and Cohere to our chatbot. Azure’s API calls would fail so we implemented model fallbacks - (e.g. if Azure fails, try Cohere then OpenAI etc.). However, provider-specific logic meant our code became increasingly complex and hard to debug.\n\n# **Solution 💡**\n\n# 1️⃣ Simplify calling existing LLM APIs\n\nThat’s when we decided to abstract our LLM calls behind a [single package - LiteLLM.](https://github.com/BerriAI/litellm) We needed I/O that just worked, so we could spend time improving other parts of our system (error-handling/model-fallback logic, etc.).\n\n[LiteLLM](https://github.com/BerriAI/litellm) does 3 things really well:\n\n* **Consistent I/O:** It removes the need for multiple if/else statements.\n* **Reliable**: Extensively tested with 50+ cases and used in our production environment.\n* **Observable**: Integrations with Sentry, Posthog, Helicone, etc.\n\n### 2️⃣ Easily add new LLM APIS - [LiteLLM UI](https://docs.litellm.ai/docs/debugging/hosted_debugging)\n\n![uploaded image](/media/?type=post&id=74316&key=user_uploads/1083928/8624880b-20dc-4f39-8b40-1537c91a5380)\n\nThe next big challenge was adding new LLM APIs. Each addition involved 3 changes:\n\n* Update list of available models users can call from\n* Adding key to our secret manager / .env file\n* Mapping the model name - e.g. `replicate/llama2-chat-...` to a user-facing alias `llama2`.\n\nSince LiteLLM integrates with every LLM API - we provide all of this out of the box with zero configuration. With a single environment variable - `LITELLM_EMAIL` you can automatically add 100+ new LLM API integrations into your production server, **without modifying code / redeploying changes 👉** [LiteLLM UI](https://docs.litellm.ai/docs/debugging/hosted_debugging)\n\n## Ask 👀:\n\n* Adding new LLM providers? Contact us at [krrish@berri.ai](mailto:krrish@berri.ai) if you need help!\n* ⭐️ us on [GitHub](https://github.com/BerriAI/litellm) to keep up with releases and news.\n* Join [our Discord](https://discord.com/invite/wuPM9dRgDw)!", "url": "https://www.ycombinator.com/launches/JKe-litellm-call-all-llm-apis-using-the-openai-format-llama2-anthropic-huggingface-etc", "votes": 26, "created_at": "2023-08-22T19:05:23.350Z" } ] }, { "id": 28815, "name": "Atla", "slug": "atla", "former_names": [ "Atla", "atla", "Atla", "atla" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f7e8e61ea6d3f763043392069ac87464fc6db2af.png", "website": "https://www.atla-ai.com/", "all_locations": "London, England, United Kingdom", "long_description": "Find and fix your agent’s most critical failures in hours, not days.\r\n\r\nAtla helps developers cut time spent on manually reviewing traces. Atla’s LLM judge evaluates your agent step-by-step, uncovers error patterns across runs, and suggests specific fixes—so you know exactly what to fix and why.\r\n\r\nAtla supports the most popular agent frameworks teams build with, including LangChain, CrewAI, and OpenAI Agents. With real-time monitoring, automated error detection, and prompt experimentation, Atla gives teams the visibility and control needed to confidently ship agentic systems that work. \r\n\r\nWe’re a team of researchers, engineers, entrepreneurs and operational leaders. Our expertise in evals was honed through training our own purpose-built LLM Judges, Selene and Selene Mini, which are available open-source and have been downloaded 60,000+ times.", "one_liner": "The improvement engine for AI agents", "team_size": 10, "industry": "B2B", "subindustry": "B2B", "launched_at": 1687465633, "tags": [ "AIOps", "Developer Tools", "Analytics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Inactive", "industries": [ "B2B" ], "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/atla", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/atla.json", "year_founded": 2023, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/atla-ai", "x_url": "https://x.com/Atla_AI", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/atla-037f", "github_url": "https://github.com/atla-ai/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f7e8e61ea6d3f763043392069ac87464fc6db2af.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": 1125799, "full_name": "Maurice Burger", "title": "Founder", "founder_bio": "Co-founder of Atla (S23). Startup veteran @ Syrup, Trim, and Merantix. Masters in CS @ University of Pennsylvania. Half an MBA @ Harvard Business School.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/maurice-burger/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a2469ba280fe36958a607550e4d8c469166187fe.jpg" }, { "user_id": 946667, "full_name": "Roman Engeler", "title": "Founder", "founder_bio": "Co-founder & CTO of atla (S23). AI safety researcher @ MATS. MSc. Robotics @ ETH, Stanford, Imperial.", "is_active": false, "linkedin_url": "https://linkedin.com/in/roman-engeler-54416212b", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/66d764f35f54a4b1f23c31849de201dfdecd99f4.jpg" } ], "jobs": [], "news": [ { "title": "Atla wants to build text-generating AI models with ‘guardrails’ | TechCrunch", "url": "https://techcrunch.com/2023/12/05/atla-wants-to-build-text-generating-ai-models-with-guardrails/", "date": "Dec 05, 2023" } ], "launches": [ { "id": 93580, "title": "Atla: The improvement engine for AI agents", "tagline": "We help teams building AI agents find and fix recurring failures.", "body": "Hey everyone! [Roman](https://www.linkedin.com/in/roman-engeler-54416212b/) here, cofounder of Atla.\n\n# **TL;DR**\n\nAtla helps teams building AI agents find and fix recurring failures. Instead of just surfacing endless traces, Atla automatically identifies the patterns behind failures and suggests targeted fixes.\n\n**Ask:** Building agents? Try us at [atla-ai.com](http://atla-ai.com) to find your agent’s most critical issues and ship improvements in hours, not days. \n\n# https://youtu.be/LtvKBJKPxKE?feature=shared\n\n# **❌ The Problem**\n\nAgents are complex black boxes, chaining together plans, tool calls, and agent-to-agent interactions. For this reason:\n\n* Failures hide inside long traces and are difficult to spot at scale.\n* Current solutions that just display traces without identifying issues are not enough.\n\nWe’ve spoken to over 100 teams who experience similar pains of digging through thousands of traces without clear signal, and fixing one issue only to have another pop up. This slows down shipping and erodes trust in the system.\n\n# **🛠 Our Solution**\n\nAtla turns all that noise into actionable insights:\n\n1. **Error annotation**: We automatically flag errors at the step level to find the root cause of a failed run.\n2. **Pattern detection**: We cluster those errors across traces to surface the recurring, high-impact failures.\n3. **Actionable fixes**: For each failure mode, Atla generates targeted recommendations specific enough to implement as small pull requests.\n\nThe result: instead of chasing symptoms, you can focus on the 2–3 failures that actually move the needle.\n\n# 📈 **Impact so far**\n\n* A legal AI startup used in over 15 countries now spots prompt failures in days instead of weeks with Atla.\n* A fast-growing productivity AI startup doubled the number of improvements shipped with Atla.\n\n# **👷 Why Us**\n\nBefore starting Atla, I led product and engineering at two fast-growing startups and researched iterative self improvement of large language models at the Stanford Existential Risks Initiative.\n\nAt Atla, we’re a small, highly technical team of AI researchers and engineers obsessed with evaluation and reliability. We previously trained Selene, an LLM-as-a-Judge model downloaded 60k+ times.\n\n![uploaded image](/media/?type=post&id=93580&key=user_uploads/946667/61832c56-f4fa-4c93-a623-eb6c31f5b1f8)\n\n# 🙏 **Our Ask**\n\nIf you’re building agents, we’d love for you to **try Atla**: https://www.atla-ai.com/\n\nKnow teams building agents? Please **share this post** with them!\n\nThank you!", "url": "https://www.ycombinator.com/launches/OLM-atla-the-improvement-engine-for-ai-agents", "votes": 79, "created_at": "2025-09-04T15:03:23.275Z" }, { "id": 88045, "title": "Selene - The World’s Most Accurate LLM-as-a-Judge", "tagline": "Half of AI’s answers are brilliant, half aren’t. We trained a model to tell them apart.", "body": "### **TL;DR**\n\nMeet Selene, a state-of-the-art LLM Judge trained specifically to evaluate AI responses. Selene is the best model on the market for evals, beating all frontier models from leading labs across 11 commonly used benchmarks for evaluators. Today, we are releasing:\n\n* API/SDK - Integrate Selene into your AI workflow\n* Alignment Platform - Build custom evaluation metrics for your use case\n\n[Get started using Selene for free.](https://www.atla-ai.com/sign-up?utm_source=ycombinator&utm_medium=community&utm_campaign=SU_YC_launchyc_communitypost_sel1launch_)\n\n![uploaded image](/media/?type=post&id=88045&key=user_uploads/1125799/e44a5253-3117-4849-9c36-3f55ea2345a3)\n\nWatch our demo [here](https://youtu.be/5A8-XqUoNBc).\n\n### **The Problem**\n\nGenerative AI is unpredictable. Even the best models occasionally hallucinate, contradict themselves, or produce unsafe outputs. Many teams rely on the same general-purpose LLMs to evaluate AI outputs, but these models weren’t trained to be judges. That leads to:\n\n* **Inaccurate evaluations** and inefficient iteration cycles in development\n* **Risky, unpredictable** AI behavior in production\n\n### **Our Solution**\n\n* **A SOTA model for evals**: Selene outperforms all frontier models (OpenAI’s o-series, Claude 3.5 Sonnet, DeepSeek R1, etc.) across 11 benchmarks for scoring, classifying, and pairwise comparisons.\n\n ![uploaded image](/media/?type=post&id=88045&key=user_uploads/1125799/8aec7a55-9ec9-4c98-86ae-ed3293fa3988)\n\n* **A platform to align our evaluator**: Adapt Selene to your exact evaluation criteria—like “detect medical advice,” “flag legal errors,” or “judge whether the agent upgraded its workflow correctly.”\n\n ![uploaded image](/media/?type=post&id=88045&key=user_uploads/1125799/ae45cd9b-5315-4d1b-a5e5-971c1da285d4)\n\n\nSelene works seamlessly with popular frameworks like DeepEval (YC W25) and Langfuse (YC W23) — just add it to your pipeline. And it runs faster than GPT-4o and Claude 3.5 Sonnet.\n\n### **Who We Are & Why We’re Building This**\n\nWe’re a small, highly technical team of AI researchers and engineers, with folks from leading AI labs and startups. Our mission is to enable the safe development of AGI. As models grow more powerful, **we need a ‘frontier evaluator’ that keeps pace with frontier AI**. We see Selene as a stepping stone toward scalable oversight of powerful AI.\n\n![uploaded image](/media/?type=post&id=88045&key=user_uploads/1125799/c1aa726d-219c-46ca-b99f-e14279c499b2)\n\n### **Our Ask**\n\n[**Try Selene for free**](https://www.atla-ai.com/api) → Integrate our API into your eval pipeline.\n\n[**Try our Alignment Platform**](https://www.atla-ai.com/sign-up?utm_source=ycombinator&utm_medium=community&utm_campaign=SU_YC_launchyc_communitypost_sel1launch_) → Craft a custom eval for your application.\n\n[**Discord**](https://discord.com/invite/qFCMgkGwUK?utm_source=ycombinator&utm_medium=community&utm_campaign=_YC_launchyc_communitypost_sel1launch_) → Leave feedback, get to know us, and brainstorm cool ideas.\n\n![uploaded image](/media/?type=post&id=88045&key=user_uploads/1125799/8b5d8436-9914-4e22-a4d6-47f4fdf842fe)\n\n", "url": "https://www.ycombinator.com/launches/Mu5-selene-the-world-s-most-accurate-llm-as-a-judge", "votes": 56, "created_at": "2025-02-26T12:41:49.232Z" } ] }, { "id": 28998, "name": "Automorphic", "slug": "automorphic", "former_names": [ "Sibyl" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6632890863ba8f97b3c98f84ce71158a24887e9a.png", "website": "https://automorphic.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Automorphic has invented a way to infuse knowledge into LLMs via fine-tuning (surpassing context window limitations), enabling developers to rapidly iterate on and successively improve custom models cheaply and efficiently.", "one_liner": "Infuse knowledge into language models with just 10 samples", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1691588844, "tags": [ "AIOps", "Deep Learning", "Developer Tools", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B" ], "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/automorphic", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/automorphic.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/automorphic", "x_url": "https://twitter.com/AutomorphicAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/automorphic-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6632890863ba8f97b3c98f84ce71158a24887e9a.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": 1146368, "full_name": "Govind Gnanakumar", "title": "Founder", "founder_bio": "Often running away from robots (that I created). Building the next generation of artifical narrow intelligence.", "is_active": true, "linkedin_url": "https://linkedin.com/in/govind-gnanakumar", "x_url": "https://twitter.com/sandkoan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2f073adeb6c9ba6591e097d17f81f051d0ae71ff.jpg" }, { "user_id": 1381983, "full_name": "Maaher Gandhi", "title": "Founder", "founder_bio": "When not working on Automorphic, I'm building AI to beat me at chess", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/maaher-gandhi-31b40928b", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/38c64b0f499364e92826147408ed30c847ad9260.jpg" }, { "user_id": 1160097, "full_name": "Mahesh Natamai", "title": "Founder", "founder_bio": "Building AI agents that can remember and self-improve like we can at Automorphic.", "is_active": true, "linkedin_url": "https://linkedin.com/in/mahesh-natamai-b17683188", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c7ac55ddb274bbc274308cc40cf70b18aa906241.jpg" } ], "jobs": [], "news": [ { "title": "Computer Science Was Too Slow, so I Dropped Out to Focus on My AI Startup", "url": "https://www.businessinsider.com/computer-science-major-dropped-out-georgia-tech-build-ai-startup-2023-9", "date": "Sep 24, 2023" } ], "launches": [ { "id": 74582, "title": "Automorphic: Infuse knowledge into language models with just 10 samples", "tagline": "Rapidly build and improve custom LLMs", "body": "**TL;DR** - [Automorphic](https://automorphic.ai) enables developers to rapidly build and improve custom fine-tuned models. Using our LLM improvement platform, you can turn raw data into a secure, production-ready, self-improving LLM in minutes.\n\n—\n\nHey everyone! We’re Govind, Maaher, and Mahesh, from Automorphic. After experiencing the challenges of fine-tuning and refining language model systems, we’re on a mission to build an interactive LLM improvement platform that turns a tedious process into an addictive one.\n\n![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/14e3e547-96a5-4a74-91b1-3ad11af28680)\n\n### **❌ The Problem:**\n\nIt takes engineers at companies like Google and Meta 12 to 18 months to take a model from research to production – and this isn’t because they run one tuning job and call it a day.\n\nThey apply a series of successive improvements that may start by supervised fine-tuning, then aligning to human preferences, then distillation and pruning unnecessary weights, and then once it’s met a certain quality threshold, they don’t just say it’s good enough. They continuously improve it with RLHF, periodic re-tunings to control for data drift, and so on.\n\nUnfortunately, such an elaborate process is unrealistic for most companies—they can’t wait a year to get a custom LLM to production even if they have the engineering talent.\n\n### **⭐️ Our Solution**:\n\nAutomorphic makes it easy for developers to continuously improve their custom LLMs.\n\n1. Upload your raw text data\n2. Start an initial fine-tuning run with your data, and continue to fine-tune as needed\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/00398557-fca1-471a-9483-b5b75e506f0f)\n\n3. One line switch from the OpenAI API endpoint to ours\n4. Try inference on your model and improve it using RLHF\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/cc6a2fe8-2b30-4a72-ada2-eeb960f6afc2)\n\n5. Use additional data to train adapters and combine and commute them however you want\n6. Publish your custom models to the [hub](https://automorphic.ai/hub), and try out existing models there\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/fc5a26f7-f7aa-4984-bb8f-1e1fc8db427b)\n\n\nYou own the model weights, so you can train and run inference in your own cloud if you’d like.\n\n# **🙌** Our Ask:\n\nSign up for our [platform](https://automorphic.ai/signup) and train your first three models for free! We’d love feedback on how we can make your language model improvement experience better.\n\nIf there’s someone in your network like an ML engineer who works with LLMs, or folks looking to replace OpenAI / implement custom LLMs, we’d love to chat with them.\n\nHere’s a short blurb you can send them:\n\n> Automorphic, a YC-backed startup, enables developers to go from raw data to a continuously improving custom language model usable in production in minutes. They’d love to chat with ML engineers and companies looking to implement custom LLMs. Email them at [founders@automorphic.ai](mailto:founders@automorphic.ai), or book a meeting [here](http://calendly.com/automorphic)", "url": "https://www.ycombinator.com/launches/JOw-automorphic-infuse-knowledge-into-language-models-with-just-10-samples", "votes": 176, "created_at": "2023-09-01T21:50:34.474Z" } ] }, { "id": 28999, "name": "Baserun", "slug": "baserun", "former_names": [ "Mochi Labs", "baserun.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/690380b4e86d38cde32c248c69764afbcf933aed.png", "website": "https://baserun.ai", "all_locations": "San Francisco, CA, USA", "long_description": "LLMs are incredibly powerful, but latency, cost, and unpredictable outputs have made productionizing LLM features challenging. Baserun is a testing and observability platform that helps AI teams streamline their development cycle from identifying an issue to evaluating their solution, so that teams ship faster with confidence.", "one_liner": "Observability and evaluation platform for LLM apps.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1691548192, "tags": [ "AIOps", "Developer Tools", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/baserun", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/baserun.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/baserun", "x_url": "https://twitter.com/baserunai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/baserun-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/690380b4e86d38cde32c248c69764afbcf933aed.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": 1038083, "full_name": "Effy Zhang", "title": "Founder", "founder_bio": "Building something new", "is_active": false, "linkedin_url": "https://linkedin.com/in/effyz", "x_url": "https://twitter.com/effyyzhang", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a25255d1e49c35b1b85e203afd7a6c0c205da71a.jpg" }, { "user_id": 815071, "full_name": "Adam Ginzberg", "title": "Founder", "founder_bio": "Adam is the CTO and co-founder of Subframe. He was an early engineer at Coda, helped lead the Coda AI team, and studied CS at Stanford.", "is_active": false, "linkedin_url": "https://linkedin.com/in/adam-ginzberg-77b6ba126", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/505718638d29d03233126fde46ccd0bfe504af7d.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 74004, "title": "baserun.ai: Ship LLM features with confidence 💪💪💪", "tagline": "A testing platform for LLM apps. ", "body": "**TL:DR:** [baserun.ai](http://baserun.ai) is a testing platform for LLM apps. From prompt playground to end-to-end tests, baserun helps you ship your LLM apps with confidence and speed.\n\n## **The problem:**\n\n**Productionizing LLM features is hard. 🥲**\n\n1. It's difficult to judge which combination of model, configurations, and prompts performs better.\n2. It's challenging to debug complex workflows that mix chained prompts and other 3rd party API calls.\n3. It's hard to understand the progression of app performance over time.\n\n![uploaded image](/media/?type=post&id=74004&key=user_uploads/1038083/cef4bc61-ae39-4dc0-b0ca-f763861c4e97)\n\n## **Our solution:**\n\n**Gain insights into your LLM features within seconds**\n\nInstall baserun SDK and immediately gain insights into your LLM features and agents during testing, and monitor their behavior in production.\n\n**Full visibility into your end-to-end tests & user journey**\n\nVisualize the precise sequence of calls, duration, and cost, along with the inputs and outputs at each stage, encompassing both custom functions and third-party API calls.\n\n**Intuitive and flexible UI for evaluating and debugging**\n\nEffortlessly compare test runs side by side, directly edit prompts, and rerun tests from the UI.\n\n![uploaded image](/media/?type=post&id=74004&key=user_uploads/1038083/87f4df81-dd31-49b5-aeb0-5d5b51f7e504)\n\n**Collaborative workspace for teams**\n\nReview results, experiment and iterate on prompts, and build test datasets with your whole team. All prompts and test results are version-controlled.\n\n![uploaded image](/media/?type=post&id=74004&key=user_uploads/1038083/3cd2684f-04b3-4835-ac39-c27ba810a69f)\n\n**Our Asks**\n\n* Try [baserun](https://baserun.ai/welcome) and give us some feedback! [Book a 15-minute call](https://calendly.com/effy-zhang/baserun-onboarding-call) with us. We’d love to hear about what you are building and help you get onboarded.\n* You can follow us on [Twitter](https://twitter.com/baserunai) or [LinkedIn](https://www.linkedin.com/company/baserun), or email us at [hello@baserun.ai](mailto:hello@baserun.ai)", "url": "https://www.ycombinator.com/launches/JFc-baserun-ai-ship-llm-features-with-confidence", "votes": 161, "created_at": "2023-08-10T19:17:49.128Z" } ] }, { "id": 29004, "name": "Chatter", "slug": "chatter", "former_names": [ "Cram" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bf0019c6ad5bec3a367e3a88e2bc4f39705513aa.png", "website": "https://chatter.anish.xyz", "all_locations": "Philadelphia, PA, USA", "long_description": "Chatter is Postman for LLMs. Our platform helps companies and developers test their LLM models. Iterate on prompts, run them across model families and evaluate them against test cases – all in one place. With collaboration features, engineers can design LLM chains while QA can write test cases. \r\n", "one_liner": "Dead simple LLM testing and iteration", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1691454323, "tags": [ "AIOps", "Developer Tools", "Generative AI", "SaaS" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": true, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/chatter", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/chatter.json", "year_founded": 2023, "city": "Philadelphia", "country": "US", "linkedin_url": "https://www.linkedin.com/company/trychatter/", "x_url": "", "fb_url": "https://www.facebook.com/trychatter", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bf0019c6ad5bec3a367e3a88e2bc4f39705513aa.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/29061/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECADIQ5IO3P%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022715Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJHMEUCIFn9f1Q%2Fd5qbfh%2BaAtaVplCeCsQqcNPXsuJRFd%2FhEVNTAiEA32K96AnrgcbRhcV%2FG1nfonPcgv%2Bc%2FqKyXAbZewGS7bkq5QMIORAAGgwwMDYyMDE4MTEwNzIiDMTF%2FTN3Ytu8H%2BXUvCrCA1e4pUvTMyQiKt1%2BeAxWJxztB3upc48xMnu8txnu6hPBB6tBT9FH%2B7KdYRpxGBnJZBkdyCmlLAJ0CXfeAAY8fyQvYDNR%2FVSROes19%2BTTnJYT9S2wCgfQCuJ0YeiFCQLChHcol7BkRYnmMNIO%2FgE29LCyEStkWRZG8WbRZY81Ca7Az%2BTBv1lMtscc5ZPC81SmhdJekCuCDpwEsSL%2FPrlMBHsQmNQ7fWZt%2FC%2F6PL9LagGFJrFwULDIrlKTRuT7%2BbaJ%2FIK4VXnF4lb94Qy6zViEkIrDXoC%2FMM8rlq8sDNGiqbGMj1TovkZ6kZSW5ZjdiSSC7qHt0t2cywP9UEqQdXuMhdWAft9xeQK1LvDjPhtv9kR6uvifQIRrMHKq%2FiRb1XsAikH6XANMUVzUJ8Ir1HZqBuS%2FEy88qF7mCLBci5FVyousO1F9Ut3gwITW0JcFOcCCd2Dsg1N%2BDATCZBDU6xN0%2FDdz8wDqBNzjAjrGbBiYgMnIOv9uk%2BsyK8iYnCmea%2FXfGo%2BiY5SuTsG%2BV8C5dxRg3phnYkIMAxeZqL7TD1qmmPu6uv9YVQKa%2BslxeaqycA%2Ba%2BafPdlHmaggog8yCaUbjFRWrhTDn%2FdrWBjqlAYDHbtBAFbJaQyRK%2Frvi4i%2BaROKi9FWEPA6onBTAu32RxbMsFc29ZArKwaNqujD8g1G3rzBr%2FC5GV%2FAXG6jj7cVGairxEVEvGC1yDegS%2FCv86Hpqs%2FT9xUImWeQ4KeT62C7Nuyj3iLifpzmHqORhRhY63cnr0feNsvCU3m42qSPRPsvIPk8NOVUKFKdgs6fuRI%2B7pHVoEPDzz%2Bdxlt1USe8vCNP5vQ%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=bcfdb2f21f9627b1a95505c9d0753f95a7491d0f981d0fdae0613b2c9ff85726", "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1056798914a02223812a847347bfbc45c64ed173.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 1383268, "full_name": "Anish Agrawal", "title": "Founder", "founder_bio": "I'm the Co-founder/CEO of Chatter (S23). Interested in AI/ML and robotics. Studied CS + Robotics @ UPenn", "is_active": true, "linkedin_url": "https://linkedin.com/in/anishagrawal1", "x_url": "https://twitter.com/anishtxt", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2a81a64a3b62eede66b2afbb6a2a26e0c0284415.jpg" }, { "user_id": 956884, "full_name": "Kasyap Chakra", "title": "Founder", "founder_bio": "Building Paragon to bring modern AI to the most important businesses in America.", "is_active": true, "linkedin_url": "https://linkedin.com/in/kasyap-chakravadhanula-357b41132", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f2291d802e8439800d86c4b2e3b91eff9d7a1818.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 73893, "title": "🧪 Chatter - Dead simple LLM iteration and testing", "tagline": "The Chatter platform helps companies and developers test their LLM models. ", "body": "**tl;dr**  Chatter is Postman for LLMs. Our platform helps companies and developers test their LLM models Iterate on prompts, run them across model families and evaluate them against test cases – all in one place. With collaboration features, engineers can design LLM chains while QA can write test cases.\\\n—\n\nHi everyone, we’re Anish and Kasyap aka the team behind [Chatter.](https://www.trychatter.ai/) We met at UPenn and dived deep into LLMs. After working on a couple projects, we realized LLM testing was a growing issue and built Chatter.\n\n![uploaded image](/media/?type=post&id=73893&key=user_uploads/1383268/18ea07e1-dd28-4625-b51c-44473efcd189)\n\n**🙋‍♂️ Who is it for?**\n\nWe built Chatter for developers and companies wanting observability while building with LLMs. If you’re building LLM-powered applications, refining your prompts, and care about their accuracy/performance (incorrect responses, hallucinations, etc), then Chatter is built for you.\n\n**⚙️ How does it work?**\n\n**🏗️ Build / Iterate Fast**, **with all the features you need**\n\nMultiple foundation models, LLM chaining, formatted responses, Jinja2 prompt templating, parameter tuning, and more – all without a single line of code. When you’re done, export the call(s) to your favorite programming language.\n\n![uploaded image](/media/?type=post&id=73893&key=user_uploads/1383268/fe7c2027-7741-4b86-bd00-3dc970d1faf2)\n\n🧪 **Test on hundreds of inputs, with automatic evaluation**\n\nEasily build a test suite as large as you need, and run it all with a single click. Add assertions for each LLM call, with automatic evaluation using anything from regex to LLM-powered methods. Everything is versioned in one place for easy comparison.\n\n![uploaded image](/media/?type=post&id=73893&key=user_uploads/1383268/8e29831b-d195-4105-9c7f-0cd1f4d3fe05)\n\n🚀 **Collaborate**\n\nNeed domain experts to review results, or your customer success team to bring in user feedback? Invite anyone into shared workspaces and make sure your users benefit from your combined expertise. \n\n![uploaded image](/media/?type=post&id=73893&key=user_uploads/1383268/d5bee1a6-612e-40e2-af2c-0463d7af8424)\n\n**🙏 Our ask**\n\nIf you know a company in your network that’s looking to improve their process for building with LLMs, we’d love a warm intro! My email is [**anish@trychatter.ai**](mailto:anish@trychatter.ai)\n\n🎡 Try the [playground](https://platform.trychatter.ai/playground) (bonus: share your feedback with us [founders@trychatter.ai](mailto:founders@trychatter.ai))\n\nShare this post!", "url": "https://www.ycombinator.com/launches/JDp-chatter-dead-simple-llm-iteration-and-testing", "votes": 27, "created_at": "2023-08-08T00:53:47.195Z" } ] }, { "id": 29134, "name": "Cognitio Labs", "slug": "cognitio-labs", "former_names": [ "Flair Technology Services Inc.", "Flair", "Flair Health", "Flair Technologies" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef89599f5919a67db2c626d5aa12b8e6f38bf8da.png", "website": "https://cognitioiq.com", "all_locations": "Los Angeles, CA, USA", "long_description": "CoAsync by Cognitio Labs is the AI back office for product quality compliance teams.\r\n\r\nWe help mission-critical food manufacturers automate repetitive, document-intensive workflows.\r\n\r\nWe started with Certificates of Analysis (COAs), one of the many workflows quality teams manage before products can ship. Today, this work is still performed manually across PDFs, emails, spreadsheets, and disconnected ERP systems. \r\n\r\nOther documents we help quality teams respond to 24x7 include supplier audit questionnaires, production schedules, lab reports & changes in product specifications and quality records.\r\n\r\nCoAsync automates the workflow by:\r\n\r\nReading supplier documents\r\nExtracting laboratory data\r\nValidating product specifications\r\nApplying compliance requirements\r\nDrafting compliant COAs\r\nOrganizing audit-ready documentation\r\n\r\nResults from one of our initial deployments with a mid-market food processor:\r\n\r\n85% less QA administrative work\r\n4× more COAs processed per shift\r\n1-2 hours saved per document\r\nNew product onboarding in under one day\r\n\r\nCOAs are just the beginning. Our vision is to build the AI back office for product quality and compliance across regulated industries by automating the many document-intensive workflows manufacturers manage every day.\r\n\r\nBacked by Y Combinator and leading investors. Founder Diva Avaya Sharma has presented example AI workflows for product quality and compliance through the World Economic Forum Centre for Advanced Manufacturing and Supply Chains' SmartStart initiative and at the International Telecommunication Union's AI for Good Global Summit, organized in partnership with the United Nations.\r\n\r\nKnow a company shipping food & product urgently? Help them get started with their own document workflows at https://cognitioiq.com", "one_liner": "AI back office for food product quality teams", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1690407835, "tags": [ "AIOps", "B2B", "Data Science", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Industrials" ], "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/cognitio-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/cognitio-labs.json", "year_founded": 2025, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cognitio-labs/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef89599f5919a67db2c626d5aa12b8e6f38bf8da.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 339188, "full_name": "Diva Avaya Sharma", "title": "Founder", "founder_bio": "Diva is the founder of Cognitio Labs (former Flair telehealth) and inventor of a patented remote patient monitoring device. Previous companies & projects at Ancestry.com, Facebook, Johnson & Johnson, and the Stanford School of Medicine. Alum: Stanford University (Bachelors)", "is_active": true, "linkedin_url": "https://linkedin.com/in/diva-sharma", "x_url": "https://twitter.com/adivawhocodes", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/66eb801562304c65890ec479268fc5fa4cacc625.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109669, "title": "Cognitio Labs - the AI Back Office for Product Quality Teams", "tagline": "CoAsync by Cognitio Labs, the AI back office for product quality teams.", "body": "CoAsync by Cognitio Labs, the AI back office for product quality teams.\n\n\n\nCertificates of Analysis are one of the sources of documentation clogging food and consumer product shipments today. Every day, millions of dollars of product sit waiting to ship because paperwork isn't ready to certify it is safe.\n\nBefore food can leave a facility, quality teams review supplier documents, lab reports, product specs, and regulatory requirements to produce a COA, the document certifying a product is safe, meets quality standards, and complies with destination-country rules. That still happens across PDFs, emails, spreadsheets, and disconnected ERPs, and a single COA can take hours.\n\nCoAsync ingests supplier documents, extracts lab data, validates specifications, applies country-specific compliance requirements, and drafts audit-ready COAs, keeping quality teams in control at every step.\n\n![uploaded image](/media/?type=post&id=109669&key=user_uploads/339188/d089cbb5-ccba-49ee-a89b-29f4ad88253f)\n\n\nResults: A Fresno-based dry fruit manufacturer cut COA processing from 8 hrs/week to under 1 hr in a six-week deployment, an 85% drop in QA admin work. A California fig grower now processes 4x more COAs per shift, saving 1 to 2 hours per document, and onboarded in under a day.\n\n![uploaded image](/media/?type=post&id=109669&key=user_uploads/339188/c9de609b-22b8-4634-a6d0-47768cd58e52)\n\n\nWe're starting with food, one of the most document-intensive, regulated supply chains in the world, building toward an AI operating system for safety and quality across consumer products adoption.\n\nOur founder @[Diva Avaya Sharma](https://www.linkedin.com/in/diva-sharma/) presented on critical supply chains adopting artificial intelligence tools at the World Economic Forum's SmartStart Manufacturing Initiative Initiative and the AI for Good Global Summit (International Telecommunications x United Nations).\n\nIf you know a food, consumer products, or ingredients manufacturer buried in paperwork overload, we welcome an introduction: .\n\n![uploaded image](/media/?type=post&id=109669&key=user_uploads/339188/7e9b5cea-da52-47c8-b61a-c419b89e02bf)\n\n\n#YCLaunch #FoodTech #SupplyChain #AI #FoodSafety", "url": "https://www.ycombinator.com/launches/SWr-cognitio-labs-the-ai-back-office-for-product-quality-teams", "votes": 2, "created_at": "2026-08-07T17:12:02.313Z" }, { "id": 104819, "title": "Cognitio Labs: The AI back office for Food & Product Quality", "tagline": "The AI back office for Food & Product Quality", "body": "[Cognitio Labs ](https://cognitioiq.com/)is building CoAsync, the AI back office for product quality teams. We started with one of food's most document-intensive workflows: the Certificate of Analysis.\n\n85% less QA admin time. 4× more COAs per shift. New products onboarded in under a day.\n\nEvery day, millions of dollars of food sit waiting to ship because the paperwork isn't ready.\n\nBefore food can leave a facility, quality teams manually review supplier documents, lab reports, product specifications, and regulatory requirements to produce a Certificate of Analysis (COA). It's just one of dozens of document-heavy workflows quality teams manage every day.\n\nToday, that work still happens across PDFs, emails, spreadsheets, and disconnected ERP systems. A single COA can take hours.\n\nOver a six-week deployment, the manufacturer reduced COA processing time from 8 hours per week to under 1 hour, centralized supplier documentation, and dramatically improved audit readiness. The result was an 85% reduction in QA administrative work.\n\nCoAsync ingests supplier documents, extracts lab data, validates specifications, applies country-specific compliance requirements, drafts compliant COAs, and prepares audit-ready documentation while keeping quality teams in control at every step.\n\nWe're starting with food because it's one of the most document-intensive, highly regulated supply chains in the world. Our vision is to build the AI back office for product quality and compliance across every regulated industry.\n\nThis summer, our founder [Diva Avaya Sharma](https://linkedin.com/in/diva-sharma) is presenting example AI workflows for product quality and compliance through the World Economic Forum Centre for Advanced Manufacturing and Supply Chains’ SmartStart initiative and at the AI for Good Global Summit, organized by the International Telecommunication Union (ITU) in partnership with the United Nations. These examples explore how AI can reduce repetitive documentation, strengthen compliance, and support the next generation of quality, manufacturing, and supply chain professionals.\n\nAre you a food quality manufacturer or processor ready to see CoAsync on your own products? Upload a supplier document, a sample lab report, and a historical COA, and watch your quality workflow automate in minutes on ", "url": "https://www.ycombinator.com/launches/RGd-cognitio-labs-the-ai-back-office-for-food-product-quality", "votes": 3, "created_at": "2026-07-06T15:52:02.427Z" }, { "id": 99119, "title": "Cognitio Labs – Real-time food traceability for safety and compliance", "tagline": "Automating real-time food traceability for FDA compliance", "body": "[Cognitio Labs](https://cognitioiq.com) is an applied AI research lab building real-time compliance infrastructure for regulated supply chains, serving as the first line of defense against recalls and regulatory failure.\n\nView our launch video at: [**https://youtu.be/iFD6vxaS0eY**](https://youtu.be/iFD6vxaS0eY)\n\nWhen a food contamination event happens, time is everything. If traceability takes days instead of hours, entire product categories get destroyed, brands lose trust, and recalls cost $10M to $100M+.\n\nYet today, most operators still rely on spreadsheets, PDFs, and manual logs. Traceability data is fragmented across facilities, systems, and partners, and critical events like temperature, handling, and lot transformations are not automatically captured or standardized. As a result, generating accurate, regulator-ready records is slow, manual, and error-prone.\n\n**Why now:** The FDA’s [FSMA 204 rule](https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods) requires companies to produce standardized, lot-level digital traceability records within 24 hours by 2028, impacting over 60,000 U.S. food facilities. The regulatory bar is rising, but the infrastructure to meet it requires improvement.\n\nWe are building that infrastructure.\n\nOur system uses sensors and AI to automatically capture key events such as temperature, humidity, handling, and lot-level movements across production, storage, and transit. We convert fragmented operational data into standardized, FSMA 204-compliant traceability records and generate regulator-ready reports in real time.\n\nCompliance is generated as operations happen, not after.\n\nThis becomes the first line of defense by enabling faster recalls, reducing spoilage, eliminating manual compliance work, and protecting contracts, insurance coverage, and brand equity. Avoiding even one major recall can save $10 million to over $100 million.\n\nCognitio Labs was founded by [Diva Avaya Sharma](https://www.linkedin.com/in/diva-sharma/), a Stanford-trained technologist who previously built a patented remote patient monitoring system integrating sensors and machine learning in regulated healthcare environments, recognized by global development institutions and national innovation programs.\n\nWe are starting with food and expanding into other regulated, high-risk supply chains.\n\nLearn more at [**cognitioiq.com**](http://cognitioiq.com).\n\nFollow our Linkedin Page [**here**](https://www.linkedin.com/company/110755112/)**.**", "url": "https://www.ycombinator.com/launches/Pmh-cognitio-labs-real-time-food-traceability-for-safety-and-compliance", "votes": 11, "created_at": "2026-03-26T19:07:46.555Z" } ] }, { "id": 29187, "name": "OpenPipe", "slug": "openpipe", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/78827e9e8ab6d62b5e66b11dffea9ae771b7d97a.png", "website": "https://openpipe.ai/", "all_locations": "Seattle, WA, USA", "long_description": "OpenPipe is an SDK that abstracts away fine-tuning custom models. We capture your existing provider’s prompt-completion pairs in the background and use them to create a new model that is faster, cheaper and often more accurate than the original.", "one_liner": "Turn expensive prompts into cheap fine-tuned models", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1690818525, "tags": [ "AIOps", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "B2B", "Engineering, Product and Design" ], "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/openpipe", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/openpipe.json", "year_founded": 2023, "city": "Seattle", "country": "US", "linkedin_url": "", "x_url": "https://twitter.com/OpenPipeAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/openpipe/openpipe", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/78827e9e8ab6d62b5e66b11dffea9ae771b7d97a.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": 203501, "full_name": "Kyle Corbitt", "title": "Founder", "founder_bio": "Repeat founder and former engineer at Google and YC. Led the Startup School team and built products that increased YC applications by over 40%.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kcorbitt", "x_url": "https://twitter.com/corbtt", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/61a7e3b87e48ae759ab6ad3f1abe5d76c39e6dbc.jpg" }, { "user_id": 323984, "full_name": "David Corbitt", "title": "Founder", "founder_bio": "I'm a co-founder at OpenPipe, a platform for turning your slow and expensive prompts into cheap fine-tuned models. My cofounder and I wrote the first web agent that ran on GPT-4 (Taxy.AI) and I've been fine-tuning models since 2021.", "is_active": true, "linkedin_url": "https://linkedin.com/in/davidcorbitt", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/df70a826af6890cc6dc3e0aabda4ca6e185e8780.jpg" } ], "jobs": [], "news": [ { "title": "Seattle startup OpenPipe raises $6.7M to help companies reduce costs for LLM models – GeekWire", "url": "https://www.geekwire.com/2024/seattle-startup-openpipe-raises-6-7m-to-help-companies-reduce-costs-for-llm-development/", "date": "Mar 26, 2024" } ], "launches": [ { "id": 74436, "title": "OpenPipe: Convert expensive LLM prompts into fast, cheap fine-tuned models", "tagline": "OpenPipe automatically captures your existing prompts and completions, then uses them to fine-tune a drop-in replacement. The replacement is faster, cheaper and often more accurate than your original prompt.", "body": "Hi there! We’re Kyle and David, and we’re building [OpenPipe](https://openpipe.ai/). OpenPipe lets you capture your existing prompts and completions, and then use them to fine-tune a model specific to your use-case. Here’s how the process works:\n\n![uploaded image](/media/?type=post&id=74436&key=user_uploads/203501/750c83fe-b66d-40b8-bec3-63049b029360)\n\n## Problem: General-Purpose LLMs are slow and expensive\n\nBefore working on OpenPipe, we each ran into limitations of GPT-3.5 and GPT-4:\n\n* **David** built an app that required searching Reddit and classifying the results on user-specific dimensions. Because the set of possible results that GPT-3.5 had to classify was so large, each search cost multiple dollars!\n* **Kyle** built (and sold) a startup that involved translating official documents. It was difficult to express the specific requirements that official translations must comply with in a way that GPT-3.5 would reliably follow, but GPT-4 was too slow to provide a good user experience.\n\nWe’ve spoken with many other companies and these issues are common. Cost and latency are two major factors blocking production deployment of LLM-backed functionality.\n\n## Solution: Replace prompts with auto-deployed fine-tuned models\n\nSmall models fine-tuned on a specific prompt are highly performant and can excel at many tasks. They’re particularly good at data extraction and classification, even on tasks that need significant world knowledge.\n\nFor example, in [one project](https://github.com/OpenPipe/OpenPipe/tree/main/examples/classify-recipes) we built to classify recipes, our model was able to determine that a recipe that calls for sautéed mushrooms needs a stovetop, despite not being explicitly trained on that connection. It outperformed GPT-3.5 in classification accuracy and reached 95% of GPT-4’s performance.\n\nAnd not only does our fine-tuned model outperform GPT-3.5, it costs 50X less to run!\n\n![uploaded image](/media/?type=post&id=74436&key=user_uploads/203501/c4655ba3-5023-4010-9fae-ea71cc0e2ccb)\n\nWe’ve built infrastructure to make fine-tuning your own model extremely easy. The process works like this:\n\n* Develop a prototype of your LLM-powered feature using GPT-3.5 or 4 like normal.\n* Collect prompts and completions over time using our reporting SDK.\n* Once you have a few hundred to a few thousand completions recorded, kick off a training job in our UI.\n* A few hours later, your model will be ready. You can either download it and self-host, or host it with us.\n* Update your SDK’s URL to point to the new model. Keep your prompts and application code exactly the same — everything will just work!\n\n## Our Ask\n\n* If you’re prototyping or rolling out a product that is hurt by the slow responses or high costs of your existing LLM provider, talk to us.\n* If you know someone who is prototyping or rolling out AI-based functionality within a large company, link them to this post or just put them in contact with us directly.\n\nYou can reach us at [founders@openpipe.ai](mailto:founders@openpipe.ai). We’d love to help out if we can!", "url": "https://www.ycombinator.com/launches/JMa-openpipe-convert-expensive-llm-prompts-into-fast-cheap-fine-tuned-models", "votes": 99, "created_at": "2023-08-28T14:59:50.515Z" } ] }, { "id": 29325, "name": "Respan", "slug": "respan", "former_names": [ "Keywords AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/511c79fc4e10857bff72dd38626234991f19c62c.png", "website": "https://respan.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Respan gives teams a unified control plane to trace and evaluate agent behavior without guesswork, automatically surface issues, and fix what breaks faster. Respan connects production observability, automated and human-in-the-loop evaluations, and an adaptive AI gateway to close the loop between detection, decision, and action - so agents improve continuously in production.\r\n\r\nRespan is trusted by 100+ AI startups and enterprise teams. 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$200K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95523, "title": "Senior Software Engineer, Frontend", "url": "https://www.ycombinator.com/companies/respan/jobs/ABWSs0E-senior-software-engineer-frontend", "location": "Alameda, CA, US / Remote (IN)", "type": "Full-time", "role": "Engineering", "role_type": "Frontend", "salary_range": "$1 - $200K", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95524, "title": "Software Engineer, AI", "url": "https://www.ycombinator.com/companies/respan/jobs/9L4xvXt-software-engineer-ai", "location": "Alameda, CA, US / Remote (IN)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$1 - $200K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95525, "title": "Software Engineer, Full-Stack", "url": "https://www.ycombinator.com/companies/respan/jobs/8s4G7k6-software-engineer-full-stack", "location": "Alameda, CA, US / Remote (IN)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$1 - 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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" } ] }, { "id": 29572, "name": "Domu Technology Inc.", "slug": "domu-technology-inc", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9f79a6b55a8ecb1f2df2490b9567644b34d67880.png", "website": "https://www.domu.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Domu automates debt-collection calls, texts, and emails for financial services and healthcare. We work with some of the largest financial institutions in the world and win by doing what nobody else is willing to do. We are hiring :)", "one_liner": "AI Agents for collections", "team_size": 50, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1719086771, "tags": [ "AIOps", "Call Center", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/domu-technology-inc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/domu-technology-inc.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/domu-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9f79a6b55a8ecb1f2df2490b9567644b34d67880.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": 972200, "full_name": "Camila Zancanella", "title": "Founder/CEO", "founder_bio": "Co-founder @ Domu | The Future of Debt Collections\nCamila has 5 years of experience in multiple business areas, data analytics, and data science. She is responsible for building the models to analyze client data to personalize communication flows and decision-making processes. She previously worked at Rappi and Otter (a unit of Cloud Kitchens) and has contributed to research papers by running statistical models for business studies.", "is_active": true, "linkedin_url": "https://linkedin.com/in/camilazancanella", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1322dec9a670c9638b1bcf7406346e37bd8ab13e.jpg" }, { "user_id": 715385, "full_name": "Nick Diaz", "title": "Founder", "founder_bio": "I’ve loved building things since I was twelve. I was a national table tennis player, and I started Domu to change how interactions work in financial services.\n\nI’ve closed more than forty deals with some of the world’s largest banks and insurance companies. But what I’m most proud of is our team; they push me every day at work, at the gym, and on our runs. I can confidently say we are one of the highest-performing teams in San Francisco.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicolasdiaz27/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/89a598a2ea2d2894751af82e1c6497afb4cec07c.jpg" } ], "jobs": [ { "id": 77017, "title": "Founding SDR or AE (Cash + Equity + Comission)", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/EUpHM75-founding-sdr-or-ae-cash-equity-comission", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$70K - $150K", "equity_range": "0.01% - 0.05%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 82888, "title": "Senior Software Engineer", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/OMYKJIa-senior-software-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $210K", "equity_range": "0.01% - 0.05%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Amazon Web Services (AWS)", "Express", "Git", "PostgreSQL", "TypeScript" ] }, { "id": 95458, "title": "UI/UX Product Designer", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/68zCbM3-ui-ux-product-designer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Design", "role_type": "UI / UX", "salary_range": "$120K - $160K", "equity_range": "0.01% - 0.05%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 97400, "title": "Software Engineer Brazil ", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/VIjsS9t-software-engineer-brazil", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$70K - $90K", "equity_range": "0.01% - 0.05%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 98587, "title": "Business Analyst Brazil", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/Hb14wWs-business-analyst-brazil", "location": "SP, BR / State of São Paulo, BR", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$40K - $60K", "equity_range": "0.01% - 0.05%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 115812, "title": "Brazil AE (Cash + Equity + Comission)", "url": "https://www.ycombinator.com/companies/domu-technology-inc/jobs/y4W1TPx-brazil-ae-cash-equity-comission", "location": "São Paulo, SP, BR / São Paulo, São Paulo, BR", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$45K - $55K", "equity_range": "0.01% - 0.03%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 81786, "title": "Domu - Building AI agents for collections", "tagline": "We automate calls, text and emails for the largest financial institutions in the world", "body": "[Domu](https://www.domu.ai/) uses generative AI to automate debt collection interactions for 8 of the 20 largest banks and insurance companies across the Americas. \n\nHi everyone, we are [Nico](https://www.linkedin.com/in/nicolasdiaz27/) and [Camila](https://linkedin.com/in/camilazancanella). We met 5 years ago while Cami was finishing her MBA at HEC Paris and Nico working at his previous AI company, after that, we participated in more than 10 hackathons together, and made 450 sales calls to understand the biggest problems in banking and communications, we realized collections was a real issue. So we decided to start Domu. \\\n\\\nAt Domu, we’ve built probably one of the biggest female teams in the current YC batch, and every day, we wake up with the conviction of helping our clients automate sales calls for collections and insurance sales.\n\n![uploaded image](/media/?type=post&id=81786&key=user_uploads/715385/7d888e91-28b8-433f-a2bf-cb0d0e246cdd)\n\n**🚨 The Problem: Nobody likes to collect debt and it’s too expensive**\n\nVery few people in the world like to make calls to charge people money. \\\n\\\nEnterprises struggle with all the back-office tasks to manage call centers or debt collection agencies due to the volume of clients. Manual processes lead to high operational costs and a lack of scalability.\n\nTake our client, a major insurance company, as an example. They manually contact more than 150,000 customers monthly, which is around 500,000 minutes spent on calls😱. This process also involves capturing data, pulling that data, and analyzing it, which usually results in suboptimal outcomes.\n\n**🫡 The Solution: Automated and personalized communications at scale** \\\n\\\nDomu automates customer interactions using GenAI, reducing the need for extensive operational teams. We automate calls and integrate with channels like SMS, WhatsApp, and emails, and give our customers real-time data to track metrics.\n\n💜  By using Domu, companies can:\n\n* Collect the same as humans in 3 months at 40% of the cost. \n* Improve debt collection and conversion rates.\n* Gain real-time insights about their clients to improve communication.\n\n**📈 Opportunity: The immense potential of our market**\n\nFor every large enterprise with thousands of clients, millions are spent on inefficient communication processes and sales agents. By automating these interactions, Domu can save companies substantial amounts and improve their customer relationships.\n\nIn the US alone, the potential savings in the debt collection sector are enormous, with trillions of dollars at stake. Domu's AI-powered solutions provide a significant opportunity for enterprises to enhance their communication efficiency and effectiveness..\n\n**Get in Touch:**\n\n* **Visit us at** [www.Domu.ai](https://www.domu.ai/)\n* **Emails us:** nick@domu.ai\n* **Chat with us over text:** (954) 445-3188‬", "url": "https://www.ycombinator.com/launches/LH8-domu-building-ai-agents-for-collections", "votes": 22, "created_at": "2024-06-27T23:42:48.328Z" } ] }, { "id": 29639, "name": "Callback", "slug": "callback", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/95750da7b7330678c36367adeb82ef6c7a2f5049.png", "website": "https://getcallback.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Leading enterprises use Callback to manage complicated, real-world workflows. We integrate with your systems, not the other way around. Our platform serves as an auditable and permissioned system of record for your operations.", "one_liner": "Effortless AI automation for business operations", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1723590693, "tags": [ "AIOps", "B2B", "Automation", "Operations" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Operations" ], "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/callback", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/callback.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/callback-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/95750da7b7330678c36367adeb82ef6c7a2f5049.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 45302, "full_name": "Alan Xie", "title": "Founder/CEO", "founder_bio": "Alan is the co-founder and CEO of Callback. He was previously a product manager at Microsoft and AWS and the co-founder of an entertainment analytics startup. He studied CS at Harvard and Columbia and has an MBA from Harvard Business School.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alanrxie", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/898cddcce840c9dcdfe73a6872cd97831e96679d.jpg" }, { "user_id": 503383, "full_name": "Paulo Bautista", "title": "Founder (COO)", "founder_bio": "Paulo is the co-founder & COO of Callback. He previously managed thousands of delivery drivers and call center agents in the Philippines holding roles like Director of Operations and Head of BizOps. Paulo holds an MBA from Harvard Business School. \n", "is_active": true, "linkedin_url": "https://linkedin.com/in/paulobautista", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5caaccc169115a1b0b65223e769c202cebb62d87.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83765, "title": "Callback - AI business process outsourcing", "tagline": "A faster and more accurate AI-native BPO.", "body": "**TLDR**: [**Callback**](https://getcallback.ai) **is the first AI-native BPO built using a modern tech stack.**\n\nOur software platform helps our customers stay focused on their core business by automating their ops processes with humans in the loop. We’ve handled tasks such as image labeling, invoice extraction, and PDF report parsing — with greater accuracy and speed than traditional BPOs or off-the-shelf software solutions. \n\n**Our team**\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeL3DPnORZSaPrv35S-22CdX2ZrxP9_EvxYzclxFTf4dxbob11k6g3VvRBAZcRLO3nXcu_LDpZmaZ_kKdYx00-KBKrk8uzJ5GqjASkFm05A_BDtSjGfDWMqZ16w2IP469vR0CfuGP2RHou2QoZ6rHAqPv0?key=rS2cFpGt_BWtrYrVA_wlvw)\n\n[Alan Xie](https://www.linkedin.com/in/alanrxie/) was a senior product manager at AWS and Microsoft, where he built enterprise productivity software used by hundreds of millions of people. He holds computer science degrees from Harvard and Columbia, as well as an MBA from Harvard Business School.\n\n[Paulo Bautista](https://www.linkedin.com/in/paulobautista/) was the first employee and Head of Business Operations at Ninja Van in the Philippines, where he managed a team of several hundred delivery couriers and grew the business to 8 figures in annual revenue. Before YC, he ran an 800-person call center as the Director of Operations and Strategy for a late-stage restaurant tech startup. Paulo studied Business Economics at the University of the Philippines and holds an MBA from Harvard Business School.\n\n**Why are we building Callback?** \n\nAs companies grow, they naturally accumulate process overhead involving manual, repetitive operations tasks. Outsourcing these processes to BPOs requires months of coordination, training, and QA — friction that only the largest multinational corporations can tolerate. \n\nWe empower **companies of all sizes** to offload their manual processes onto our platform. Callback is a software layer for business operations that makes your processes more **transparent**, **efficient**, and **cost-effective**. \n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXerz8QHBBJ7s8VeO6FMk5ZD43yxGNXTCfbO727NYabKPet-cckWwJEbP9eKkHDAdv-JUu1mnzYrC-gxCNxYgZCUp4jt4AOILGazXQLoCIG3SsbL5JMT73ZuzGViPAJyknEVRvM-UeYNTHAvwBnXavIU_igy?key=rS2cFpGt_BWtrYrVA_wlvw)\n\nOnboarding onto our platform is straightforward:\n\n1. Customers define a specific process workflow as a **Blueprint**\n2. They execute **Jobs** based on their Blueprints\n3. Jobs rely on pre-defined **automation** as well as **human QA**\n\nDo you have any ops processes you wish you could delegate? Unstructured data that you need to turn into a specific structured format? Email us at [founders@getcallback.ai](mailto:founders@getcallback.ai) or book time with us [here](https://calendly.com/paulo-getcallback/can-you-do-this-for-me).", "url": "https://www.ycombinator.com/launches/Ln3-callback-ai-business-process-outsourcing", "votes": 96, "created_at": "2024-09-04T21:33:17.602Z" } ] }, { "id": 29643, "name": "Sage Care", "slug": "sagecare", "former_names": [ "Clara", "Sage" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8391ef28b31f763fdf577fc8631f259459023eb8.png", "website": "https://sagecare.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Sage is a HIPAA compliant, AI-native communication platform that makes home care agency client-intake handling smarter, faster, and more reliable.\r\n\r\nTeams that use Sage can save over 100 minutes of busywork per prospect, freeing up their teams to manage a larger, faster growing business.", "one_liner": "Automating home care agency operations with AI", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Healthcare IT", "launched_at": 1722734654, "tags": [ "AIOps", "Automation", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Healthcare", "Healthcare IT" ], "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/sagecare", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/sagecare.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/sagecare-ai", "x_url": "", "fb_url": "https://www.facebook.com/HomeCareNearMe/", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8391ef28b31f763fdf577fc8631f259459023eb8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 1865044, "full_name": "Jon Levinson", "title": "Founder/CEO", "founder_bio": "Supercharging home care operations with AI at Sage Care. Previously product leader at Uber. Tufts 2013.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jonlevinson1/", "x_url": "https://x.com/levinsonjon", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3cef75f29ab94e8713551046472dd4d0a06c80b6.jpg" }, { "user_id": 1545228, "full_name": "Ian Gillis", "title": "Founder/CTO", "founder_bio": "Co-founder & CTO @ Clara", "is_active": true, "linkedin_url": "https://linkedin.com/in/ian-gillis-3a503837", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a54d98c343e8a7e3b1fa57e274db0abd221f9286.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 82720, "title": "🧓 Clara - Better home care for seniors", "tagline": "Marketplace software for discovering, hiring, and managing in-home caregivers", "body": "# **TL;DR**\n\nClara is a home care marketplace that makes it easy for families to find and employ their ideal caregiver.\n\nOur platform uses AI to match customers with the best possible caregiver and automates compliance with built-in payroll, tax, and insurance support, all for 15-25% less than a home care agency.\n\nClara is currently live in the SF Bay Area. Sign up today at [**clarahomecare.com**](https://www.clarahomecare.com/)\n\n![uploaded image](/media/?type=post&id=82720&key=user_uploads/1865044/7f7548dc-0c0c-4f41-b967-1b48178fb055)\n\n---\n\n# _____________________\n\n# **The Problems with Home Care**\n\n**_🧑‍⚕️🌊  More Americans than ever before need care_**\n\n* **Silver Tsunami -** 11,000 Americans will turn 65 every day from now through 2027. 69% of those will require long-term care, for an average of 3 years (HHS). \\~88% of them would prefer to age at home rather than in a facility (AARP)\n* **Care work is the fastest growing profession in the U.S.**, projected to add >1M jobs by 2028. Job growth in this sector is greater than the second and third U.S. occupations with the most job growth combined (BLS)\n\n**_💸  Care costs for families are high and not correlated with quality_**\n\n* Non-medical care is **not covered by Medicare** or standard health insurance plans\n* The average out-of-pocket cost of a home health aide in SF today is [**$102,960/year**](https://www.genworth.com/aging-and-you/finances/cost-of-care)\n* Most care is delivered by **home care agencies that take 30-50% margin**, taking advantage of unsophisticated consumers who rely on referrals from overworked case managers/social workers\n* Care recipients often suffer from high caregiver turnover, with **families reporting >100 caregivers cycling through their homes**\n* Fed up with agencies, many families pay independent care workers cash under the table, opening them up to **costly tax penalties and significant personal liability**\n\n**_😞 Caregivers are underpaid and disempowered_**\n\n* Avg. **hourly wages for care workers total just $14-$16/hr**. 45% of care workers live at, or near poverty.\n* **Industry-wide turnover is** [**\\~80%**](https://homehealthcarenews.com/2024/07/home-cares-industry-wide-turnover-rate-reaches-nearly-80/) due to poor pay, difficult working conditions, and unpredictable schedules\n\n---\n\n---\n\n---\n\n# _____________________\n\n### **The Solution**\n\n# **Clara helps families legally hire and manage caregivers, without the use of agency**\n\n![uploaded image](/media/?type=post&id=82720&key=user_uploads/1865044/36bde3e7-9041-47e2-8b59-ff2416e2aa6e)\n\n**Our platform:**\n\n* 👫 Uses AI to match families with local Home Health Aides (HHAs), Certified Nursing Assistants (CNAs), or experienced companion caregivers, all thoroughly vetted and background-checked\n* 📝 Makes 100% legal, on-the-books employment frictionless by automating payroll, taxes, and insurance\n* 📱Helps caregivers deliver the best care possible, while keeping families in the loop with gen-AI care management features\n\n**Our approach:**\n\n1. Improves care quality, consistency, and transparency for care recipients and their families\n2. Increases take-home pay and stability for caregivers, while providing access to key benefits like unemployment insurance and social security\n\nThat’s why our customers call us “refreshing”, and our caregivers call us “too good to be true”\n\n![uploaded image](/media/?type=post&id=82720&key=user_uploads/1865044/6982a009-931e-411e-a09f-757acae0a0d5)\n\n_Jon and Lydia, a Clara care recipient_\n\n---\n\n---\n\n# _____________________\n\n# **Our Team**\n\n[Jon](https://www.linkedin.com/in/jonlevinson1/) and [Ian](https://linkedin.com/in/ian-gillis-3a503837) met as Product and Engineering management counterparts at Uber, where they launched and grew Uber Direct, a new line of business, from $0 —> \\~$1B in revenue.\n\nWhen Jon and his family faced the frustrations of finding and managing care for his father, he reached out to Ian to collaborate on a new approach. Leveraging their combined 20 years of experience building lovable consumer and marketplace tech products, they reimagined home care in America.\n\n![uploaded image](/media/?type=post&id=82720&key=user_uploads/1865044/cdcf2927-89be-4749-a1d5-c348067ece95)\n\n---\n\n---\n\n---\n\n# _____________________\n\n# **🙏 How you can help us**\n\n1. If you know any fantastic caregivers looking for better home care employment opportunities, kindly refer them to us at \n2. We’d also love **introductions to Social Services Directors**, Case Managers, or other professionals with similar roles at post-acute rehab/skilled nursing facilities in the SF Bay Area. We’re building tooling to make their lives easier and would love early partners\n3. Show us some ❤️ by liking/following us on [Facebook](https://www.facebook.com/HomeCareNearMe/), [Instagram](https://www.instagram.com/clara_home_care/), and [LinkedIn](https://www.linkedin.com/company/clarahomecare)\n\n# _____________________\n\n# **💝 How we can help you**\n\nTo celebrate our launch we’re offering 50% off our service fee for the YC community. If you (or someone you know) is looking for a caregiver, or wants to explore new caregiving options for an aging loved one in the Bay Area, send them our way! Just have them complete our intake form at [clarahomecare.com,](http://clarahomecare.com) and send an email to [yc@clarahomecare.com.](mailto:yc@clarahomecare.com) Mention this offer and we’ll take 50% off their first 6 months.", "url": "https://www.ycombinator.com/launches/LWC-clara-better-home-care-for-seniors", "votes": 118, "created_at": "2024-08-05T17:36:05.806Z" } ] }, { "id": 29649, "name": "Wordware", "slug": "wordware", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ff410def03c8c58c3adc7db9615becc8164804e3.png", "website": "https://wordware.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Every tool you use was built as an island, so you became the glue between them. You carry a name from Gmail into the CRM, a time from the CRM into Calendly, a note from Calendly into a doc. AI showed up and mostly became an eleventh window to paste into. \r\n\r\nSauna is a hybrid of AI and software standing on one shared brain. It holds your context and hands you real software built on top of it. You talk to it like a chatbot and use what it builds like an app: software for the path you repeat, AI for the edge that never repeats. Keep it to yourself, or share the brain with your team. \r\n\r\nThe clearest way to see it is a tool doing what its whole category cannot. An ordinary Calendly shows a stranger your free slots. Your Calendly writes the invite email itself, because it knows how the two of you know each other, and it offers a board member the slot over your gym block, because it knows which of your busy hours bend and for whom. \r\n\r\nWordware is the team behind it, 15 people backed by Spark Capital, Felicis, and Y Combinator with a $30M seed, the largest in YC's history. We build from an office in the Presidio, fifty metres from Crissy Field beach, and we work absurdly hard because we can see and feel the outcome every day.", "one_liner": "AI agents you can rely on", "team_size": 15, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1715713232, "tags": [ "AIOps", "Developer Tools", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Consumer" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/wordware", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/wordware.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/saunabywordware/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/wordware-6b65", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ff410def03c8c58c3adc7db9615becc8164804e3.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": 286908, "full_name": "Filip Kozera", "title": "Founder/CEO", "founder_bio": "Cofounder and CEO at Wordware -- an IDE for Natural Language Programming and Agent Orchestration. Previously I cofounded and exited KRISTALIC, which tried to augment the human memory. Before that, I studied Deep Learning at Cambridge. \n\nThe most interesting fact about me outside of work is that I travelled to 103 countries and was part of multiple high-altitude mountaineering expeditions and rescue operations in Kyrgyzstan and Nepal. I also do sailing, paragliding, kitesurfing and boxing.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rafaell0pez/", "x_url": "https://twitter.com/kozerafilip", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/97f0e2d2b3e5ed0937b9bcde43b6fc1d4d8b1177.jpg" }, { "user_id": 1377439, "full_name": "Robert Chandler", "title": "Founder/CTO", "founder_bio": "Robert Chandler is a founder & CTO at Wordware. \n\nHe started his career as an early engineer at a self-driving car startup, Five AI, which was acquired by Bosch. Here he lead the Offline Perception team which accelerated the dev-test cycle through automated ground truthing and was awarded 5+ patents in the process.\n\nHe has been in the AI space for over a decade since studying Information Engineering at the University of Cambridge. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/robertjhchandler", "x_url": "https://twitter.com/bertie_ai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a1a5c68c003591a1edfc51c175f2295bc1c399a6.jpg" } ], "jobs": [ { "id": 100381, "title": "Ex-YC Founder", "url": "https://www.ycombinator.com/companies/wordware/jobs/0Uw5LLO-ex-yc-founder", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 100382, "title": "Past Founder with Track Record and Experience", "url": "https://www.ycombinator.com/companies/wordware/jobs/KnEnNLv-past-founder-with-track-record-and-experience", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 100386, "title": "Senior Full Stack Engineer (Back-End)", "url": "https://www.ycombinator.com/companies/wordware/jobs/q25ojiS-senior-full-stack-engineer-back-end", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $240K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 100409, "title": "Head of Growth", "url": "https://www.ycombinator.com/companies/wordware/jobs/3zXoo6V-head-of-growth", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$200K - $280K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 107156, "title": "Founding Growth Marketer", "url": "https://www.ycombinator.com/companies/wordware/jobs/E2cby3G-founding-growth-marketer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$150K - $250K", "equity_range": "", "experience": "1+ years", "visa": "Will sponsor", "skills": [] }, { "id": 107959, "title": "Senior Applied AI Engineer", "url": "https://www.ycombinator.com/companies/wordware/jobs/ngciYZN-senior-applied-ai-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180 - $240", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 107960, "title": "Content Producer", "url": "https://www.ycombinator.com/companies/wordware/jobs/O2FEogq-content-producer", "location": "San Francisco / Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 109286, "title": "Growth Engineer", "url": "https://www.ycombinator.com/companies/wordware/jobs/tWwK61i-growth-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 110533, "title": "Senior Product Engineer", "url": "https://www.ycombinator.com/companies/wordware/jobs/NbKjip9-senior-product-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $240K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 110567, "title": "Staff Engineer", "url": "https://www.ycombinator.com/companies/wordware/jobs/r5nar82-staff-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$280K - $400K", "equity_range": "", "experience": "8+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 115019, "title": "Senior Platform Engineer", "url": "https://www.ycombinator.com/companies/wordware/jobs/Obb6hO2-senior-platform-engineer", "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": [] } ], "news": [ { "title": "Wordware raises $30 million to make AI development as easy as writing a document | VentureBeat", "url": "https://venturebeat.com/ai/how-wordware-30m-seed-round-could-disrupt-the-entire-ai-development-industry/", "date": "Nov 21, 2024" } ], "launches": [ { "id": 82674, "title": "Wordware — Build your AI stack 20x faster", "tagline": "First toolkit for building AI Agents with Natural Language Programming", "body": "Wordware is an IDE that enables anyone to build complex AI Agents and applications. Domain experts and engineers can now iterate 20x faster with prebuilt tools, API deployment, tracing, and more.\n\nCurrently, prompting is mostly done by engineers inside the code base. This is just a coincidence, and it yields poor quality and a long iteration cycle due to communication overhead with domain experts.\n\nWordware is the go-to tool for these cross-functional teams. Our clients range from AI tinkerers developing their ideas to large companies using Wordware as their whole LLM backend.\n\nIt’s a tool (an IDE) that enables you to quickly build custom AI agents for specific use cases, like legal contract generation, marketing content automation, invoice analysis, candidate screening, generating PRDs, and many more. We call applications built on Wordware ‘WordApps’ because you can create them using natural language—in other words, using words (pun intended).\n\n![uploaded image](/media/?type=post&id=82674&key=user_uploads/89898/b9693831-0217-438e-a787-e99a4d9d7031)\n\nOur core belief is that the domain expert—not the engineer—knows what good LLM output looks like. For example, lawyers building legal SaaS need to be deeply involved in the process, and working directly in the codebase or going back and forth with engineers isn’t the way to go. Most of our clients are cross-functional teams, including less technical members, who need to collaborate with engineers on LLM applications, such as assessing prompt outputs, and care about the speed of iterations.\n\nWe are in production with 7 YC Companies and looking to give other early-stage companies with their AI Stack on fire unreasonable amounts of attention. Start for free or book a demo at [www.wordware.ai.](https://www.wordware.ai/)\n\nI met Robert (my CTO) at the University of Cambridge. We have both worked with LLMs since BERT and created well-performing stacks before AI Ops was a term. We both have exits in the field.\n\n![uploaded image](/media/?type=post&id=82674&key=user_uploads/286908/9e4aec68-a8db-44af-884c-71d8201cecb9)\n\n", "url": "https://www.ycombinator.com/launches/LVS-wordware-build-your-ai-stack-20x-faster", "votes": 18, "created_at": "2024-08-02T14:15:10.409Z" } ] }, { "id": 29660, "name": "Hyrex", "slug": "hyrex", "former_names": [ "Kura" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c33003b33c54f32f641d1dd6956b58f331c3842d.png", "website": "https://www.hyrex.io", "all_locations": "San Francisco, CA, USA", "long_description": "Hyrex is a task orchestration framework that uses PostgreSQL (or Hyrex Cloud) as a durable task queue. Define tasks in Python or TypeScript, send them to be processed asynchronously, and let Hyrex handle the rest.\r\n\r\nHyrex tasks are COLD: Controllable, Observable, Large-Scale, and Durable!", "one_liner": "The COLD Task Framework", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1724200248, "tags": [ "AIOps", "Developer Tools", "Cloud Computing", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/hyrex", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/hyrex.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/hyrex-labs", "x_url": "https://x.com/hyrex_io", "fb_url": null, "cb_url": null, "github_url": "https://github.com/hyrex-labs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c33003b33c54f32f641d1dd6956b58f331c3842d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/29c89a57406f27700f9b8b7efac971895519ec3f.png" ], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 668182, "full_name": "Mark Dawson", "title": "Founder/CEO", "founder_bio": "Software Engineer. Second time founder. Currently focused on AI + Cloud infrastructure.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/markedwarddawson/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6790b0a938ff2f969081aec2eac59e12ba759360.jpg" }, { "user_id": 1967655, "full_name": "Trevor Reed", "title": "Founder/CTO", "founder_bio": "Co-founder at Hyrex, building the COLD task orchestration framework. Former Mars Rover driver at JPL with over 1km of drive distance on Mars and software engineer at Google processing worldwide YouTube data.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/trevorar", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0f6e2f6d5eafc4fc06afb8ca024e54677782c091.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 93335, "title": "Hyrex - The Open-Source COLD Task Framework", "tagline": "Production-grade background tasks built on Postgres", "body": "![uploaded image](/media/?type=post&id=93335&key=user_uploads/1967655/4c6a3685-1d2e-4f71-8808-ffcbd2a920a8)\n\n\\\nWe’re Mark and Trevor, founders of [Hyrex](https://hyrex.io). We’ve written a lot of software over the years at Google, NASA, and multiple startups. As our focus has shifted to scalable web apps, we’ve come to appreciate the magic of asynchronous background task queues. They make it easy to build lightning-fast apps that can scale dynamically, while ensuring that essential tasks are completed with full confidence, in priority order. This distributed approach is more important than ever for building dynamic AI agents, indexers, and web-first apps.\n\nHere’s a basic task orchestration architecture - you send tasks (i.e. function calls) to a queue, and workers pull them off and complete them in parallel.\n\n![uploaded image](/media/?type=post&id=93335&key=user_uploads/1967655/f29e6d7d-890b-4863-aa19-eea241e05714)\n\n**The Problem**\\\nUnfortunately, none of the existing frameworks supported everything on our checklist. As we talked with developers working on startups, we learned that most shared the same experience. Some frameworks require extensive code rewrites, while others are difficult to configure and debug, or simply unmaintained. Most lack a deep commitment to open-source, and many require dependencies on outdated or niche technologies. As you can probably guess by now, we decided to build our own!\\\n\\\n**Our Approach**\n\nWe started with a few core creative constraints.\n\n1. Only required dependency is a Postgres database.\n2. Existing functions can be converted to tasks using just a decorator/wrapper. No changes to your core code that lead to lock-in.\n3. Open-source first, with a higher-performance managed service with an identical SDK. Easy to switch back-and-forth.\n4. Can be set up and tested in minutes.\n\nAnd we ended up with Hyrex!\n\nIf you’ve used a task framework before, everything should work as you expect (and probably better)! If you’re new to task frameworks, everything you need to know is at [hyrex.io/docs](https://hyrex.io/docs)\n\n![uploaded image](/media/?type=post&id=93335&key=user_uploads/1967655/8472e657-3969-4e48-aa91-d4a8ecdc7f85)\n\n\\_(Hyrex Cloud in action)\\_\\\n\\\n**What is COLD?**\n\nWhy do we call ourselves “the COLD task framework”? For each function you wrap as a Hyrex task, you get 4 properties needed for production-grade systems:\n\n* **Controllable** - tasks can be sent from anywhere, anytime, with any configuration\n* **Observable** - tasks status, results, logs, and metadata are easily available via a UI or programmatically\n* **Large-scale** - workloads are horizontally scalable across any cloud infra (even multi-cloud)\n* **Durable** - workers are fault-tolerant and self-healing, and tasks can’t be dropped once they’re in the system\n\nWith these built-in properties, your Python/TypeScript functions are ready to send with confidence. You can run them on schedules and easily chain them together into task trees to build products like Deep Research and dynamic AI agents.\n\n![uploaded image](/media/?type=post&id=93335&key=user_uploads/1967655/a9e116eb-a557-4687-8f6a-fd8242f72e3d)\n\nHyrex open-source requires only a Postgres database to run. Hyrex Cloud ([hyrex.io/cloud](https://hyrex.io/cloud)) provides higher throughput, GitHub integration with automatic deployments when you push changes, and built-in scalable worker pools. Both options use the same SDKs, so you can easily switch at any time!\n\n**Hyrex Cloud pricing**\n\n* $200/month - individual plan (1 project + 1 worker)\n* $400/month - team plan (up to 5 members and 5 projects + 2 workers)\n\n**Our ask:**\n\n* Sign up for a 1:1 demo at [hyrex.io](http://hyrex.io)\n * We’d love to chat directly and help write your first Hyrex task!\n* Try it out on your own!\n * Build your first Hyrex task in minutes using our quickstart guides: https://hyrex.io/docs/learn/getting-started\n* Join our Discord ([discord.gg/hyrex](https://discord.gg/hyrex)) for questions and discussion", "url": "https://www.ycombinator.com/launches/OHP-hyrex-the-open-source-cold-task-framework", "votes": 17, "created_at": "2025-08-27T13:44:23.405Z" }, { "id": 83339, "title": "Kura | The AI DevOps Copilot", "tagline": "Kura is an intelligent copilot that helps software teams manage and optimize their cloud infrastructure.", "body": "**TL;DR:** [Kura](https://www.usekura.com/) integrates directly with services like AWS, helping software teams respond to incidents, provision infrastructure, and optimize complex cloud systems.\n\n![uploaded image](/media/?type=post&id=83339&key=user_uploads/1967655/a1945e14-3f49-4491-8e31-d40335e6f5ab)\n\nHello YC! We’re [Trevor](https://www.linkedin.com/in/trevorar) and [Mark](https://www.linkedin.com/in/markedwarddawson/), founders of Kura. After building distributed systems for a combined \\~15 years at places like Google and NASA, we know the challenges and triumphs of cloud-based products. We started Kura with the core belief that recent AI advancements can unlock the potential of software teams building on the cloud.\n\n# **What’s the problem?**\n\nAs web apps become increasingly core to the world, maintaining cloud infrastructure requires increasingly more time and resources.\n\n* Distributed systems are getting more complex than ever\n* Managed solutions trade off ease against control and cost\n* Cloud infra engineers are few and far between, leaving much of the work to teams who may not have extensive cloud experience\n\n# **What can Kura do?**\n\nKura is a chatbot and workspace that integrates directly with a live index of your cloud infra from services like AWS, GCP, and Azure.\n\n![uploaded image](/media/?type=post&id=83339&key=user_uploads/1967655/be157dd9-7ee9-48bf-af07-aca46efca63c)\n\n**Kura can answer questions about your cloud system**, providing links and live information for queries like:\n\n* “Is my main API cluster healthy?”\n* “Which container version is running in prod?”\n* “Can you help me deploy a new container via CDK?”\n\n**Kura can run pre-packaged and user-defined “runbooks”** to proactively surface issues via notifications. For example:\n\n* Scheduled checks for over/under-utilized computing resources\n* Discovering and surfacing cost spikes and trends\n* Searching for common security vulnerabilities\n\nWith Kura, software teams can build faster and more confidently on the cloud, all while maintaining full control of their critical infrastructure.\n\n# **Our ask:**\n\n* If your team is building on the cloud and would like to learn more or sign up as a pilot customer, please reach out to [founders@usekura.com](mailto:founders@usekura.com).\n* If you know software teams or developers running into roadblocks due to cloud complexity, please send this post their way - we’d love to chat and learn more!\n* Follow us at [@kura_labs](https://x.com/kura_labs) to follow along on the journey!", "url": "https://www.ycombinator.com/launches/LgB-kura-the-ai-devops-copilot", "votes": 57, "created_at": "2024-08-23T01:15:08.727Z" } ] }, { "id": 29676, "name": "Elevate", "slug": "elevate-2", "former_names": [ "Elev4te" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/de959e87a5bda5b32dabf7dc904953d947ff5842.png", "website": "https://www.useelevate.dev/", "all_locations": "New York City, NY, USA", "long_description": "Elevate is bringing AI to private equity-backed roll-ups. \r\n\r\nFor example, one of our customers is Guardian Restoration Partners, which is backed by Alpine Investors, the leading roll-up focused private equity firm. In three months, we integrated 10 acquisitions and enabled them to pilot AI agents built on top of our data warehouse. \r\n\r\nWe know this problem personally. Before Elevate, Scott was a Vice President at a $15B private equity firm where he was leading a roll-up. Steve and Jeff are both software engineers with backgrounds across finance, big tech, and start-ups. The founding team has been friends for over a decade from their Duke undergraduate days. ", "one_liner": "Automated AI data integrations for roll-ups", "team_size": 10, "industry": "B2B", "subindustry": "B2B", "launched_at": 1718146562, "tags": [ "AIOps", "Finance", "B2B", "Automation", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B" ], "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/elevate-2", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/elevate-2.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/elev4te-ai", "x_url": "https://x.com/useElevateAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Elev4te-AI", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/de959e87a5bda5b32dabf7dc904953d947ff5842.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": 1977390, "full_name": "Scott Cara", "title": "Founder/CEO", "founder_bio": "Scott is the co-founder and CEO of Elevate AI. \n\nScott was previously a private equity investor at Sycamore Partners where he partnered with \nc-suite executives at leading companies to drive growth. Prior to Sycamore, Scott was a management consultant at Bain & Co. \n\nScott studied Computer Science and Mathematics at Duke University and holds an MBA from Harvard Business School. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/scott-cara-31829041", "x_url": "https://twitter.com/scara29", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e4dea25fd46cb2e2d9d206eb935a3ab620819877.jpg" } ], "jobs": [ { "id": 93042, "title": "Forward Deployed Engineer (FDE)", "url": "https://www.ycombinator.com/companies/elevate-2/jobs/FFGZvQq-forward-deployed-engineer-fde", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Data science", "salary_range": "$130K - $170K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python", "SQL", "Data Warehousing", "ETL", "Data Modeling", "Data Analytics" ] }, { "id": 93166, "title": "AI Deployment Lead", "url": "https://www.ycombinator.com/companies/elevate-2/jobs/KyJ5rH5-ai-deployment-lead", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Data science", "salary_range": "$110K - $150K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python", "SQL", "Data Warehousing", "Data Modeling", "Data Analytics" ] } ], "news": [], "launches": [ { "id": 93226, "title": "Elevate - AI-powered data management for roll-ups", "tagline": "Providing private equity-backed companies with better data and access to AI tools", "body": "[Scott](https://www.linkedin.com/in/scott-cara-31829041/) here from [Elevate](https://www.useelevate.dev/), showcasing our AI-powered data management for roll-ups.\n\n# **⚠️ Problem: Private equity-backed businesses struggle to use AI because of data challenges.**\n\n📉 75% of M&A deals underperform due to **integration** challenges \n\n🔥 #1 cause of M&A integration problems is **data** fragmentation \n\n🤖 #1 hurdle blocking companies from using AI is **data** quality – #2 is **data** privacy & protection\n\n# 🚀 **Solution:** **Elevate’s AI-powered data management platform acts as a translator enabling PE-owned businesses to use AI in their daily operations.**\n\nOur platform helps PE-backed acquisitive companies (aka roll-ups) reach for data nirvana through:\n\n1. **Migrations:** Transitioning data from legacy systems to the core tech stack,\n2. **Warehousing:** Consolidating, cleaning, and enriching data across acquisitions and systems, providing a single source of truth, and\n3. **AI Services:** Unlocking agentic AI automations built on top of clean warehouse data.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdhAUwhGszwvyPlBFEYAfccNMFkNhz9tFht9DK0R7Mn8efaeTgqsJiQHHFbvf8pZM5f2k76wHg75gJpT49XnlnomjZo6kfiN74m4yTCrGcUYzi-tncfAfe-DlchOMmNSR_HHxeo8w?key=d4EvylbdqETj4Y-psv5-Bw)\n\nBut don’t trust us, ask our customers. \n\nhttps://youtu.be/H1sDiTtCV44\n\n# 🧑‍💻 **Team: Expertise at the intersection of finance x tech.**\n\n[Scott](https://www.linkedin.com/in/scott-cara-31829041/) (CEO) was most recently a Vice President at Sycamore Partners, a $10B+ AUM private equity firm. Scott is also a Harvard Business School grad where he hob-knobbed with too many finance bros + gals. \n\n[Steve](https://www.linkedin.com/in/steve-mazzari-38b470b1/) (CTO) was a software engineer at Meta, the ~~social media giant,~~ ~~metaverse pioneer,~~ AI talent acquisition firm. He has also seen this movie before as a founding engineer at multiple start-ups. \n\n[Jeff](https://www.linkedin.com/in/jeffrday/) (CPO) was a software engineer at a $17B+ hedge fund, before giving into the crypto-craze. His Circle stock sustains us. \n\nThe three founders have known each other for over a decade since their CS undergrad majors together at Duke in the not-so-dirty-anymore Durham, NC. \n\n# **🎺 Asks:**\n\n**PE / roll-up connections:** Find your friends in private equity and tell them about this cool new AI solution that will make their portfolio companies better, them richer, and their lives less miserable.  \n\n**AI solutions:** If you are building an AI tool that could be useful for PE-backed companies (👋 that’s \\~all of you!), reach out + let’s chat to see if there could be a use case for our customers. \n\nContact us: [hello@useelevate.dev](mailto:hello@useelevate.dev) \n\n**Elevate**dly Yours, \n\n[Scott](https://www.linkedin.com/in/scott-cara-31829041/), [Jeff](https://www.linkedin.com/in/jeffrday/), and [Steve](https://www.linkedin.com/in/steve-mazzari-38b470b1/)\n\n![uploaded image](/media/?type=post&id=93226&key=user_uploads/1977390/a54f9874-662a-4886-80d7-fb0bf1954f08)\n\n", "url": "https://www.ycombinator.com/launches/OFe-elevate-ai-powered-data-management-for-roll-ups", "votes": 32, "created_at": "2025-08-21T01:30:29.370Z" } ] }, { "id": 29782, "name": "Laminar", "slug": "laminar", "former_names": [ "Laminar AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f68c82dc13524e235198c783318ae1b1aa5d7432.png", "website": "https://laminar.sh", "all_locations": "London, England, United Kingdom", "long_description": "Laminar is open-source observability for AI agents. Trace complex workflows, replay and debug agent runs, and detect anomalies across trajectories at scale.", "one_liner": "Understand why your AI agent breaks. Iterate fast to fix it.", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1721337823, "tags": [ "AIOps", "Developer Tools", "SaaS", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/laminar", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/laminar.json", "year_founded": 2024, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/lmnr-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/lmnr-ai/lmnr", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f68c82dc13524e235198c783318ae1b1aa5d7432.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1771812, "full_name": "Robert Kim", "title": "Founder/CEO", "founder_bio": "Co-founder and CEO @ Laminar (lmnr.ai). Previously, I interned at Palantir where I built semantic search package which now powers many internal AI teams and worked on resource allocation engine at core infrastructure team. I also interned at Bloomberg where I scaled market tick processing pipeline by 10x to 10M ticks/s.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/skull8888888/", "x_url": "https://twitter.com/skull8888888888", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b1164aa2d27587dbe70dd830f8ae356064e709d5.jpg" }, { "user_id": 1980084, "full_name": "Din Mailibay", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO at Laminar (laminar.sh). Previously, I have worked at Amazon for 2 years building and scaling critical payments infrastructure. Before that, I've spent a year creating ML infrastructure for a drug discovery biotech startup in Korea.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/dinmukhamedm", "x_url": "https://twitter.com/din_mlb", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c56af6f86306ce69d00244d2229612165fb00abc.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98606, "title": "Laminar – Understand why your agent failed. Iterate fast to fix it.", "tagline": "Open-source observability platform for long-running agents — trace in one line, replay-debug from any step, detect patterns at scale.", "body": "Hey everyone, Robert from Laminar (YC S24) here. The entire observability space was built for request-response LLM apps. Nobody was building for Agents that run for 40 minutes and fail in ways you can't reproduce. So we did.\n\n## Tracing that actually helps you understand what happened\n\nMost observability platforms just collect data and wait until you go look at it. And when you do, you get a tree of spans. You click into one, read it, go back, click the next one, try to hold the context in your head. For a simple LLM call, that's fine. For an agent that ran for 30 minutes and made 200 decisions, it's practically useless.\n\nWe built Laminar's tracing to give you as much information as quickly as possible.\n\nOur trace timeline and **reader mode** lay out the agent's reasoning and actions as a clean, readable feed.\n\n![uploaded image](/media/?type=post&id=98606&key=user_uploads/1771812/73741d82-1719-4062-8cc2-9bd8de880010)\n\nYou open a trace and immediately see what the agent did, what it was thinking, and where things went wrong (we also capture and show application level exceptions). For browser agents, we record **full browser sessions synced with traces**. You literally see what the agent saw at every step → .\n\nIf a trace is too complex to parse visually, you can **chat with it**: ask questions about what happened in natural language, instead of manually digging through hundreds of steps. We take into account the whole context of the trace, not just a single span.\n\n![uploaded image](/media/?type=post&id=98606&key=user_uploads/1771812/87cc29b4-f5c3-4924-ab96-5c52af1fae76)\n\nWe can confidently say that we have the best tracing DX on the market. It’s one line of code to integrate. Laminar SDK auto-patches vast majority of AI frameworks and SDKs, including **Claude Agent SDK, AI SDK, LiteLLM, Browser Use, Stagehand, OpenHands SDK**, and much [more](https://docs.laminar.sh/tracing/integrations/openai). We are the only platform that traces **Claude Agent SDK sub-agents**. When Claude delegates work to sub-agents, you get full visibility into that entire chain, not just the top-level call. Here’s an example of a trace .\n\n## The debugger\n\nThis is the feature we wished existed when we were building agents ourselves. When your agent fails 15 minutes into a run, the normal workflow is: restart from scratch, wait for it to reach the same state, hope it reproduces the failure. That's insane.\n\nLaminar starts a local dev server that connects to our platform. You can run your agent directly from our UI, and when a run fails, you go to the exact step where it went wrong. You tweak your prompt or tool definitions right in the UI. And you rerun from that step — with full context preserved.\n\nHere's how it works under the hood: our tracing SDK sits right before the LLM call boundary. When you rerun from a step, we mock all the LLM calls that happened before that point, replaying their original responses. This means the agent walks through its prior steps instantly without actually calling any LLMs and spending any tokens — and crucially, it properly restores external state along the way. If your agent was controlling a browser, the browser gets back to the exact page and DOM state. If it was working in a sandbox, the sandbox is restored. By the time execution reaches your breakpoint, everything — conversation history, tool state, external environment — is exactly as it was. You tweak your prompt, hit rerun, and the agent picks up from there with the real world intact.\n\nHere’s a demo of Laminar debugger.\n\n\n\n## Signals\n\n![uploaded image](/media/?type=post&id=98606&key=user_uploads/1771812/19af455c-7dfa-46e7-aa40-fbac2b4696a6)\n\nIn development, you care about individual runs. In production, you have thousands of runs and the question changes — it's no longer \"why did this run fail,\" it's \"what's going wrong across all my runs and how often.\"\n\nThat's what Signals solve. You write a short natural language description of what you want to detect — something like \"agent gets stuck in a retry loop\" or \"user gets frustrated and rephrases their request.\" Laminar runs this against every trace, extracts matching events, and then clusters them into patterns. So instead of manually sampling traces hoping to spot trends, you get a structured view of what's actually happening. We use this ourselves to monitor our own agents, and it catches things we'd never find by skimming through traces.\n\n## SQL editor, evals, and more\n\nAll of your data lives is accessible with built-in SQL editor (both in UI and API). You can run arbitrary queries against your traces, spans, and events — build custom dashboards, do ad-hoc analysis, or bulk-create datasets from production traces. Those datasets plug directly into our evals pipeline, so you can run evaluations on real production data instead of synthetic test cases.\n\n## Laminar\n\nLeading agent companies like Browser Use, OpenHands, Rye.com, Alai and many more use Laminar in production. Laminar is fully open source and extremely fast (written in Rust). Self-host it anywhere or use our managed platform ().\n\n**Our ask:**\n\n* If you're shipping agents and debugging is painful, we'd love to help — [founders@lmnr.ai](mailto:founders@lmnr.ai)\n* Intros to teams running long running agents would be huge\n* Star us on GitHub: \n\n🔗 ", "url": "https://www.ycombinator.com/launches/PeQ-laminar-understand-why-your-agent-failed-iterate-fast-to-fix-it", "votes": 6, "created_at": "2026-03-09T00:35:53.684Z" }, { "id": 89930, "title": "Laminar: The Missing Developer Tool for Browser Agents", "tagline": "Laminar captures browser session recordings and syncs them with agent traces.", "body": "Robert and Din from Laminar here. We created a new type of observability, which enabled us to build the SOTA open-source browser agent.\n\n# The Problem\n\nBrowser agents are powerful but notoriously difficult to develop and debug. When your agent fails, traditional traces just show a wall of text logs without context. You can't see what the agent was looking at when it made decisions, why it chose certain actions, or what exactly went wrong. It's like trying to debug a self-driving car without any camera footage – practically impossible.\n\nThis lack of proper observability tools has been the biggest barrier to building reliable browser agents.\n\n# The Solution\n\n![uploaded image](/media/?type=post&id=89930&key=user_uploads/1771812/0da20a73-6dc1-4d3d-8561-dc38dd8cb624)\n\nLaminar created the missing piece in browser agent development: complete visual context for every agent action and decision. Our platform records high-quality browser sessions and perfectly synchronizes them with agent traces. Now developers can:\n\n1. **See what the agent sees** - Watch the complete visual journey of your agent as it navigates websites\n2. **Pinpoint exact failure points** - Identify precisely where and why agents make mistakes\n3. **Debug with context** - Understand agent decisions with full visual and trace context\n\n# Results\n\nUsing our own tools, we built Index - open-source browser agent that achieves 92% on WebVoyager benchmark, setting a new state of the art. Index can handle everything from research tasks to complex automations in apps like Google Sheets. In other words, we created a reliable **API layer to any website** that you can simply prompt with natural language.\n\n# Integrations\n\nLaminar seamlessly integrates with AI browser frameworks like **Stagehand** and **Browser Use** - integration takes just 2 lines of code.\n\n# Ask\n\n* **Build better agents:** Try Laminar today for your browser and AI agent development => \n* **Explore our open source agent:** Check out Index at [github.com/lmnr-ai/index](https://github.com/lmnr-ai/index)\n* **Dive into docs:** Comprehensive guides at \n\nWe're looking for early adopters and feedback! Reach out to us at [founders@lmnr.ai](mailto:founders@laminar.ai) and join our [discord](https://discord.com/invite/nNFUUDAKub).", "url": "https://www.ycombinator.com/launches/NOU-laminar-the-missing-developer-tool-for-browser-agents", "votes": 11, "created_at": "2025-04-28T22:25:16.716Z" }, { "id": 83572, "title": "Laminar - Open-source observability and analytics for complex LLM apps", "tagline": "Observability and analytics which helps you track LLM apps in production.", "body": "**tldr: Laminar is an open-source developer platform that provides full instrumentation of LLM applications and combines trace data with event-based analytics.**\n\n**—**\n\nHey everyone, we’re Robert, Din, and Temirlan. Previously, we built infrastructure at Palantir, Amazon, and Bloomberg — now, we’re building an open-source platform to help developers understand how their LLM apps perform in production.\n\n![uploaded image](/media/?type=post&id=83572&key=user_uploads/1771812/e4140bd4-1ae9-453d-8457-2564c3820cbc)\n\n# Why do LLM apps need observability from day 0?\n\n![uploaded image](/media/?type=post&id=83572&key=user_uploads/1771812/81e46417-9b77-4368-ab0f-c04b8b84556e)\n\nLLMs are stochastic, and designing robust software around them (e.g., RAG, Agents) is an iterative process. A great observability platform not only facilitates this process, but actually makes it more productive. Hence, many AI developers adopt observability tools early on.\n\nLaminar goes beyond single LLM call tracing and provides tools for entire app instrumentation and powerful UI for full trace visualization, trace search, and session grouping.\n\n## What is different about analytics for LLM apps?\n\n![uploaded image](/media/?type=post&id=83572&key=user_uploads/1771812/29d881c9-de4c-416a-be18-d17638d67135)\n\nLLM apps produce traces, which are essentially very rich text. Traditional event-based analytics tools are not built for extracting metrics from this kind of data.\n\nCurrently, AI devs spend a good chunk of their time manually inspecting traces to understand usage patterns of their LLM apps. As they scale, manual inspection is not feasible anymore.\n\nLaminar tackles this problem by using other LLMs to process rich text outputs in the background. With Laminar, developers can define custom events, such as “USER SENTIMENT,” to collect user sentiments and track this metric at scale as they deploy their LLM apps into production.\n\nEach event is linked to the trace that produced it, and developers can understand when and why certain semantic events have happened. It gives developers deeper understanding of the performance and usage of their LLM apps.\n\n## Our ask\n\n* Star our repo → \n* Start tracing and tracking events with a managed version of our open-source platform here → \n* [Join our Discord](https://discord.com/invite/nNFUUDAKub)\n* Connect us with anyone who builds software around LLMs and would greatly benefit from a tool like this!", "url": "https://www.ycombinator.com/launches/Ljw-laminar-open-source-observability-and-analytics-for-complex-llm-apps", "votes": 17, "created_at": "2024-08-29T15:16:33.971Z" }, { "id": 82294, "title": "Laminar - Developer platform to ship reliable LLM agents 10x faster", "tagline": "Combining orchestration, evals, data, and observability into a single platform.", "body": "# tl;dr: Laminar is a developer platform that combines orchestration, evaluations, data, and observability to empower AI developers to ship reliable LLM applications 10x faster. Get started for free → [lmnr.ai.](https://www.lmnr.ai)\n\nHey everyone, we’re Robert, Din and Temirlan. Previously, we built infrastructure at Palantir, Amazon, and Bloomberg— now our goal is to help AI developers ship reliable LLM applications faster.\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/1a4a4a99-7e50-4bcd-86ff-8d2a5474a091)\n\n# ❌ The Problem\n\nLLMs are stochastic, and designing robust software around them (e.g. LLM agents) demands rapid iteration on core logic and prompts, constant monitoring and a structured way of testing new changes. Existing solutions are vertical and the burden of maintaining “glue” between them is still on the developers, which inevitably slows them down.\n\n# ✅ Our Solution\n\nLaminar is a dev platform that combines orchestration, evaluations, data, and observability to help AI devs to ship reliable LLM applications 10x faster. We provide:\n\n* a GUI to build LLM applications as dynamic graphs with seamless local code interfacing.\n* an [open-source package](https://github.com/lmnr-ai/lmnr-python) to generate abstraction-free code from these graphs directly into developers' codebases.\n* a state-of-the art evaluation platform that lets devs build fast and custom evaluators without managing evaluation infrastructure themselves.\n* a data infrastructure with built-in support for vector search over datasets and files. Data can be easily ingested into LLMs and LLMs can write to the datasets, creating a self-improving data flywheel.\n* a low latency logging and observability infrastructure.\n\n# Orchestration\n\nAs devs, we love to code everything ourselves, but we’ve realized the fastest way of iterating on LLM application logic is via graph UI. So, we’ve built the ultimate LLM “IDE”, where you build your LLM applications as dynamic graphs. You can build cyclical flows, route to different tools, and collaborate with your teammates in real-time!\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/be5a68c2-4440-4fd3-92e7-71c7436e13c8)\n\nGraphs can seamlessly interface with local code. “Function node” can call local functions on your server, right from our UI or our SDK. It’s a huge game changer for testing of LLM agents which call different tools and then circle the response back to LLMs. In the gif below, local function “save_result_to_db“, which runs on a server on my computer, is directly called from our UI.\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/17ade57e-5551-4db3-a036-0bed9956a79e)\n\nUsing our [open-source package](https://github.com/lmnr-ai/lmnr-python), you can generate zero-abstraction code from graph definition, which exactly replicates the graph functionality. Code is generated as pure functions right inside your repo, and you have total freedom to modify it however you want. It is extremely valuable for the devs who are tired of frameworks with myriads of layers of abstraction.\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/6955178c-ef6a-4b26-bac9-39fdebe6f1ac)\n\nYou can also deploy LLM pipelines as API endpoints on our infrastructure and easily call them via our Python/TS sdks.\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/d071c79f-9b3c-4376-9de1-c9cccd401e6b)\n\n# Evaluations\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/799c93b7-c273-4b65-ac0c-50a3e360952c)\n\nLaminar pipeline builder can be used to build custom and flexible evaluation pipelines that seamlessly interface with local code. You can start from something simple like exact matching and then build a custom LLM-as-a-judge pipeline tailored to your specific use case. You can upload large datasets and run evaluations on thousands of data points at the same time, and get all statistics about the run in real time. All of this without the pain of managing evaluation infrastructure yourself.\n\n# Observability\n\n![uploaded image](/media/?type=post&id=82294&key=user_uploads/1771812/f6d0e26d-60d7-4371-a558-a36006d8cd43)\n\nWhether you decide to host LLM pipelines on our platform or generate code from graphs, all pipeline runs are logged and you can easily inspect the traces in the convenient UI.\n\n# Conclusion\n\nLaminar aims to deliver the best developer experience for AI developers. We remove unnecessary friction and burden of managing infrastructure. We let you focus on building the best AI products and ship them 10x faster!\n\n# Our ask\n\n* Start building reliable LLM agents right now for free → \n* Check out [open-source code gen package](https://github.com/lmnr-ai/lmnr-python)\n* Connect us with anyone who builds software around LLMs and would greatly benefit from a tool like this!\n* [Join our Discord](https://discord.com/invite/nNFUUDAKub)", "url": "https://www.ycombinator.com/launches/LPK-laminar-developer-platform-to-ship-reliable-llm-agents-10x-faster", "votes": 97, "created_at": "2024-07-20T04:14:28.826Z" } ] }, { "id": 29825, "name": "Sepal AI", "slug": "sepal-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/78c48b77fe0ad236d148f827dd2ba0b86450afd3.png", "website": "https://www.sepalai.com", "all_locations": "San Francisco, CA, USA", "long_description": "Sepal is a data research company on a mission to advance human knowledge and capabilities through safe AI.\r\n\r\nWe partner with the world’s leading AI labs and enterprises to help their models get better at the tasks people actually want them to do.\r\n\r\nWe’ve built a Cloud-Native Agent Dataset Factory which turns the process of generating evaluation and training data from manual, inconsistent, and labor-intensive into something automated, standardized, and scalable.\r\n\r\nSepal AI was founded in 2024 by engineers and operators from Vercel and Turing. We went through Y Combinator, raised several million dollars from leading investors, and already count multiple Fortune 500s and top AI research labs as paying customers.", "one_liner": "Data Development for Advanced AI", "team_size": 15, "industry": "B2B", "subindustry": "B2B", "launched_at": 1722966351, "tags": [ "AIOps", "Reinforcement Learning", "Data Labeling", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Acquired", "industries": [ "B2B" ], "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/sepal-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/sepal-ai.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/sepalai/", "x_url": "https://x.com/sepal_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/78c48b77fe0ad236d148f827dd2ba0b86450afd3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 1996512, "full_name": "Robi Lin", "title": "Founder", "founder_bio": "Co-Founder @ Sepal AI\n\nBuilt the enterprise workflow products and fulfillment strategy at Turing.com. Scaled Turing’s LLM trainer business line from 50 to 800+ onboarded developers in 5 months for foundational LLM and enterprise customers. Previously was at Bain & Co.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/robert-lin1/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4668b119bdaddeda68e7781c88272985fc03575c.jpg" }, { "user_id": 860488, "full_name": "Kat Hu", "title": "Founder", "founder_bio": "Cofounder @ Sepal AI\n\nBuilt Turing’s Foundational LLM trainer business GTM. Ran orgs of 500+ AI trainers & built corresponding operations for scale. Previously was at McKinsey.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kathleenhu/", "x_url": "https://www.twitter.com/katqhu1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0060040b5fd066406e4be3460a6c0d0209a59029.jpg" } ], "jobs": [], "news": [ { "title": "Sepal AI supports the launch of Anthropic's Claude 3.7 Sonnet with Pre-launch Testing", "url": "https://x.com/sepal_ai/status/1894157588142690695", "date": "May 01, 2025" } ], "launches": [ { "id": 82824, "title": "🌱 Sepal AI - Confidently deploy your AI models", "tagline": "We build benchmarks, evals, & training datasets for enterprises.", "body": "# **Tl;dr:** Sepal provides frontier data and tooling for advancing responsible AI development.\n\n# **__________________________________________________________**\n\n[Sepal AI](https://www.sepalai.com/?ref=launch_yc) is on a mission to advance human knowledge and capabilities with the responsible development of artificial intelligence.\n\n# **🧐 Responsibly advance human knowledge with AI? What does that mean?**\n\nWe believe in a world where AI advances scientific research and empowers economic growth. \n\nTo achieve that future, AI product & model builders need:\n\n1. **Golden Datasets and Frontier Benchmarking:** To iteratively measure model performance on specific use cases.\n2. **Training Data:** To improve model capabilities using fine-tuning and RLHF.\n3. **Safety / Red-teaming:** To measure and forecast the safety of LLMs before putting them out in the wild.\n\n# **__________________________________________________________**\n\n# ⚠️ **Okay, well why does it matter?**\n\nFrontier data for AI development is vital for safe deployment & scaling. However, developing this data is difficult. \n\nMost frontier data requires domain knowledge that can be hard to source and curate (e.g., finance, medical, physics, biology, etc.). Publicly available benchmarks (e.g., MMLU, GPQA, MATH, etc.) are contaminated and too general to be useful to actual product & model builders.\n\n# **__________________________________________________________**\n\n# **🌱 How do we do this?**\n\nWe’ve built Sepal AI - the data development platform that enables you to curate useful datasets.\n\n**The Platform:** We bring data generation tooling, human experts, synthetic data augmentation, and rigorous quality control into one platform so you can manage the production of high-quality datasets.\n\n![uploaded image](/media/?type=post&id=82824&key=user_uploads/860488/81dd4889-9694-4c67-8505-389edf7c3361)\n\n**Our Expert Network:** We’ve built a network of 20k+ experts across STEM and professional services (think academic PhDs, business analysts, medical professionals, marketing and finance consultants) to support campaign design & data development. \n\nSample engagements we’ve run:\n\n* **🧬 Cell and Molecular Biology Benchmark:** An original benchmark to evaluate complex reasoning across models. Produced by a team of PhD biologists from top institutions in the US.\n* **💼 Finance Q&A + SQL Eval:** A Golden Dataset to test the ability of an AI agent to query a database and produce human-expert-level answers to complex finance questions.\n* **📏 Uplift Trials & Human Baselining:** End to end support for conducting secure in-person evaluations on model performance. \n* _…. \\[insert your custom use case next?\\]_\n\n# **__________________________________________________________**\n\n# **🙏 Asks:**\n\n1. If you are building an AI application and need to measure or improve your model, or\n2. If you are a researcher at an AI lab building or evaluating models for new capabilities / risk areas, or\n3. If you’re passionate about the development of AI, AI safety, or evals in general…\n\nLet’s chat — please check us out at [www.sepalai.com](http://www.sepalai.com) \n\n# **__________________________________________________________**\n\n# **👪 Our team:**\n\n![uploaded image](/media/?type=post&id=82824&key=user_uploads/860488/a1187807-573a-4248-9181-6378d6e2b042)\n\nMeet [Kat](https://www.linkedin.com/in/kathleenhu/) (on the left), [Robi](https://www.linkedin.com/in/robert-lin1/) (in the middle), [Fedor](https://www.linkedin.com/in/f3d0r/) (on the right)!\n\nRobi and Kat previously built the technical LLM training business for Turing. Kat on the go-to-market & operations side. Robi on the product & fulfillment side. Fedor is a long-time close friend - he was an early engineer at Vercel & Newfront where he built out foundational infrastructure.\n\nSay hi: [founders@sepalai.com](mailto:founders@sepalai.com).", "url": "https://www.ycombinator.com/launches/LXs-sepal-ai-confidently-deploy-your-ai-models", "votes": 51, "created_at": "2024-08-07T18:46:33.791Z" } ] }, { "id": 29950, "name": "Pipeshift", "slug": "pipeshift", "former_names": [ "Xylem AI", "Pipeshift AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6dc8136429eadec250bda86f1102143f81a24beb.png", "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": [ "AIOps", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "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/pipeshift", "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", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/7bbe0c7a8e3c5d237daced05c9c6ccf3e3d2c253.png" ], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "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", "x_url": "https://x.com/arkobuilds", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/39136e6e723e1f76e5a1bdc560206943aee81027.jpg" }, { "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", "x_url": "https://x.com/FerraoEnrique", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a33343830babab394b79c61cdb3cffd378b0f8f5.jpg" }, { "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", "x_url": "https://x.com/pranavreddyg", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b8b0ad79f3f331999efdd9b6b917e168ce8ed31e.jpg" } ], "jobs": [], "news": [ { "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" } ], "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" } ] }, { "id": 30078, "name": "Abundant", "slug": "abundant", "former_names": [ "Abundant (prev. Scorecard)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac0261264b03915325cd897f5bcb7484eec48903.png", "website": "https://www.abundant.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Hello! 👋\r\n\r\nWe are a team of former ML engineers, founders, roboticists and ops leads who obsess about data and its impact on safe, reliable AI.\r\n\r\nWe specialize in creating environments and datasets for RL by leveraging our experience in simulation and model training.\r\n\r\nBy the numbers:\r\n • Powering 3 of the top 6 global AI labs and multiple Fortune 500 enterprises\r\n • Billions of training tokens generated each month, 2x month over month\r\n • Exclusive, global network of over 500 domain experts\r\n\r\nWe believe humans are inherently creative, and thrive by pushing the frontier.\r\n\r\nWe are working towards an abundant future--one where everyone has access to infinite intelligence, services and goods.\r\n\r\nBased in San Francisco, CA. We enjoy good food and good company.\r\n\r\n-- more info below --\r\n\r\nAbundant is building the NVIDIA of training data. AI models rely on two fundamental ingredients: compute and data. NVIDIA, the leader in compute, has a peak market cap of $5T and generated $130B in revenue last year as the need for scaling compute has exploded. We believe the need to scale data is just beginning, as we move beyond SFT and human supervision to RL and Learning from Experience.\r\n\r\nOur founding team consists of second-time founders, ML engineers and data leads from Waymo, Google, Meta and AWS. Our team has previously collaborated with DeepMind to classify hate speech in YouTube videos, trained SOTA models for self-driving, and scaled data pipelines with thousands of human annotators. Our pioneering work in human computation, synthetic data, imitation learning and RL give us a solid advantage in delivering results to our customers.\r\n\r\nWhy now? Training data is more important and more scarce than ever before. Scaling laws dictate that linear improvement in model performance demands an exponential increase in training data. But there is only one World Wide Web and most of it has already been trained on. The next advances will require new, diverse, and high-quality datasets, making training data more important and scarce than ever before.\r\n\r\nWhat happens if we succeed? Abundant will be the core enabler for AGI and beyond. Most of the challenges in model training are already solved. What’s missing is the data necessary to move from general knowledge to domain expertise; from chatbots to agents; and from digital intelligence to physical AI. Ask any AI researcher or roboticist: the core bottleneck to progress is the availability of data, i.e. “abundant data”.\r\n\r\nAbundant works with the most advanced AI labs and startups, as well as F500 enterprises.", "one_liner": "Agent simulation and RL for researchers", "team_size": 7, "industry": "B2B", "subindustry": "B2B", "launched_at": 1731958841, "tags": [ "AIOps", "API", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B" ], "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/abundant", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/abundant.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/abundant-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/abundant-ai/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac0261264b03915325cd897f5bcb7484eec48903.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 7473, "full_name": "Jesse Hu", "title": "Founder", "founder_bio": "Working towards an abundant future. ex-Waymo ML engineer.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jessehu", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a8a4ef665af22a7083c89e314f2a5a8c24ab7468.jpg" }, { "user_id": 1890020, "full_name": "Meji Abidoye", "title": "Founder", "founder_bio": "datum.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/meji-abidoye/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a0ad58a12c200357c79faba7e5d500eb2a88b767.jpg" }, { "user_id": 1694208, "full_name": "Ke Huang", "title": "Founder", "founder_bio": "make shit happen!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ke-huang-07611058", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5ea8c4d7a206a1d43a8f4dd32d912144ff22d86c.jpg" } ], "jobs": [ { "id": 98753, "title": "Member of Technical Staff, Platform Engineering", "url": "https://www.ycombinator.com/companies/abundant/jobs/qj6SIWp-member-of-technical-staff-platform-engineering", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$135K - 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Customers give us the parts they want to put into designs and we give them footprints, symbols and 3d models made from the manufacturers datasheet.", "one_liner": "PCB component librarian", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1740421634, "tags": [ "AIOps", "Electronics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "Switzerland", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/stillwind", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/stillwind.json", "year_founded": 2024, "city": "Zürich", "country": "CH", "linkedin_url": "https://www.linkedin.com/company/augento/", "x_url": "https://x.com/stillwinddotai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/stillwind-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c1b36aa3102c0461123d8bc84ffc46b58b33f3a9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 1922635, "full_name": "Linus Meierhöfer", "title": "Founder/CEO", "founder_bio": "Co-founder & CEO @ Stillwind. Previously researched quantified complexity theory. Worked in Quantitative Engineering @ DRW. Studied CS @ ETH Zurich.\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/linus-meierhöfer-7a4596259", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/06909f44e7c94b237b0204cc7c1a836a03b02675.jpg" }, { "user_id": 1909734, "full_name": "Hannes Furmans", "title": "Founder/CTO", "founder_bio": "Co-founder & CTO @ Stillwind\nDid CS at ETH Zürich. Did open source work on libp2p. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hannes-furmans-52923625a/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e0a27bfd69e63a8fec7f4d02d6dbb8aaf39703df.jpg" }, { "user_id": 2235211, "full_name": "Josef Zoller", "title": "Founder", "founder_bio": "Co-founder & Chief Engineering Officer @ Stillwind. CS @ ETH Zürich. I’m a full-stack software engineer but specialize in networks, systems programming, and ML.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zollerboy1", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/edf4c9e6c9d2f70ba0ed4cf881d83e2cddf92b3f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 104942, "title": "Stillwind Builder — Zero to BOM in minutes instead of days", "tagline": "AI agent that turns your PCB design intent into a complete, verified bill of materials.", "body": "❌ **The problem**\n\nEvery PCB design is a constraint problem: you need components that are electrically and functionally compatible with each other. Solving it by hand means hours — often days — of tracking down parts, reading datasheets, adding a component, discovering a conflict, and backtracking. Pick an MCU and the cascade begins: it needs a specific crystal, external flash on the QSPI bus, a regulator that can source its peak current. Every choice ripples through the whole design.\n\n✅ **The solution**\n\nStillwind Builder formalizes your design constraints as an explicit system of equations and solves for matching components.\n\n* **Verified specs.** Before we show you a part, a second model independently re-reads its datasheet and verifies the specs match — so the numbers you design against are what the datasheet actually says, not extraction artifacts.\n* **Dependency-aware.** When you lock in a component, the agent reads its datasheet for the supporting parts it requires — voltage regulators, crystals, external flash — and updates the constraint system. The ripple effects of each choice are handled instead of discovered later.\n* **You stay in control.** You set the direction and make the calls that matter; the agent handles the combinatorial grind.\n\nAt the end, you download your BOM and start wiring.\n\n🚀 **Try it**\n\nThe public beta is live and free during launch week: [**https://stillwind.ai**](https://stillwind.ai)\n\nBuilding hardware and want to tell us what's broken? We'd genuinely love that — grab time with us: ", "url": "https://www.ycombinator.com/launches/RIc-stillwind-builder-zero-to-bom-in-minutes-instead-of-days", "votes": 5, "created_at": "2026-07-07T21:57:20.206Z" }, { "id": 88188, "title": "Augento 🤖 - DeepSeek reinforcement fine-tuning as-a-service", "tagline": "Improving your AI agents with reinforcement learning.", "body": "Hey all 👋, we’re Linus, Hannes, Lukas, and Josef—co-founders of [**Augento**](https://www.augento.ai/)! 🕊️\n\n**TL;DR**\n\nWe align your agents with reinforcement finetuning. You give us your agent, tell us where it fails and we’ll improve it. 🚀\n\nWe are actively looking for **design partnerships**. If you are interested, please shoot us a message at [**founders@augento.ai**](mailto:founders@augento.ai) 😄\n\n**The Problem ❌**\n\n🗣️ AI Agents struggle in real-world environments. Even state-of-the-art reasoning models score below 50% accuracy on non-trivial benchmarks.\n\nThe solution for many is still prompt engineering & expanding the models' guardrails. However, anyone who’s fought with large prompts knows how draining it can be. You’re never quite sure if the model is really following your instructions or if your tweaks make any difference.\n\n**Our Solution ✅**\n\nWe tackle this by replacing prompt engineering with fine-tuning + RL on **your** feedback. We integrate with a two lines of code change in your existing system.\n\n[**https://www.youtube.com/watch?v=Sulzf7VZr3k**](https://www.youtube.com/watch?v=Sulzf7VZr3k)\n\n**The Workflow 💡**\n\n1\\. Swap out your LLM connector URL with ours.\n\n2\\. We intercept every prompt and output, displaying them in our UI.\n\n3\\. Where necessary, you give high-level feedback, like your preferred tone or how a tool should actually be used.\n\n4\\. We continuously post-train the model to match up to your feedback.\n\n5\\. Once you deem it good enough and want to switch over to the model, you can do that with a click of a button, no changes to your code required.\n\n**Our Ask**\n\nWe’d love your input and are looking for early users to test-drive Augento. Shoot us a message at [**founders@augento.ai**](mailto:founders@augento.ai)\n\n**The Team**\n\nLinus previously studied CS @ ETH in Zurich and did research in complexity theory. During his studies, he worked as an ML Engineer & as a Quantitative Developer in High-Frequency Trading.\n\nHannes previously studied CS @ ETH Zurich and worked on decentralized and distributed systems. During his studies, he worked as a paid contributor for a big open source project and as the technical lead for his previous startup.\n\nJosef previously studied CS @ ETH Zurich, and worked on computer systems and networks, as well as ML. During his studies, he worked as a full-stack software engineer and embedded systems developer.", "url": "https://www.ycombinator.com/launches/MwO-augento-deepseek-reinforcement-fine-tuning-as-a-service", "votes": 18, "created_at": "2025-03-02T02:49:45.257Z" } ] }, { "id": 30272, "name": "Lucidic AI", "slug": "lucidic-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f666d84f3ea0fbb63226b5b84cd46c97e83f4414.png", "website": "https://lucidic.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Lucidic is trying to find a new paradigm to teach models. The last decade made models intelligent, but intelligence is not the same as experience. Humans do not get better by memorizing thousands of examples; we build learning systems around ourselves: skills, memories, critique, practice, tools, and guidance. And every person learns differently because every task is different. Lucidic brings that idea to AI agents by training a custom learning system for each agent, so it learns what to remember, what skills to build, when to ask for help, and how to improve from experience. The model provides the intelligence, Lucidic gives it a way to learn.", "one_liner": "Reimagining how Machines Learn ", "team_size": null, "industry": "B2B", "subindustry": "B2B", "launched_at": 1736904770, "tags": [ "AIOps", "Developer Tools", "SaaS", "Automation", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B" ], "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/lucidic-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/lucidic-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/105580616/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f666d84f3ea0fbb63226b5b84cd46c97e83f4414.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": 2183616, "full_name": "Abhinav Sinha", "title": "Founder/CEO", "founder_bio": "Hi, I’m Abhinav the founder and CEO for Lucidic AI! I’m from Stanford where I studied Computer Science with an AI specialization (both bachelor's and master's) and did research at Stanford's AI Lab. I’ve worked at Apple as a software engineer and at Citadel Securities and Susquehanna International Group as a quant. \n\nIn my free time, I like to play basketball, pickleball, lift, and rewatch Christopher Nolan movies!\n\nFeel free to reach out, would love to chat -- abhinav@lucidic.ai ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/abhinavsinha15/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b9b8b7c2a005b7d2a2f4231aba78117677082f03.jpg" }, { "user_id": 2373805, "full_name": "Andy Liang", "title": "Founder/CTO", "founder_bio": "Founder/CTO at Lucidic AI, working on an analytics, testing, and simulations framework for AI agent devs. Stanford BS/MS Computer Science (AI specialization). Ex Citadel, AppLovin. \n\nPreviously built GrocerCheck, a COVID-19 web app helping shoppers social distance effectively, with over 200,000 users and endorsed by the Government of Canada. \n\nReach me at andy@lucidic.ai", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/andyliang223/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3c501422ab10290f03dc7c6e7b85cf3be1715f93.jpg" }, { "user_id": 2373806, "full_name": "Jeremy Tian", "title": "Founder", "founder_bio": "Hi! I'm Jeremy Tian, one of the founders and the Chief Scientist at Lucidic AI, working to explain your AI models' decisions. I have a BS/MS in Computer Science with an AI specialization from Stanford University. Ex. Quantitative Trader at DRW and software engineering/machine learning engineering at Steel Dynamics. Contact me at jeremy@lucidic.ai!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jeremy-tian-a2076a239/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a05c261a89cb8f8bde42c10b8654a299f061adaf.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30333, "name": "TensorPool", "slug": "tensorpool", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/79bd0c6f85962c1282260ab60adb0462fdb87ec8.png", "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", "launched_at": 1738629191, "tags": [ "AIOps", "Developer Tools", "SaaS", "DevOps", "Cloud Computing" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "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/tensorpool", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/tensorpool.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/105594641/", "x_url": "https://x.com/tensorpool", "fb_url": null, "cb_url": null, "github_url": "https://github.com/tensorpool/tensorpool", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/79bd0c6f85962c1282260ab60adb0462fdb87ec8.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 2359695, "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/", "x_url": "https://x.com/TychoSvoboda", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/71640aeaca285abc7a66cfc35382cb816fb97f60.jpg" }, { "user_id": 969804, "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, "linkedin_url": "https://www.linkedin.com/in/joshkmartinez/", "x_url": "https://x.com/joshkmartinez", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c836606ceba2f01352bc677895c11d93dbd10733.jpg" }, { "user_id": 1709047, "full_name": "Hlumelo Notshe", "title": "Founder/CPO", "founder_bio": "Co-Founder & CPO of TensorPool\nEx-DeepMind & NextDoor\nStanford BS & MS in Computer Science + Mathematics", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hlumelo-notshe", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4f64adb6ddd63e6cd1c2d5f8c9773281f1ced8d6.jpg" } ], "jobs": [], "news": [], "launches": [ { "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 \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" } ] }, { "id": 30455, "name": "Paper Instruments", "slug": "paper-instruments", "former_names": [ "The LLM Data Company" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b0612dcacbdc26af352611e30ccd5d9b8e4dfc31.png", "website": "https://www.paperinstruments.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Paper Instruments post-trains LLMs to accelerate knowledge work.", "one_liner": "Open-source frontier models for knowledge work", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1747787326, "tags": [ "AIOps", "Generative AI", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B" ], "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/paper-instruments", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/paper-instruments.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/the-llm-data-company/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b0612dcacbdc26af352611e30ccd5d9b8e4dfc31.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 2567589, "full_name": "Daanish Khazi", "title": "Founder/CEO", "founder_bio": "Building Paper Instruments ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daanishkhazi/", "x_url": "https://x.com/bertgodel", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d801ee4030acebe9150bb04ac835dc66ac53e17f.jpg" }, { "user_id": 1654752, "full_name": "Gavin Bains", "title": "Founder", "founder_bio": "Building Paper Instruments", "is_active": true, "linkedin_url": "https://linkedin.com/in/thegavinbains", "x_url": "https://x.com/thegavinbains", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/97ae09d79028e06f59656d6104a43065f47bb315.jpg" }, { "user_id": 812898, "full_name": "Joseph Besgen", "title": "Founder", "founder_bio": "Building Paper Instruments", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jpbesgen/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/006ef838d361412552b6f85a35f53bcce895d81a.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90688, "title": "The LLM Data Company - Workspace for Evals", "tagline": "Write, version and execute evals and RL rewards", "body": "[**The LLM Data Company**](https://www.thellmdatacompany.com/) creates tooling to write, version and execute evals for models and agents. We help you measure performance and define rewards for reinforcement learning.\n\n[https://www.youtube.com/watch?v=56LpwoPxqjc](https://www.youtube.com/watch?v=56LpwoPxqjc%5C)\n\nEvaluating model performance is an opaque art and vanilla LLM judges do not provide the necessary signal to measure output quality. Additionally, modern GRPO-style RL techniques offer rapid, generalizable improvements to model performance, but they require well-specified eval datasets to be used as rewards. We help you quickly specify high signal evals across a variety of unstructured tasks with aligned graders, fine-grained rubrics and fast iteration.\\\n\\\nOur eval workspace – [**doteval**](https://www.doteval.com/) – offers a Cursor-like experience to edit evals-as-code against our .yaml schema. Version evals across checkpoints, replace manual effort with AI-generated diffs, and compare eval runs on tight-execution loops to align them with your IP. This way, you can confidently determine whether you should upgrade to Claude 4 or stay on Gemini 2.5 Pro, or whether this new 12,000 word prompt actually creates net improvement over that 8,000 word prompt. You can also one-click export the spec to be used as the training set for RL and post-train your own model.\n\n![uploaded image](/media/?type=post&id=90688&key=user_uploads/2567589/e6c9c568-81ef-4057-bdff-4ad49982f76c)\n\n\\\nToday, we work with frontier AI teams to benchmark performance for complex model tasks. If you are interested in early access to doteval, need help creating an evaluation dataset, or are trying out GRPO or RFT and want something to quickly train against, please reach out at:\\\n[**founders@thellmdatacompany.com**](mailto:founders@thellmdatacompany.com)", "url": "https://www.ycombinator.com/launches/Nai-the-llm-data-company-workspace-for-evals", "votes": 105, "created_at": "2025-05-23T18:42:39.878Z" } ] }, { "id": 30500, "name": "Besimple AI", "slug": "besimple-ai", "former_names": [ "Simple Annotation", "besimple ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8305d688995d7c8f184d463f02f3ded6947c12e7.png", "website": "https://besimple.ai", "all_locations": "San Francisco, CA, USA", "long_description": "We are building the data layer for AI, starting with audio. \r\n\r\nWe start with data collection, curating our own proprietary set of diverse conversational data covering a wide range of languages, dialects and accents. We then leverage human expert audio annotators and our own annotation platform to process audio data for Automatic Speech Recognition. \r\n\r\nWith human level transcription and diarization, our data help push the audio model frontier. Today we have over millions of hours of conversational data, and growing. \r\n\r\nIf you need audio data for training or evaluating your voice models or voice agents, reach out! We offer flexible licensing deals that work for startups and enterprises, with minimal process. \r\n\r\nAudio data should besimple :) ", "one_liner": "Voice data for AI", "team_size": 10, "industry": "B2B", "subindustry": "B2B", "launched_at": 1747861843, "tags": [ "AIOps", "Data Labeling", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B" ], "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/besimple-ai", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/besimple-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/besimple-ai/", "x_url": "https://x.com/besimple_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8305d688995d7c8f184d463f02f3ded6947c12e7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/14e2e0920e736148a86789dc8875ba75c3eb4e88.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/0009a9ebc2ee89017db7e4d65efbcee0f22e1954.png" ], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 2527745, "full_name": "Yi Zhong", "title": "Founder/CEO", "founder_bio": "Currently co-founder & CEO at Besimple AI, building the human-interaction data layer for emotionally intelligent AI. \n\nPrior to Besimple, I was a PM lead at Meta for its AI Integrity team, building large scale AI systems to detect problematic contents and accounts. I also worked as a PM at Microsoft and Dropbox, after graduating from MIT in 2016.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yzhong94/", "x_url": "https://x.com/yiz_be_building", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/efec15c007a345ab4e139c02c9ec73a69f2fb207.jpg" }, { "user_id": 2544844, "full_name": "Bill Wang", "title": "Founder/CTO", "founder_bio": "Shenzhen-born, Rhode Island-brewed, Bay Area-domesticated. While at Meta, launched 7 products and killed 2. Most recently, led the GenAI Annotation team, developing an in-house annotation platform for training LLaMa. Previously, managed an engineering organization responsible for improving connectivity for over 300 million users and optimizing 70%+ of Meta's annual SMS spend.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/8illwang/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/76fe485d198fa8330635f73a2b97cb0314d66721.jpg" } ], "jobs": [ { "id": 96880, "title": "Audio QA Lead - Part Time Contractor", "url": "https://www.ycombinator.com/companies/besimple-ai/jobs/mKH99fq-audio-qa-lead-part-time-contractor", "location": "San Mateo, CA, US / Remote (US)", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$25 - $45 / hourly", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 97432, "title": "Strategic Projects Lead — Audio Data", "url": "https://www.ycombinator.com/companies/besimple-ai/jobs/yWfhhOR-strategic-projects-lead-audio-data", "location": "San Mateo, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$140K - $160K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 110630, "title": "Community Growth Manager, Contributor Acquisition", "url": "https://www.ycombinator.com/companies/besimple-ai/jobs/pDSSREY-community-growth-manager-contributor-acquisition", "location": "San Mateo, CA, US / Remote (US)", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$30 - $50 / hourly", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 116834, "title": "RL Environment Engineer (Full-time, Part-time, or Contract)", "url": "https://www.ycombinator.com/companies/besimple-ai/jobs/dPIlkr6-rl-environment-engineer-full-time-part-time-or-contract", "location": "San Mateo, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "AI engineer", "salary_range": "$160K - $360K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 116900, "title": "Linguist - Speech & Conversational AI", "url": "https://www.ycombinator.com/companies/besimple-ai/jobs/uTWqXak-linguist-speech-conversational-ai", "location": "San Mateo, CA, US / Remote (US)", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$30 - $60 / hourly", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Announcing our seed round led by YCombinator - Besimple AI", "url": "https://besimple.ai/blog/announcing-our-seed", "date": "Dec 05, 2025" } ], "launches": [ { "id": 90653, "title": "Besimple AI – Spin up your own data annotation platform in 60 seconds🚀", "tagline": "We make data annotation simple", "body": "Hey there! We are [Yi](https://www.linkedin.com/in/yzhong94/) and [Bill](https://www.linkedin.com/in/8illwang/), from [Besimple AI](https://www.ycombinator.com/companies/besimple-ai)! Struggling with data annotation? Keep reading!\n\n💎 **TLDR**\n\nWith Besimple, you can generate your own platform for annotating AI eval and training data instantly.  Simply paste or stream any type of raw data and we'll instantly generate a tailored annotation interface, clear guidelines, an automated human-in-the-loop workflow, and AI judges to scale your insights.\n\nhttps://www.youtube.com/watch?v=Hw-EVKdOhds\n\n## **❌ The Problem**\n\nHigh-quality, human-reviewed data is essential for improving AI models. But teams today face significant challenges:\n\n* **Complex LLM Workflows**: Most AI startups and even the largest AI companies use spreadsheets for annotations to “move fast”. It is brittle. And it breaks down quickly for dynamic, multimodal, agent-based LLM use cases.\n* **Annotation Bottlenecks**: As models get better, only domain experts and someone on your team can improve model performance.  They quickly get overwhelmed when guidelines and data change constantly. This is expensive and slows model iteration.\n* **No Feedback Loop**: After a model ships, interesting production data isn’t automatically fed back into evaluation or training, missing opportunities for ongoing improvement.\n\n## **✅ Our Solution:** \n\n1. Instant custom UI: Just paste or stream your data and we create a custom annotation UI for your task.  We support text, chat, audio, video, LLM traces, and more. \n2. Tailored guidelines: Import any existing guidelines or we will draft new ones aligned with your business goals, ready for annotation\n3. AI Judges for real-time evaluation: LLM-based “judges” continuously learn from incoming annotations to evaluate live traffic and flag borderline cases for human review.\n4. Enterprise-grade deployment: On-prem optional install, and robust user management for internal SMEs, external vendors, or Besimple’s vetted annotators.\n5. Lightning fast setup: No code, no plugins—just drop in your data, set guidelines, and you’re good to go.\n\n**Traction:** The leading AI grading company Edexia uses besimple to annotate hundreds of decisions and improve their evals.\n\n## **The Team**\n\nWe’re **Yi Zhong** and **Bill Wang** —who built the annotation platform for Meta’s Llama models. We founded Besimple AI to help everyone spin up a Scale AI in 60s.\n\n# **🙏 Our asks**\n\nWe’re on a mission to make _continuous, high-quality data_ the easiest part of AI development. If that resonates, let’s chat!\n\n1. Reach us at [**founders@besimple.ai**](mailto:founders@besimple.ai)\n2. Book demo [here](https://calendly.com/yi-besimple/besimple-ai-intro-call).\n3. Or join our self-serve waitlist on our [website](https://besimple.ai/)!\n\nWe’ll show you how to launch a custom annotation platform in under a minute!", "url": "https://www.ycombinator.com/launches/Na9-besimple-ai-spin-up-your-own-data-annotation-platform-in-60-seconds", "votes": 332, "created_at": "2025-05-22T20:14:47.012Z" } ] }, { "id": 30521, "name": "Kairos", "slug": "kairos", "former_names": [ "Janus" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3b44f7b050ecc3ee1451baeb4e0a733b19937955.png", "website": "https://www.withkairos.co/", "all_locations": "San Francisco, CA, USA", "long_description": "Kairos closes the last-mile reliability gap in AI deployments. \r\n\r\nWe bring frontier techniques to companies in critical industries, deploying specialized agents that encode operator expertise and reliably automate their most manual workflows.", "one_liner": "Specialized AI for Critical Industries", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1748466933, "tags": [ "AIOps", "Machine Learning", "Reinforcement Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 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/kairos", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/kairos.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/withkairos/", "x_url": "https://x.com/with_kairos", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3b44f7b050ecc3ee1451baeb4e0a733b19937955.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Andrew Miklas", "url": "https://www.ycombinator.com/people/andrew-miklas" }, "founders": [ { "user_id": 1090038, "full_name": "Shivum Pandove", "title": "Founder", "founder_bio": "Kairos | Prev. ML/CS at Carnegie Mellon, Scaled 3+ startups as a SWE/PM from 0 -> 1, and conducted DL research across comp. bio labs", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shivump1/", "x_url": "https://x.com/shivump101", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8567c0d26b02bc6e0b04899282d36f727db954a9.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30539, "name": "mlop", "slug": "mlop", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3af9294b241ac810c538d00aea0a921d1229099f.png", "website": "https://mlop.ai", "all_locations": "London, England, United Kingdom", "long_description": "A fully open-source, performant and actionable ML model training platform\r\n", "one_liner": "Experiment tracking for training ML models", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1746826132, "tags": [ "AIOps", "Developer Tools", "Machine Learning", "SaaS" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "B2B" ], "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/mlop", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/mlop.json", "year_founded": 2025, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/mlop", "x_url": "http://x.com/mlop_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/mlop", "github_url": "https://github.com/mlop-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3af9294b241ac810c538d00aea0a921d1229099f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 2374707, "full_name": "Stephen Sun", "title": "Founder/CTO", "founder_bio": "Building high quality tools for ML engineers", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jqssun", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4cc063b4584f3db8491006411bbfb2a837c4b88b.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30596, "name": "Adaptional", "slug": "adaptional", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8ea52a7c833e951c693eafd9561453164d5bf74c.png", "website": "https://www.adaptional.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Adaptional is building agentic AI for insurance claims. Claims are 75% of every dollar in the $1T insurance industry -- and one of the most under-optimized areas, still managed by adjusters working hundreds of files by hand. We're changing this with AI claims review agents, already deployed at some of the major P&C carriers. \r\n\r\nIn the future, claims will be powered by an intelligence layer that manages files and escalates actions to human adjusters. Adaptional is building the AI system that will be the foundation for every claims department across P&C insurance.", "one_liner": "AI-powered insurance claims review", "team_size": null, "industry": "B2B", "subindustry": "B2B", "launched_at": 1758589841, "tags": [ "AIOps", "Insurance", "Enterprise", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B" ], "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/adaptional", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/adaptional.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/adaptional", "x_url": "https://x.com/adaptionalAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8ea52a7c833e951c693eafd9561453164d5bf74c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 423912, "full_name": "Kevin Cox", "title": "Founder/CTO", "founder_bio": "Co-founded and sold digital insurance brokerage Savvy (W20). Led engineering teams at Clipboard Health while revenue grew profitably from $30M -> $130M. Building Adaptional - AI for commercial insurance.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kevin-cox-90337b47/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2b7259298331b37a4b0148298d7000aa1740d138.jpg" }, { "user_id": 350876, "full_name": "Suril Kantaria", "title": "Founder", "founder_bio": "Cofounder of Adaptional S25 and Savvy W20, a digital insurance brokerage for startups & SMBs. Previously on the early team at X/xAI and Primer, a NLP lab that raised $250m+.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/suril-kantaria-59522922/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6d48636a05ad637f49098c12abee3f0706b5ab03.jpg" } ], "jobs": [ { "id": 93126, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/adaptional/jobs/k7W6ge9-founding-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $280K", "equity_range": "1.00% - 2.00%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 99740, "title": "Founding GTM Lead", "url": "https://www.ycombinator.com/companies/adaptional/jobs/D8ofuWJ-founding-gtm-lead", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$140K - $185K", "equity_range": "0.35% - 0.75%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [] }, { "id": 30651, "name": "Alter", "slug": "alter", "former_names": [ "Alter AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3a30946452aaca1a7c8b4b8fefafd9807f194243.png", "website": "https://alterauth.com", "all_locations": "New York City, NY, USA", "long_description": "Alter is a zero-trust identity and access control platform purpose-built for AI agents. It wraps every tool call in strong authentication, fine-grained authorization, and real-time guardrails, so agents can move fast without breaking things.\r\n\r\nEach request is verified at the parameter level, authorized against granular policies, executed with least-privilege access, and fully audited in real time. Unsafe actions, whether it’s a rogue DROP TABLE or a payment above policy limits, are blocked before they touch production. Behind the scenes, Alter manages credentials, issuing ephemeral, scope-narrowed access for every interaction, then rotating or expiring it in seconds. The result: no long-lived secrets, no blind spots, and no surprises in audit.\r\n\r\nWith Alter, teams can move fast on AI agent initiatives while staying fully compliant with SOC 2, HIPAA, GDPR, and internal security standards. A CISO-ready dashboard delivers real-time visibility, detailed audit logs, and compliance-ready controls, removing silos, eliminating excessive permissions, and providing complete oversight of every agent workflow.", "one_liner": "Privileged Access Management for AI Agents", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1752476002, "tags": [ "AIOps", "Developer Tools", "DevSecOps", "Security", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Security" ], "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/alter", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/alter.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/alterai-dev", "x_url": "https://x.com/alter_ai_labs", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3a30946452aaca1a7c8b4b8fefafd9807f194243.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Jon Xu", "url": "https://www.ycombinator.com/people/jon-xu" }, "founders": [ { "user_id": 736424, "full_name": "Srikar Dandamuraju", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO of Alter (YC S25), a zero-trust identity and access control platform for AI agents. Former Platform Lead at Goldman Sachs, where I scaled GSAM’s post-trade infrastructure from single to multi-cluster and helped launch the GM Card. Before that, software engineer at Addepar and Clear Street.\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/srikar-d-473390136", "x_url": "https://x.com/realsreeks", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cab46acc391807e07306cfd19067daaf2b55c346.jpg" }, { "user_id": 1363069, "full_name": "Kevan Dodhia", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO of Alter (YC S25), a zero-trust identity and access control platform for AI agents. Previously technical co-founder of Compute.ai, where we built a compute engine 5x faster than EMR Spark and sold into highly regulated enterprises like LSEG. Compute.ai was acquired by Terizza in 2025.", "is_active": true, "linkedin_url": "https://linkedin.com/in/kevan-dodhia-760183129", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ab81ff957341010eea7e1ee52d81382311c83b1c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 93044, "title": "Alter – Identity and Access Control Platform To Secure Agents", "tagline": "Build secure, compliant agents without the fear of breaking production", "body": "### **TL;DR**\n\nAlter sits in the middle of every AI agent interaction, verifying identity and applying fine-grained RBAC & ABAC to check every parameter against policy. It instantly rejects dangerous actions and provides clear audit trails logging every request/response and decision.\\\n\\\nInterested? Learn more and request beta access at [alterai.dev](http://alterai.dev)\n\nDon’t want to read? Here is a video explaining what we do in a nutshell\n\n[Identity And Access Control Platform To Secure Agents | Alter (YC S25)](https://www.youtube.com/watch?v=UX0ACmMXpMg)\n\n**The Problem**\n\n**Dangerous:** Long-lived credentials and over-scoped service accounts mean a single leak can give an agent the keys to production letting it run destructive commands, exfiltrate sensitive data, or trigger costly transactions.\\\n**Opaque:** Scattered logs and siloed security make it impossible to trace “which agent did what, with which parameters” when something goes wrong, leaving teams blind in audits or incident response.\\\n**Slow:** Security reviews drag on for weeks because least-privilege and policy checks are hard to implement at scale, delaying projects and eroding velocity.\\\n**Overexposed:** Agents often get blanket root access, with no guardrails to stop prompt-injection or malicious payload swaps, so one bad input can become a catastrophic action.\\\n**The result:** enterprises face agents that _can_ and _do_ take unsafe actions in production, forcing teams to choose between unacceptable risk or shelving AI initiatives entirely.\\\n**\\\nOur Solution**\n\nAlter is the safest way to run AI agents in production without giving them blanket credentials or risking compliance breaches. From one central control layer, organizations can:\n\n* Host or connect to all their tools. We support MCP and Native tools (A2A coming soon)\n* Approve or deny any agent request in real time, with identity verification baked into every call.\n* Apply parameter-level RBAC & ABAC so dangerous actions like drop table commands in prod, during a freeze never reach production.\n* Instantly grant seconds-lived, scope-narrowed tokens so agents get only the access they need, only for that task.\n* Remove the need for long-lived API keys entirely, eliminating one of the biggest security liabilities.\n* See exactly what every agent did, when, and with what parameters, all in one CISO-ready dashboard.\n* Pass SOC 2, HIPAA, and GDPR audits without slowing down because compliance controls run automatically in the background.\n* Scale to any number of agents and tools without creating security silos or over-permissive access.\n\n### **The Team**\n\nWe are Srikar and Kevan.\\\n\\\nWe previously built enterprise-grade infrastructure at ComputeAI and Goldman Sachs, powering mission-critical systems for the London Stock Exchange and the Apple Card launch. Now we’re using that experience to make AI agents safe for production by giving them only the access they need and only for as long as they need it.\\\n**\\\nOur Ask**\n\nWe’re looking to connect with:\n\n* Engineering and security leaders running AI agents in production\n* Enterprises in regulated industries (finance, healthcare, government) with strict compliance requirements\n* Startups building AI-first products that need zero-trust access controls\n* Infosec teams looking to eliminate long-lived credentials and enforce least-privilege by default\n\nAs a bonus, we have also partnered with former OpenAI cybersecurity experts to provide you with ongoing red teaming, catching prompt-injection, data exfiltration, and other exploits before attackers can.\n\nIf you or someone you know is interested, please reach out to us at [**founders@alterai.dev**](mailto:founders@alterai.dev) or book a time to chat.", "url": "https://www.ycombinator.com/launches/OCi-alter-identity-and-access-control-platform-to-secure-agents", "votes": 30, "created_at": "2025-08-15T19:42:34.047Z" } ] }, { "id": 30804, "name": "Luminal", "slug": "luminal", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/200fe1fa3e40bd9aed419582490f17ed6cf00c37.png", "website": "https://luminal.com", "all_locations": "San Francisco, CA, USA", "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.", "team_size": 7, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1752295982, "tags": [ "AIOps", "Developer Tools", "Cloud Computing", "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": "Growth", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": [ { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Most people running their own AI models are lighting money on fire and don’t even know it. The rush to get to market first means nearly every AI company is leaving thousands of dollars on the table every MONTH by not optimizing their models. That’s why we made Luminal.\r\n\r\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!" }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "At Luminal, we’re taking a bold swing at redefining how machine learning engineers interact with the core compute layer powering AI. When we succeed, the world’s intelligence stacks will be faster, simpler, and more robust. The hardware is ready for AI performance, the low-level code is not.\r\n\r\nSolving the software layer destroys NVIDIA’s moat and democratizes compute for AI" } ], "url": "https://www.ycombinator.com/companies/luminal", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/luminal.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/luminalai", "x_url": "https://x.com/luminal_ai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/luminal-ai/luminal", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/200fe1fa3e40bd9aed419582490f17ed6cf00c37.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/73/1757458252-72779f72997f54317b8c01694f81904a/3f224a54c068a1a328bad1346d270df1.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022731Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=baa04fa440a7a89278a425e5d379c70e68fcc86cdadb2696474ed7dae2cc989c", "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/afe07b7cdad741133663ccd98ae75b789d6181ec.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/4246eb727ea8c653fa0d76f735c92584d36369c7.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1374217, "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", "x_url": "https://x.com/joefioti", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0583e75ba29d754c8f1ed1c3827cf290ad883aae.jpg" }, { "user_id": 1927303, "full_name": "Matthew Gunton", "title": "Founder/CTO", "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", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/matthewjgunton/", "x_url": "https://x.com/matthewjgunton", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/15de4b81bdb4fc504eb3cf421a355e8a3e6a6d8e.jpg" }, { "user_id": 427411, "full_name": "Jake Stevens", "title": "Founder", "founder_bio": "Cofounder at Luminal: generating GPU kernels automatically to speed up ML models. Ex-Apple. Talk to me about donuts or compilers or both :)", "is_active": true, "linkedin_url": "https://linkedin.com/in/jake-stevens-", "x_url": "https://x.com/stake_jevens", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b4844719d9e9c9bd7f7cd8d3f02862f691340f0d.jpg" } ], "jobs": [ { "id": 80694, "title": "Compiler Engineer", "url": "https://www.ycombinator.com/companies/luminal/jobs/GAXg6qE-compiler-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $250K", "equity_range": "0.15% - 0.75%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [ "Rust", "CUDA" ] }, { "id": 83575, "title": "Cloud Inference Engineer", "url": "https://www.ycombinator.com/companies/luminal/jobs/J87U2SS-cloud-inference-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $250K", "equity_range": "0.15% - 0.75%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Torch/PyTorch", "CUDA" ] }, { "id": 94688, "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)", "visa": "Will sponsor", "skills": [ "SASS", "CUDA" ] } ], "news": [ { "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" } ], "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" } ] }, { "id": 30903, "name": "Kestrel AI", "slug": "kestrel-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a17c93b508bc423bb59667db4befdfd07cfd6f68.png", "website": "https://usekestrel.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Kestrel is the AI automation layer for platform engineering teams.\r\n\r\nPlatform teams use Kestrel to automate incident response, cloud provisioning, CI/CD, security, and developer requests across their stack.\r\n\r\nKestrel turns natural-language prompts into deterministic, production-ready workflows with 40+ integrations, 200+ pre-built actions, and support for custom HTTP and webhook actions.\r\n\r\nDeveloper-first, with a CLI, Python SDK, and MCP server to manage workflows from where platform teams already work.", "one_liner": "AI Agents for Platform Engineering", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1761187283, "tags": [ "AIOps", "Developer Tools", "DevOps", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B", "Security" ], "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/kestrel-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/kestrel-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/kestrel-ai-security/", "x_url": "https://x.com/kestrel__ai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/KestrelAI/Kestrel-Operator", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a17c93b508bc423bb59667db4befdfd07cfd6f68.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1567ed15193a6530e6a8ca6468ddada7553e0aa0.png" ], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 2950276, "full_name": "Raman Varma", "title": "Founder/CEO", "founder_bio": "Co-founder & CEO. Prev: CS @ Berkeley; ML @ Sky Computing / BAIR labs, Square, Coursera; SWE @ Illumio.\nBuilding Kestrel – AI agents for platform engineering teams.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ramanvarma/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b2b5f7e9806eb261a5342845ec47b959b65e467e.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 104092, "title": "Kestrel - AI Agents for Platform Engineering", "tagline": "Automate incident response, CI/CD, developer requests, security, and more with AI agents that build deterministic workflows across your platform stack.", "body": "TL;DR: Kestrel turns natural-language prompts into deterministic, production-ready workflows that automate incident response, cloud provisioning, CI/CD, security, and developer requests. It supports 30+ integrations and is developer-first, with a CLI, Python SDK, and MCP server so you can manage workflows from where you (and your coding agents) already work. Build your first workflow in <5 mins with $1,000 in usage credits at [usekestrel.ai](http://usekestrel.ai).\n\n\n\n**The Problem**\n\nCoding agents made every developer faster. But the platform team did not get faster with them.\n\nDevelopers now ship with Cursor/Claude/Codex and generate infrastructure, deployments, and provisioning requests at a rate no platform team was staffed for.\n\nAt illumio I once waited more than three weeks just to get Kafka clusters provisioned for a new service I was building. And provisioning is only part of it. The platform team is also on the hook for incident response when production breaks, repairing the CI/CD glue that ties the deployment pipeline together, platform security, and every \"can you just\" developer request in Slack.\n\nMost platform teams pick one of two bad options. They hand developers (and their agents) direct access to infra, CI/CD, and cloud accounts, and hope nothing breaks. Or they gatekeep and become the bottleneck every developer has to wait on.\n\nBuilding a platform that developers actually like has been a luxury only the largest engineering orgs could staff, and an understaffed platform effort usually does more harm than good.\n\n**The Solution**\n\nSee how it works: \n\nPlatform teams use Kestrel to automate incident response, cloud provisioning, CI/CD, security, developer requests, and more across their stack. You describe what you want to automate in plain English, and the Workflow Agent builds the whole thing. It draws from 140+ pre-built actions across 30+ integrations, plus custom HTTP APIs and webhooks for anything that isn't built in yet.\n\nOnce a workflow is configured it runs deterministically with no LLM deciding what to do at runtime.\n\nYou can define workflows that handle self-service developer requests without handing over the keys; scope who can trigger each workflow; fence it to specific clusters, cloud accounts, or repos; and require approval before anything touches production. Developers get a golden path without free rein, and the platform team keeps the guardrails without gatekeeping.\n\nKestrel analyzes your connected integrations and suggests ready-to-deploy workflows, and the Workflow Assistant turns your existing runbooks and scripts into production-ready automations, so you don't have to start from scratch: \n\nEvery workflow you build in Kestrel is fully observable, with a dashboard that shows what's running, what's waiting on approval, and what failed; real-time step-level logs; and a failure diagnosis agent that fixes failed workflows for you: \n\nThe [Kestrel CLI](https://docs.usekestrel.ai/workflows/cli), [Python SDK](https://docs.usekestrel.ai/workflows/sdk), and [MCP server](https://docs.usekestrel.ai/workflows/mcp) let you and your coding agents build and manage workflows from the terminal and application code.\n\n**How It Works**\n\nFirst, connect your platform stack. Kestrel supports 30+ integrations across infrastructure, PaaS, AI compute, observability, on-call, networking, databases, CI/CD, and IaC:\n\n![uploaded image](/media/?type=post&id=104092&key=user_uploads/2950276/c11a044c-2df5-4c23-8035-5c23b99ad45c)\n\nThen, open the Workflow Agent and describe what you want to automate. Here are a few examples:\n\n* “When a PagerDuty alert fires for the production Kubernetes cluster, run root cause analysis, post the findings to #incidents in Slack, create a P1 Jira ticket, and apply the recommended fix after approval in Kestrel.”\n* “When a developer submits a cloud provisioning request in Slack, route it to the team lead for approval, provision the AWS resources with Terraform in the infra repo, and notify the developer once the pull request is merged.”\n\nEverything you do in the dashboard is available via the CLI/SDK/MCP. E.g. you can generate a workflow, activate it, and clear an approval from the terminal:\n\n![uploaded image](/media/?type=post&id=104092&key=user_uploads/2950276/e5c068f1-98e7-4e35-9e83-407f2d4807a6)\n\n**Ask**\n\nIf you're a platform engineer or eng leader tired of manually responding to developer requests, incidents, or CI/CD failures, you can try it yourself today with a 14-day free trial and $1,000 in usage credits. Use suggested workflows and the Workflow Assistant to build your first platform automations in minutes.\n\nIf you want to learn more about what onboarding looks like or dedicated infrastructure/on-prem deployment options, [book a demo](https://calendly.com/hello-usekestrel/30min) and I'll walk you through it.\n\n[Try Kestrel today](https://platform.usekestrel.ai/register)", "url": "https://www.ycombinator.com/launches/R4u-kestrel-ai-agents-for-platform-engineering", "votes": 20, "created_at": "2026-07-01T23:54:04.445Z" }, { "id": 95330, "title": "Kestrel: AI Copilot for Kubernetes Ops & Security", "tagline": "Run Kubernetes securely and fix incidents in seconds, not hours.", "body": "Hey everyone, we’re [Raman](https://www.linkedin.com/in/ramanvarma/) and [Evan](https://www.linkedin.com/in/evanchopra/) from Kestrel 👋\\\n\\\n**TL;DR:** Kestrel helps teams using Kubernetes detect, investigate, and fix issues without manual firefighting. It monitors cluster health and security 24/7, finds root causes, and generates ready-to-apply YAML fixes - so incidents get resolved before you even reach for your laptop.\\\n\\\nLaunch video: https://youtu.be/HA2YziEBMVw\n\n**Ask:** If your team runs Kubernetes, we’d love to hear from you! You can reach out to us at [raman@usekestrel.ai](mailto:raman@usekestrel.ai) or [book a demo here](https://calendly.com/raman-usekestrel/demo). Or get started in minutes with a [14-day trial](https://platform.usekestrel.ai/register)!\n\n# **The** **Problem**\n\nRunning Kubernetes at scale is **chaos**.\\\n\\\nAs founding engineers on the Kubernetes Security team at Illumio, we saw both startups and large **organizations struggle with its complexity and scale**. For example, network policies are so tricky that many teams default to allowing everything, and even basic ops tasks like debugging failed rollouts mean spending hours digging through logs and events just to find where things went wrong.\\\n\\\nOver time, this complexity compounds and clusters begin to drift - configs diverge, policies break, and “temporary” workarounds become production. And when something goes wrong, **engineers are buried under alerts and reports** from disconnected tools, trying to debug blind at 3 a.m.\n\n# **Our Solution**\n\nKestrel is redefining how teams manage Kubernetes with the **first agentic platform that unifies ops and security**. It replaces manual triage with autonomous investigations and human-in-the-loop remediations - so issues that used to take hours to debug can be understood and fixed in seconds.\n\n💬 **AI Chat Assistant**: Investigate **any Kubernetes issue** across all of your clusters in plain English - skip the complex queries and context switching, and get answers with ready-to-apply YAML fixes from a single point of command.\n\n![uploaded image](/media/?type=post&id=95330&key=user_uploads/2950276/4cabf3fd-7053-4e7b-8562-4feaa5f72df4)\n\n🚨 **Incident Response**: Kestrel continuously monitors your clusters to detect incidents, trace root causes, and generate **YAML fixes that can be applied with a single click**. It's like having a dedicated on-call SRE team - without the burnout or headcount.\n\n![uploaded image](/media/?type=post&id=95330&key=user_uploads/2950276/218cbd5b-4100-4341-a02b-4b8ff04d5a26)\n\n🌐 **Topology Map**: Visualize your Kubernetes infra with classified intra-cluster, cross-cluster, and external traffic overlaid in real time.\n\n![uploaded image](/media/?type=post&id=95330&key=user_uploads/2950276/280f095a-4030-46bf-824e-1f0d421cf761)\n\n⚠️ **Risk Assessments**: Launch a swarm of specialized AI agents that discover vulnerabilities and provide **exact remediation steps** across network security, RBAC, privilege escalation, container image security and more. Validate fixes with a single click.\n\n![uploaded image](/media/?type=post&id=95330&key=user_uploads/2950276/53b80638-b9b4-4986-be1d-7a5ed6db5571)\n\n# **The Team**\n\n[Raman](https://www.linkedin.com/in/ramanvarma/) and [Evan](https://www.linkedin.com/in/evanchopra/) were founding engineers on Illumio’s Kubernetes team, where they built distributed systems to secure clusters for Fortune 500 companies.\\\n\\\nRaman previously worked on ML systems research at Sky Lab and BAIR. Evan was a security researcher during his Master’s in Computer Science, focusing on the Signal Protocol.\n\n![uploaded image](/media/?type=post&id=95330&key=user_uploads/2950276/7eeab8b1-9e35-4d4a-bac4-41b196c89fc7)\n\n", "url": "https://www.ycombinator.com/launches/Ona-kestrel-ai-copilot-for-kubernetes-ops-security", "votes": 37, "created_at": "2025-11-09T16:01:39.639Z" } ] }, { "id": 31165, "name": "IncidentFox", "slug": "brownie", "former_names": [ "Brownie" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/43fb964e8e0ca12dcc1000b4c4c2d6be7ffc203e.png", "website": "https://incidentfox.ai", "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, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1771353224, "tags": [ "AIOps", "Developer Tools", "AI" ], "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, "question_answers": null, "url": "https://www.ycombinator.com/companies/brownie", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/brownie.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/incidentfox/", "x_url": "https://x.com/incidentfox", "fb_url": null, "cb_url": null, "github_url": "https://github.com/incidentfox/incidentfox", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/43fb964e8e0ca12dcc1000b4c4c2d6be7ffc203e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/7a65d44099243d6d951e4e99e1e1e11e7a489905.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "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", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/98daac7c797ee12d1d4782adddd38231d21056a0.jpg" }, { "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/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0e2b5e314557a818ba205643be47cf15fc6f9dc8.jpg" } ], "jobs": [], "news": [], "launches": [ { "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: \n\n👉 Open source (Apache 2.0): \n\n👉 Launch video: \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: < 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" } ] }, { "id": 31295, "name": "Carrot Labs", "slug": "carrot-labs", "former_names": [ "Anyware" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c9d56ba5bab1fb6c9a7734a1a8aab7b6fbc48ed.png", "website": "https://superpenguin.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "The problem: \r\nAI has quietly become one of the largest and fastest-growing line items for companies. Spend is scattered across a dozen provider consoles that each show one number at the end of the month and nothing about why it moved. There's no unified view, and no way to trace a dollar back to the customer, feature, team, or even the pull request that caused it.\r\n\r\nWhich customers are unprofitable once you subtract their AI costs? Which feature is quietly burning your Anthropic budget? Is this week's spike a runaway loop or real growth? How much did that AI-written PR actually cost to ship?\r\nToday teams reverse-engineer answers by exporting CSVs from each provider and stitching them together in a spreadsheet, and by the time they do, the money is already spent.\r\n\r\nWhat SuperPenguin does: SuperPenguin tracks AI spend across 14 providers (OpenAI, Anthropic, Google Gemini, Deepgram, ElevenLabs, AWS Bedrock, Azure, Modal, Cursor, OpenRouter, and more).\r\n\r\n* Zero-code setup: connect an API key and costs sync automatically with model-level breakdowns, trends, and forecasts\r\n* Per-request attribution: add two lines with our Python or TypeScript SDK to tag every AI call by customer, feature, team, or environment (or any other metadata).\r\n* AI coding cost per PR: connect Cursor to see engineering spend attributed to each pull request, repo, and developer, so you know what it actually costs to ship.\r\n* Alerts on budget thresholds and spend anomalies, delivered to Slack, email, or Discord.\r\n\r\nMost teams are set up in under five minutes. We help companies see where their AI money goes and whether it's worth it.", "one_liner": "AI Cost Management: Track and attribute AI spend across every provider", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1772517831, "tags": [ "AIOps", "FinOps", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "B2B" ], "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/carrot-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/carrot-labs.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/carrot-labs-ai/", "x_url": "https://x.com/carrotlabs__ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6c9d56ba5bab1fb6c9a7734a1a8aab7b6fbc48ed.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": 3198622, "full_name": "Christopher Acker", "title": "Founder/CEO", "founder_bio": "Carrot Labs Founder. \n\nBuilding the continuous learning platform for AI Agents\n\nhttps://carrotlabs.cal.com/chris/20-min-meeting", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/christopher-acker-4711a3177/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f633a5168fb74667f84f441be1418cfd690860eb.jpg" }, { "user_id": 3309323, "full_name": "Yuta Baba", "title": "Founder", "founder_bio": "Co-Founder of Carrot Labs.\n\nPreviously Senior Data Scientist at Snowflake, where I was in charge of ML account-based bookings forecasts that shaped financial planning as the company scaled from $250M pre-IPO to $3.6B in revenue.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yutababa/", "x_url": "https://x.com/yuta__baba", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/39f087e64dd3676f6e4b5be30a474291e4b0a441.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98534, "title": "Carrot Labs: Custom models tuned to your tasks that never stop improving", "tagline": "Your model is your moat. Stop renting your intelligence and start growing it.", "body": "**TL;DR:** Carrot Labs helps you build models for your agents that are faster and better, forever. Use our platform to build proprietary models using open source that progressively improve.\n\n[**Our Launch Video**](https://youtu.be/Qiw-HG14AVE)\n\n## **The Problems:**\n\nFrontier models from Anthropic, OpenAI and Google are constantly pushing the boundary of intelligence further, but great quality comes with high latency and cost. Smaller models are fast, but can't get the job done right.\n\nSo you start modifying system prompts. It takes you far enough, but at some point you hit a ceiling. And every time a new model comes out, your prompts don't work the same way anymore! (just February 2026 alone, Opus 4.6 and Sonnet 4.6 from Anthropic, Gemini 3.1 Pro from Google, GPT-5.3-Codex from Open AI, Qwen 3.5 from Alibaba came out to name a few.)\n\n## **Solution:**\n\nWe build you a proprietary model tuned to your tasks so you always have access to the most performant model for your agent.\n\nOur platform records your agent’s activity and tunes an optimized model from an open source base model. Once it's ready, swap out the frontier model for yours. That's it. As new production data flows in, we continuously evaluate and retrain your model to boost performance. What you get is a model specialized to your tasks that outperforms frontier.\n\n## **Why this matters to your startup:**\n\nIn the short run, you need your agent to be faster and more accurate, and prompt tuning will only get you so far. We are the fastest way to get there.\n\nIn the long run, every company building agents is using the same frontier models. Your competitor has the same intelligence you do. The thing that differentiates you is the knowledge you distill into your own model. Your model is your moat.\n\n## **Team:**\n\nChris led AI at Skylo Technologies, the world's largest commercial direct-to-device satellite network that provides satellite communications services to Google, Samsung, and Garmin devices.\n\nYuta was a senior data scientist at Snowflake where he developed financial forecasts through ML that supported the company's hypergrowth from pre-IPO to billions in revenue.\n\n## **How you can help:**\n\nIf you are building agents or know anyone building agents who would appreciate a model that outperforms frontier on their tasks, let us know at [contact@carrotlabs.ai](mailto:contact@carrotlabs.ai).\n\nOur self-serve agent eval SDKs are available. Feel free to use it! You can sign up from here: .", "url": "https://www.ycombinator.com/launches/PdG-carrot-labs-custom-models-tuned-to-your-tasks-that-never-stop-improving", "votes": 22, "created_at": "2026-03-06T10:07:11.227Z" } ] }, { "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": [ "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/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", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/948ce8964e024f509e8d2c3db33ab94f3acb5b17.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "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" } ], "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" } ] }, { "id": 33050, "name": "Derya", "slug": "derya", "former_names": [ "Derya Maritime / Meridian Forwarding (forwarding office)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e0512b453e994207fdf56052eb221e687d29dc51.png", "website": "http://usederya.com", "all_locations": "San Francisco, CA, USA", "long_description": "Derya deploys supply chain automations for startups, SMEs, and enterprises that fulfil their entire supply chain function, from prospecting and procurement to distribution of goods.\r\n\r\nOur mission is to rebuild the supply chain from the ground up, turning one of the world’s hardest coordination problems into an AI native system that moves commodities by being integrated into both the supply chain infrastructure and the customer. We work across robotics, commodities, and food & beverages companies; procuring goods and managing various operation functions for them.", "one_liner": "AI Brokers for Industrial Trade", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Supply Chain and Logistics", "launched_at": 1784839627, "tags": [ "AIOps", "Logistics", "Supply Chain", "Procurement", "Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Supply Chain and Logistics" ], "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/derya", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/derya.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/derya-maritime", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e0512b453e994207fdf56052eb221e687d29dc51.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/734e64627fe82417c09de0dc89e3ab054b81f94f.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 2282645, "full_name": "Mert Turna", "title": "Co-Founder", "founder_bio": "Building agentic supply chain automations @ Derya (YC S26). CS @ UCLA, built a ship operations software used in 30+ vessels ($200M Fleet Value).\n\nPast experience:\n- Co-Founder, @ Hemut (YC P25), truck management operations.\n- Several VC and IB internships\n- UCLA Debate Team Ops & Highest Speaker in the team\n- National Debating Team", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mert-turna/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/12fc51cbbdae084e45eb08501c53376a7c27c1b8.jpg" }, { "user_id": 1979081, "full_name": "Oguzhan Karaca", "title": "Cofounder", "founder_bio": "Building agentic supply chain automations @ Derya (YC S26). Law at Oxford, built a 6-figure ARR education business on AI autopilot.\n\nPast experience:\n- CEO @ Ivy Consultants, scaling to six-figure ARR\n- Legal @ Ceva/CMA CGM\n- Editor @ Oxford Law Journal\n- Treasurer @ Pembroke (Oxford) Student Union\n- National Debating Team", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/oguzhan-karaca/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1bc38d2953cef10b16a1977c66084ed9a3eea127.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109885, "title": "Derya: Your supply chain autopilot for mapping, planning, and execution.", "tagline": "We build agents that help hardware companies design supply chain processes and eliminate friction.", "body": "TL;DR: We map out all your procurement, logistics, assembly, and distribution operations and then execute them for hardware companies. If you build hardware, physical AI, robots, we'll set up the operations while you focus on designing your product and sales.\n\n**Ask:** If you are spending too much time sourcing components, managing manufacturers, or coordinating shipments, let's see how we can save time and make money for you. Reach us at [contact@usederya.com](mailto:contact@usederya.com) or book a meeting at[ https://calendar.app.google/vSabvozH51TCM9V18](https://calendar.app.google/vSabvozH51TCM9V18) \n\n \n\n**The Problem**\n\nRedundant labor and inefficiencies represent $1.3 trillion of the $13 trillion logistics market. You shouldn't be spending your Tuesday chasing a customs broker, comparing three carrier quotes you don't trust, and hoping your parts clear China customs before your build slips a week.\n\n**Solution**\n\nWe’re setting up your supply chain for you:\n\n* **Shipping & customs automation** for cheaper and risk-free freight and imports\n* **Procurement** support to autonomously order, ship, and track all your inputs\n* **Production** visibility and execution to get your products ready to ship, quick\n\n**Why us**\n\nWe already handled more than $15M in freight before starting to handle the whole supply chain for companies ranging from lab robotics to biochemical manufacturers.\n\nWe’ve been friends for almost 10 years and built our first business together in high school. Mert was the CTO of the trucking AI company Hemut (YC P25), UCLA CS major, and the son of a ship captain. He built software for 25+ ships, a $450M fleet running on his software. \n\nOguzhan is an Oxford law grad who worked at CMA CGM/Ceva (3rd-largest shipper), previously built a six-figure-ARR AI-native business, and grew up in a farming family, providing him exposure as both the carrier and the customer.", "url": "https://www.ycombinator.com/launches/SaL-derya-your-supply-chain-autopilot-for-mapping-planning-and-execution", "votes": 7, "created_at": "2026-08-10T19:30:00.323Z" } ] }, { "id": 33177, "name": "HyperProbe", "slug": "hyperprobe", "former_names": [ "HyperTest inc" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b4b91e0b78bbed1c83add57d22b06ab632567450.png", "website": "https://hyperprobe.co", "all_locations": "San Francisco, CA, USA", "long_description": "With tools like Cursor and Claude Code, writing software is no longer the bottleneck. Debugging is. Agents cannot fix production crashes using logs and traces that were sized for human eyeballs. The gap is missing data.\r\n\r\nHyperProbe allows agents to debug live software autonomously. Our SDK drops non-breaking probes straight into running services to read exact variable states from memory at the moment of failure, instead of reasoning backwards from flat logs.\r\n\r\nZero redeploys. Under 1% CPU overhead. Automatic PII redaction.\r\n\r\nWe turn 4-hour production war rooms into 10-minute automated resolutions. As agents write and operate more of the world's software, HyperProbe is the runtime observability layer that lets them understand what they built.\r\n\r\n", "one_liner": "AI engineer that debugs and fixes production incidents in realtime", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1783455456, "tags": [ "AIOps", "Developer Tools", "SaaS", "DevOps", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/hyperprobe", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/hyperprobe.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b4b91e0b78bbed1c83add57d22b06ab632567450.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": 3173209, "full_name": "Shailendra Singh", "title": "Founder/CEO", "founder_bio": "Founder at HyperProbe. Also founded HyperTest. Previously headed and scaled engineering and product functions at a unicorn and few small startups.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shailendra-singh-6540b8b/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/804a582f290d73d89dadb5a73815fc0814ca121d.jpg" }, { "user_id": 417680, "full_name": "Karan Raina", "title": "Founder/CTO", "founder_bio": "Founder and CTO at HyperTest (YC S26)\nMS CS Georgia Tech\n\n2nd time founder\nPreviously led a 50 member engineering team at Limetray", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/karanraina/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bd379360253464d14cb5b5a47a3911c2139eaa6d.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 110401, "title": "Hyperprobe: On-call Agent for engineering teams", "tagline": "The fastest way for coding agents to debug incidents live in production.", "body": "# \\*\\*\\*\\*\n\n**TL;DR:** AI writes most of our code now. Debugging it when it breaks in production is still slow, because the value that explains the failure was never logged. HyperProbe is an on-call agent for engineering teams. It takes an open incident to a confirmed root cause by dropping read-only probes into your running service and capturing the exact variable state logs don't have. No redeploys, no restarting the app. \\\n\\\n📹 **Launch Video: [https://youtu.be/QtaZrQ8_Uk8](https://youtu.be/QtaZrQ8_Uk8)**\n\n**🚧 The Problem**\n\nTeam ships faster than ever with agents that write most of our code. This is already shrinking the useful context engineers need to debug AI written code. Not helped by the limited telemetry added in the same code by AI. So when something breaks in prod, the first instinct for engineers is to open logs or throw them to agents. But if the line you are looking for is not there, agents will start guessing the root cause on limited data, forcing engineers to collect more logs by redeploys.\n\nThe result: our best engineers become our on-call team, incidents stay open longer, and patches come from data that never capture production context, so issues recur.\n\nYou can see where that leads. In September last year, a public fintech in India paid out $5 million after failed transactions were incorrectly marked successful. NYSE the same year miscalculated price bands for 40 symbols, printing trades so wrong they had to be reversed. Same pattern: the code ran clean, wrote the wrong state, and neither logs nor traces could show it.\n\n ✅ **What Hyperprobe does**\n\nHyperProbe gives engineering teams an on-call agent that works the incident and takes it to a confirmed root cause, live in production.\n\n* **Plan:** the agent picks up the alert from PagerDuty, Datadog, or Slack, then reads your logs and traces to locate the file and line. The easy incidents resolve right here.\n* **Probe:** logs not enough? places read-only virtual breakpoints on the suspect lines and captures the exact variable state live, across services. No redeploys, no restarts. \n* **Confirm:** the diagnosis is verified against real captured evidence, not a guess. Your engineer gets a confirmed RCA and commits the fix.\n\nThere are two pieces. An SDK that runs inside your service, and an MCP server your coding agent talks to. The SDK is what makes setting probes (virtual breakpoints, log or metric) possible without a redeploy. \n\n### **🔍 A few examples**\n\n* **An intermittent 500 that won't reproduce locally:** catch it live with full variable state \n* **A third-party API misbehaving:** see the exact payload and response\n* **A function returns a wrong value with no error:** see what it computed and why\n* **Anything that only happens in prod:** stop redeploying with console.logs\n\n🚀 **The team**\n\nWe've both lived this problem. Shailendra ran production at scale. Karan was a senior architect at a SaaS company. Before this, both spent three years building production SDKs for a testing product, learning the hard parts of pulling real runtime state out of a running service without breaking it.\n\nEvery incident we watched came down to the same question: what was the actual value at the moment it broke. Nobody built it for the way engineers debug with coding agents now. So we did.\n\n![uploaded image](/media/?type=post&id=110401&key=user_uploads/3173209/ddf3d8d0-2bd4-4a8c-9547-fa5ea2496469)\n\n### **🎁 Why now**\n\nFor thirty years the expensive part of software was writing it, and every tool got built for that. Generation is now nearly free, but the cost didn't disappear. It moved downstream, to figuring out why the generated code broke in production.\n\nAnd that system is now mostly code no human wrote or holds in their head. When it breaks, the engineer has less context than ever, and their first move is already to hand it to the coding agent. That agent only has your logs and traces, so it guesses. We give it the evidence instead, captured live from the running code at the point of failure.\n\n### **🙏 Our ask**\n\n* If you're an engineer tired of the redeploy loop, debugging code you didn't write at 2 AM, try it on your own service.**,** first investigation in 10 min:[ https://docs.hyperprobe.co/quickstart](https://docs.hyperprobe.co/quickstart)\n* If your best engineers have become your on-call team and you want that fixed, let us work one real incident from your stack, free: ", "url": "https://www.ycombinator.com/launches/Sif-hyperprobe-on-call-agent-for-engineering-teams", "votes": 82, "created_at": "2026-08-17T15:30:20.732Z" } ] }, { "id": 33320, "name": "OneCLI", "slug": "onecli", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0405005492b7122ac5f4784e260fc64d7bd99053.png", "website": "https://onecli.sh", "all_locations": "San Francisco, CA, USA", "long_description": "OneCLI (YC S26) is an open-source platform for delegating business-critical work to AI agents, from Salesforce updates to data analysis and engineering investigations.\r\n\r\nAgents work across your company’s apps, prepare changes, and pause for approval when your rules require it. Your team controls what they can access and change, while credentials stay out of the agents’ reach and every call is logged.\r\n\r\nhttps://www.onecli.sh", "one_liner": "AI teammates for your business-critical work.", "team_size": 0, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1783888956, "tags": [ "AIOps", "B2B", "Workflow Automation", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Productivity" ], "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/onecli", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/onecli.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/onecli", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0405005492b7122ac5f4784e260fc64d7bd99053.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": 406823, "full_name": "Jonathan Fishner", "title": "Founder/CEO", "founder_bio": "Co-founder of OneCLI", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jonathanfishner", "x_url": "https://x.com/jonathanfishner", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6230ecfcb0be73a0475915f586f61d5139a12a58.jpg" }, { "user_id": 1935982, "full_name": "Guy Ben Aharon", "title": "Founder/CTO", "founder_bio": "Co-founder of OneCLI", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/guy-ben-aharon/", "x_url": "https://x.com/GuyBenAharon", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d2d3562621d02f714e16c1c63903eab6e5f71f23.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111185, "title": "OneCLI - Give every employee a secured, sandboxed pro assistant agent", "tagline": "Self-hosted agent harness for teams: sandboxed, policy-controlled, and never touching a real credential. Works from web and Slack.", "body": "Hi everyone 👋, we're Jonathan and Guy, founders of OneCLI.**\\\n\\\n🏆 TL;DR**\n\nOneCLI is an open source agent harness for teams. Each employee gets their own sandboxed pro assistant agent, connected to their GitHub, Gmail, Notion, Dropbox, or CRM right from the chat/Slack, while the whole workspace runs under one policy your admins control. \\\n\\\nAgents never hold real secrets: the credential is injected at the network layer, per request, after the call is authorized. You can't steal what isn't there.\n\n**Launch Video**\\\n\n\n🚧 **The Problem**\n\nLately, autonomous agents have arrived and shown up everywhere. Tools like OpenClaw and Hermes have become extremely popular and been adopted by open source communities, mostly by individuals. Companies, however, have been left behind: they need integration with their identity providers to enable easy provisioning of agents on behalf of an employee's identity, with least-privilege access.\n\nThose tools have fundamental flaws. Security fundamentals, like secret handling and organization-level control, are missing by default. So companies must choose between weak agents or making them powerful but risky.\n\n✅ **What OneCLI Does**\n\nOneCLI gives teams the whole harness, open source and self-hosted, so agents can be powerful and safe at once:\n\n* **Sandboxed agent per employee:** each agent runs isolated with its own memory, skills, and permissions.\n* **Secrets injected at the network layer:** the agent gets a placeholder, the gateway swaps in the real credential per request, after the call is authorized. Credentials never enter the agent's context, memory, or logs.\n* **Policy enforced outside the model:** admins define org-wide policy. Block endpoints and scope access per agent.\n* **Human-in-the-loop where it counts:** sensitive actions (send the email, delete the Linear ticket, empty the S3 bucket) wait for deterministic approval in the chat.\n* **Full identity trail:** every agent is bound to an employee, every call logged with who it acted for and which policy allowed it.\n\n🚀 **Team**\n\nWe've spent our careers in security. Jonathan built zero trust network access at Axis Security (acquired by HPE). The core ZTNA idea is that you never trust the client and enforce access outside it, at the network layer. Guy was the first employee at Argon (AppSec company acquired by Aqua Security), protecting client codebases. Agents need exactly the treatment ZTNA gives humans, so we built it.\n\n🙏 **Our Ask**\n\n* Do you want to provide each of your employees with a secure OpenClaw isolated in a sandbox? Shoot me an email: [jonathan@onecli.sh](mailto:jonathan@onecli.sh)\n* Star/try the repo:[ https://github.com/onecli/onecli](https://github.com/onecli/onecli). Self-host in minutes, free.", "url": "https://www.ycombinator.com/launches/SvJ-onecli-give-every-employee-a-secured-sandboxed-pro-assistant-agent", "votes": 29, "created_at": "2026-08-20T14:00:00.677Z" }, { "id": 106907, "title": "OneCLI - Your AI agents shouldn't be holding raw secrets", "tagline": "Agents hold placeholder tokens, real secrets get injected at the network layer", "body": "# **🏆 TL;DR**\n\n[**OneCLI**](http://onecli.sh) is an open-source credential gateway for **AI agents**.\\\nWhen an agent makes a call, OneCLI swaps in the real credential at the network layer and forwards it. The agent never touches the secret, **you can't steal what isn't there**.\\\n320K+ downloads, 2,500+ GitHub stars, and the default credential layer in NanoClaw (30K+ stars).\n\n**Ask:** If your team runs coding agents (Claude Code, Codex, Cursor) or autonomous agents in production. And if you can intro us to Head of DevOps or heads of DevSecOps at companies rolling out AI agents, email us at [jonathan@onecli.sh](mailto:jonathan@onecli.sh) \\\n\\\n**Sign up for free:** \n\n![uploaded image](/media/?type=post&id=106907&key=user_uploads/406823/5294ff50-4b88-4ee7-9c9c-3c776b5b45b5)\n\n\n\n# The Problem\n\nAI agents are doing real work now, which means they need real credentials: API keys, database passwords, OAuth tokens. And there's no safe way to hand them over.\n\nToday, every team does one of two things: hardcode secrets into **.env files** the agent can read, or build a one-off proxy hack. Both mean the **agent holds the secret**. An agent that holds a secret can leak it, through prompt injection, a malicious MCP server, a compromised dependency, or simply by pasting it into a log or a commit.\n\nSecrets management was designed for humans and **deterministic software**. Autonomous agents that read untrusted webpages, install packages, and write their own code are a fundamentally different threat model. The blast radius of one leaked production key is enormous, and teams are granting agents more access every week.\n\n# Our Solution\n\nOneCLI moves the credential out of the agent entirely.\n\nYou run one command, and every outbound call from your coding agent (Claude Code, Codex, Cursor, Cline) or autonomous agent (OpenClaw, Hermes, NanoClaw) routes through OneCLI. The agent only ever sees placeholder tokens. On each request, the proxy:\n\n1. Matches the request by host/path\n2. Checks it against policy\n3. Swaps the placeholder for the real credential\n4. Forwards the request\n\nThe agent never holds the secret, so there's nothing to steal, log, or exfiltrate. \\\nSecrets are AES-256-GCM encrypted at rest. The whole thing runs in a single Docker container with no external dependencies. It works with anything that can set HTTPS_PROXY.\n\nFor teams, we already support integration with IdPs and organization-wide policy setup, giving you full control and visibility over every access made by your agents.\n\n# Why this and not the PAM/identity vendors?\n\nBecause the credential problem for agents is a **network problem, not an identity problem**. Vault-based tools (1Password, HashiCorp Vault) still hand the secret to the agent to authenticate. The agent holds it, so the agent can leak it. OneCLI injects the credential at the wire: transparent injection, policy enforced on every outbound request, and the secret never enters the agent's context in the first place.\n\n# The Team\n\nWe've spent our careers in security. Guy (CTO) was the first hire at Argon (acquired by Aqua Security). Jonathan (CEO) worked on ZTNA and network security at Axis Security (acquired by HPE), after serving in Israeli Military Intelligence.\n\nWe hit this problem ourselves. Before OneCLI we built ChartDB together, an open-source database tool with 20K+ GitHub stars, and while building an agent orchestration layer on top of it, we kept hitting the same wall: there was no safe way to hand an autonomous agent credentials. Every team we talked to was either hardcoding API keys or duct-taping proxies. So we built the thing we wished existed.\n\n![uploaded image](/media/?type=post&id=106907&key=user_uploads/406823/ff451063-51d2-4c13-9c5f-cd1863b8c648)\n\n# The Ask\n\nIf you know a Head of DevOps or DevSecOps lead at a company rolling out AI agents, we'd love an intro.\n\n[jonathan@onecli.sh](mailto:jonathan@onecli.sh)", "url": "https://www.ycombinator.com/launches/RoJ-onecli-your-ai-agents-shouldn-t-be-holding-raw-secrets", "votes": 11, "created_at": "2026-07-23T13:44:46.846Z" } ] }, { "id": 33342, "name": "Nex", "slug": "nex", "former_names": [ "GarageSpace", "Inc.", "Nex", "Nex.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b8f563ce5c802b34a01e9ad0f5641eb4bb092c6c.png", "website": "https://nex.ai", "all_locations": "San Francisco, CA, USA", "long_description": "We build and deploy AI agents that manage prospecting, qualification, CRM hygiene, forecasting, lead routing, revenue recovery, and all the complex revenue work in between, on autopilot.", "one_liner": "Grok bot for GTM & Revenue teams", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Sales", "launched_at": 1784187262, "tags": [ "AIOps", "Workflow Automation", "Sales" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Sales" ], "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/nex", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/nex.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/nexdotai/", "x_url": "https://x.com/nexdotai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b8f563ce5c802b34a01e9ad0f5641eb4bb092c6c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 1371335, "full_name": "Najmuzzaman (Nazz) Mohammad", "title": "Founder/CEO", "founder_bio": "CEO/Co-founder at Nex (YC S26).\n\nFounding hire at VentureBasecamp, an ed-tech product acquired by Wadhwani Foundation within 2 years.\nEarlier founded a profitable e-commerce business during college.\nPreviously Senior PM at HubSpot and Clipboard Health.", "is_active": true, "linkedin_url": "https://linkedin.com/in/najmuzzaman", "x_url": "https://x.com/najmuzzaman", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5bd335c3878a06175e6944df3acdedf99db2ab1b.jpg" }, { "user_id": 3636126, "full_name": "Francisco Dias", "title": "Founder/CTO", "founder_bio": "Just trying to help out.\n\n4th engineer on HubSpot CRM (pre-IPO, ~0 customers)\nCo-wrote HubSpot's first component library\nFirst product-focused engineer on HubSpot's AI infrastructure team; helped grow it into a new group\nMentored 10+ ICs into technical leads\n\nHelped ship one of the most ambitious responsive websites, BostonGlobe.com\n\n22 marathons and a sub-11 Ironman in 22 months\n\nBOS > ATL > SFO", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/frandias/", "x_url": "https://x.com/ffrandias", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fef4552be6d6bed0049c187c08b25e3fd19b792f.jpg" } ], "jobs": [], "news": [ { "title": "Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git) | Hacker News", "url": "https://news.ycombinator.com/item?id=47899844", "date": "Apr 24, 2026" } ], "launches": [ { "id": 111167, "title": "Nex - Claude Cowork for high-volume workflows, starting with GTM", "tagline": "AI that builds and runs the complex, high-volume GTM workflows only the world's best GTM engineers know how to build, at the price of a coffee.", "body": "**TL;DR:** Nex is Claude Cowork for high-volume workflows, starting with GTM. Describe a goal, and Nex builds an agent that runs it end to end across 1000+ integrations. The kind of work that would burn your entire weekly limit overnight on Claude or Codex runs on autopilot on Nex, for the price of a coffee, while that coffee brews. Try it at [nex.ai/signup](http://nex.ai/signup).\n\n _(Our AI-generated “The Office” parody video that went viral was taken down by YouTube. You can still [watch it here on LinkedIn](https://www.linkedin.com/posts/nexai-yc-s26-is-the-ai-gtm-engineer-that-ugcPost-7495214587882004480-Ch_b/?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAAseA8sB3xUI6osd_UWpcPBpx2tIFENh3N8)_)\n\n👋 Hey friends, we're Nazz and Francisco. We spent years at HubSpot running its three largest customer platforms, and we're backed by HubSpot founder Dharmesh Shah.\n\n**The problem: general-purpose agents aren't built for high-volume GTM**\n\nThe best GTM engineers can wire HubSpot, Clay, Outreach, Gong, and Slack into agents that run a revenue team. But run that same work through a general agent and it re-reasons every step, burns your entire weekly limit overnight, and still isn't reliable at volume. Almost nobody can hire enough GTM engineers to do it properly.\n\n**How Nex works**\n\nDescribe a goal, and Nex builds and runs the workflow across 1000+ integrations:\n\n* Lead generation, qualification, and enrichment\n* Scraping public records\n* Outbound over email and socials\n* CRM cleanup and hygiene\n* Re-engaging lost deals\n\nBecause most GTM work is repetitive and deterministic, Nex has AI write code for the predictable parts and run it fast without wasting tokens, saving AI judgment for the genuinely hard parts. That's how it breezes through high-volume work while not burning through your monthly AI budget in a day.\n\n**What that looks like**\n\nOn a single onboarding call, Nex audited thousands of records in a customer's HubSpot, flagged missing data, archived owners, bad associations, and zombie deals, and started fixing them before the call was over.\n\n**Why this matters**\n\nGTM is the beachhead. The same engine runs any high-volume workflow that's too slow and too expensive to run straight through a general model, and that pattern shows up in every operations-heavy function: finance reconciliations, support triage, recruiting outreach.\n\n**Our ask**\n\n* Try it free at [nex.ai/signup](http://nex.ai/signup)\n* If you run RevOps or GTM at a fast-moving company, we'd love to talk\n* If you know a RevOps or GTM leader, an intro would mean a lot: [founders@nex.ai](mailto:founders@nex.ai)", "url": "https://www.ycombinator.com/launches/Sv1-nex-claude-cowork-for-high-volume-workflows-starting-with-gtm", "votes": 7, "created_at": "2026-08-17T22:00:00.274Z" } ] }, { "id": 33738, "name": "CoArena", "slug": "coarena", "former_names": [ "Coasty" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1543623e6506eaf893b1c43e614688a6b2a32bda.png", "website": "http://coarena.ai", "all_locations": "San Francisco, CA, USA", "long_description": "CoArena is a live arena where anyone can use the world's top computer-use models racing two of them on the same real computer task and judging which one did it better. Every battle becomes something the AI labs can't build themselves, an honest test of their agents on real work, and the data to make them better.", "one_liner": "The biggest crowdsourced benchmark for Computer-Use", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1782294083, "tags": [ "AIOps", "Machine Learning", "Reinforcement Learning", "Data Labeling", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/coarena", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/coarena.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/coastyai", "x_url": "https://x.com/coastyai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/coasty", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1543623e6506eaf893b1c43e614688a6b2a32bda.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/cfec1f74a71f276cfe85bcc102647baf48c51fff.png" ], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 1194042, "full_name": "Prateek Jannu", "title": "Founder/CEO", "founder_bio": "Prateek Jannu, Co-founder & CEO - Stanford (MS. EE) and Purdue (BE. CE). Invented Solo-Synth-GAN, which generates labeled training data from a single example. Built Coasty to #1 on the OSWorld benchmark (82%), beating Agent S3, UiPath, Kimi K2.5, and Claude Sonnet 4.5. Now building CoArena.", "is_active": true, "linkedin_url": "https://linkedin.com/in/prateekjannu", "x_url": "https://x.com/prateekjannu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/26ed45c60c34293a10373502e615a8c6499d356f.jpg" }, { "user_id": 2457255, "full_name": "Nitish Kovuru", "title": "Founder", "founder_bio": "Co-founder and CTO/COO of Coarena, launched Coasty prev, SOTA CUA framework on OSWorld at 83% accuracy. Did CS at Columbia (Vision track) and hold a BS in Computer Engineering from Purdue, where I TA'd a graduate-level AI course as an undergrad and later went on to build enterprise sales automation agentic systems for my previous company.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nitish-kovuru-2423351b4", "x_url": "https://x.com/llmhubdev", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/29f588d1c332c655eee8ea69c5e1c27e9ec4fce9.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111689, "title": "Coarena - The biggest crowdsourced benchmark for Computer-Use", "tagline": "We build the most advanced and biggest benchmark for computer use", "body": "We are officially launching CoArena (YC S26). Released 2 weeks ago, users have doubled every week since, and the arena went from $0 to $60,000 in revenue since. It's a live arena where the world's top Computer-Use models race to finish the same real computer task, and real people judge which one did it better, blind.\n\n\n\n![uploaded image](/media/?type=post&id=111689&key=user_uploads/1194042/43ddaa95-1f50-4b30-b0a4-9b085433ec3c)\n\n**The problem**\n\nEvery AI lab says their agent is the best at using a computer, and every claim points at a benchmark score. We know those scores well: earlier this year we built the #1 agent on OSWorld (82%). That's exactly how we learned the scores mean very little. Benchmarks are static, agents memorize them, and they say nothing about the messy tasks people actually need done.\n\n**What we built**\n\nOn CoArena, anyone can type a real task (\"find me the cheapest flight,\" \"assemble this expense report\"), watch two anonymous frontier agents race it live, and vote on the winner. Names are revealed only after you vote, so the judging is blind. Every battle becomes a fresh eval no model has ever seen, and the failure cases labs never catch: prompt injection, spending money when it should ask, silent side-effects.\n\n**What the arena has already shown**\n\n\\- Only \\~66% of agent runs finish their task at all 1 in 3 dies to a failure before any score exists\n\n\\- On identical tasks, the best frontier agent completes \\~87% of runs; the worst completes \\~42%\n\n\\- One major frontier model times out in roughly a third of its runs\n\n\\- In \\~7% of battles, human judges ruled that BOTH agents failed\n\n\\- The #1 spot on our leaderboard changed hands three times in three days, real tasks don't saturate\n\n**What you get**\n\nIt's completely free, including models you can't use anywhere else (Fable, Sol, and unreleased previews). Run your real errands with frontier agents, keep whatever they produce, and find out which model is actually best at YOUR kind of work, not the benchmark's.\n\n**Our asks**\n\n1\\. Use it, it's free. Bring your daily work: the flights, the spreadsheets, the research. Two frontier agents will race it, you keep the result, and your vote makes the leaderboard more honest: \n\n2\\. If you're an AI lab working on computer use or vision, or a robotics company building on vision, we'd love to talk. We can send you sample evals and data today: [founders@coasty.ai](mailto:founders@coasty.ai)", "url": "https://www.ycombinator.com/launches/T3R-coarena-the-biggest-crowdsourced-benchmark-for-computer-use", "votes": 59, "created_at": "2026-08-22T19:00:00.303Z" }, { "id": 105157, "title": "Coasty: The computer-use agent that doesn't break on real software", "tagline": "Coasty helps developers kill RPA.", "body": "**TL;DR: Coasty gives developers a computer-use agent that reliably automates back-office work in legacy desktop software where RPA scripts and other CUAs fail. It’s achieved 82.81% on OSWorld and it’s great at automating workflows where quiet mistakes cost the most: regulatory filings, patient records, payments, and gives developers precise control and observability over its work.**\n\n\\\n(_The portals and the data in it, are mockups_)\n\n**Replayable audit trail (real run):**  \n\n**OSWorld submission (Coasty scored 82.81% on OSWorld Verified, confirmed by the OSWorld team, with our latest internal run at 85.60%.):**[ https://github.com/coasty-ai/coasty-osworld](https://github.com/coasty-ai/coasty-osworld)\\\n\\\n**The Problem**\n\nMost back-office work doesn't happen in clean web apps. It happens in legacy desktop software, full of surprise pop-ups, wandering menus, and impenetrable UIs.\n\nToday, developers automating this back-office “real” work are stuck choosing between brittle RPA scripts and CUAs that demo beautifully and score high but break on contact.\n\nThe problem isn't getting an agent to click. It's trusting it when a silent mistake gets someone fired.\n\n### **Why Coasty is reliable where others aren't**\n\n**It recovers mid-task.** Coasty catches its errors and fixes them mid-run. If it’s crunching on a 20-step workflow and makes a misclick at step 3, it will recover and start from the error. First-pass accuracy is vanity. Recovery keeps a run alive.\n\n**It survives messy software.** Pop-ups, modals that steal focus, legacy desktop apps. Coasty doesn’t record clicks, it reads the screen. So when the UI changes overnight and every RPA script breaks, Coasty looks at the new screen and keeps going.\n\n**It knows when it's done.** An agent that thinks it finished but didn't is worse than one that fails loudly. Coasty verifies its own output and leaves a timestamped audit trail you can replay.\n\n**It swarms.** One task across forty state portals, each with its own ancient interface, all at once. Forty agents, one verified result set. What takes a team a week, is done overnight.\n\n### **Traction**\n\nCoasty has been verified by the OSWorld team and will be published soon, but also another internal benchmark we published (85.60%): < >.\n\nWe have paying customers on four continents, a few thousand active users, and we have actually grown 24x since Claude Cowork came out.\n\n### **Why us**\n\nWe started working on this idea when models weren’t good enough for computer use (last summer), so we’ve built frameworks to test and run the perfect combination of models and harnesses to see what works in practice. Prateek worked as an ML engineer at multiple startups and dropped his Stanford admit while I did CS at Columbia and built my previous company’s entire agentic sales automation. We’ve grown with this field and have been working on it even before it got much attention, so we understand where CUAs mess up and how to deal with these messes realistically.\n\n### **The ask**\n\nSend us one workflow your team runs by hand. We’ll tell you straight whether Coasty can run it reliably today. If it looks automatable, we’ll build a working proof of concept on your real software before a long sales process.\n\nWe’re especially looking for intros to operators and executives in accounting, tax, insurance, healthcare, compliance, and data privacy - anywhere teams are still babysitting RPAs, clicking through legacy portals, reconciling records, filing forms, or moving data across old desktop software.\n\nHere's our Calendly:[ https://cal.com/coasty/15min](https://cal.com/coasty/15min)", "url": "https://www.ycombinator.com/launches/RM5-coasty-the-computer-use-agent-that-doesn-t-break-on-real-software", "votes": 12, "created_at": "2026-07-10T17:30:25.886Z" } ] }, { "id": 33969, "name": "OpenTag", "slug": "opentag", "former_names": [ "Gini Agent", "Gini", "OpenTag", "Open Tag" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c893158d06fb0a2865c30711a691fd486b8a7dcf.png", "website": "https://www.tryopentag.com/", "all_locations": "San Francisco, CA, USA", "long_description": "OpenTag is an AI coworker in your Slack with full context on your company. It answers questions and takes real work off your plate. It also keeps your wiki up to date automatically, so knowledge never goes stale.", "one_liner": "Model Agnostic AI coworker in Slack", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1787093671, "tags": [ "AIOps", "B2B", "Workflow Automation", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "B2B" ], "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/opentag", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/opentag.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/109881466", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c893158d06fb0a2865c30711a691fd486b8a7dcf.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/790aa3ec5de0bba9ece0a6519e8daba6f10a2cff.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 802295, "full_name": "Tony Kam", "title": "Founder/CEO", "founder_bio": "Co-founder and CEO at OpenTag. Previously at Tesla and founded Lilac Labs (S24). Studied EECS at Berkeley.", "is_active": true, "linkedin_url": "https://linkedin.com/in/tonyshekkam", "x_url": "https://x.com/tonykamshek", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/79ba45d01b5c5889e6430069721b3a279fa04e29.jpg" }, { "user_id": 3880379, "full_name": "Wilson Nguyen", "title": "Founder", "founder_bio": "Co-founder at OpenTag. Previously founding engineer at Lilac Labs (YC S24). Studied Computer Science at UC Berkeley.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nguyenwilson/", "x_url": "https://x.com/potatoparser", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/19a40eb53a4be1df58ecf47644f1c5154bd9ff7e.jpg" }, { "user_id": 1378729, "full_name": "Shelden Shi", "title": "Founder/CTO", "founder_bio": "Co-Founder and CTO at OpenTag. Previously founded Lilac Labs (S24). Studied CS and CogSci at UC Berkeley.", "is_active": true, "linkedin_url": "https://linkedin.com/in/sheldenshi", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/92d85249cf52d37811253ebfde1164c62804c115.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111999, "title": "OpenTag - Model Agnostic Claude Tag", "tagline": "AI Super Coworker in Slack.", "body": "Hey everyone. You’ve might of heard of Open AI, Open Router, OpenCode, OpenWork….\n\nIntroducing [OpenTag](https://www.tryopentag.com/?utm_source=yc&utm_medium=referral&utm_campaign=yc-launch&utm_content=launch-page) - the model agnostic Claude Tag.\n\nWe started early access a 2 weeks ago and are live with 10 teams.\n\nIf you've seen Claude Tag, you know the interface: tag an AI in Slack, hand it a task, get the result in the thread. We think that's the right interface. We built OpenTag because we think the multiplayer harness should be model agnostic. When a better model ships, your coworker gets better the same day, benefiting from the performance and cost improvements.\n\nOpenTag is an AI coworker that lives inside of Slack. You invite it into the channels you want. It picks up how the company runs, how decisions are made, who owns what. It helps the team coordinate and builds automations for workflows that it sees as repetitive. It asks for tools and visibility the way an A-player hire does, and it tries to grow its own scope and agency. It starts as an intern and tries to earn the COO job. As it works, it writes a company wiki of how you actually run. That wiki compounds as a company brain, so OpenTag only gets sharper with time. Throw it anything. It does its best, and it asks for help when it’s blocked. The result lands in the thread, with sources, where the whole team can see it.\n\n**Four parts, one coworker:**\n\n1. **Ambient and proactive agents -** properly sandboxed with its own computer and browser, so you can trust them with actual tasks instead of babysitting chat windows.\n2. **A wiki that maintains itself —** no more stale handbooks. OpenTag writes down how the company actually runs and keeps it current. Teammates can read it. Agents can read it.\n3. **Automations that suggest themselves -** OpenTag watches how your team works and surfaces the repetitive stuff worth automating, so you're not designing workflows from a blank page.\n4. **Built for multiplayer** - OpenTag is designed with proper permissioning that matches how your team operates.\n\n**2 minutes to start.** Add OpenTag to Slack, invite it to the channels where work happens, give it a job. It’ll ask for the tools it needs (whatever the job touches). It only reads the channels you invited it to.\n\n**Built by software engineers who’ve shipped products used by millions.**\n\nThe three of us worked together at Lilac Labs (YC S24) and all went to Berkeley. Tony (CEO) was software at Tesla and Intel. Shelden (CTO) was at Flatiron Health, Roche, and Certik (cyber security). Wilson (Chief Engineer) was at Hewlett Packard Enterprise and Vestible, a regulated securities platform.\n\nOpenTag is the coworker we wanted at our last company, when we were the ones running every job.\n\n**Try OpenTag today**\n\nTry it at [www.tryopentag.com](http://www.tryopentag.com)\n\nTake 30 minutes with me: [book a demo.](https://cal.com/tony-kam-ympx2p/intro?overlayCalendar=true)\n\nWould love to hear your thoughts and feedback. You can email me at [tony@tryopentag.com](mailto:tony@tryopentag.com)\n\n![uploaded image](/media/?type=post&id=111999&key=user_uploads/802295/8d1c1925-f213-405e-bfd4-f9b9effd0095)\n\n", "url": "https://www.ycombinator.com/launches/T8R-opentag-model-agnostic-claude-tag", "votes": 6, "created_at": "2026-08-25T22:00:00.175Z" }, { "id": 111423, "title": "Gini: AI coworker that lives in Slack", "tagline": "Does the work in your channels and turns what it learns into a company brain.", "body": "Hi everyone, [Shelden](https://www.linkedin.com/in/sheldenshi), [Wilson](https://www.linkedin.com/in/nguyenwilson), and I ([Tony](https://www.linkedin.com/in/tonyshekkam/)) are cofounders of [Gini](https://ginicomputer.com/?utm_source=yc&utm_medium=referral&utm_campaign=yc-launch&utm_content=launch-page).\n\n[Gini](https://ginicomputer.com/?utm_source=yc&utm_medium=referral&utm_campaign=yc-launch&utm_content=launch-page) is a model agnostic proactive AI coworker that lives inside of Slack. You invite it into the channels you want. It picks up how the company runs, how decisions are made, who owns what. It helps the team coordinate and builds automations for workflows that it sees as repetitive. It asks for tools and visibility the way an A-player hire does, and it tries to grow its own scope and agency. It starts as an intern and tries to earn the COO job. As it works, it writes a company wiki of how you actually run. That wiki compounds as a company brain, so Gini only gets sharper with time. Throw it anything. It does its best, and it asks for help when it’s blocked. The result lands in the thread, with sources, where the whole team can see it.\n\n\n\n**Four parts, one coworker:**\n\n1. **Ambient and proactive agents -** properly sandboxed with its own computer and browser, so you can trust them with actual tasks instead of babysitting chat windows.\n2. **A wiki that maintains itself —** no more stale handbooks. Gini writes down how the company actually runs and keeps it current. Teammates can read it. Agents can read it.\n3. **Automations that suggest themselves -** Gini watches how your team works and surfaces the repetitive stuff worth automating, so you're not designing workflows from a blank page.\n4. **Built for multiplayer** - Gini is designed with proper permissioning that matches how your team operates.\n\n**2 minutes to start.** Add Gini to Slack, invite it to the channels where work happens, give it a job. It’ll ask for the tools it needs (whatever the job touches). It only reads the channels you invited it to.\n\n**Built by software engineers who’ve shipped products used by millions.**\n\nThe three of us worked together at Lilac Labs (YC S24) and all went to Berkeley. Tony (CEO) was software at Tesla and Intel. Shelden (CTO) was at Flatiron Health, Roche, and Certik (cyber security). Wilson (Chief Engineer) was at Hewlett Packard Enterprise and Vestible, a regulated securities platform.\n\nGini is the coworker we wanted at our last company, when we were the ones running every job.\n\n**Try Gini today**\n\nTry it at [ginicomputer.com](https://ginicomputer.com/?utm_source=yc&utm_medium=referral&utm_campaign=yc-launch&utm_content=try-cta).\n\nTake 30 minutes with me: [book a demo.](https://cal.com/tony-kam-ympx2p/intro?overlayCalendar=true)\n\nWould love to hear your thoughts and feedback. You can email me at [tony@ginicomputer.com](mailto:tony@ginicomputer.com)\n\n![uploaded image](/media/?type=post&id=111423&key=user_uploads/802295/f974cf1c-1978-4418-a1e6-5fdc6de258f8)\n\n", "url": "https://www.ycombinator.com/launches/Sz9-gini-ai-coworker-that-lives-in-slack", "votes": 101, "created_at": "2026-08-20T13:00:00.616Z" } ] }, { "id": 34761, "name": "Veeza AI", "slug": "veeza-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1ff98253605935aaa53bb31e8330be6a74309c6d.png", "website": "https://veeza.ai", "all_locations": "", "long_description": "Billions of people need tourist visas to travel the world. We use AI agents to prepare applications, verify documents, secure appointments, and handle the visa process end to end.\r\n", "one_liner": "AI tourist visa agent for people with weak passports.", "team_size": 2, "industry": "Consumer", "subindustry": "Consumer -> Travel, Leisure and Tourism", "launched_at": 1786858536, "tags": [ "AIOps", "Consumer", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Consumer", "Travel, Leisure and Tourism" ], "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/veeza-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/veeza-ai.json", "year_founded": 2026, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/veeza-ai", "x_url": "https://x.com/useveeza", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1ff98253605935aaa53bb31e8330be6a74309c6d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Grey Baker", "url": "https://www.ycombinator.com/people/grey-baker" }, "founders": [ { "user_id": 578651, "full_name": "Aly Moursy", "title": "Founder/CEO", "founder_bio": "Founder at Veeza AI (YC F26). Previously Head of Product and AI at Efreshli, where he built an AI interior-design product. Earlier co-founded Dlvvr, a same-day delivery platform that became one of the highest-rated delivery apps on Shopify. Started at Bitaccess (YC S14) as their first intern. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alymoursy/", "x_url": "https://x.com/alymoursy", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6bd07941a7e50d179bdb3c964edd166de233bb8d.jpg" } ], "jobs": [], "news": [ { "title": "Veeza AI Wants to Make Global Mobility Easier for the Global South", "url": "https://scenenow.com/Business/Veeza-AI-Wants-to-Make-Global-Mobility-Easier-for-the-Global-South", "date": "Oct 17, 2026" }, { "title": "One Egyptian founder and AI enthusiast is out to fix the broken visa process plaguing “residents of the Global South” - EnterpriseAM Egypt", "url": "https://enterpriseam.com/egypt/2026/08/10/one-egyptian-founder-and-ai-enthusiast-is-out-to-fix-the-broken-visa-process-plaguing-residents-of-the-global-south/", "date": "Aug 09, 2026" } ], "launches": [ { "id": 122484, "title": "Veeza AI (F26) — AI tourist visa agent for people with weak passports", "tagline": "Pick your destination. We'll handle everything else.", "body": "**TL;DR** [Veeza AI](https://veeza.ai) helps people with weak passports get tourist visas. Upload your passport, and our computer-use agents verify your documents, fill in all the forms and book your visa appointment.\n\n\n\n**The problem**\n\nHey, I'm Aly. I'm Canadian-Egyptian, and I've traveled on a strong passport and a weak one. With a strong one, you book a flight. With a weak one, you spend hours filling out forms (the UK's is 68 steps long), collect a stack of documents, then fight for an appointment like Taylor Swift tickets. And just like with Taylor Swift tickets, scalpers get there first. Their bots grab the slots and resell them for as much as $1,000.\n\nMy friends and I have missed weddings, engagements and conferences because of this. I get that countries need to vet who comes in. It just shouldn't be this hard.\n\n**How Veeza AI works**\n\n1. Pick your destination from 33 countries, including the US, the UK, Canada and Schengen countries.\n2. Upload your passport and documents. Our AI verifies them, flags anything missing, fills in all the forms and books your visa appointment.\n3. A human reviews everything to make sure it's done right.\n\nBehind the scenes, our computer-use agents work through the government portals for you.\n\n![](https://bookface.ycombinator.com/media/?type=post&id=122484&key=user_uploads/578651/140a58f6-93a5-4631-aaff-51ce99d2417f)\n\nSince joining YC, we've grown from 1,000 to 7,000+ users. People are recommending us to their families and coming back for their next visa. A woman applied through Veeza AI, then her mom did, then her grandmother. One traveler has had four visas approved through us since June.\n\nAbout 5 billion people have weak passports. We estimate the Global South files 200 million tourist visa applications a year. We're starting with MENA, then the entire Global South. And as personal AI agents start planning and booking people's trips, Veeza AI will be where they turn for visas.\n\n**How you can help**\n\nKnow a travel agency or company whose customers need tourist visas? I'd love an intro for B2B2C partnerships.\n\nYou can reach us at [**founders@veeza.ai**](mailto:founders@veeza.ai).\n\nAly", "url": "https://www.ycombinator.com/launches/VrY-veeza-ai-f26-ai-tourist-visa-agent-for-people-with-weak-passports", "votes": 68, "created_at": "2026-10-19T17:00:00.202Z" } ] }, { "id": 35258, "name": "Mirrors", "slug": "mirrors", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e98a564b21ba044528e16126406d35b7464044b7.png", "website": "https://www.mirrors.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Production agents handle many different customer workflows. Currently, when one handles a situation badly, an engineer reads the trace and patches the prompt. The next situation needs another exception. Soon, improving the agent becomes a recurring engineering job, and the prompt keeps growing.\r\n\r\nMirrors finds patterns in past runs. Engineers review guidance for a specific situation and preview where it would apply. Mirrors then delivers that guidance while the agent works, inside the team’s existing setup. The team can check whether the change improved the outcome.\r\n\r\nWe’re building Mirrors for engineering teams running customer-facing agents across varied workflows, so they can improve behavior without hardcoding every lesson in the global prompt.", "one_liner": "Give AI agents the right context at the right moment", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1789011304, "tags": [ "AIOps", "Developer Tools", "SaaS", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Engineering, Product and Design" ], "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/mirrors", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/mirrors.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/runmirrors/", "x_url": "https://x.com/mirrorsdotdev", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e98a564b21ba044528e16126406d35b7464044b7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/d29b93262a911b75f36289e28863218346e9644c.png" ], "primary_partner": { "name": "Andrew Miklas", "url": "https://www.ycombinator.com/people/andrew-miklas" }, "founders": [ { "user_id": 3174410, "full_name": "Aryan Singhal", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO @ Mirrors (YC F26). Published four AI/ML research papers in high school at top AI conferences such as NeurIPS and EMNLP. Prev. ML research at UT Austin and UCSB.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/aryan-singhal-ai", "x_url": "https://x.com/ai_singhal", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fdc726196feb99cd0b97b4d0a2fa4012a5a31466.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 35266, "name": "TrueMile", "slug": "truemile", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/94408cc02a79b94fd281d47aabc0056465683df6.png", "website": "https://TrueMile.AI/", "all_locations": "San Francisco, CA, USA", "long_description": "TrueMile is building a fully autonomous logistics company, starting with trucking. Delayed pickups, empty miles and poor planning eat into a carrier’s profits. Our agents coordinate loads, routes, driver schedules and fuel stops to earn more from each truck and lower the cost of moving goods.\r\n\r\nWe’re starting with our own fleet so we control execution, learn from every delivery and keep the gains. Human drivers come first. As autonomous trucks become available on our routes, we’ll have the customers, freight and infrastructure ready for them.\r\n\r\nAs we drive toward AGI, people will still need groceries, clothes and materials to build their homes. Someone has to move all of it. We want every leap in AI to show up in what people pay for everyday goods.", "one_liner": "An AI-operated logistics company.", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Supply Chain and Logistics", "launched_at": 1790031761, "tags": [ "AIOps", "Machine Learning", "Logistics", "Data Science" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Supply Chain and Logistics" ], "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/truemile", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/truemile.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/truemile/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/94408cc02a79b94fd281d47aabc0056465683df6.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/a12b26e20f33313f399874daf66fede460128c50.png" ], "primary_partner": { "name": "Andrew Miklas", "url": "https://www.ycombinator.com/people/andrew-miklas" }, "founders": [ { "user_id": 3107234, "full_name": "Harpreet Dhaliwal", "title": "Founder/CEO", "founder_bio": "Harpreet has generated over $7M+ in trucking revenue, running his family's fleet since 19 and building the operation agents that became TrueMile. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/harpreet-dhaliwal/", "x_url": "https://x.com/harpreetd14", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c44eac0421520202a48251098ea059ed7d9a460b.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 35525, "name": "Orca Aerospace", "slug": "orca-aerospace", "former_names": [ "Otter Aerospace Inc.", "Orca Aerospace Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/afa6ce6fe3345bb9e7b6296e9877371ada8e2155.png", "website": "https://orcaaerospace.com/", "all_locations": "San Francisco, CA, USA", "long_description": "At Orca, we are focused on creating the future of spacecraft operations by moving control onto the vehicle. By allowing AI models to control the satellite using deterministic tooling, we are able to achieve the same holistic, high-quality decisions on \"what\" to do to carry out a mission while maintaining the safety of systems that decide \"how\" to execute it. ", "one_liner": "AI spacecraft operators that lives onboard the vehicle", "team_size": 5, "industry": "Industrials", "subindustry": "Industrials -> Aviation and Space", "launched_at": 1788312802, "tags": [ "AIOps", "Space Exploration", "Satellites" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Industrials", "Aviation and Space" ], "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/orca-aerospace", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/orca-aerospace.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/orcaaerospace", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/afa6ce6fe3345bb9e7b6296e9877371ada8e2155.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": 2936461, "full_name": "Jacob Ososke", "title": "Founder/CEO", "founder_bio": "Jacob spent over a decade at Lockheed Martin in guidance, navigation, and control, rising from intern to GNC Manager and founding the company's first AI-GNC team. His work focused on autonomous fault recovery and self-healing systems for aerospace platforms. Jacob has an MS in Robotics from Santa Clara University and an MS in Computer Science from Georgia Tech.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ososke/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/61f6732a8596fd1d04b234779c4e8dac84f7b219.jpg" }, { "user_id": 4003515, "full_name": "Hakan Chunton", "title": "Founder/CTO", "founder_bio": "Founder at Orca Aerospace (YC F26). CS/Aero at MIT (MEng, SB). Research entails on-board satellite collision avoidance, trajectory optimization, and imitation learning. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hakan-chunton/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/28ee067c626f71f46a6c1bd2ce1f9538051d3717.jpg" } ], "jobs": [ { "id": 107693, "title": "Founding Flight Software Engineer", "url": "https://www.ycombinator.com/companies/orca-aerospace/jobs/eMihWDs-founding-flight-software-engineer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Embedded systems", "salary_range": "$100K - $200K", "equity_range": "0.25% - 2.00%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "C++", "Python", "Linux" ] } ], "news": [], "launches": [] }, { "id": 36084, "name": "Covera", "slug": "covera", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e711e58f600a9ea3c53a45d934c825989e9b5b57.png", "website": "https://www.covera-agents.com/?utm_source=ycombinator&utm_medium=company_profile&utm_campaign=yc_f26", "all_locations": "San Francisco, CA, USA", "long_description": "Covera is the AI-native operating system for independent P&C insurance agencies. It lives inside the agency's AMS and inbox and runs agentic AI workflows across the whole client journey, from the first email to the next renewal. Producers quote first and win more business; CSRs and account managers stop retyping data.\r\n\r\nSubmissions, for producers: every new request becomes a ready file, one click keys every carrier portal for a first quote during the call, quotes come back side by side, and binding updates the AMS with no double entry.\r\n\r\nServicing, for CSRs and account managers: answers about any account with their source, COIs and follow-ups drafted, renewal reminders 90, 60 and 30 days out with remarketing when premiums jump, and coverage gaps across the book turned into cross-sell leads.\r\n\r\nAgencies using Covera go from lead to quote in 5 minutes, save 10 hours a week per user and win 20% more deals against competitors. Every action is reviewed by the agency team and logged for E&O. Works with Applied Epic, AMS360, QQCatalyst, HawkSoft, EZLynx, Momentum, Gmail and Outlook.\r\n\r\nBuilt by the founders of Simio (conversational AI, acquired in 2024).", "one_liner": "AI-native OS for insurance brokers", "team_size": 2, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1787324338, "tags": [ "AIOps", "SaaS", "B2B", "Insurance", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Fintech", "Insurance" ], "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/covera", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/covera.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/covera-agents/", "x_url": "", "fb_url": "https://www.facebook.com/covera.agents", "cb_url": "https://www.crunchbase.com/organization/covera-agents", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e711e58f600a9ea3c53a45d934c825989e9b5b57.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": 3982969, "full_name": "Tiphaine Venet", "title": "Founder/CEO", "founder_bio": "Founder & CEO at Covera. Previously founded Simio, a conversational AI platform for retail enterprises (Samsung, Carrefour, Air France), and sold it in 2024. Before that, VC at Serena, co-leading investments. Educated at HEC Paris and École Polytechnique.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tiphaine-venet/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6becc198efbb64b0f02ef9867232ea4ed76a8123.jpg" }, { "user_id": 3987641, "full_name": "Thibaut Regerat", "title": "Founder/CTO", "founder_bio": "Founder at Covera. Previously co-founder/CTO at Simio, building a WhatsApp commerce platform for e-commerce brands. Engineer's degree in Computer Science from CentraleSupélec. Early experience in data science and business intelligence before transitioning to founding companies.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/thibaut-regerat/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7515f95f0ecfb7251e72672750bdd224992ad4be.jpg" } ], "jobs": [ { "id": 105751, "title": "Founding GTM/ Account Executive", "url": "https://www.ycombinator.com/companies/covera/jobs/DAQuYrq-founding-gtm-account-executive", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$60K - $100K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [] }, { "id": 36643, "name": "Papaya", "slug": "papaya", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/90437a276bb22b842ea04cd42e19389e216c984c.png", "website": "https://papaya.fyi/", "all_locations": "San Francisco, CA, USA", "long_description": "Papaya is the optimization engine for AI agents. Engineers don't have time to continually improve the agents they've shipped — the context bloat, overly chatty subagents, and broken tool calls sit in production traces nobody reads, quietly hurting agent efficiency. With Papaya they connect their agent directly via SDK, get actionable recommendations on how to improve quality, latency, and cost, and the ones they approve are pushed to production as pull requests.\r\n\r\nPapaya runs 200+ research-backed analyses across context, prompts, prompt caching, subagents, and tool calls. Every recommendation is ranked by impact and comes with the production runs that produced it — typically a 20%+ quality improvement on the first workflow analysis.", "one_liner": "Continuously improve the quality of your AI agents.", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1789407516, "tags": [ "AIOps", "Developer Tools", "B2B", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B" ], "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/papaya", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/papaya.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/papayaai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/90437a276bb22b842ea04cd42e19389e216c984c.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Andrew Miklas", "url": "https://www.ycombinator.com/people/andrew-miklas" }, "founders": [ { "user_id": 2135001, "full_name": "Macy Mody", "title": "Founder/CEO", "founder_bio": "Co-Founder & CEO at Papaya (YC F26). Previously led GTM and Ops at SafeBase from $0-10M ARR and through its acquisition. Also led analytics products at HqO and managed investments for AmEx Digital Labs. UMich undergrad and HBS MBA.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/macy-berg/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/570e127be1ea69af0971fe4c5f2a81e394bae22b.jpg" }, { "user_id": 48440, "full_name": "Niyaz Puzhikkunnath", "title": "Founder/CPO", "founder_bio": "Founder at Papaya. Previously Sr. SDE at Amazon for 10+ years building large-scale ML systems across Computer Vision, Classification, LLMs/Agents. Led teams on brand and product classification, built agent evaluation workflows, and created a self-service ML training pipeline. Earlier, one of the first engineers at Zovi.com, where he helped build the front-end architecture and internal analytics.", "is_active": true, "linkedin_url": "https://linkedin.com/in/niyazpk", "x_url": "https://x.com/niyazpk", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/104b41ff2bd7bbe701b32887f45470ba3e89badd.jpg" }, { "user_id": 3166801, "full_name": "Faiz Vadakkumpadath", "title": "Founder", "founder_bio": "Founder at Papaya (YC F26). Previously Senior Data Engineer at Amazon for 10 years, leading teams for the enterprise Data Lake, solving problems at Exabyte scale. Earlier scaled Vodafone's MPESA mobile money system in Africa from 25 to 200 tps at IBM. Co-founded OpenReporting, a SaaS financial reporting tool.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vpfaiz", "x_url": "https://x.com/vpfaiz", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cfa4f0888a12e8d75c59b3bd551968fc62ced0d5.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 115486, "title": "Papaya - Continuous Optimization for your AI agents", "tagline": "Papaya finds what's hurting your agent's quality, cost, and latency, and fixes it for you.", "body": "Hey everyone! 👋 \\\n\\\nWe're Macy, Faiz and Niyaz - co-founders of[ Papaya](https://papaya.fyi) \n\n**TL;DR**\n\nPapaya is the optimization engine for AI agents. Connect your production agent and Papaya watches every action, finds what's hurting quality, cost, and latency, and gives you the fix. Once a fix is in place, Papaya makes sure your agent doesn't regress. Teams running Papaya are seeing 20%+ improvements to quality, latency, and cost.\n\n**Ask:** If you run an agent in production, connect it to Papaya. [Grab a time to get started here](https://cal.com/macy-mody/macy-papaya).\n\n\n\n**The Problem**\n\nWe’ve been building agentic systems for years (6 years to be exact…before it was cool), including really large ones that helped run major workflows inside Amazon. We know firsthand that the hard part isn’t getting an agent to work, it’s keeping it working at a high quality bar. We consistently saw that every model update and every prompt change shifted behavior in ways nobody caught until something downstream looked wrong. The only way to find out what changed was to pull traces and read them by hand.\n\nMost companies shipping agents today are focused on the next feature for customers and they don't have time to fully maintain what's already been released. When we built our own agent as a three-person team, we hit the same wall. We needed to make sure our agent had high quality answers, was fast for our customers, and as low cost as possible, but we had to look at everything by hand. Every hour we spent examining traces was an hour we couldn’t spend on new product development. That's why we built Papaya.\n\n**How Papaya works 🔧** \n\nAt Papaya, our goal is to do the hard AI engineering work for you, staying up to date on the latest and greatest research so you can take advantage of this knowledge through optimizations. Simply stream your production traces through a lightweight SDK. From there, Papaya:\n\n* **Finds your use cases**: groups every run by the job the agent is doing, so you're looking at \"refund requests,\" not \"run 4812.\"\n* **Runs 200+ analyses**: checks every use case against 200+ research-backed failure patterns across quality, cost, and latency.\n* **Ranks the fixes by impact**: every recommendation comes with an estimated effect on quality, latency, and cost. Approve the ones you want directly in Papaya and Papaya opens the PR.\n* **Watches for regression**: once a fix ships, Papaya keeps checking that the improvement holds.\n\n![uploaded image](/media/?type=post&id=115486&key=user_uploads/2135001/0a49f356-ac11-44d5-bcc1-69caf4ba64d9)\n\n**Our Ask 🙏**\n\n* Run an agent in production? Connect it to Papaya. [Grab a time with us here](https://cal.com/macy-mody/macy-papaya).\n* Know any companies spending tons of time maintaining and fixing their agents? Intro us at [macy@papaya.fyi](mailto:macy@papaya.fyi).\n* Building agents and want a second set of eyes? Ping us. We are happy to help however we can!\n\n**The Team**\n\nWe've spent over 10 years building large scale agentic systems at Amazon, and we've scaled other YC startups from $0 to $10M ARR through acquisition. We started out building an analytics agent. The harness we built to optimize our own agent became the center of every prospect conversation, so we pivoted to it. Meet our team below :) \n\n![uploaded image](/media/?type=post&id=115486&key=user_uploads/2135001/ba5c9344-fb83-47ed-8c8c-deef533840ce)\n\n", "url": "https://www.ycombinator.com/launches/U2g-papaya-continuous-optimization-for-your-ai-agents", "votes": 38, "created_at": "2026-09-16T18:04:02.706Z" } ] }, { "id": 37830, "name": "Tilo", "slug": "tilo", "former_names": [ "Podesta AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5ee5f7b68452b3800941d6ce5bfbfacd28c318f2.png", "website": "https://tilo.so", "all_locations": "San Francisco, CA, USA", "long_description": "Tilo is a Linux-based operating system built for AI agents to work alongside people. On Tilo, computer use is up to 10,000x cheaper than on a normal OS.\r\n\r\nToday agents use computers the way people do. They take screenshots and click through apps, which is slow, expensive, and brittle. In Tilo, every app exports actions, so agents work over MCP while people use the UI. You still see what each agent does in your apps, and anything it changes can be undone.\r\n\r\nAny number of people and agents can work in one workspace in real time, across apps. Tilo runs as your OS, in our cloud, or headless on your machine, and you can open and share any workspace from the browser.", "one_liner": "A Linux-based, AI-native operating system.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1792039962, "tags": [ "AIOps", "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", "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/tilo", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/tilo.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://code.tilo.so/tilo", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5ee5f7b68452b3800941d6ce5bfbfacd28c318f2.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Grey Baker", "url": "https://www.ycombinator.com/people/grey-baker" }, "founders": [ { "user_id": 3674518, "full_name": "Xaver Emmerling", "title": "Founder/CEO", "founder_bio": "Founder/CEO at Tilo (YC F26). Building a OS for the future.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/xaver-emmerling-915228278/", "x_url": "https://x.com/XaEmmerling", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/77851cdcba5a5afc0a817b9d4747669c6be1c137.jpg" }, { "user_id": 3674027, "full_name": "Quinten Wolff", "title": "Founder/CTO", "founder_bio": "Built a patent workflow automation platform at BDPE, a Band 1 Chambers-ranked litigation firm in Munich. Early admission to Computer Science at Technical University of Munich. Now building the future of human & agent computer use.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/quinten-wolff-44810140b", "x_url": "https://x.com/NetniuqFlow", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/accf3868a2df4e89dbe035edd52e23b027c0ff9a.jpg" } ], "jobs": [], "news": [], "launches": [] } ]