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Our customers are genetics testing companies, governments of countries, and pharma companies. We train our machine learning algorithms to enable large scale comparison of genomes to power novel discoveries, enabling new diagnostics and drugable targets.  ", "one_liner": "Large Scale Genomic Data Mining Software", "team_size": 7, "industry": "Healthcare", "subindustry": "Healthcare -> Healthcare Services", "launched_at": 1548889511, "tags": [ "Machine Learning", "Genomics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2019", "status": "Inactive", "industries": [ "Healthcare", "Healthcare Services" ], "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/spiral-genetics", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2019/spiral-genetics.json", "year_founded": 2009, "city": "Seattle", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/spiral-genetics", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/822d8282aa459c05cf652eefd3f059118ec38f90.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 309274, "full_name": "Adina Mangubat", "title": "Founder", "founder_bio": "Adina Mangubat is the CEO and co-founder of Spiral Genetics. Spiral Genetics makes software to compare large populations to enable novel drugs and diagnostics. She was featured in Forbes 2013 “30 Under 30″ for Science and Healthcare. In 2017, Forbes named her as one of the All-Star Alumni for the 30 Under 30 in Science and Healthcare. Adina is passionate about world-changing technology and having people be empowered to make the difference in the world that they are committed to making.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/amangubat/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/56c4057b642d5f829a8232449d313eaf07ab9446.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 12531, "name": "Shiru", "slug": "shiru", "former_names": [ "Shiru Bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/96f8b3ffa1e973f19d7d140a20c5160d4529a01e.png", "website": "https://www.shiru.com/", "all_locations": "San Francisco, CA, USA; Emeryville, CA, USA", "long_description": "", "one_liner": "Shiru leverages ML to create proteins to feed the world sustainably. ", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Industrial Bio", "launched_at": 1555989874, "tags": [ "AI-powered Drug Discovery", "Cellular Agriculture", "Machine Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2019", "status": "Active", "industries": [ "Healthcare", "Industrial Bio" ], "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/shiru", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2019/shiru.json", "year_founded": 2019, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/shiru-inc/?viewAsMember=true", "x_url": "https://twitter.com/shiruinc", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/96f8b3ffa1e973f19d7d140a20c5160d4529a01e.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": 331631, "full_name": "Jasmin Hume", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jasminhume/", "x_url": "https://twitter.com/jasminhume", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/662b2bc9648cc21183f4da21d847675348658598.jpg" } ], "jobs": [], "news": [ { "title": "Biotech startup Shiru sprouting plant-based ingredients with new capital infusion – TechCrunch", "url": "https://techcrunch.com/2021/10/27/biotech-startup-shiru-sprouting-plant-based-ingredients-with-new-capital-infusion/", "date": "Oct 27, 2021" } ], "launches": [] }, { "id": 12572, "name": "Short Story", "slug": "short-story", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dac97eb3343bbee255adbecf7060b7b9ac299faf.png", "website": "https://shortstorybox.com", "all_locations": "San Francisco, CA, USA", "long_description": "Short Story is a style discovery platform to help petite women find clothes that fit. The average American woman is 5'4\" and petite, yet this remains an enormously underserved segment. We’re a team of curators passionate about finding style, quality, and fit for petite women. Our mission is to remove the “ugh” from the shopping equation and support petite women in telling their stories, stylishly and confidently. 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I also invest in YC companies and try to be as supportive as I can. In college, I co-founded a startup that sold textbooks to students across 117 campuses. Before Short Story, I worked at Tower Research and Waymo.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hoffstaetter/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/320c2159dd299fa50ce8e78523fe878e3399befc.jpg" }, { "user_id": 273905, "full_name": "Isabella Sun", "title": "Founder", "founder_bio": "I'm the cofounder of Short Story. I also invest in YC companies through Pioneer Fund.\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/isabellasun/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8f03f0fa96940faafeaf2740c5087776eabd5e7e.jpg" } ], "jobs": [ { "id": 95138, "title": "Senior Software Engineer, Full Stack (San Francisco)", "url": "https://www.ycombinator.com/companies/short-story/jobs/vLBbq3y-senior-software-engineer-full-stack-san-francisco", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$200K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95139, "title": "Founding Recruiter", "url": "https://www.ycombinator.com/companies/short-story/jobs/rHoOs6t-founding-recruiter", "location": "San Francisco", "type": "Full-time", "role": "Recruiting & HR", "role_type": "Recruiter", "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 95140, "title": "Senior Webflow Developer & CRO Specialist", "url": "https://www.ycombinator.com/companies/short-story/jobs/VIPsqqc-senior-webflow-developer-cro-specialist", "location": "Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95141, "title": "Merchandise Planner", "url": "https://www.ycombinator.com/companies/short-story/jobs/dewMApe-merchandise-planner", "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": 95142, "title": "Staff Engineer, Full Stack (San Francisco)", "url": "https://www.ycombinator.com/companies/short-story/jobs/ieSp0D1-staff-engineer-full-stack-san-francisco", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$250K", "equity_range": "", "experience": "8+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95144, "title": "Styling Team Manager (Remote)", "url": "https://www.ycombinator.com/companies/short-story/jobs/OXoM0Hf-styling-team-manager-remote", "location": "Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95145, "title": "Community & Growth Operator (Contract to Hire)", "url": "https://www.ycombinator.com/companies/short-story/jobs/62QgeUq-community-growth-operator-contract-to-hire", "location": "San Francisco", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 95157, "title": "Senior Software Engineer, Back-End (San Francisco)", "url": "https://www.ycombinator.com/companies/short-story/jobs/eFv4XQW-senior-software-engineer-back-end-san-francisco", "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": "Startup Year One: Short Story, the clothing subscription service built just for petites | Fortune", "url": "https://fortune.com/2022/07/24/startup-year-one-short-story-clothing-subscription-box/", "date": "Jul 25, 2022" }, { "title": "Isabella Sun — Reinventing Fashion for Petite Women (#143) ⋆ Asian Hustle Network", "url": "https://www.asianhustlenetwork.com/podcast/isabella-sun-reinventing-fashion-for-petite-women-143/", "date": "Jul 22, 2022" }, { "title": "Co-founder of clothing subscription company Short Story on customizing styles for petite woman | KTLA", "url": "https://ktla.com/morning-news/clothing-subscription-box-short-story-customizing-personal-styles-for-the-modern-petite-woman/", "date": "May 18, 2021" } ], "launches": [] }, { "id": 12608, "name": "TRM Labs", "slug": "trm-labs", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/19aa5130e3a0a349d200e7510f0c2fe8439a646f.png", "website": "https://trmlabs.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "TRM is on a mission to build a safer world for billions of people. \r\n\r\nAI is extending the capability gap between attackers and defenders. 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Prior to founding MindsDB, he worked for a number of data-intensive start-ups, most recently working with Aneesh Chopra (the first CTO in the US government) building data systems that analyze billions of patients records and lead to highest savings for millions of patients.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/torresjorge/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d2501f3e40dd4908825af00de60d6ba5bbd5e244.jpg" } ], "jobs": [], "news": [ { "title": "How MindsDB Became a Hot AI Startup Worth $160 Million", "url": "https://www.businessinsider.com/how-mindsdb-became-hot-ai-startup-2023-6", "date": "Jun 14, 2023" }, { "title": "MindsDB Scores $7.6m Seed Funding to Democratize Machine Learning – Towards AI — The World’s Leading AI and Technology Publication", "url": "https://towardsai.net/p/news/mindsdb-scores-7-6m-seed-funding-to-democratize-machine-learning", "date": "Nov 01, 2021" } ], "launches": [] }, { "id": 13112, "name": "Orbiter", "slug": "orbiter", "former_names": [ "In Good Company", "Orbiter (by In Good Company)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/81f62b42761b3850734765fe84470eb84ad0de1b.png", "website": "http://www.getorbiter.com", "all_locations": "San Francisco, CA, USA", "long_description": "Orbiter’s ML models monitor business metrics automatically and send alerts when those metrics experience abnormal drops or spikes. Any company can onboard in less than five minutes with just their database credentials - no engineering required. ", "one_liner": "ML models automatically monitor business performance", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1584147330, "tags": [ "Machine Learning", "SaaS", "B2B" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2020", "status": "Inactive", "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/orbiter", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2020/orbiter.json", "year_founded": 2020, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/orbiterai", "x_url": "https://twitter.com/orbiterai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/81f62b42761b3850734765fe84470eb84ad0de1b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 376672, "full_name": "Mark Wai", "title": "Founder", "founder_bio": "Building Polished\n\nPreviously Growth @ Tesla, Facebook", "is_active": false, "linkedin_url": "https://linkedin.com/in/markwai", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e7835121f75384f2b6a95aa37523c3607b9931ae.jpg" }, { "user_id": 336794, "full_name": "Victor Zhang", "title": "Founder", "founder_bio": "Co-founder of Cega\nBringing exotic options to DeFi\n🇨🇦eh?", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/zhangvictorz/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0dc72137d734239f768d6c6583992dec473bb98b.jpg" }, { "user_id": 382880, "full_name": "Winston Zhang", "title": "Founder", "founder_bio": "VP Revops @EvenUp. Co-founded Cega (acquired) and Orbiter (YC W20). Ex-PM @DoorDash (YC S13)", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/winstonzhang/", "x_url": "https://twitter.com/zhangwins", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c4198ee93d44c9cc498e4eb35fca3269d7a5826f.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 13121, "name": "Handoff", "slug": "handoff", "former_names": [ "1build" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7c28f259776f3ae753c7e797a8482d385f032fef.png", "website": "https://www.handoff.ai/", "all_locations": "Austin, TX, USA", "long_description": "Handoff creates instant construction estimates and automates business operations for contractors with AI.\r\n\r\nUsing generative AI technology and access to localized construction costs, Handoff provides remodelers with a fast, accurate, and intuitive way to create construction cost estimates in minutes.", "one_liner": "AI estimator & agent for remodelers.", "team_size": 20, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction -> Construction", "launched_at": 1578363908, "tags": [ "Machine Learning", "Construction", "B2B", "Proptech", "API" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2020", "status": "Active", "industries": [ "Real Estate and Construction", "Construction" ], "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/handoff", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2020/handoff.json", "year_founded": 2019, "city": "Austin", "country": "US", "linkedin_url": "https://www.linkedin.com/company/handoff-ai/", "x_url": "https://twitter.com/HandoffAI", "fb_url": "https://www.facebook.com/handoffai", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7c28f259776f3ae753c7e797a8482d385f032fef.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": 428748, "full_name": "Dmitry Alexin", "title": "Founder", "founder_bio": "I am the CEO & Founder of Handoff. Previously, I led the ML team at CloudKitchens as one of the founding employees. Before that I worked for a global macro hedge fund and the IMF.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/dzhdankin/", "x_url": "https://twitter.com/dmitry_alexin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8b010f58fb3d0257945c9a1b225395720d5d5beb.jpg" } ], "jobs": [ { "id": 88875, "title": "Account Executive", "url": "https://www.ycombinator.com/companies/handoff/jobs/wPWUYMh-account-executive", "location": "Austin, TX, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$120K - $130K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 105419, "title": "Senior Product Manager", "url": "https://www.ycombinator.com/companies/handoff/jobs/TyN2pBE-senior-product-manager", "location": "Austin, TX, US", "type": "Full-time", "role": "Product", "role_type": null, "salary_range": "$165K - $200K", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "As inflation fears spike, 1build raises $14M to help construction firms optimize their cost estimates – TechCrunch", "url": "https://techcrunch.com/2021/09/22/1build-series-a/", "date": "Sep 22, 2021" } ], "launches": [ { "id": 106241, "title": "Handoff H1: a takeoff model that outperforms human estimators", "tagline": "Autonomous takeoffs, available today via API", "body": "**TL;DR:** H1 reads construction blueprints and produces a complete material takeoff autonomously, from the raw PDF. It scores 81.6% on our benchmark — ahead of human estimators (77.6%) and \\~20 points clear of the best frontier model. It runs in \\~2 hours vs. up to a week for a human.\n\n![uploaded image](/media/?type=post&id=106241&key=user_uploads/428748/ccaeb6b3-ba3f-4330-acd0-8e8ff779cca2)\n\n\n**The problem**\n\nBefore a contractor can bid a job, someone has to do a takeoff: read every sheet, count materials trade by trade, measure walls and roofs, cross-reference dimensions that live on different pages. It takes an experienced estimator 8–20 hours per project and years of training to do at all. It's the tax on every bid in a huge, fragmented industry.\n\nYou'd think a frontier model could do this now. It can't. Foundation models today aren't capable of understanding construction drawings to a level that is actually useful for a construction business. So this massive $2 trillion industry still runs on blueprints that are being analyzed by humans pretty much the same way they have been for the last few hundred years.\n\n**What we built**\n\nHandoff has developed H1: an AI model that understands and analyzes blueprints better than a human estimator, thus resolving this manual bottleneck once and for all.\n\n![uploaded image](/media/?type=post&id=106241&key=user_uploads/428748/e3a9fd8f-cdf2-48f1-b4d6-9e30aa807ec5)\n\nH1 has three layers:\n\n* **Scope analysis** — a detailed, trade-by-trade breakdown of what the project actually involves, read straight from the drawings.\n* **Takeoff with quantities** — a complete material quantity list per trade: framing members, sheets, linear and square footage, counts, all with units.\n* **Localized construction costs** — accurate pricing on those quantities, localized to your ZIP code.\n\nAll of it programmatic and autonomous. No human needs to be involved in the process of creating the entire takeoff. You can simply upload plans and get structured scope, quantities, and costed output back.\n\n**The benchmark (TakeoffBench-V1)**\n\nAs part of this, we are also releasing the industry's first takeoff benchmark that enables us and anybody else in the research community to compare models against 15 real, permissioned, PII-stripped residential blueprint sets, each paired with a consensus-validated expert takeoff.\n\n![uploaded image](/media/?type=post&id=106241&key=user_uploads/428748/b4f0b802-c841-403e-8867-5b1ac2e9fcb2)\n\n![uploaded image](/media/?type=post&id=106241&key=user_uploads/428748/abdfeb2b-4ed7-4764-bc47-a6305f6d8ff9)\n\nHere's how we performed on the benchmark:\n\n* Handoff-H1: 81.6%\n* human estimator (with takeoff software): 77.6%\n* best frontier model (Claude Fable): 61.4%\n\nThe rest cluster in the low-to-mid 50s (gpt-5.6, Claude, Kimi) down to 34.7% (DeepSeek-V4-Pro).\n\n**Try it**\n\nH1 is live in the [Handoff](https://handoff.ai) product and on the API. We have one of the world's largest suppliers already using our model in every one of their locations across the country, including thousands of projects every month.\n\nIf you're looking for an intelligence layer to extract detailed structured data from blueprints with construction costs priced out to the nearest cent, hit us up at [api@handoff.ai](mailto:api@handoff.ai)", "url": "https://www.ycombinator.com/launches/RdZ-handoff-h1-a-takeoff-model-that-outperforms-human-estimators", "votes": 7, "created_at": "2026-07-22T20:04:49.600Z" }, { "id": 93974, "title": "Handoff - AI Agent for Residential Contractors", "tagline": "Handoff makes you more money, saves time, and runs your residential contracting business with AI.", "body": "### **The Problem**\n\nThere are 1.2M+ residential contractors (remodelers, home builders, etc.) in the US. They are severely understaffed and overworked:\n\n* 40% of their time goes to paperwork instead of building.\n* They rely on pen & paper or outdated software.\n* The US needs 4.7 million new homes (and contractors need to keep up with that demand).\n\nhttps://www.youtube.com/watch?v=jd85wN1hhBo\n\n### **The Solution: Handoff AI Agent**\n\nHandoff is the AI agent that helps owners of residential construction businesses automate their back office with AI.\n\nHere are some of the things Handoff can do:\n\n* Generate accurate construction estimates from short project descriptions and files (blueprints, photos, videos).\n* Automatically create material shopping lists (with links to purchase).\n* Send invoices, accept payments, and issue consumer loans.\n* Build project schedules, subcontractor bid invites, daily logs, and more.\n\nHandoff takes on the roles of:\n\n1. Cost estimator\n2. Project coordinator\n3. Admin assistant\n\nThis reduces the operational burden of scaling a construction business by $500k+/year, at a price point of just $149/month.\n\n![uploaded image](/media/?type=post&id=93974&key=user_uploads/428748/c2abd311-42d3-4f56-aed6-87253717f18e)\n\n![uploaded image](/media/?type=post&id=93974&key=user_uploads/428748/69f03428-53c5-4f46-9def-875366b532d9)\n\n### **The Secret Sauce**\n\nHow did we build Handoff? In short, we became obsessed with creating the world’s most accurate API for construction costs and a knowledge base of construction logic.\n\nWe work directly with top suppliers like Home Depot and Lowe’s to integrate their catalogs into Handoff, giving our customers real-time pricing and inventory.\n\nWe’ve also trained Handoff on 10,000+ construction cost estimates we completed by hand while running a manual cost estimating service in our early days.\n\n![uploaded image](/media/?type=post&id=93974&key=user_uploads/428748/9349123e-4a79-47c3-a7b6-7cb7d0575a47)\n\n### **Send This to Your Contractor**\n\nAre you getting some remodeling work done?\n\nGet a free account on [Handoff](https://handoff.ai/) and generate an estimate for your project. Share it with remodelers bidding on your job and ask them to explain any cost variance versus Handoff’s numbers.\n\nYou might be able to save yourself some money and help us generate more leads!", "url": "https://www.ycombinator.com/launches/ORi-handoff-ai-agent-for-residential-contractors", "votes": 14, "created_at": "2025-09-19T20:14:46.930Z" }, { "id": 70854, "title": "🏗️ 1build - Plaid for construction cost data", "tagline": "Track live pricing on 68M datapoints for construction materials, labor, and equipment in the US.", "body": "**TL;DR: 1build is launching the API for construction pricing data. Developers can use our API to access up-to-date information on construction costs for workflows like construction estimating, procurement, and loan/insurance underwriting. The API can be either queried with GraphQL or easily integrated into any application with one line of HTML.**\n\n📚 **Backstory**\n\nAs wild as it sounds, the construction industry still uses actual books that are published once a year to track construction market pricing. I previously ran the data science team at CloudKitchens where I needed an API for tracking live construction pricing data--but instead of an API I had to deal with these cost books.\n\n![](https://t4207102.p.clickup-attachments.com/t4207102/24e83b10-e8f3-4b04-b043-a050c01c2eb1/image.png)\n\n📊 **Problem**\n\nConstruction costs have been extremely volatile over the last several years. For example, lumber prices skyrocketed 400% and then crashed in 2021. At the same time, the construction tech space is booming ($5B+ is invested annually), but it still lacks its own \"Plaid\" for mission-critical market pricing data.\n\n🛠️ **Our solution**\n\n1build has an easy-to-use API as well as a JavaScript embed widget that gives you access to live pricing data on 68 million construction materials, labor, and equipment anywhere in the US.\n\n🚀 **How it works**\n\nYou can either use our GraphQL API ([docs](https://developer.1build.com/1build-api-reference)) or JavaScript embed widget ([docs](https://developer.1build.com/1build-widget-reference/)) to integrate construction pricing data into your application. Sign up [here](https://app2.1build.com/public/register?signupType=SOFTWARE_DEVELOPER) to get your API key.\n\nHere are just some of the use cases for our data:\n\n* Your contractor sent you an estimate and you want to validate their quote vs market rates.\n* You're serving bank underwriters who need to check whether their client's construction loan application has an accurate budget.\n* You're working with insurance brokers who need to approve a quote for home repairs.\n\n![uploaded image](/media/?type=post&id=70854&key=user_uploads/428748/7736013a-63ee-4a2c-ae3e-72873fb8f515)\n\n**Features**\n\n⚫ Search or browse our catalog of 68 million construction items and assemblies localized to your project. Include labor to get all-in installation costs.\n\n![](https://t4207102.p.clickup-attachments.com/t4207102/d8aac21a-8be8-46b3-813c-1b71484d5362/image.png)\n\n⚫ Add this line of HTML in your app to embed our construction pricing catalog:\n\n![](https://t4207102.p.clickup-attachments.com/t4207102/31a4f86e-5567-4937-926c-452f8f6274aa/image.png)\n\n⚫ Or query our API with GraphQL:\n\n![](https://t4207102.p.clickup-attachments.com/t4207102/5c1a6e76-3c97-4ed2-ae2d-2273f8357b79/image.png)\n\n👷 **Asks**\n\nTry [1build](http://1build.com) now! [Get your API keys](https://app2.1build.com/public/register?signupType=SOFTWARE_DEVELOPER) or [contact us](https://meetings.hubspot.com/dmitry-alexin/30-min) to embed live construction data into your application or ML model.\n\nWe are hoping to discover new use cases and applications for our data, so don't hesitate to reach if you want to bounce around a few ideas.\n\nWe are also looking for feedback on the API / JS widget's ease of use, so let us know about your experience with our docs/integration.", "url": "https://www.ycombinator.com/launches/IQo-1build-plaid-for-construction-cost-data", "votes": 24, "created_at": "2023-04-10T19:14:40.977Z" } ] }, { "id": 13147, "name": "Replicate", "slug": "replicate", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/256be79b4f9ac851425e3bf2a60af5ae33ca72ba.png", "website": "https://replicate.com", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Run machine learning models in the cloud", "team_size": 36, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1647283616, "tags": [ "Developer Tools", "Machine Learning", "Community", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2020", "status": "Acquired", "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/replicate", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2020/replicate.json", "year_founded": 2019, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/replicate/", "x_url": "https://twitter.com/replicate", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/replicate", "github_url": "https://github.com/replicate", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/256be79b4f9ac851425e3bf2a60af5ae33ca72ba.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": 35139, "full_name": "Ben Firshman", "title": "Founder", "founder_bio": "Product at Docker, creator of Docker Compose. 4x founder.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bfirsh/", "x_url": "https://twitter.com/bfirsh", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6368e7ef32404188b9649557032af9a2ba4c9625.jpg" }, { "user_id": 421489, "full_name": "Andreas Jansson", "title": "Founder", "founder_bio": "Built research tools & infrastructure at Spotify. PhD in ML for music.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/janssonandreas", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0ef030fe6748a13d137bd184875dff315f2a04b7.jpg" } ], "jobs": [], "news": [ { "title": "Replicate Raises $40 Million For Its Library Of Open Source AI Models", "url": "https://www.forbes.com/sites/rashishrivastava/2023/12/05/replicate-raises-40-million-for-its-library-of-open-source-ai-models/?sh=de345433eee4", "date": "Dec 05, 2023" }, { "title": "Businesses are building on open-source AI – Replicate", "url": "https://replicate.com/blog/series-b", "date": "Dec 05, 2023" }, { "title": "Replicate wants to take the pain out of running and hosting ML models • TechCrunch", "url": "https://techcrunch.com/2023/02/21/replicate-wants-to-take-the-pain-out-of-running-and-hosting-ml-models/", "date": "Feb 21, 2023" } ], "launches": [] }, { "id": 13161, "name": "Clayboard", "slug": "clayboard", "former_names": [ "Modulo", "Slingshow", "Clayboard", "Pathspace" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/95390ad4c8c9633ca7bffdf9af67b0ae984c5cd9.png", "website": "http://clayboard.com", "all_locations": "New York City, NY, USA; New York, NY, USA; Remote", "long_description": "", "one_liner": "Create active, collaborative learning experiences ", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1660775581, "tags": [ "Machine Learning", "SaaS", "B2B", "Productivity", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2020", "status": "Inactive", "industries": [ "B2B", "Productivity" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/clayboard", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2020/clayboard.json", "year_founded": 2020, "city": "New York City", "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/95390ad4c8c9633ca7bffdf9af67b0ae984c5cd9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 324353, "full_name": "Jorge Fuentes", "title": "Founder", "founder_bio": "YC Badge: 0x2f0d643602a0a1cf421f674206f9e8d6fe666a1f\n", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/jsfuentes", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fc87e8d129b8eb4b526d450e915d7ccb932f468d.jpg" }, { "user_id": 206328, "full_name": "Nilay Modi", "title": "Founder", "founder_bio": "Product & design", "is_active": false, "linkedin_url": "https://linkedin.com/in/thenilaymodi", "x_url": "https://twitter.com/nilaymod", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/617b53a187d9dc0a6cfef02a8a19fc213baed841.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 65449, "title": "🧠 Pathspace: Collaborative brainstorming. Supercharged with A.I.", "tagline": "Solve problems together effectively with design thinking best practices—no learning curve.", "body": "Hey! Super thrilled to share Pathspace with all of you!\n\nPathspace is the easiest and most effective way to solve problems together using design thinking best practices and the power of GPT-3.\n\n**TL;DR Solve problems together for free at** [**pathspace.com**](http://pathspace.com)**. No learning curve. No signup required.** \n\n![](https://uploads-ssl.webflow.com/62d313720e07abf87ece7618/62df4aaf229ef36da3537997_Step%202.png)\n\n—\n\n### **❌ The Problem**\n\nThere’s been an enormous shift towards remote work, which is excellent for flexibility and accessing global talent but not so great for collaborative creativity & brainstorming.\n\nMost group brainstorming involves a few people throwing out ideas. Then, a team leader guesses consensus and prioritizes. **This typical brainstorming process is far from ideal and has the following flaws:**\n\n1. **Single-threaded** - With only one person talking at a time, there is a single train of thought, and many good ideas are left unsaid and unexplored\n2. **Groupthink** - The desire for quick decisions and group cohesion can make bad decisions seemingly unanimous\n3. **Controlled by Leaders and Extroverts** - The loudest team members or those with the most authority can drown out the opinions of the more introverted and junior team members.\n4. **Self-censorship** - Fear of judgment and social anxiety can cause some members to withhold crazier ideas.\n5. **Poor engagement** - Doing nothing and waiting for the meeting to end passively is easy, especially remotely.\n\n—\n\n### **✅ The Solution**\n\nWe created a brainstorming tool for remote teams with built-in design thinking best practices from expert facilitators.\n\nIt’s a quick, customizable step-by-step process to unleash your team's creativity and prioritize.\n\n**Pathspace is easier to use than a “digital whiteboard” and more robust than a doc:**\n\n1. **Structured and intuitive** - Rather than a single generic interface like a doc or whiteboard, each step in the process has a unique user interface.\n2. **AI suggestions** - Leverage the power of GPT-3 to generate more out-of-the-box ideas.\n3. **Anonymous (no groupthink)** - Discover the best ideas and reach a democratic consensus with anonymous ideas and voting.\n4. **Organized and exportable** - Instead of scattered stickies that are hard to parse, we automatically keep everything organized so you can easily understand results and export them.\n5. **Customizable** - Create and customize Pathspace to your process by mixing and matching different tactics.\n\n![](https://uploads-ssl.webflow.com/62d313720e07abf87ece7618/62df4aafd1d3f877f7a7d04d_Step%205.png)\n\n—\n\n### **Brainstorm for free. No signup required.**\n\nIf you’re reading this, most likely you’re a founder. Here are some prompts we recommend brainstorming with your team and wish we had done earlier :)\n\n1. [Who is our ideal customer/persona?](https://app.pathspace.com/create?prompt=Who%20is%20our%20ideal%20customer/persona?)\n2. [How might we validate our product?](https://app.pathspace.com/create?prompt=How%20might%20we%20validate%20our%20product?)\n3. [How might we improve retention?](https://app.pathspace.com/create?prompt=How%20might%20we%20improve%20retention?)\n4. [How might we get more leads/signups?](https://app.pathspace.com/create?prompt=How%20might%20we%20get%20more%20leads/signups?)\n5. [What features should we build next?](https://app.pathspace.com/create?prompt=What%20features%20should%20we%20build%20next?)\n\nClicking on the prompts above will take you straight into Pathspace.\n\n**You can learn more & use it for free at** [**pathspace.com**](http://pathspace.com)**.**", "url": "https://www.ycombinator.com/launches/H1d-pathspace-collaborative-brainstorming-supercharged-with-a-i", "votes": 18, "created_at": "2022-08-17T22:31:25.270Z" } ] }, { "id": 13209, "name": "Scout", "slug": "scout", "former_names": [ "Probe", "Croft", "Scout", "CRONCH!", "Cronch", "Munchies" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d57f772c91256fa7714735af9ad73b3b7c3b1648.png", "website": "https://www.scout.ml/", "all_locations": "San Francisco, CA, USA; Berkeley, CA, USA", "long_description": "", "one_liner": "Dead simple CI/CD for ML teams", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1584176238, "tags": [ "Machine Learning", "DevOps" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2020", "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/scout", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2020/scout.json", "year_founded": null, "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/d57f772c91256fa7714735af9ad73b3b7c3b1648.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 232703, "full_name": "Pranav Sekhar", "title": "Founder", "founder_bio": "2x founder with a background in machine learning. 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With Tidepool, product teams can find patterns in their user text interactions to help make better product decisions.\n\n[**Check our our demo video here!**](https://youtu.be/Ss-zLj53OlM)\n\n### **The Problem:**\n\nLLM apps have introduced [**a new paradigm**](https://www.nngroup.com/articles/ai-paradigm/) for interacting with software, where users can work iteratively with the software via a natural language interface, generating user inputs and model responses consisting of unstructured text.\n\nTraditional product analytics techniques don't deal well with large amounts of unstructured text - it's hard to summarize, it's hard to aggregate, and it's hard to effectively sample. 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After you upload user text interaction events, Tidepool will:\n\n1. **Automatically group your data by similarity.** Tidepool runs embedding clustering on your users’ text interactions to surface interesting attributes: things like prompt topics, prompt languages, and common usage patterns that can be turned into shortcuts.\n2. **Summarize common attributes in your data**, using LLMs to determine what each cluster “contains.” For example, understanding that the most common topics that users discuss are business, education, and art.\n3. **Track attributes in production traffic**, allowing you to uncover how a specific attribute might be correlated to good / bad product outcomes. We utilize lightweight models running on foundation model embeddings to scalably extract these attributes from hundreds of millions of production interaction events.\n\n### **About Us:**\n\nOver the last few years, Aquarium has worked a lot with computer vision companies to help them curate labeled datasets and improve their fine-tuned models using our core embedding technology.\n\nOur mission has always been to make it easier for people to build and improve production ML systems that solve real-world problems. When we saw the Cambrian explosion of LLM apps earlier this year, we realized that our core embedding technology and expertise was very useful for getting these new apps to product-market fit even faster!\n\n### **Our Ask:**\n\n* **Try it out!** [Sign up here to get started.](https://www.tidepool.so/)\n* **Share this post with your friends!** We’d love to get exposure to as many different AI applications as possible.\n* **Give us feedback!** [Join our Slack community](https://join.slack.com/t/tidepoolcommunity/shared_invite/zt-1zp4f82tc-78rxSFZ6E3eXguvH3pzdwQ) and leave your thoughts, or [schedule some time with us to talk](https://calendly.com/pgao/tidepool-demo).", "url": "https://www.ycombinator.com/launches/J5E-tidepool-product-analytics-for-ai-apps", "votes": 25, "created_at": "2023-07-24T18:15:32.331Z" } ] }, { "id": 22032, "name": "Intelligent", "slug": "intelligent", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b6ed23c59f075554e641d7d208bcb56abf6880d1.png", "website": "https://www.intelligent.services/", "all_locations": "San Francisco, CA, USA; Los Altos, CA, USA; Remote", "long_description": "We help businesses cut operational costs, while still exceeding customer expectations.\r\nMost businesses are consistently under- or over-staffed, which either damages customer experience or leaves money on the table (by unnecessarily increasing staffing costs). 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We exclusively partner with top engineering companies with world-class search, discovery, and ads departments to get the last +5-10% of revenue at scale. We work with many of the biggest names in e-commerce.\n\n## Dramatically Better Search\n\n**Increase revenue by +9%** with unified search, discovery, and native ads. We always outperform all in-house systems and vendors in A/B testing, at scale, forever. Demos: \\[[ebay](https://youtu.be/PlFxYfPlZYY)\\] \\[[amazon](https://youtu.be/lWauI9s7jUY)\\] \\[[walmart](https://youtu.be/Ya0LtKW8ZCg)\\]\n\n## Dramatically Better Ads\n\nAdd **native ads and sponsored search**. Our ads integrate with our organic ranking for dramatically better performance. We optimize ad load, profitability, and sales.\n\n## Build In-House Better\n\nAll of our customers have **existing search, discovery, and ads teams**. We help you build in-house better. 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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": 23796, "name": "Slope", "slug": "slope", "former_names": [ "AirDesk" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bb362ee6e3bfce450e6a8196b555db1534feef1b.png", "website": "http://www.slopepay.com", "all_locations": "San Francisco, CA, USA", "long_description": "Accept and reconcile B2B payments seamlessly.", "one_liner": "The B2B Payments Platform ", "team_size": 16, "industry": "Fintech", "subindustry": "Fintech -> Payments", "launched_at": 1625009192, "tags": [ "Fintech", "Machine Learning", "Payments", "Fraud Detection", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Fintech", "Payments" ], "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/slope", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/slope.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/slope-tech/", "x_url": "https://twitter.com/slopepay", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bb362ee6e3bfce450e6a8196b555db1534feef1b.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": 135035, "full_name": "Lawrence Lin Murata", "title": "Founder", "founder_bio": "Lawrence Lin Murata, co-founder and CEO. 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He was previously a software architect at Datadog, helping launch and scale the APM product across 100+ software runtimes and integrations. He has also built developer-facing products at Confluent and Bloomberg. He leads the technical team at Lariat Data, guiding product and architecture direction.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/talwai", "x_url": "https://twitter.com/AadityaTalwai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53586e1009253153eb206d40d628e8e4aab543d1.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 24205, "name": "Jovian", "slug": "jovian", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d6f4074567727ce4d14de635e6debbbdc71b55f5.png", "website": "https://www.jovian.com", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Jovian is an online university for software development and data science. We offer practical and industry-focused programs that help professionals learn technical skills, build real-world projects, and advance their careers.\r\n\r\nStudents learn practical skills, build real-world portfolio projects, and undergo job readiness training. Our tutors offer 24x7 guidance & mentorship over Slack & Zoom. Students also get access to jobs with 200+ hiring partners.", "one_liner": "Online University for Tech Professionals", "team_size": null, "industry": "Education", "subindustry": "Education", "launched_at": 1625134988, "tags": [ "AI-Enhanced Learning", "Developer Tools", "Education", "Machine Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Education" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/jovian", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/jovian.json", "year_founded": 2019, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/school/jovianhq/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/jovianhq", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/d6f4074567727ce4d14de635e6debbbdc71b55f5.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 154969, "full_name": "Aakash N S", "title": "Founder", "founder_bio": ".", "is_active": true, "linkedin_url": "", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/879bb544f405971d5880e49337e109c639fa0067.jpg" } ], "jobs": [], "news": [ { "title": "jovian: Machine learning edtech startup Jovian bags $1.5 million in funding - The Economic Times", "url": "https://economictimes.indiatimes.com/tech/funding/machine-learning-edtech-startup-jovian-bags-1-5-million-in-funding/articleshow/91767290.cms", "date": "May 24, 2022" } ], "launches": [] }, { "id": 24303, "name": "Cero", "slug": "cero", "former_names": [ "CERO.AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/28978000b8dcc240b064a1480e88b2e3fa90b056.png", "website": "https://cero.ai", "all_locations": "Santiago, Santiago Metropolitan Region, Chile; Remote", "long_description": "At Cero, we solve specific coordination tasks between hospitals and patients, such as appointment confirmations and last minute cancellations, by automatically communicating with patients over WhatsApp. For example, a patient can confirm and reschedule their appointment without needing to call anyone.\r\n\r\nAlmost half of the medical appointments in Latin America are missed because patients simply do not show up. This ranges from missing a regular dentist check to missing an expensive MRI scan. With increasing pressure to improve access to care in developing countries, optimizing communication and coordination with patients is a key task to achieve. This is a massive problem especially in places where most people don't know how to effectively use self-service mediums (or are too busy to use them).\r\n \r\nWe bootstrapped the company and we are profitable. As of August 2021 we have $97K in monthly revenue from 15 clients with a CMRG of 20% in the last year. We have 100% logo retention and we grow with our clients. We are currently coordinating over 600,000 medical appointments every month, just in Chile.\r\n\r\nThis problem represents a $3.5B market opportunity in Latin America only. Latin America has 650M people, each one having an average of 3 medical consultations per year. We charge for every time we effectively communicate with a patient. We plan to handle several coordination interactions with every patient for each consultation, which considers a roadmap of scheduling, confirming, booking, reimbursements, payments, among others.\r\n\r\nWe are a team mixing strong knowledge about the healthcare industry with deep technical skills. Felipe (CEO) is a former dentist that has led deep changes in healthcare payments in Chile. Mauricio (CTO) and Jorge (R&D), both PhD in Computer Science, led the creation of the most advanced Spanish Language Neural Network used daily by scientists and practitioners in LatAm (BETO: Spanish BERT).\r\n\r\nIn a weekend project, Mauricio and Jorge automated the communication with students attending a summer school obtaining impressive engagement. Felipe was struggling with contacting his patients to coordinate appointments, and saw a big opportunity in this technology. 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Currently co founder & CEO at CERO.\nPreviously, I founded Excedent, a solution for eligibility and payment through API's for medical services connecting over 500 health providers with the top 3 health insurances in Chile. In 2013 I founded Deenty where we worked to improve access to oral care in Chile, being recognized as one of Chile's 100 young leaders of the year. Founder of Rompientes, NGO focused on preserve coastal environments and surfbreaks", "is_active": true, "linkedin_url": "https://cl.linkedin.com/in/luis-felipe-rodríguez-besa-31ba772a", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c7f84bd6acf710eb6813dd0b4b8e814e8370c828.jpg" }, { "user_id": 741576, "full_name": "Jorge Pérez", "title": "Founder", "founder_bio": "Jorge has a PhD in Computer Science and more than 10 years of experience in research with a focus on Web Data, Natural Language Processing and Deep Learning. He is a Microsoft Research PhD Fellow and he has published his research in top Data Science and AI conferences such as NeurIPS, ICLR, IJCAI, WWW, SIGIR and VLDB. He has developed open scientific NLP tools for the Spanish language that are used in a daily basis by the industry and academia all across Latam.", "is_active": true, "linkedin_url": "https://www.linkedin.com/NA", "x_url": "https://twitter.com/perez", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9a2c4c3082da1b175196a6f9168c8ec13d7a8c25.jpg" }, { "user_id": 741797, "full_name": "Mauricio Quezada", "title": "Founder", "founder_bio": "PhD in CS from Universidad de Chile, Engineer and Software Developer. Member of the winning local ICPC team of 2011 and organizer of several training camps of Chile and other Latam countries for programming contests. I've published research articles in known Data Science venues such as SIGIR, EPJ Data Science, and PloS One.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mauricio-quezada-317100199/", "x_url": "https://twitter.com/waxkun", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/56a89766f3fcb0f60c7e49e85955d78d32fb93b8.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 24346, "name": "MindFi", "slug": "mindfi", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6b7be3c4ba661d5b0f410a12fded0d9add5f93d1.png", "website": "https://www.mindfi.co", "all_locations": "Singapore, Singapore", "long_description": "MindFi is an app that reduces burnout and improves mental health for employees. We sell to companies in Asia such as Deutsche Bank, KPMG, PatSnap, Visa. 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", "one_liner": "Transforming Mental Health for Corporate Asia", "team_size": 25, "industry": "B2B", "subindustry": "B2B -> Human Resources", "launched_at": 1628416801, "tags": [ "Machine Learning", "Mental Health Tech", "B2B", "HR Tech", "Health & Wellness" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "B2B", "Human Resources" ], "regions": [ "Singapore", "Southeast 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/mindfi", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/mindfi.json", "year_founded": 2017, "city": "Singapore", "country": "SG", "linkedin_url": "https://sg.linkedin.com/company/mindfi.co", "x_url": "https://twitter.com/mindfico", "fb_url": "https://www.facebook.com/mindfico", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6b7be3c4ba661d5b0f410a12fded0d9add5f93d1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 435477, "full_name": "Gangeshwar Krishnamurthy", "title": "Founder", "founder_bio": "AI Scientist turned startup founder. 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$400K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 113501, "title": "ML Engineer, Product", "url": "https://www.ycombinator.com/companies/mach9/jobs/uUwtMzD-ml-engineer-product", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $300K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 114030, "title": "ML Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/mach9/jobs/6vBDUBp-ml-infrastructure-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$160K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 115423, "title": "Perception Intern", "url": "https://www.ycombinator.com/companies/mach9/jobs/iaBeLx3-perception-intern", "location": "San Francisco", "type": "Internship", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": null, "visa": "US citizen/visa only", "skills": [] }, { "id": 116444, "title": "Business Development Representative", "url": "https://www.ycombinator.com/companies/mach9/jobs/15HcGWq-business-development-representative", "location": "San Francisco", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$105 - $120", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Mach9 Unveils Digital Surveyor 2 A New Standard For Lidar Feature Extraction", "url": "https://informedinfrastructure.com/post/mach9-unveils-digital-surveyor-2-a-new-standard-for-lidar-feature-extraction", "date": "Mar 20, 2026" }, { "title": "Between the Scanner and the Deliverable: How Mach9 is Building the Missing Layer in Geospatial Production", "url": "https://www.xyht.com/surveying/between-the-scanner-and-the-deliverable-how-mach9-is-building-the-missing-layer-in-geospatial-production/", "date": "Mar 19, 2026" }, { "title": "Mach9 Unveils an AI-Assisted Workflow Focused on Trust and Control at Scale", "url": "https://www.geoweeknews.com/sponsored/mach9-unveils-an-ai-assisted-workflow-focused-on-trust-and-control-at-scale", "date": "Feb 10, 2026" }, { "title": "Mach9 wants to create maps 30 times faster than previous standards | Geo Week News | Lidar, 3D, and more tools at the intersection of geospatial technology and the built world", "url": "https://www.geoweeknews.com/sponsored/mach9-digital-surveyor-lidar-data-feature-extraction-automation", "date": "Feb 06, 2024" }, { "title": "Mach9 launches new geospatial software platform aimed at infrastructure space", "url": "https://www.geoweeknews.com/news/tech-reality-capture-mach9-launches-new-geospatial-software-platform-aimed-at-infrastructure-space?utm_source=social&utm_medium=linkedin&utm_campaign=5msocial", "date": "Mar 16, 2023" } ], "launches": [ { "id": 99241, "title": "Mach9 - Make Maps Fast. AI-Powered CAD Software for Reality Capture", "tagline": "We help infrastructure survey and engineering teams turn raw point cloud data into engineering-ready CAD/GIS deliverables up to 100x faster.", "body": "Hi everyone! \n\nWe’re [Alex](https://www.linkedin.com/in/alexbaikovitz/), [Zach](https://www.linkedin.com/in/zachsussman/), [Haowen](https://www.linkedin.com/in/haowensh/), and [Michael ](https://www.linkedin.com/in/mmong/)- co-founders of [Mach9](https://mach9.ai/). \n\n**📍TLDR**: \n\nMach9's Digital Surveyor 2 is a geospatial platform that turns LiDAR point clouds into engineering-ready deliverables up to 100x faster. Its AI-powered extraction engine identifies and extracts linear features — including curbs, pavement edges, guardrails, and barriers — and learns from each user's edits to improve accuracy over time.\n\nSurvey and engineering teams at Langan, Olsson, HDR, Woolpert, Michael Baker International, and major utilities and state DOTs already use it in production.\n\nCheck out our Digital Surveyor 2 Launch Video:\n\n[**https://youtu.be/XoISK5nx87E?si=mzcrxg5uI3hynSm2**](https://youtu.be/XoISK5nx87E?si=mzcrxg5uI3hynSm2)\n\n**❌ The Problem**\n\nEvery road, bridge, fiber network, and utility corridor project starts with a survey. Increasingly, that survey is done with LiDAR scans encompassing millions of points.\n\n**But here's the thing**: Collecting data isn't the hard part anymore, turning it into engineering ready deliverables is. Today, that work is being done manually. Skilled technicians sit in front of a screen and trace features one by one across potentially thousands of features. Paint lines, curbs, poles, signs, guardrails. Mile after mile.\n\n**As a result**: Firms turn down work they could win, deadlines slip, and the most expensive bottleneck of the workflow is the part that hasn't changed in 20 years.\n\n**✅ Our Solution**\n\nDigital Surveyor 2 is a web-based CAD application where AI works alongside drafters to extract features from point cloud data dramatically faster — without sacrificing the precision their clients require.\n\nHere's what makes it different:\n\n🔹 **AI that drafts with you**. \n\nThe feature extraction engine in Digital Surveyor 2 suggests linear features, such as curbs, pavement edges, guardrails, and barriers directly from LiDAR point clouds — and adapts to the user’s drawing patterns. As users draw, the software picks up on their patterns and refines its suggestions in real time.\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/ca66f2e8-267b-4693-83ec-81b92eabb558)\n\n🔹 **Field-to-finish in one environment**. \n\nIngest point cloud data from any system such as RIEGL, Trimble, or Leica scanners. Extract features with AI-assisted workflows and precision tools. Easily QA everything in 3D. Export directly into AutoCAD, MicroStation, or ESRI. No switching between tools. No preprocessing pipeline.\n\n🔹 **Built for the people who do this work**. \n\nDS2 was designed so that a surveyor who knows where points go in the field can be productive on day one. Surveyors, field techs, and project managers can easily be working in it with minimal ramp time or CAD expertise.\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/e5e82676-f1fd-4bcd-bd86-fcca46636e29)\n\n🙏 **Our Ask**\n\n* **👉 Follow us** — Stay up to date with what we're building. Follow Mach9 on[ ](https://www.linkedin.com/company/mach9)[LinkedIn](https://www.linkedin.com/company/mach9geo).\n* **🔗 Intro us** — Know someone at a electric / telecom utility, data center owner, DOT, or survey/engineering firm that works with LiDAR or mobile mapping data? A warm intro goes a long way. We're especially looking to connect with survey and engineering leaders, mobile mapping teams, CAD/GIS technicians, and geospatial production leads.\n* **🚀 Try it yourself** — If your team is turning point clouds into deliverables today, we'd love to show you what DS2 can do. [Get started here](http://mach9.ai/get-started?utm_source=YC&utm_medium=article&utm_campaign=launch_post).\n* 💼 **We're hiring** — We're building engineering software to transform the $3 trillion global infrastructure industry. If you want to work on hard problems at the intersection of AI, geospatial, and computer graphics, we're scaling across GTM, engineering, and operations. Check out our open roles [here](https://jobs.ashbyhq.com/mach9).\n\n![uploaded image](/media/?type=post&id=99241&key=user_uploads/311199/d7592b72-f824-4cf8-9921-48b5b14674fc)\n\n", "url": "https://www.ycombinator.com/launches/Pof-mach9-make-maps-fast-ai-powered-cad-software-for-reality-capture", "votes": 29, "created_at": "2026-03-30T20:07:04.564Z" } ] }, { "id": 24590, "name": "Waterplan", "slug": "waterplan", "former_names": [ "Water Plan" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/56794ad6ea12a6e16aa87fe26ec5e7109ba0e46d.png", "website": "http://waterplan.com", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Waterplan is the world’s leading climate platform to measure, respond, report, and monitor companies increasingly changing climate water risk. Customers include multinational companies like Coca-Cola, Diageo, Colgate & Ab InBev. The platform combines companies’ operational data with local water satellite imagery to provide a continuously updated financial assessment of water risk. Based on that, it offers tailored mitigation and adaptation opportunities, from conventional infrastructure to nature-based solutions. The company was founded by a team of tech and water 2nd-time entrepreneurs with two exits and almost ten years of experience working for Fortune 500 companies in water projects. Early investors include YCombinator, Giant Ventures, David Helgason, Leonardo DiCaprio, Richard Branson's family, Manu Ginobili, among others waterplan.com\r\n", "one_liner": "Water risk mitigation for industrial sites", "team_size": 20, "industry": "B2B", "subindustry": "B2B", "launched_at": 1627999929, "tags": [ "Machine Learning", "SaaS", "B2B", "Climate", "ClimateTech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "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": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/waterplan", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/waterplan.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/waterplan-ai", "x_url": "https://twitter.com/WeAreWaterplan", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/waterplan", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/56794ad6ea12a6e16aa87fe26ec5e7109ba0e46d.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": 739834, "full_name": "Jose Ignacio Galindo", "title": "Founder", "founder_bio": "Jose Ignacio is co-founder & CEO of Waterplan, helping orgs tackle water risk. Born in Patagonia’s Ushuaia, he’s a second-time founder with a background in software engineering. Before Waterplan, he co-founded Herolens (acquired by Innovid, NYSE: CTV) and was CSO at Wolox (now Accenture).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/joseignaciosg/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4b78ec09084c1184008961e312e7a72543d648ae.jpg" }, { "user_id": 739877, "full_name": "Nicolas Wertheimer", "title": "Founder", "founder_bio": "New on the tech world. Medical doctor and social water entrepreneur for almost 10 years. Founder of Proyecto Agua Segura (Safe Water), a Latin American consulting firm that provides safe water access & hygiene training to more than 300k people and delivered 3260 water projects with Fortune 500 companies. Global Shaper of the World Economic Forum, member of the Youth Congress for Sustainable Americas, Forbes 30 under 30, Chivas Venture Finalist. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nicolas-wertheimer/", "x_url": "https://twitter.com/NicoWertheimer", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ae9034cb77caaa55a945b6f6836a21f2347b4eec.jpg" } ], "jobs": [], "news": [ { "title": "Making Waves: Waterplan Secures $11M In Funding", "url": "https://www.forbes.com/sites/craigsmith/2023/05/24/making-waves-waterplan-secures-11m-in-funding/?sh=60e79d143a54", "date": "May 24, 2023" }, { "title": "Waterplan Announces $11 Million in Series A Financing", "url": "https://www.globenewswire.com/news-release/2023/05/23/2674060/0/en/Waterplan-Announces-11-Million-in-Series-A-Financing-Round.html", "date": "May 23, 2023" }, { "title": "From Droughts to Floods, Water Risk Is an Urgent Business Issue", "url": "https://hbr.org/2022/11/from-droughts-to-floods-water-risk-is-an-urgent-business-issue", "date": "Nov 09, 2022" }, { "title": "Can a Start-Up Help Companies Monitor and Manage Their Water Use? - The New York Times", "url": "https://www.nytimes.com/2022/10/26/business/water-use-companies-waterplan.html", "date": "Oct 26, 2022" }, { "title": "Waterplan: DiCaprio Backs $7m Round for Startup Tackling Water Crisis", "url": "https://www.businessinsider.com/water-crisis-waterplan-pitch-deck-leonardo-dicaprio-richard-branson-2022-4", "date": "Apr 08, 2022" } ], "launches": [] }, { "id": 24616, "name": "Vibe Kanban", "slug": "vibe-kanban", "former_names": [ "bloop" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/773b39b8261f5827258bdc470dd1356bfb4e9ee4.png", "website": "https://vibekanban.com", "all_locations": "London, England, United Kingdom", "long_description": "As AI agents become more capable at writing code, developers are spending more of their time planning and reviewing what those agents produce. Vibe Kanban helps them do exactly that, seamlessly integrating with leading coding agents like Anthropic’s Claude Code and OpenAI’s Codex.", "one_liner": "Plan and review AI generated code", "team_size": 10, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1629492105, "tags": [ "Machine Learning", "Open Source", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "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/vibe-kanban", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/vibe-kanban.json", "year_founded": 2021, "city": "London", "country": "GB", "linkedin_url": "", "x_url": "https://x.com/vibekanban", "fb_url": null, "cb_url": null, "github_url": "https://github.com/bloopai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/773b39b8261f5827258bdc470dd1356bfb4e9ee4.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": 63418, "full_name": "Louis Knight-Webb", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/knightwebb/", "x_url": "https://twitter.com/LouisKnightWebb", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e45ae7df06a70770c53ef0611314c83458149b76.jpg" }, { "user_id": 717838, "full_name": "Gabriel Gordon-Hall", "title": "Founder", "founder_bio": "Co-Founder Vibe Kanban (formerly bloop), ex. ML Research Engineer", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gabriel-gordon-hall-54a18812a/", "x_url": "https://twitter.com/ggordonhall", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e02ffbfaae5aadac25aca88cb3309799e72f58d6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92068, "title": "Vibe Kanban - 10X your Claude Code usage", "tagline": "Run coding agents in parallel, streamline review and planning", "body": "![uploaded image](/media/?type=post&id=92068&key=user_uploads/63418/85136151-1f80-4ccc-9487-61f9f5883df0)\n\nHi 👋 We’re Louis and Gabriel, the founders of Vibe Kanban.\n\nRecently, we reached an exciting milestone: **over 50% of our code is now written by coding agents** like Claude Code, Gemini CLI, Codex, and Amp.\n\nThis means that our roles have evolved. Instead of spending most of our time writing code ourselves, **we now primarily plan, review, and orchestrate** the efforts of these AI agents.\n\nPreviously, our bottleneck to shipping was the speed of writing code. Today, it’s how quickly we humans can effectively plan, review and keep on top of the work produced by AI. **Time spent watching coding agents working is time wasted.**\n\nhttps://youtu.be/TFT3KnZOOAk\n\n**With Vibe Kanban you can:**\n\n1. Easily switch between different coding agents\n2. Orchestrate the execution of multiple coding agents in parallel or in sequence\n3. Quickly review work and start dev servers\n4. Track the status of tasks that your coding agents are working on\n5. Centralise configuration of coding agent MCP configs\n\n**To launch Vibe Kanban:**\n\n```\n# Make sure you've already authenticated with Claude Code, Gemini CLI, Codex or Amp, then run:\n\nnpx vibe-kanban\n```\n\nYou can also read the docs at [vibekanban.com](https://vibekanban.com)\n\n**Our ask:**\n\nIf you use coding agents, please try starting your next task with Vibe Kanban. If you reach out to [Louis on X](https://x.com/LouisKnightWebb) with bugs and suggestions, I'll get Claude Code to work on your feedback ASAP.", "url": "https://www.ycombinator.com/launches/Nwy-vibe-kanban-10x-your-claude-code-usage", "votes": 27, "created_at": "2025-07-11T15:13:20.819Z" } ] }, { "id": 24666, "name": "Baubap", "slug": "baubap", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5b8f09f3d507f587a964edfec2e799e29ee3b8d5.png", "website": "https://www.baubap.com", "all_locations": "Mexico City, CDMX, Mexico; Remote", "long_description": "Baubap is LatinAmerica's firts AI-powered, credit-focused bank. Today we're the leading AI lending app in Mexico", "one_liner": "Smart micro financing for everyone", "team_size": 150, "industry": "Fintech", "subindustry": "Fintech -> Credit and Lending", "launched_at": 1625680094, "tags": [ "Machine Learning", "Lending" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2021", "status": "Active", "industries": [ "Fintech", "Credit and Lending" ], "regions": [ "Mexico", "Latin America", "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/baubap", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2021/baubap.json", "year_founded": 2018, "city": "Mexico City", "country": "MX", "linkedin_url": "https://www.linkedin.com/company/baubap", "x_url": "https://twitter.com/Baubapmx", "fb_url": "https://www.facebook.com/baubapmx", "cb_url": "https://www.crunchbase.com/organization/baubap", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5b8f09f3d507f587a964edfec2e799e29ee3b8d5.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": 405153, "full_name": "Roberto Salcedo", "title": "Founder", "founder_bio": "Cofounder and CEO at alternative lending fintech Baubap. Prior to founding Baubap, I was the CFO of a Mid Market Media Conglomerate with revenue >$100M and a Corporate Banking VP at Mexican Bank Invex. Earlier, I worked at HSBC where I designed and deployed the bank’s very first financial inclusion efforts 15 years ago to later be part of the Bank's Corporate Banking Executive Directory. Proud to hold a BA degree from UNAM and a Global Leadership diploma from Cambridge", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/roberto-salcedo-nieto/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6749e50f4bc54f77f3e18060187e679d15cbf41b.jpg" }, { "user_id": 399071, "full_name": "Luis Villarreal", "title": "Founder", "founder_bio": "Co-Founder and CTO @Baubap. Enthusiast and entrepreneur, passionate about helping people and making an impact in their life.\nBuilding the bridge for financial freedom in Latin America.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/luisvcastilla/", "x_url": "https://www.twitter.com/luisvcastilla/", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/19286c73fdd95155e81b48da12e4879248f0a107.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 24737, "name": "CellChorus", "slug": "cellchorus", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b81eadcf3657043bd35cb166adc3ac4e9ad367e8.png", "website": "https://cellchorus.com", "all_locations": "Houston, TX, USA", "long_description": "At CellChorus, we apply artificial intelligence to evaluate thousands of microscopy videos in parallel to evaluate how immune cells move, interact and perform over time. 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", "one_liner": "Serverless Infrastructure Platform for AI", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1641981854, "tags": [ "Machine Learning", "Infrastructure", "AI" ], "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": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/cerebrium", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/cerebrium.json", "year_founded": 2021, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cerebrium", "x_url": "https://twitter.com/cerebriumai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/730897d24e909ee905c210f74fec1b439d21c4a7.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": 704363, "full_name": "Michael Louis", "title": "Founder", "founder_bio": "Michael is a South African entrepreneur that has founded multiple businesses across ML & A.I, Blockchain, Retail, and consumer marketplaces. Previously he was the CTO of OneCart which sold to Walmart/Massmart for an undisclosed sum. Currently, he is a co-founder at Cerebrium, a machine learning platform that makes it easier for businesses to train, deploy and monitor ML models in production.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/michael-louis-94104a113/", "x_url": "https://twitter.com/MichaelLouis_za", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/54b64443a0c4ba80ed7cb858105cd333c6837813.jpg" }, { "user_id": 831262, "full_name": "Jonathan Irwin", "title": "Founder", "founder_bio": "Co-founder & CTO of Cerebrium\n\n8+ years experience as a Javascript developer including a lot of time spent in a lead developer role.\n\nBComm undergrad and Finance Honours at the University of Cape Town. Data Science at Tilburg University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jono-irwin/", "x_url": "https://twitter.com/jonoirwin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5602f37dccfdb644c18dcdb0789f74f5a2740c09.jpg" } ], "jobs": [], "news": [ { "title": "Cerebrium blog | Cerebrium Raises $8.5M led by Gradient to Scale the Leading High-Performance Serverless AI Platform", "url": "https://www.cerebrium.ai/blog/cerebrium-raises-8-5m-led-by-gradient-to-scale-the-leading-high-performance-serverless-ai-platform", "date": "Jul 08, 2025" } ], "launches": [ { "id": 69873, "title": "Cerebrium: An AWS Sagemaker alternative", "tagline": "A framework that makes it easier to train, deploy and monitor ML models across serverless GPU's", "body": "# TL;DR\n\n[Cerebrium](https://www.cerebrium.ai/) is a machine learning framework that makes it easy to train, deploy and monitor ML models. You can:\n\n* [⚙️](https://emojipedia.org/gear/)**Train** LLM’s such as FlanT5, GPT-NEOx-20b etc with a few lines of code (beta)\n* [🚀](https://emojipedia.org/rocket/)**Deploy** LLM’s or even custom models using just one line of code to serverless CPU’s/GPU’s.\n* [📈](https://emojipedia.org/chart-increasing/)**Monitor** your ML models with our built in integrations with Arize and Censius.\n\nHi Everyone! We’re Michael and Jonathan and we are so excited to introduce you to Cerebrium!\n\n# [❌](https://emojiguide.com/symbols/cross-mark/) **The Problem**\n\nImplementing machine learning based applications in a business remains challenging. You have multiple infrastructure concerns, such as expensive GPU’s, scaling issues, fragmented tooling while additionally also struggle to find machine learning talent and keep up with the latest research and techniques. These barriers to entry prohibit smaller companies from implementing machine learning into their business sooner.\n\n# [🚀](https://emojipedia.org/rocket/) **The Solution**\n\nCerebrium helps teams easily train, deploy and monitor machine learning models in production. Using our framework, you can train and deploy models (custom built or LLM’s) to serverless CPU’s/GPU’s using one line of code. You are only charged for inference time and we automatically handle the scaling and the versioning of models.\n\nWhen it comes to training and/or inference, we use the latest research techniques to reduce training and inference times by 20-40%. You just focus on adding value to your use case and we will take care of the rest!\n\n# [⚙️](https://emojipedia.org/gear/) **How it works**\n\n1. Create a free account [here](https://dashboard.cerebrium.ai). You get $10 in free serverless GPU credits.\n2. Install our Python library: **_pip install cerebrium_**\n3. Write one line of code to deploy your custom model or deploy a pre-built template\n4. Get an endpoint that automatically scales based on traffic\n5. Check out our [documentation](https://docs.cerebrium.ai) to see a greater list of examples and features.\n\n![Demo](https://cerebrium-assets.s3.eu-west-1.amazonaws.com/Cerebrium-Demo-1-March-2023.webp)\n\n### [🤔](https://emojipedia.org/thinking-face/) **Why we Built Cerebrium**\n\nAt our previous business, machine learning made a huge impact across various teams but it took months of building, testing and experimenting to get it there. We wanted to make the same sort of impact accessible to companies who don’t have the resources or know-how - and of course make it available in a fraction of the time\n\n### [🙏](https://emojidictionary.emojifoundation.com/folded_hands) **Our Asks**\n\n* Try out our framework! It takes the average user less than 4 minutes to get a model up and running (yes even if its custom) and give us your feedback - its free.\n* Share our framework with people you know who are building ML based applications or are struggling to fine-tune/deploy their ML models.\n* Join our [Slack](https://join.slack.com/t/cerebriumworkspace/shared_invite/zt-1lvbha401-lPsnCWYnVlGymGKS2E5fvA) and [Discord](https://discord.gg/ATj6USmeE2) communities\n* Upvote and share this post!", "url": "https://www.ycombinator.com/launches/IAz-cerebrium-an-aws-sagemaker-alternative", "votes": 21, "created_at": "2023-03-02T17:32:30.939Z" } ] }, { "id": 25538, "name": "Lexter.ai", "slug": "lexter-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/13fb400d02f64e196fddff48bb201121acd1fa1d.png", "website": "https://www.lexter.ai", "all_locations": "São Paulo, SP, Brazil; Remote", "long_description": "Lexter is the first legal LLM company in Brazil, already working with 3 out of the top 5 law firms in the Country", "one_liner": "LLM for Legal in Brazil", "team_size": 18, "industry": "B2B", "subindustry": "B2B -> Legal", "launched_at": 1644264235, "tags": [ "Machine Learning", "LegalTech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Legal" ], "regions": [ "Brazil", "Latin America", "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/lexter-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/lexter-ai.json", "year_founded": 2020, "city": "São Paulo", "country": "BR", "linkedin_url": "https://www.linkedin.com/company/lexter-ai", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/lexter-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/13fb400d02f64e196fddff48bb201121acd1fa1d.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": 416169, "full_name": "Pedro Jahara", "title": "Founder", "founder_bio": "I'm a Co-founder and CEO of Lexter.ai. \nPrior to Lexter I've founded BelugaDB (a self-service BI tool for big data) and R2U.io (platform that improves e-commerce conversion rates via AR). I was also the CEO of Revmob and a consultant at Bain&Co.\nI love music, japanese culture (anime, food, ...), crossfit and snowboarding", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pedrojahara/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/957d760a44a7b9cbb17414c21fd00430f10f3af0.jpg" }, { "user_id": 857122, "full_name": "Pedro Calderon", "title": "Founder", "founder_bio": "I've studied naval engineering at USP, in Brazil, in my graduation year I discovered that I liked programming much more than I liked boats! Shortly after as a developer at RevMob I discovered a new passion, creating products.\n\nIn 2016 I created my first startup eDoc, a health tech with the mission to optimize inefficiencies in the Brazilian health care market, focusing in patients primary care.\n\nFinally in 2020 I joined the Lexter.ai founding team to try to change the Brazilian lawtech scene.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/pedrocalderon/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2791da975a6bb1101d4b49a929296bbdc2fb7b88.jpg" }, { "user_id": 857111, "full_name": "Guilherme Delai", "title": "Founder", "founder_bio": "Lexter.ai technical cofounder", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/guilherme-delai/", "x_url": "https://twitter.com/guilhermedelai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7c6e9392e8a7d27978e0e536cd65612838ed28cd.jpg" }, { "user_id": 857583, "full_name": "Lucas Kawazoi", "title": "Founder", "founder_bio": "Co-founder of Lexter.ai", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lucaskawazoi/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3c70e422b0048df0232347a35f7ad80baeb76ed0.jpg" } ], "jobs": [], "news": [], "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": 25607, "name": "Axis", "slug": "axis", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f3e7a7c395e202f91209c183250f4e6d0835ea29.png", "website": "http://axis.xyz", "all_locations": "New York City, NY, USA; New York, NY, USA; Remote", "long_description": "Axis is a knowledge base of government regulations and officials that helps large companies do business in foreign markets. For example, if you want to operate in Saudi Arabia - our software tells you which laws you need to comply with and which officials you need approvals from. We currently cover major markets in Europe, Middle East, and Africa", "one_liner": "Knowledge base for corporate affairs teams", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1640038883, "tags": [ "Machine Learning", "B2B", "Subscriptions", "Regtech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "B2B", "Productivity" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/axis", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/axis.json", "year_founded": 2018, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/axis-xyz/", "x_url": "https://twitter.com/axis_hq", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f3e7a7c395e202f91209c183250f4e6d0835ea29.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": 876872, "full_name": "Mishaal Al Gergawi", "title": "Founder", "founder_bio": "I grew up in Dubai and was there most of my life till moving to New York in 2018. I studied finance and worked in investment banking. I became a writer in 2008 and in 2011 built Delma, a risk consultancy. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mishaal-al-gergawi/", "x_url": "https://twitter.com/algergawi", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7d4fce7505a5fd4d12eeba1c48c29617fa12e26d.jpg" } ], "jobs": [], "news": [ { "title": "Breaking New Grounds: Axis Founder Mishaal Al Gergawi", "url": "https://www.entrepreneur.com/article/422109", "date": "Mar 13, 2022" } ], "launches": [] }, { "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). 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", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/oban/", "x_url": "https://twitter.com/Obanmact", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/865ea81c5261591da4c1d4c61a26fae1a2014dce.jpg" } ], "jobs": [ { "id": 98588, "title": "Backend Engineer (Mid/Sr)", "url": "https://www.ycombinator.com/companies/spade/jobs/k9ZLlCc-backend-engineer-mid-sr", "location": "New York, NY", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$170K - $230K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] }, { "id": 98589, "title": "Finance Lead", "url": "https://www.ycombinator.com/companies/spade/jobs/ahCOFcZ-finance-lead", "location": "New York, NY / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$170K - $200K", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 98592, "title": "Technical Customer Support", "url": "https://www.ycombinator.com/companies/spade/jobs/2rT8fjC-technical-customer-support", "location": "New York, NY / Remote (US)", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "$85K - $108K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Spade raises $40M for transaction data enrichment", "url": "https://www.axios.com/pro/fintech-deals/2026/03/24/spade-40m-bank-transaction-data-tools", "date": "Mar 24, 2026" }, { "title": "Fintech Spade's Series a Pitch Deck Used to Raise $10 Million", "url": "https://www.businessinsider.com/fintech-spades-series-a-pitch-deck-raise-10-million-2023-12", "date": "Dec 18, 2023" }, { "title": "Spade digs into credit card fraud detection intelligence following new capital raise | TechCrunch", "url": "https://techcrunch.com/2023/12/06/spade-credit-card-fraud-detection/", "date": "Dec 06, 2023" }, { "title": "Spade raises $5m in seed funding round led by a16z", "url": "https://blog.spade.com/spade-raises-5m-in-seed-funding-led-by-a16z/", "date": "Feb 27, 2023" }, { "title": "Spade turns credit card transaction gibberish into clear, actionable data | TechCrunch", "url": "https://techcrunch.com/2023/02/27/spade-credit-card-data-a16z/", "date": "Feb 27, 2023" } ], "launches": [] }, { "id": 25975, "name": "Ploomber", "slug": "ploomber", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b5abaea13a47b9e7cb04c8d183b6a2056824f626.png", "website": "https://ploomber.io/", "all_locations": "New York City, NY, USA; New York, NY, USA; Remote", "long_description": "We are building a cloud platform to help companies deploy and scale AI applications.", "one_liner": "Heroku for AI", "team_size": 7, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1643229196, "tags": [ "Developer Tools", "Machine Learning", "Analytics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Inactive", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/ploomber", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/ploomber.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ploomber", "x_url": "https://twitter.com/ploomber", "fb_url": "https://www.youtube.com/channel/UCaIS5BMlmeNQE4-Gn0xTDXQ", "cb_url": "https://www.crunchbase.com/organization/ploomber", "github_url": "https://github.com/ploomber", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b5abaea13a47b9e7cb04c8d183b6a2056824f626.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 564961, "full_name": "Eduardo Blancas", "title": "Founder", "founder_bio": "Eduardo Blancas is the co-founder and CEO of Ploomber, a technology company developing the most data scientist-friendly tooling on the planet. Originally from Mexico, Eduardo moved to the US in 2015 and spent some time in Chicago, New York, and Boston. Eduardo studied at Columbia University and Tec de Monterrey.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/edublancas", "x_url": "https://twitter.com/edublancas", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8d3de165ec32594a2ea0952f5edee76a60a86bab.jpg" }, { "user_id": 506869, "full_name": "Ido Michael", "title": "Founder", "founder_bio": "Ido is a founded Ploomber to help data scientists build faster. He'd been working at AWS leading data engineering/science teams. Single handedly he built 100’s of data pipelines during those customer engagements together with his team. Originally from Israel, he came to NY for his MS at Columbia University. He decided to build Ploomber since he constantly found that projects dedicated about 30% of their time just to refactor the dev work (prototype) into a production pipeline. ", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/ido-michael", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/62ff2c180ec1d3a578b30edc6d9241f1f5551fa9.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 78826, "title": "Ploomber: Heroku for AI", "tagline": "A platform to deploy AI and data applications quickly", "body": "**tl;dr; **[**Ploomber**](https://www.platform.ploomber.io/register)** is a cloud platform that quickly deploys container-based AI and data applications (Streamlit, Shiny, Panel, Flask, FastAPI, etc.)**\\\n—\n\nHey all! We're [Eduardo](https://www.linkedin.com/in/edublancas) and [Ido](https://www.linkedin.com/in/ido-michael) from Ploomber, and we're excited to launch our cloud platform!\n\n**Problem**\n\nWith the rise of AI and data, many companies are deploying applications whose requirements differ from typical web applications. The most common differences are larger computational resources and libraries that many PaaS platforms do not allow to install (case in point: one of our customers migrated to our platform after several failed attempts to install a C++ library required for their Python app to run). These restrictions hinder the ability of many companies to ship complex AI and data applications.\n\n**Our Solution**\n\nOur container-based platform provides greater flexibility when deploying applications: you can quickly provision more resources and install anything your app requires to run.\n\nWe provide official support for frameworks like Streamlit, Shiny, Panel, Flask, and FastAPI, but you can use any framework you want.\n\nWe also provide a feature that stops your application automatically if it doesn't receive traffic and starts it again on an incoming request; this offers significant savings for many of our customers who deploy low-traffic applications.\n\n**How it works**\n\n![uploaded image](/media/?type=post&id=78826&key=user_uploads/564961/8070baa6-6dc5-468e-8041-cb35cf860f53)\n\nYou can deploy applications with the UI or via the command-line interface (you can also use the CLI to deploy on each commit to your git repository).\n\n**Live examples**\n\n![uploaded image](/media/?type=post&id=78826&key=user_uploads/564961/50219f4a-395d-4d0d-98cb-4e123ad50efa)\n\n[Arxiv Chat:](https://summer-wind-5194.ploomberapp.io/) a chatbot that allows you to search, ask questions, and download papers from Arxiv. \n\n[Analytical dashboard](https://broad-fire-8217.ploomberapp.io) built with Panel.\n\n[Flask app](https://wild-bonus-9050.ploomberapp.io/) with user authentication and API key management.\n\n**Try it out!**\n\n[Create an account](https://www.platform.ploomber.io/register) and check our [documentation](https://docs.cloud.ploomber.io/en/latest/intro.html) to get started!\n\n**We can help you migrate!**\n\nIf you already deployed an application, we can help you migrate at no cost! Please email us at [contact@ploomber.io](mailto:contact@ploomber.io)! We also have discounts for the YC community, so contact us!", "url": "https://www.ycombinator.com/launches/KVO-ploomber-heroku-for-ai", "votes": 10, "created_at": "2024-02-27T20:05:32.375Z" } ] }, { "id": 25976, "name": "GoJom", "slug": "gojom", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f6a49e808e5f793fd59cf2dacc4bd426cfbce9e.png", "website": "https://gojom.com", "all_locations": "Lima, Callao Region, Peru; Lima, Lima Province, Peru", "long_description": "GoJom is a One Stop Shop for Real Estate Solutions. It’s technology creates a faster and easier way to buy, sell and rent properties in LaTam. ", "one_liner": "One-stop-shop for Real Estate Solutions. ", "team_size": 140, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction", "launched_at": 1639692064, "tags": [ "Fintech", "Machine Learning", "Proptech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Active", "industries": [ "Real Estate and Construction" ], "regions": [ "Peru", "Latin America", "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/gojom", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/gojom.json", "year_founded": 2020, "city": "Lima", "country": "PE", "linkedin_url": "https://www.linkedin.com/company/gojom/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/casas360", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f6a49e808e5f793fd59cf2dacc4bd426cfbce9e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 697477, "full_name": "Andrés González - Olaechea", "title": "Founder", "founder_bio": "Entrepreneur with experience as investment banker and asset manager in U.S and LaTam.", "is_active": true, "linkedin_url": "https://linkedin.com/in/andr%C3%A9s-gonz%C3%A1lez-olaechea-837736b4", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9d276f7a56f9cea016ddbfdb126ba1e92a12c700.jpg" }, { "user_id": 697660, "full_name": "Horacio Faudella", "title": "Founder", "founder_bio": "Serial digital entrepreneur since 1997:\nAdonde.com 1997, first search engine in Peru for 10 years.\nAdondeVivir.com, Peru's leading real estate portal, first exit in Peru.\nTurismoi.com, global distribution platform for tours in +10 countries.\n\nAward-winning startups such as the BID Challenge Network with AdondeCompras.com in 2005, Digi Award (IAB PE) 2010 - AdondeVivir.com, 1st place of the Venture Day of IE Business School (Madrid) 2014 - Turismoi.es, Creatividad Empresarial 2019 - Turismoi.pe.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/horacio-faudella/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b6570f410e750ee8009b46ea09a085101a3ac2a0.jpg" }, { "user_id": 697568, "full_name": "William Wong Garay", "title": "Founder", "founder_bio": "Self-taught software engineer with 20+ years of experience, now building AI systems that run in production. Co-Founder and CTO of GoJom (YC W22), Latin America's first intelligent real estate portal, and before that CTO of Turismoi. I led AI development at TEDI (500 Global LatAm B17), where the assistant I designed handled 40,000+ conversations with over 90% resolved without humans, and I am VP of Software Engineering at ConstructAI. I build LLM agents and RAG with Python, FastAPI and LangGraph.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/willywg/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0be42a1d5b29252aff8b5335b02476b384cf1965.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 26238, "name": "Flike", "slug": "flike", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7958871792e5be922aaf99f654b521726b10126e.png", "website": "https://flike.app/", "all_locations": "San Francisco, CA, USA", "long_description": "Flike trains AI models on your product marketing knowledge, enabling your sales team to generate relevant&hyper-personalized emails that are aligned with your brand voice throughout the entire sales funnel (from cold outbound to upselling). And we're already partnering with some of the largest YC companies.", "one_liner": "Flike's AI crafts sales emails that convert", "team_size": 8, "industry": "B2B", "subindustry": "B2B", "launched_at": 1648043554, "tags": [ "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2022", "status": "Inactive", "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/flike", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2022/flike.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/80920022", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/flike", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7958871792e5be922aaf99f654b521726b10126e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 457452, "full_name": "Tobias Müller", "title": "Founder", "founder_bio": "Building Supply Chain AI.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/tobias-mueller-rome/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ac153dbbb2b8c57bffde659df7f8fe4e50f4e1e1.jpg" }, { "user_id": 833490, "full_name": "Yannick Müller", "title": "Founder", "founder_bio": "\"Failure is an option here. If you're not failing, you're not innovating enough\" - Elon Musk\n\nAsk me about:\n- Machine Learning\n- Web Development\n- Ironman", "is_active": false, "linkedin_url": "https://linkedin.com/in/yajm", "x_url": "https://twitter.com/yajmch", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bc4dc1c894785694420ad0ec7f9574eff3904eab.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 78416, "title": "Flike - AI Sales Co-Pilot", "tagline": "Replicate your winning emails. Personalized & Relevant!", "body": "**TLDR:** Flike learns from your Gmail emails to figure out the relevancy, personalization, and tone of your winning sales messages. Use Flike for cold outbound, event-based responses and dynamic follow-ups. It works where you work and supports generating messages directly in your Gmail and LinkedIn.\n\n![uploaded image](/media/?type=post&id=78416&key=user_uploads/833490/5e8db11a-77d2-499c-9db0-9f2dc7c61c9d)\n\n### **Relevancy gets you the deal**\n\nFlike’s relevancy engine analyzes 1,000+ data points per prospect to evaluate whether including them will make the prospect more likely to reply. The data points are collected from:\n\n* LinkedIn, Crunchbase, X & G2\n* Your previous conversations on Gmail & LinkedIn\n* Additional first-party data like CRM activity and prospect intentions are available in the growth and enterprise plan\n\n### **Use-Cases**\n\n* Cold Outbound, leveraging your best-performing winners\n* Dynamically suggests a follow-up message whenever any new activity from the prospect is detected\n* Event-based responses including addressing a competitor, timing is off & not interested\n\n![uploaded image](/media/?type=post&id=78416&key=user_uploads/833490/38386c07-2437-4e8c-9e15-f3b8e8efa53f)\n\n### **Integrating all your data sources**\n\nThe more data sources that are connected the more impressive Flike AI becomes. In the free trial & pro plan you can connect your Gmail & LinkedIn. On the growth and enterprise plan, you can tap into your Outreach, Salesloft, Hubspot, and Salesforce data.\n\n![uploaded image](/media/?type=post&id=78416&key=user_uploads/833490/96f272c3-3625-4b92-99b8-e2a2b087175f)\n\n### **Get up & running in 2 minutes**\n\n* [**Try Flike 7 days for free**](https://flike.app/)\n* If you have a custom sales workflow and are curious about what’s possible [**set up a call here**](https://calendly.com/yannickflike/flike-exploration)\n* If you want to chat about sales workflows in general & are curious about product updates, [**join our Slack channel here**](https://join.slack.com/share/enQtNjYxNTI3NzE3MTIyMi1jZjEzYTc2ZGQ4NjlhNWVkNzQ3N2Y1YTcyMWYzNGYyYTFiMTM4YzQzOGJiYzQxNDA2ZWQwNjMyZDRiNWNhNmY5)", "url": "https://www.ycombinator.com/launches/KOm-flike-ai-sales-co-pilot", "votes": 6, "created_at": "2024-02-14T15:05:23.054Z" } ] }, { "id": 26675, "name": "Birch Biosciences", "slug": "birch-biosciences", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6d5ede074951640a2e67e11927c78affb1e31b81.png", "website": "http://www.birchbiosciences.com", "all_locations": "Portland, OR, USA", "long_description": "Birch Biosciences engineers enzymes for plastic recycling using synthetic biology and machine learning. Our enzymes function as high performance “molecular scissors” that efficiently break down plastic polymers into chemical building blocks that can be used to manufacture high quality recycled plastic products. Today, plastic manufacturing is unsustainable and a major driver of climate change. Birch Biosciences is developing an economical end-to-end plastic recycling process that reduces carbon emissions by 70% and enables a circular plastic economy.\r\n", "one_liner": "We recycle plastic using engineered enzymes", "team_size": 10, "industry": "Industrials", "subindustry": "Industrials -> Climate", "launched_at": 1656393851, "tags": [ "Machine Learning", "Synthetic Biology", "Biotech", "Climate", "ClimateTech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Industrials", "Climate" ], "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/birch-biosciences", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/birch-biosciences.json", "year_founded": 2021, "city": "Portland", "country": "US", "linkedin_url": "https://www.linkedin.com/company/birch-biosciences-llc", "x_url": "https://twitter.com/BirchBiosci", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/birch-biosciences", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6d5ede074951640a2e67e11927c78affb1e31b81.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 913224, "full_name": "Johan Kers", "title": "Founder", "founder_bio": "Johan is co-founder and Chief Executive Officer of Birch Biosciences. He has spent the last 15 years programming bacteria and genetic circuits to produce novel therapeutic proteins and other natural products at leading companies including Absci and Ginkgo Bioworks. Johan is especially interested in solving major sustainability problems using biotechnology. He holds a PhD from Cornell University and completed a postdoctoral fellowship at Stanford University School of Medicine.", "is_active": true, "linkedin_url": "http://linkedin.com/in/johankers", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/77c9dbc7ddcfc0cea9d901a92e53475050f42ea4.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 64291, "title": "🌳 Birch Biosciences — Engineered Enzymes for Plastic Recycling 🌳", "tagline": "We recycle plastic using engineered enzymes", "body": "**The problem:**\n\nToday, plastic recycling doesn't work.  Less than 9% of plastic is recycled in the United States, a number unchanged over the past 15 years.  The current mechanical recycling process involves melting and reforming plastic at high temperatures, generally resulting in major greenhouse gas emissions while producing low quality plastic at high cost.  This is a major problem as consumers demand more sustainable plastic products, but brands are unable to meet this demand due to sustainability, quality, yield, and price issues with mechanically recycled plastic.  If we don’t develop a circular plastic economy, plastics will be responsible for 15% of global CO2 emissions by 2050.\n\n**The solution:**\n\nBirch Biosciences is engineering an enzyme-mediated process to recycle plastic. Our high performance enzymes work as “molecular scissors'' at low temperatures to break down plastic polymers into monomer “building blocks,” which can be re-polymerized into high quality recycled plastic products. Birch recycling technology will:\n\n* **Reduce** **carbon emissions** associated with plastic recycling by > 70%.\n* Generate recycled plastic of **equivalent** **quality** **to new plastic**.\n* Produce recycled plastic at **equal or lower cost** than mechanically recycled plastic.\n* Increase the variety of plastic products that can be **effectively recycled**\n\nBirch’s recycling process enables a true ‘circular’ plastic economy where plastic products are continuously recycled into the same plastic products, rather than being manufactured from oil or gas.  This is especially important as consumers, brands, and our planet demand more sustainable products.\n\n**Our technology focus:**\n\nEnzymes as “molecular scissors” are the centerpiece of our recycling technology. We discover and develop high performance enzymes using iterative machine learning and quantitative high-throughput screening of novel protein designs. Our technology stack enables design of robust enzymes for industrial scale plastic recycling using a fermentation-like bioprocess.\n\nWe’re currently focused on engineering enzymes for PET plastics (i.e., beverage bottles, polyester clothing) and polyurethane plastics (i.e., foam for furniture, bedding, insulation). In addition to YC, our technology development is supported by the US Department of Energy and the National Science Foundation.\n\n**How you can help:**\n\nIf you or anyone in your network might be interested in learning more or helping out, we would love to chat.  We're especially interested in talking with:\n\n* Companies and brands that use PET plastics\n* Experts in the plastic manufacturing and recycling industry\n* Operators of recycling facilities\n* Nonprofits with an interest in sustainability and plastic recycling\n\nWe can be reached at [founders@birchbiosciences.com](mailto:founders@birchbiosciences.com)", "url": "https://www.ycombinator.com/launches/Gix-birch-biosciences-engineered-enzymes-for-plastic-recycling", "votes": 246, "created_at": "2022-07-02T17:42:00.694Z" } ] }, { "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": 26863, "name": "Provision", "slug": "provision", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/385a0231f7ab5d980e79ffdfbfa16bc2183b283d.png", "website": "https://provision.com", "all_locations": "Toronto, ON, Canada", "long_description": "Provision is building the AI Estimator for construction. We identify risks and obligations in contracts, specs, and drawings so contractors can reduce time spent reviewing documents and focus on winning and delivering projects profitably. Backed by Y Combinator (S22).", "one_liner": "Ironclad for construction", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1653315975, "tags": [ "Documents", "Machine Learning", "Construction", "Productivity", "NLP" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "Canada", "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/provision", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/provision.json", "year_founded": 2022, "city": "Toronto", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/provision-software-co", "x_url": "https://twitter.com/UseProvision", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/provision-1d32", "github_url": "https://github.com/provisionsoftwarecorporation", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/385a0231f7ab5d980e79ffdfbfa16bc2183b283d.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": 351083, "full_name": "Luigi La Corte", "title": "Founder", "founder_bio": "Luigi is the co-founder and CEO of Provision. Before co-founding Provision, he worked in the construction industry for seven years between Arup (a multi-disciplinary engineering design firm) as a civil engineer and Plenary (infrastructure developer) as a Director. He managed $3B+ of large infrastructure projects with some of the most sophisticated stakeholders across the value chain (owners, GCs, sub-contractors, facility managers). ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/luigi-r-la-corte", "x_url": "https://twitter.com/_luigilacorte", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b63b6fd552c5f9f80531f108ea4793a7cf9be3e2.jpg" } ], "jobs": [ { "id": 69811, "title": "Account Executive (Mid-Market, Toronto)", "url": "https://www.ycombinator.com/companies/provision/jobs/B1WvXZy-account-executive-mid-market-toronto", "location": "Toronto, ON, CA", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$140K - $170K CAD", "equity_range": "0.20% - 0.65%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 86646, "title": "Senior Machine Learning Engineer", "url": "https://www.ycombinator.com/companies/provision/jobs/dseNXSQ-senior-machine-learning-engineer", "location": "Toronto, ON, CA / Toronto, Ontario, CA / Remote (CA)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$140K - $300K CAD", "equity_range": "0.10% - 0.50%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [ "Google Cloud", "Python", "Torch/PyTorch", "Machine Learning" ] } ], "news": [], "launches": [ { "id": 65358, "title": "Provision 🏗️📃 - Ironclad for construction", "tagline": "Provision helps owners and constructors understand their commercial documents, collaborate with team members, and reduce risk, increasing profits by 5-10%. ", "body": "We’re Brendan and Luigi and we’re the co-founders of Provision.\n\n**Problem**\\\nThe construction industry spends $500B annually (\\~4% of market value) on issues stemming from document complexity. Documents are becoming longer, harder to read, and contain obligations that go unchecked eroding profitability. Currently, people use CTRL+F to navigate thousands of pages and Excel to extract information, often resulting in missed obligations, and unexpected costs.\n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/8f4cc15a-b090-4ac2-a0d5-a9eee38cc0eb)\n\n\n**Need**\\\nConstruction professionals need to understand and manage all of the requirements held within these documents, because missing them means delays, budget overruns and even failed projects.\n\n**Enter Provision**\\\nProvision ingests contracts, specs and drawings, and gives users tools to manage risks, obligations and compliance.\n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/edbcd4d6-2298-48c2-9025-edcd7959b3d4)\n\n\n**Backstory**\\\nBoth Luigi and Brendan worked in the construction industry for years before starting Provision. Brendan left the industry, and built two startups over a couple of years but always had aspirations to go back to construction. Luigi worked as a Director at an infrastructure developer for 6 years, where he saw even the most sophisticated constructors fail to understand their documents. We came together in December of 2021 through YC to build something impactful. \n\n![uploaded image](/media/?type=post&id=65358&key=user_uploads/280788/dc0bd0eb-5339-4efa-9159-cd50050156cb)\n\n\\\n**Ask**\\\nUpvote this post! :)\n\nAnd if anyone has any warm connections to GCs or subcontractors in North America who builds big projects (50M+), we would love an intro. [founders@getprovision.co](mailto:founders@getprovision.co)\n\nAlso, we’re hiring for Data Engineer and SWE roles. [careers@getprovision.co](mailto:careers@getprovision.co) \n\nFollow us on [LinkedIn](https://www.linkedin.com/company/86033411), and drop us a line if you want to chat.\n\nThanks!", "url": "https://www.ycombinator.com/launches/H0A-provision-ironclad-for-construction", "votes": 16, "created_at": "2022-08-14T15:53:59.802Z" } ] }, { "id": 27038, "name": "Fini", "slug": "fini", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b044a080c88a5aedab72016a60feff3dd053fc64.png", "website": "https://www.usefini.com", "all_locations": "Amsterdam, NH, Netherlands", "long_description": "Fini builds AI agents that autonomously resolve up to 80% of customer support tickets for enterprises specially in Fintech, E-commerce, Gaming and Fitness tech.\r\n\r\nOur agents understand complex workflows, take real actions (like refunds or KYC checks), and improve in real-time. Built on a proprietary knowledge engine, our agents deliver 98%+ accuracy and integrate deeply with tools like Zendesk, Salesforce, and internal APIs.\r\n\r\nEnterprises like Bitdefender and Training Peaks use Fini to slash support costs by 50% while boosting CSAT by 10%.", "one_liner": "Fini | Automate 80% of enterprise support with AI agents", "team_size": 14, "industry": "B2B", "subindustry": "B2B -> Operations", "launched_at": 1656434795, "tags": [ "Generative AI", "Machine Learning", "SaaS", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Operations" ], "regions": [ "Netherlands", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/fini", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/fini.json", "year_founded": 2022, "city": "Amsterdam", "country": "NL", "linkedin_url": "https://www.linkedin.com/company/usefini/", "x_url": "https://x.com/finichatAI", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/fini-ea8a", "github_url": "https://github.com/ask-fini", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b044a080c88a5aedab72016a60feff3dd053fc64.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1010279, "full_name": "Deepak Singla", "title": "Founder", "founder_bio": "Deepak Singla is the co-founder of Fini. Deepak leads Fini’s product strategy, and the mission to automate customer interactions for tech companies around the world. Originally from India, Deepak graduated from IIT Delhi where he received a Bachelor degree in Mechanical Engineering, and a minor degree in Business Management ", "is_active": true, "linkedin_url": "http://linkedin.com/in/deepak-singla-68345667/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a0c990cd0a8a29c0f957affa6d442d8590fb49eb.jpg" }, { "user_id": 996529, "full_name": "Hakim K", "title": "Founder", "founder_bio": "Hakim is leading tech at Fini. Hakim has a background in software engineering and analytics across 4+ organizations and 10+ years of experience. Most recently at Uber, Hakim met Deepak. Originally from Sweden, Hakim graduated from École Polytechnique with a master's degree in Engineering Mathematics.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hakim-k/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/10faa8b98308ba56ea25b67d0072eedbfc40eace.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 123337, "title": "Scout by Fini: An AI teammate for your support team", "tagline": "Turn customer conversations into insights, knowledge updates and bug reports, from Slack.", "body": "![uploaded image](/media/?type=post&id=123337&key=user_uploads/1010279/d5b943fa-4982-44a2-95a5-41a6821f5a48)\n\nHey everyone, Deepak here, co-founder of Fini (S22). We just launched **Scout, an AI teammate that turns customer conversations into better support knowledge, bug reports and insights.**\n\nAt Fini, we build AI agents that answer customer questions. But answering a ticket doesn’t fix the outdated help article or product issue that caused it. Someone still has to investigate, gather evidence and make the fix. That’s the work we built Scout to take on.\n\n**Scout lives in Slack and works across your support tickets, customer reviews and threads.** You can ask it:\n\n* “Why did CSAT drop last month, and has it recovered?”\n* “What are customers asking that our help center doesn’t cover?”\n* “Which product issues keep coming up, and who’s affected?”\n\nIt investigates and brings back answers with evidence and customers’ own words. It can then draft a knowledge update or file a Jira/Linear bug with the findings attached. Your team approves every change.\n\nIt also runs recurring reviews, so the work keeps moving without someone remembering to ask. Teach it a rule like “Always exclude our test tickets” and it remembers for future work.\n\n**What customers are seeing:**\n\nAt TrainingPeaks, weekly knowledge upkeep went from 6 hours to 1. At Snapdocs, the share of customer questions covered by approved knowledge grew from 44% to 72% in 11 weeks.\n\nScout works with your existing support stack. You don’t need to use Fini’s customer-facing agent.\n\n**We’d love feedback from YC founders and support teams.** Reply here or DM me and we’ll help you get set up.\n\n\n\nWhat’s one thing buried in your customer conversations that you wish your team had time to investigate?", "url": "https://www.ycombinator.com/launches/W5J-scout-by-fini-an-ai-teammate-for-your-support-team", "votes": 3, "created_at": "2026-10-19T16:29:07.812Z" }, { "id": 96968, "title": "Fini: Accuracy-first AI for high-stakes support", "tagline": "AI customer support with 100% compliance and 0% hallucinations", "body": "Hey everyone! 👋\n\nWe’re Deepak and Hakim. Before founding Fini (YC W22), we led product and growth teams at Uber, operating at the scale of 200 million+ support tickets every year. We founded Fini believing that AI support can (and should!) be held to a higher standard than human support.\n\n**TL;DR** If you run support for a bank, fintech, insurance company, or any regulated industry, chances are you’ve tried your hand at generic AI support chatbots and realized they’re not a fit. A single hallucinated response can lead to lawsuits and PR disasters, destroying customer trust you’ve worked hard to build over decades. Fini AI was built specifically for these high-stakes environments, resolving 80% of support queries with full compliance and zero hallucinations.\n\n![uploaded image](/media/?type=post&id=96968&key=user_uploads/1010279/fe000980-ba95-4470-9469-32c24fb5516a)\n\n\n### **Support in regulated industries is broken**\n\nCompanies in finance, insurance, and healthcare face a brutal trade-off:\n\n* **Scale support with humans:** Expensive, hard to hire, and training takes months.\n* **Scale it with generic AI:** High risk of hallucinations, compliance breaches, and protocol violations.\n\nIn regulated spaces, hallucinations or protocol violations aren’t just isolated bad user experiences; it’s a potential regulatory fine. A class-action lawsuit. A PR disaster waiting to happen. Because of this, most teams are forced to stick with slow, expensive manual support, leaving customers waiting even for straightforward queries.\n\n### **The (accuracy) problem**\n\nIf you lead a support team in a regulated industry, you can't just let an LLM loose on your knowledge base.\n\n* Generic models prioritize \"fluency\" over \"factuality.\"\n* They often make up policies or promise refunds that don't exist.\n* Audit trails are often non-existent or hard to parse.\n\nYou end up blind to what the AI is actually saying until it’s too late. This risk forces many companies to pull back on AI support entirely, leading to burnt-out agents and frustrated customers.\n\n### **Fini solves this**\n\nWe built Fini to be accuracy-first. We monitor every interaction to ensure compliance and use specialized AI agents that don’t just \"chat\" — they solve.\n\n* **98% Accuracy:** We hold our AI to even higher standards than human agents.\n* **Human-in-the-Loop:** We seamlessly loop in human experts for edge cases.\n* **Self-learning:** The AI learns from your best people whenever they intervene to solve a new problem.\n* **Proven Scale:** We already resolve over 1 million tickets monthly for customers like the US Chamber of Commerce Foundation, Atlas, Found, and Bitdefender.\n\n### **Our Asks**\n\n* **Do you lead Support or CX** in a regulated industry (Fintech, Insurtech, Health) and want to automate tickets without the risk? **Let’s chat.**\n* **Know a great operator?** We’re hiring a Head of Growth, Product Marketing Lead, and an AI Product Manager. Please introduce them to us!", "url": "https://www.ycombinator.com/launches/PE0-fini-accuracy-first-ai-for-high-stakes-support", "votes": 6, "created_at": "2026-01-20T18:12:08.355Z" }, { "id": 64064, "title": "Fini - ⚡️ Lightning ⚡️ quick Customer Contact Resolution", "tagline": "Fini helps companies collect, categorize and resolve customer issues using AI ", "body": "**Fini helps companies collect, categorize and resolve customer interactions.**\n\nHey all! We’re Deepak & Hakim, and we have worked on AI systems for customer interaction at companies like Uber, Accenture, and PwC.\n\nWe’ve seen **companies in multiple sectors lose millions of dollars** trying to solve various customer interactions manually through agents - spanning from healthcare claims for insurance companies to millions of customer inbound tickets for B2C enterprises.\n\nAt Fini we categorise and resolve all your customer inbounds in real-time across 50+ languages, and integrates with your CRM systems in less than a week.\n\n**Problem:**\n\nWhen a company tries to scale its operations, it often runs into the following roadblocks in terms of handling customer contact:\n\n1. Customer requests come from multiple channels (emails, CRM, typeform, chatbots, in-app)\n2. These requests are currently solved manually by agents. Actioning this feedback at scale is slow and expensive.\n3. Customer insights are not generated in real time\n\n**How can Fini help me?** \n\nWe can help you to:\n\n* **Integrate channels into one overview:** Collecting requests from across your systems.\n* **Product driven resolution:** Our software redirects customers to self-serve product integrations to solve their issues without any human touch.\n* **Get real-time product insights:** Dashboards tailored to your use-case precisely illuminating customer pain points.\n\n**Want us to help you?** Email [hello@usefini.com](https://mailto:hello@usefini.com) to get set up within a week!", "url": "https://www.ycombinator.com/launches/GfI-fini-lightning-quick-customer-contact-resolution", "votes": 34, "created_at": "2022-06-25T00:31:19.513Z" } ] }, { "id": 27042, "name": "Artemis Search", "slug": "artemis-search", "former_names": [ "Artemis", "Artemis Labs", "Umbria (by Artemis Labs)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/002e53ddce5bb4318d71a6b7c48c2417fb3ad159.png", "website": "https://search-artemis.com/", "all_locations": "San Francisco, CA, USA; Rochester, MN, USA; Remote", "long_description": "Artemis Search is a vector database search with a twist. We use special-purpose deep-learning models instead of using textual / semantic similarity to evaluate how good a search result is.\r\n\r\nThis enables us to actually reason how well search results match the intent of the search query, eliminating the problems that come from evaluating search results on how much they look like the search query.", "one_liner": "A search technology that actually finds what you’re looking for.", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1656613108, "tags": [ "Machine Learning", "SaaS", "B2B", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "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": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/artemis-search", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/artemis-search.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/search-artemis", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/002e53ddce5bb4318d71a6b7c48c2417fb3ad159.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 982880, "full_name": "Austin McCoy", "title": "Founder", "founder_bio": "Austin is a Caltech CS graduate with an emphasis in applied mathematics and finance. Austin has an academic background developing and publishing computer graphics technology and machine learning models at Caltech's Aerospace Robotics and Control (ARCL) laboratory. Austin also has experience launching and selling several small technology businesses ($100k - $1M ARR) centered around developing and selling B2B and B2C software products.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/austin-mccoy-833600229/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7e2b5d37a856a3a6ac6422a57220edd8c664fcaa.jpg" }, { "user_id": 1018952, "full_name": "Manvir Singh", "title": "Founder", "founder_bio": "Manvir is a self-taught software engineer who specializes in building robust and intuitive technology accessible to clients from all backgrounds. He has coordinated with his co-founder, Austin, on previous ventures and looks forward to bringing his experience to bear with Artemis.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/manvir-singh-abb298247/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5c1de568e5bc43f567f2cd40177ec5a5ca6092d6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 75378, "title": "Umbria: Get designer-grade docs fast", "tagline": "Join us for a 24-hour YouTube livestream!", "body": "### 🖥️ The Product\n\nUmbria is the fastest way to launch designer grade docs!\n\n**Here’s how it works:**\n\n1. Drag and drop components\n2. Add your text\n3. Publish\n\nSee our own site & docs built with Umbria here: [Umbriadocs.com](http://Umbriadocs.com)\n\n### 🚀 The Pitch\n\nYou can get started for free — or contact Kyle for a special deal! ([kyle@umbriadocs.com](mailto:kyle@umbriadocs.com))\n\n### 👀 24 Hour Docs Livestream\n\nWe’re going to build docs for 24 hours straight on a YouTube stream next Wednesday (the 11th)!\n\nWhy? Becuase we can. Reach out for a shoutout, we’ll feature your docs!\n\n![uploaded image](/media/?type=post&id=75378&key=user_uploads/1018852/37abfcf5-e64c-4b6b-b81f-aa9e7a213169)\n\n", "url": "https://www.ycombinator.com/launches/Jbm-umbria-get-designer-grade-docs-fast", "votes": 5, "created_at": "2023-10-04T20:27:51.119Z" }, { "id": 66800, "title": "Artemis: Data Science Project Management", "tagline": "Document, track and visualize every aspect of your workflow in one place", "body": "**tl;dr** Artemis is the best platform to document, track, and visualize complex data science projects. We help teams log when and how they use resources (scripts, models, databases, etc.) to create the through yet compensable documentation for their projects.\n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/4e301d6d-975c-4497-8692-91a0834cdaba)\n\n**❌ The Problem:** Data science teams employ a variety of resources often spread across platforms. The way these resources interact in a project is often poorly documented. Documentation systems that do exist rely on direct peer-to-peer collaboration and produce results that are difficult to understand.\n\n**✅ The Solution:** Artemis is a platform for organizations to document and map every time a resource is used and how it works so projects can be understood at a glance.\n\n🏆 **Two Steps To Success:**\n\n**First,** Artemis teams create archives to document every time they use a resource. The Archive helps users document their resource’s location(s), relationships to other resources, and functions in addition to recording how they’re employed in a specific project. \n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/718ed567-d130-450d-a425-e25dfdf7c8ee)\n\n**Second**, resource deployments archived with Artemis are automagically mapped out to create interactive visual networks. These networks offer a comprehensive, one of a kind perspective that informs leadership decisions and helps individuals understand their work and their peers’ work in context.\n\n![uploaded image](/media/?type=post&id=66800&key=user_uploads/1018852/188a66ae-e107-4524-b084-54d62a18eec3)\n\n**▶️  Price and Contact**\n\nArtemis free to try for 30 days and free to academic institutions forever. Enterprise seats cost $20/month.\n\nIf you’d like to try Artemis or are interested to learn more, please reach out: [austinmccoy@artemisar.com](mailto:austinmccoy@artemisar.com)", "url": "https://www.ycombinator.com/launches/HNQ-artemis-data-science-project-management", "votes": 13, "created_at": "2022-10-18T22:04:26.831Z" }, { "id": 65058, "title": "Artemis: Retool for desktop applications", "tagline": "Drag-and-drop to make interfaces for any program", "body": "Hello, we’re [Austin](https://www.linkedin.com/in/austin-mccoy-833600229/), [Manvir](https://www.linkedin.com/in/manvir-singh-abb298247/) and [Kyle](https://www.linkedin.com/in/kyle-h-202515237/) — we founded [Artemis](https://artemisdevtool.com/). \n\n**_tl;dr_** _It takes too long to give desktop internal tools usable GUIs. Artemis fixes that._\n\n### **Problem:** \n\nArtemis was born in a Caltech robotics lab when our founder Austin realized too much time was spent giving internal tools usable GUIs. Building front-ends for internal tools written in programming languages like Python, C, C++ and Matlab is a tedious process that makes it slow and difficult to deploy even the simplest projects.\n\nExisting solutions aimed at solving this problem like Java’s Netbeans or Python’s QT only aid the user in designing how their interface will look, leaving the task of coding each element’s functionality. The complexity of these tools also present a high barrier to entry – especially for users who are just trying to make simple interfaces. These users don’t need all the bells and whistles of QT, they just need a simple solution they can learn and employ quickly.\n\n### **Artemis Solution:**\n\nArtemis’ drag and drop interface lets developers rapidly design front-ends comprised of elements that function out-of-box. This prepackaged functionality means integration is a breeze.\n\nHere’s how it works:\n\n1) Create your interface in our drag-and-drop webtool:\n\n![uploaded image](/media/?type=post&id=65058&key=user_uploads/1018852/93a29830-bbc4-4a98-a054-995f401064a4)\n\n2) Integrate into codebase with three steps (minimal overhead)\n\n![uploaded image](https://bookface.ycombinator.com/media/?type=post&id=65058&key=user_uploads/982880/bf607bd0-7015-4777-b264-72c6128ada94)\n\n3) Launch your new interface (runs in browser to facilitate cross platform use, but still works offline) \n\n![uploaded image](/media/?type=post&id=65058&key=user_uploads/1018852/c497ce1e-e1f0-4d6b-ab4d-f25340a53401)\n\n### **Project history, use cases, and pricing:**\n\n**History:** We began life as an augmented reality development platform that functioned in almost the same manner. However after realizing how small that market is, we’ve pivoted to a broader one. That being said: we remain so cross platform you can even build frontends to be displayed in AR and VR.\n\n**Use Cases:** Artemis is a must-have for anybody who builds internal tools. \n\nSome examples include: Machine learning dashboards • GUIs for engineering tools • Control panels for machinery & IoT devices • GUIs for physicists’ models and calculators • And whatever else you can build!\n\n**Pricing:** $9 a month after 1 week free trial", "url": "https://www.ycombinator.com/launches/GvK-artemis-retool-for-desktop-applications", "votes": 21, "created_at": "2022-08-02T04:40:30.973Z" } ] }, { "id": 27092, "name": "Lamin", "slug": "lamin", "former_names": [ "Lamin Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/65bf3f581eea5d1a5b7640c508e3fc0e7c8930f9.png", "website": "https://lamin.ai", "all_locations": "", "long_description": "We make it easy to query, trace, and govern R&D data in the age of AI, serving big pharma enterprises, biotech, and academia.\r\n\r\nOur core product is an open-source, lineage-native, format-agnostic lakehouse for agents and teams.", "one_liner": "Open data platform for traceable, multimodal AI", "team_size": 12, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1655969538, "tags": [ "Developer Tools", "Machine Learning", "Biotech", "Open Source", "Data Engineering" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "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/lamin", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/lamin.json", "year_founded": 2022, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/lamin-labs", "x_url": "https://twitter.com/laminlabs", "fb_url": null, "cb_url": null, "github_url": "https://github.com/laminlabs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/65bf3f581eea5d1a5b7640c508e3fc0e7c8930f9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1014343, "full_name": "Alex Wolf", "title": "Founder/CEO", "founder_bio": "Building open-source data infra for biology at Lamin.\n\nPreviously, created Scanpy and led the build-up of Cellarity’s compute platform.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/falexwolf/", "x_url": "https://twitter.com/falexwolf", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/534fd23c014da309d8eb5c4831d5e1693d48ddc3.jpg" }, { "user_id": 1014442, "full_name": "Sunny Sun", "title": "Founder", "founder_bio": "", "is_active": true, "linkedin_url": "http://linkedin.com/in/sunnyosun", "x_url": "https://twitter.com/sunnyosun", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f46137e52adf8e8d65c130efd41cc0e1224d0c92.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 27201, "name": "Lavo Life Sciences", "slug": "lavo-life-sciences", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/590ccfba5d4f0f97ed00fd962f2fdea307dbea2f.png", "website": "https://www.lavo.ai/", "all_locations": "", "long_description": "Lavo Life Sciences runs simulations of drug molecules on computers. Pharma companies use these simulations to guide their experiments and ultimately save time and money in the lab. This will de-risk and expedite clinical trials and FDA approval.", "one_liner": "AI for drug formulation", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1674082073, "tags": [ "AI-powered Drug Discovery", "Machine Learning", "Biotech", "Drug discovery" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Inactive", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/lavo-life-sciences", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/lavo-life-sciences.json", "year_founded": 2022, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/lavo-life-sciences/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/590ccfba5d4f0f97ed00fd962f2fdea307dbea2f.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 148651, "full_name": "Scott Swarthout", "title": "Founder", "founder_bio": "Software Engineer @ DoorDash, former co-founder of Lavo Life Sciences", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/scottswarthout", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/af903ba732a9393676eeef2136e55d10a933cf34.jpg" }, { "user_id": 1053804, "full_name": "Zach Glick", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/zach-glick-800b75219/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/54056b9978aecbffd03e4a215347e4766fa753b7.jpg" }, { "user_id": 156580, "full_name": "Derek Metcalf", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/derekmetcalf", "x_url": "https://twitter.com/derek_metcalf", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/59b13bc66f7b72fb061c26d95cd51d227f632c4a.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69547, "title": "⚛️ Lavo Life Sciences - Computer simulations of drug molecules", "tagline": "We use AI to de-risk drug development and save time in the lab", "body": "### **TL;DR**\n\nLavo Life Sciences runs simulations of drug molecules on computers. Pharma companies use our simulations to guide their experiments, saving hundreds of millions of dollars and years of time in the lab. This will de-risk clinical trials and expedite FDA approval.\n\n![uploaded image](/media/?type=post&id=69547&key=user_uploads/148651/80bc9d1a-e8b4-41cc-8ca1-5deed312ecbc)\n\nThis is what a pill looks like at the molecular level. Drug molecules pack into repeating patterns–or “crystal structures”–which control the drug’s stability. This particular drug (Ritonavir) was recalled when the developers chose an unstable crystal structure. This recall could have been avoided with effective simulation.\n\n### **🤔 The Problem**\n\nLavo’s simulation software predicts the crystal structure of drug molecules. The crystal structure refers to the three-dimensional pattern in which individual molecules pack to form a solid. Choosing the best crystal structure before clinical trials maximizes the efficacy, shelf-stability, and safety of the drug. Failure to accurately determine the crystal structure can lead to problems in manufacturing, legal protection of the drug patent, and success in clinical trials.\n\nFrom conversations with pharma companies, we’ve learned that they want more of these predictions, but existing vendors are too slow and expensive–think hundreds of thousands of computer hours for a single molecule. This is a limitation of their methodology and is also our key differentiator.\n\nThis market is large and growing. 15,000 relevant drugs enter R&D each year, and pharma companies spend over $100M on crystal structure predictions annually. Also, we have learned that pharma companies need predictions on larger drugs than are currently possible with today's methods, so we have an opportunity to expand the market with more efficient products.\n\n### **🔨 Why now, why us?**\n\nWe are uniquely positioned to solve this problem – Zach and Derek have PhDs in computational chemistry and Scott is a former Google Cloud PM. We are prepared to tackle this scientific challenge at an industrial scale.\n\n### **💡 Our Solution**\n\nWe started working on this problem 4 months ago and our simulations are already 10x faster than current methods\n\nWe use AI and quantum chemistry algorithms to improve every step of the crystal structure prediction workflow:\n\n* We use custom algorithms to quickly get an understanding of the landscape of possible crystals.\n* We use AI to accurately rank crystal structures 100x faster than traditional techniques\n* We scale our software on the cloud to achieve rapid turnaround times.\n\n### **❤️ Our Asks**\n\nWe are interested in meeting people in the pharma industry. Please email [founders@lavo.ai](mailto:founders@lavo.ai) to connect with us!\n\nThanks,\n\nScott, Zach, and Derek", "url": "https://www.ycombinator.com/launches/I5j-lavo-life-sciences-computer-simulations-of-drug-molecules", "votes": 30, "created_at": "2023-02-22T17:14:00.733Z" } ] }, { "id": 27206, "name": "Delfino AI", "slug": "delfino-ai", "former_names": [ "Be Golden" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0ca7311a440acc157fc34997e9d9bbdd9f1873fd.png", "website": "https://www.delfino.ai/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Delfino AI helps automate the repetitive phone calls that providers' offices make to payors", "one_liner": "Generative AI for administrative automation in healthcare ", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Healthcare IT", "launched_at": 1657044467, "tags": [ "Generative AI", "Machine Learning", "Health Tech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Healthcare", "Healthcare IT" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/delfino-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/delfino-ai.json", "year_founded": 2021, "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/0ca7311a440acc157fc34997e9d9bbdd9f1873fd.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 513088, "full_name": "Sneh Patel", "title": "Founder", "founder_bio": "Sneh has a Doctorate in Pharmacy and MBA from Harvard. She previously worked at McKinsey and an NEA-backed startup. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/snehpatel1/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7ff7972cb9551df4529bb0739c13f0f2951e1ac2.jpg" }, { "user_id": 679130, "full_name": "Kimberly McManus", "title": "Founder", "founder_bio": "Kimberly’s expertise in consumer health comes from leading a data science team at 23andMe. She has a Bachelor’s degree from MIT, and a Master’s degree in Biomedical Informatics and a Ph.D. in Biology from Stanford University. She’s published over ten papers in genomics, healthcare and machine learning and holds one patent, with three patents pending.", "is_active": true, "linkedin_url": "http://www.linkedin.com/in/kimberly-mcmanus-phd-5384293a", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1d0d9b2cdd817a778b97a6206035835f7a9854c0.jpg" } ], "jobs": [ { "id": 60809, "title": "Healthcare Operations Associate - Part Time ", "url": "https://www.ycombinator.com/companies/delfino-ai/jobs/2HyErWK-healthcare-operations-associate-part-time", "location": "US / Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$15 - $18", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 64075, "title": "Founding AI Engineer", "url": "https://www.ycombinator.com/companies/delfino-ai/jobs/3OuXift-founding-ai-engineer", "location": "Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$80K - $180K", "equity_range": "0.20% - 5.00%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [ "CSS", "Django", "HTML", "JavaScript", "PostgreSQL", "Python", "Machine Learning", "Software Architecture" ] }, { "id": 72851, "title": "Healthcare Operations Associate - Full Time ", "url": "https://www.ycombinator.com/companies/delfino-ai/jobs/GMYf77b-healthcare-operations-associate-full-time", "location": "US / Remote (US)", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$17 - $19", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "AI Voice Agents: 2025 Update | Andreessen Horowitz", "url": "https://a16z.com/ai-voice-agents-2025-update/", "date": "Jan 29, 2025" }, { "title": "The Future of Voice: Our Thoughts On How It Will Transform Conversational AI - Lightspeed Venture Partners", "url": "https://lsvp.com/stories/the-future-of-voice-our-thoughts-on-how-it-will-transform-conversational-ai/", "date": "Sep 26, 2024" }, { "title": "Hi, AI: Our Thesis on AI Voice Agents | Andreessen Horowitz", "url": "https://a16z.com/ai-voice-agents/", "date": "May 29, 2024" } ], "launches": [ { "id": 64318, "title": "Be Golden helps measure and manage inflammation levels.", "tagline": "Learn more and pre-order here: https://www.begolden.online/ ", "body": "### **THE PROBLEM**\n\n\\>50% of American adults have high inflammation levels.  Chronic inflammation may lead to many negative outcomes including:\n\n* **Chronic disease (e.g., cancer, heart disease, Alzheimer’s)**\n* **Fatigue**\n* **Symptoms of IBS, migraine and pain (fibromyalgia and myofascial pain)**  \n* **Negative impact on IVF and IUI success**\n\n \n\n### **THE SOLUTION/HOW IT WORKS**\n\nBe Golden helps you measure and manage your inflammation levels using an at home finger prick based blood testing kit and a digital platform.  How it works:\n\n1. MEASURE your baseline inflammation levels; Receive your results and how they compare to other people like you\n2. TRY A NEW HABIT from our list of personalized options from the scientific literature (or something else) and track how often you complete it\n3. RE-MEASURE every 1-2 months and receive analytics on changes to your habits and inflammation scores\n\n**THE ASK**\n\nIf you or anyone you know has health goals related to the below, please sign up to learn more here: \n\n* Help **prevent** **disease** (e.g., cancer, heart disease, Alzheimer’s)\n* **Feel good** \n* Help reduce the frequency of **migraine** symptoms\n* Help prevent **irritable bowel syndrome (IBS)** symptoms \n* Help take control of your **fertility**\n\n \n\nIf you know any of the following experts, we would love a connection:\n\n* chronic inflammation experts that we can speak to about inflammation biomarkers\n* OB/GYNs \n* Gastroenterologists\n\n  ", "url": "https://www.ycombinator.com/launches/GjO-be-golden-helps-measure-and-manage-inflammation-levels", "votes": 30, "created_at": "2022-07-05T03:44:25.192Z" } ] }, { "id": 27221, "name": "CAPSULE", "slug": "capsule", "former_names": [ "Dryftwell", "GLIMPSE" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/19f195e9a25d1cff1f914f65ceb3e7458c6d52b9.png", "website": "https://www.shopcapsule.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "CAPSULE is a mobile app that makes it easy to save and buy the things you find on any social media platform. Just snap a screenshot of anything you like, from any platform, and we search the entire internet to instantly give you shoppable links. \r\n\r\nInstead of searching hundreds of websites and sifting through thousands of products on your own, CAPSULE lets you find inspiration from anywhere and uses advanced machine learning to return results that feel like magic. ", "one_liner": "Buy anything you find on social media", "team_size": 4, "industry": "Consumer", "subindustry": "Consumer -> Apparel and Cosmetics", "launched_at": 1661989948, "tags": [ "Generative AI", "Machine Learning", "Computer Vision", "E-commerce", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2022", "status": "Inactive", "industries": [ "Consumer", "Apparel and Cosmetics" ], "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/capsule", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/capsule.json", "year_founded": 2022, "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/19f195e9a25d1cff1f914f65ceb3e7458c6d52b9.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": 917276, "full_name": "Michele Van Ruiten", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "http://www.linkedin.com/in/michelevanruiten/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d93fe8ad5e2cfc5d231a7e28a3bc3d66c96d1168.jpg" }, { "user_id": 276919, "full_name": "Kelsey Pedersen", "title": "Founder", "founder_bio": "", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/kelseypedersen/", "x_url": "https://twitter.com/kelsey_pedersen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f3b491709d3fd754dd8270d093c773cbbdd12d2.jpg" } ], "jobs": [], "news": [ { "title": "Capsule, a New 'Shazam' for Fashion App, Could Change the Way You Shop - Business Insider", "url": "https://www.businessinsider.com/capsule-shazam-for-fashion-app-change-the-way-you-shop-2024-8", "date": "Aug 12, 2024" }, { "title": "This new 'Shazam' for fashion app could change the way the world shops online", "url": "https://www.yahoo.com/tech/shazam-fashion-app-could-change-102802967.html", "date": "Aug 12, 2024" }, { "title": "Our 11 favorite companies from YC's S22 Demo Day", "url": "https://techcrunch.com/2022/09/07/11-favorite-yc-demo-day-s22-startups/", "date": "Sep 07, 2022" } ], "launches": [] }, { "id": 27260, "name": "Coverage Cat", "slug": "coverage-cat", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9bd0bbec7d523edd1bef0a7fed1c32a4c5e91d07.png", "website": "https://www.coveragecat.com/", "all_locations": "New York City, NY, USA; Remote", "long_description": "Coverage Cat, the AI-native insurance broker, fixes your coverage limits and finds cheaper premiums. \r\n\r\nWe provide both AI and human support so you can get fast answers on your policy and quickly get a response from a human agent so you can protect you assets or close on a new home. \r\n\r\nThe AI-first approach drives core search, support and shopping experiences to ensure you get the best prices with the correct coverage. ", "one_liner": "Consumer Optimized Insurance", "team_size": 4, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1659454514, "tags": [ "Fintech", "Machine Learning", "Insurance" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Fintech", "Insurance" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/coverage-cat", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/coverage-cat.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/coverage-cat", "x_url": "https://twitter.com/coveragecats", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/coverage-cat", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9bd0bbec7d523edd1bef0a7fed1c32a4c5e91d07.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1047431, "full_name": "Max Cho", "title": "Founder", "founder_bio": "I once knocked my teeth out and spent the next few weeks reading insurance policies to try to pay for their replacements. Prediction tasks with software and data are my jam. I love studying why people do what people do, esp. with ads & marketing.\n\nPreviously: Marketing products @ Google, financial prediction software @ Two Sigma, and catching software exceptions @ Microsoft. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/max-cho-0a376a24/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ba94ef687ad8abdeed36a551eb2f5d11a42d83c9.jpg" }, { "user_id": 123258, "full_name": "Gabriel Botelho", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO @ Coverage Cat. \n\nShop for umbrella and home insurance at www.coveragecat.com! ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/gb-cc", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/32db7698c55b453d919d3891cce7d25bd1d9d225.jpg" } ], "jobs": [ { "id": 60678, "title": "[PAUSED] Senior Engineer", "url": "https://www.ycombinator.com/companies/coverage-cat/jobs/rOkcL9B-paused-senior-engineer", "location": "CA, US / Remote (CA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$140K", "equity_range": "0.05% - 0.50%", "experience": "6+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 88545, "title": "[PAUSED] Fractional Operations Specialist (Contract)", "url": "https://www.ycombinator.com/companies/coverage-cat/jobs/TH81d0Y-paused-fractional-operations-specialist-contract", "location": "Remote (US)", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$10 - $15 / hourly", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 94308, "title": "[PAUSED] Growth Engineer (Contract)", "url": "https://www.ycombinator.com/companies/coverage-cat/jobs/NJXH199-paused-growth-engineer-contract", "location": "Remote", "type": "Contract", "role": "Engineering", "role_type": "Full stack", "salary_range": "$15 - $25 / hourly", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [ "Growth design" ] } ], "news": [ { "title": "How Silicon Valley Turned on Silicon Valley Bank - WSJ", "url": "https://www.wsj.com/articles/how-silicon-valley-turned-on-silicon-valley-bank-ee293ac9", "date": "Mar 12, 2023" }, { "title": "The biggest moonshots in YC's S22 batch • TechCrunch", "url": "https://techcrunch.com/2022/09/08/the-biggest-moonshots-in-ycs-s22-batch/", "date": "Sep 08, 2022" }, { "title": "Coverage Cat is backed by YC", "url": "https://www.coverager.com/coverager-cat-is-backed-by-yc/", "date": "Aug 03, 2022" } ], "launches": [ { "id": 116242, "title": "Shop for umbrella insurance via your personal agent (Muse, Instinct, OpenClaw, Hermes, etc.)", "tagline": "Your agent + Coverage Cat's API/MCP = better insurance", "body": "![uploaded image](/media/?type=post&id=116242&key=user_uploads/123258/c95ab970-fa7e-4c6c-b106-0343c5321734)\n\nWe’re proud to release our agent-native solution for consumers to compare and shop for umbrella and home insurance.\n\nPoint your preferred agent (Instinct, Codex, ClaudeCode, OpenClaw, Hermes, Jo, etc.) or any tool-using assistant (ideally with access to your context). Just tell it to:\n\n`Shop for umbrella insurance with Coverage Cat`\n\nOr, point it at [coveragecat.com](http://coveragecat.com) and tell it to do the same. What used to be a week of forms, phone tag, and PDF underwriting shrinks to a minimal prompt, review of your information, and credit consent.\n\nYour agent gathers your context, calls the calculators, compares carriers, and delivers a bindable quote — the complex, cross-carrier judgment call happens where it belongs: inside the loop with you and your AI, not on hold with a sales person.\n\nIt will pre-fill applications from what it already knows about you, ask for your review and confirmation, then hand back a resumable checkout link — all in a single conversation.\n\nWant something more packaged? Check out other agentic interface access options below:\n\nBrowse the catalog at [coveragecat.com/ai/skills](https://www.) or the source at [github.com/Coverage-Cat/skills](https://github.com/Coverage-Cat/skills).\n\nWant to use our MCP? Install it up and get access to both the comparison/purchase tools as well as read-only calculators, agent finders, and estimate tools — discovery lives at [coveragecat.com/api/agent](https://www.coveragecat.com/api/agent), the OpenAPI spec is at [coveragecat.com/api/agent/openapi.yaml](https://www.coveragecat.com/api/agent/openapi.yaml).\n\nPower users can install the CLI for operators:\\\n`npm install -g @coveragecat/cli # npm`\n\nor via Homebrew\n\n```\nbrew tap Coverage-Cat/coveragecat && brew install coveragecat # Homebrew\n```\n\nWant your agent to check out our broader developer portal? .\n\nHappy shopping!", "url": "https://www.ycombinator.com/launches/UEs-shop-for-umbrella-insurance-via-your-personal-agent-muse-instinct-openclaw-hermes-etc", "votes": 4, "created_at": "2026-09-22T15:38:06.824Z" }, { "id": 106902, "title": "Coverage Cat AI Insurance Leaderboard", "tagline": "How do frontier models perform on complex insurance tasks?", "body": "# Coverage Cat AI Insurance Bench\n\nWe’re Coverage Cat. We built an AI insurance benchmark to measure how frontier models perform on real insurance workflows, not trivia or generic reasoning tasks.\n\n### TL;DR\n\n![uploaded image](/media/?type=post&id=106902&key=user_uploads/123258/e54c9091-f0a5-4500-b9f8-125255d46418)\n\nCoverage Cat’s AI Insurance Benchmark compares 7 frontier models on two insurance-specific evals:\n\n* Price estimation: can a model estimate umbrella insurance quote ranges accurately?\n* Brokerage/Agent task: can a model answer practical insurance questions the way a good broker or agent would?\n\nThe public leaderboard ranks models by Elo Rating, Win Rate, Quote Coverage, Quote MAPE, Winkler Loss, and judged brokerage-task quality.\n\nView it here: Coverage Cat AI Insurance Benchmark ()\n\n### The Problem\n\nMost AI benchmarks don’t tell you whether a model is useful inside a real insurance workflow or whether consumers can really trust it for insurance information.\n\nInsurance work is full of messy edge cases: pricing uncertainty, underwriting rules, coverage terminology, household risk factors, and questions where the answer needs to be both correct and concise.\n\nThere has been good prior work on insurance AI benchmarks, including UNDERWRITE, which evaluates multi-turn underwriting agents with domain experts, noisy tools, and simulated users. That work focuses on enterprise underwriting agent behavior and found meaningful gaps between general model performance and enterprise readiness.\n\nOur benchmark is different: it focuses on consumer insurance workflows Coverage Cat actually needs to automate, including umbrella quote estimation and broker/agent-style answers. It is also designed as a public, continuously updated leaderboard rather than a one-time research benchmark.\n\n### What We Built\n\nThe first public version includes:\n\n* 2,800+ price-estimation scenarios\n* 19,000+ quote model results\n* 1,200+ brokerage/agent task questions\n* 8900+ QA model results\n* 7 tested models\n\nThe benchmark uses anonymized eval data, no-retention/no-logging model platforms, and aggregate-only public snapshots. Customer data is not exposed to model providers.\n\n### What’s Next\n\nWe’ll update the leaderboard soon with newer frontier models as they become available through our approved model-provider paths. We also expect to expand the benchmark with more brokerage workflows, richer human review, and additional insurance product lines.\n\nOur goal is simple: make it obvious which AI models are actually good at insurance work and help those models improve.\n\nView the leaderboard: Coverage Cat AI Insurance Benchmark ()\n\nIf you’re shopping for umbrella coverage, you can also start here: Coverage Cat Umbrella Insurance ().", "url": "https://www.ycombinator.com/launches/RoE-coverage-cat-ai-insurance-leaderboard", "votes": 2, "created_at": "2026-07-22T22:45:07.812Z" }, { "id": 92734, "title": "Coverage Cat: Your AI-Native Insurance Agent", "tagline": "Insurance questions cause headaches. Coverage Cat is the relief.", "body": "Coverage Cat, the AI insurance agent, answers any questions you might have about home, auto, umbrella, life, and disability insurance. No more waiting on hold or puzzling over policy jargon. Coverage Cat's assistant can help you figure out if you should buy that policy, file that claim, or shop around for a better price at the click of a button.\n\n---\n\n## Expert Answers for Home, Auto, and Umbrella Insurance\n\nCoverage Cat is trained to provide the right answers about personal lines coverage:\n\n* **Home Insurance**: Learn what your policy covers, from water damage to liability limits.\n* **Umbrella Insurance**: Understand when and how your extra liability protection kicks in.\n* **Auto Insurance**: Get clear explanations on coverage. Never worry about what collision or comprehensive mean again.\n\n---\n\n## Policy Reviews Powered by AI\n\nDon't get bamboozled by the fine print. Just upload your insurance documents, and Coverage Cat examines policy terms to highlight important coverage details.\n\n---\n\n## Estimates for Claims and Coverage\n\nCurious about how a claim or coverage change might impact your premiums? Coverage Cat can create estimates based on specialized tools tools to offer cost and claims insights. Understand your financial exposure before you make your decisions!\n\n---\n\n## Coverage Cat Saves You Time and Money\n\nComplexity benefits insurers, not policyholders. Coverage Cat leverages our rich insurance data sets to create price transparency, and put valuable information into your hands.\n\n---\n\n## Easy Insurance Conversations Start Now\n\nInsurance need not frustrate. Coverage Cat turns your questions into straightforward answers. Clarity makes all the difference when dealing with a stressful part of your personal finances.\n\n---\n\n## Try Coverage Cat Today!\n\nJust visit us at [coveragecat.com](http://coveragecat.com) where you can get answers and shop for insurance.\n\n![uploaded image](/media/?type=post&id=92734&key=user_uploads/123258/9fdcf95e-d78a-44c5-956f-5ea7d8728fed)\n\n", "url": "https://www.ycombinator.com/launches/O7i-coverage-cat-your-ai-native-insurance-agent", "votes": 13, "created_at": "2025-08-05T02:01:00.343Z" }, { "id": 65072, "title": "Coverage Cat - Insurance For People Who Don't Believe In Insurance", "tagline": "The Best Way to Buy Umbrella/Home/Life/Disability/Auto Insurance", "body": "Do you have any of: a net worth over $1million; a house; a car; a high paying job? Then Coverage Cat is the best way to buy insurance. We provide **real quotes**, aggressively comparison shop for you across policy types, and **never sell your data**. The result is thousands of dollars saved and weeks of work and phone calls.\n\n![uploaded image](/media/?type=post&id=65072&key=user_uploads/123258/92e2c8d5-44e3-4cf8-9df6-2ddecf5f19a4)\n\n### Fill out one form and we handle the rest\n\nDon’t waste another minute getting harassed by spam/sales people or figuring out if you bought the right policy. We will shop for you, explain your options, and ensure you get you the best policy for your needs. \n\n* Need umbrella insurance? Shop and learn on our [explainer page!](https://www.coveragecat.com/umbrella-insurance)\n* Need home, car, life, or disability insurance? Visit our [homepage](https://coveragecat.com).\n* Want to learn more about insurance? Check out our [blog](https://www.coveragecat.com/blog).\n* Live in California or Florida and worried about a price jump or non-renewal on your homeowners insurance? See our reviews of carriers that are still underwriting in both states: [Delos \\[CA\\]](https://www.coveragecat.com/review/delos), [Bamboo \\[CA\\]](https://www.coveragecat.com/review/bamboo), [Kin \\[FL\\]](https://www.coveragecat.com/review/kin).\n\n👋 We’re Max & Gabriel, co-founders of Coverage Cat.\n\n### Opportunity: Insurance - An essential product, often poorly purchased\n\nInsurance is an all-time great financial innovation. It represents a way for people to pool and mitigate significant risks when events have shaken out for the worst. \n\nBut the way insurance is been bought and sold has generated a mismatch between the protection from risk that insurance products SHOULD provide. This forces tens of thousands of people to go bankrupt every year from risks that could be properly insured– Often while overpaying for coverage that doesn't protect them from catastrophic risk.\n\n### Problem: Insurance is complicated\n\nThe web of carriers, brokers, contracts, and claims that ensure your risks are properly handled can be an absolute nightmare for people who just want to feel like the things that are important to them are protected.\n\n### The Solution: A one-step process to find the best insurance\n\nWe collect information on how much you feel comfortable losing, in addition to standard insurance questions. Coverage Cat’s AI and team of specialists then searches across available carriers to find the policies and prices for your needs. If you’re in CA, FL, NY, TX, or WA we’d appreciate the opportunity to save you a bundle!\n\nCheers, Max & Gabriel 🐱", "url": "https://www.ycombinator.com/launches/GvY-coverage-cat-insurance-for-people-who-don-t-believe-in-insurance", "votes": 46, "created_at": "2022-08-02T16:06:51.203Z" } ] }, { "id": 27361, "name": "Polymath Robotics", "slug": "polymath-robotics", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d6f5a2035d19900b2a4411b03d2c1998b0aaa5e.png", "website": "http://www.polymathrobotics.com", "all_locations": "San Francisco, CA, USA", "long_description": "Polymath is building a general autonomy stack for cautious vehicles. Our software allows any industrial vehicle - whether it's a tractor in a field or a bulldozer in a mine, drive itself. We bundle together AI, ML, Controls, ROS, Safety and best-in-class deployment practices to enable our customers to tell automated vehicles to do via a REST API.\r\n\r\nWe're on more robots than we have engineers, are seeing our revenue (and robotic fleet) grow rapidly, and are looking for folks who want to help automate the world.", "one_liner": "General Autonomy for Industrial Vehicles", "team_size": 25, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1659110769, "tags": [ "Hard Tech", "Machine Learning", "Robotics", "Unmanned Vehicle", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2022", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/polymath-robotics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2022/polymath-robotics.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/polymath-robotics/", "x_url": "https://twitter.com/polymathrobots", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/polymath-robotics", "github_url": "https://github.com/polymathrobotics", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0d6f5a2035d19900b2a4411b03d2c1998b0aaa5e.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/27421/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECAPVLLM2EU%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022709Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJGMEQCIERmcK8Cf9%2Btw509DI59PWqE38RRTAlvZa4J3JDwHNfvAiAayM%2BKkNtOvjgd758WgPB4q6%2BUu3dhFEeIBKJdEEYmHirlAwg5EAAaDDAwNjIwMTgxMTA3MiIMT2KDc6%2BsqQRelJt7KsIDHKXozlWP2zRm6TyaI63cZu7UdMNJI84LhfrBVoBzKOZrazhBds2e6DhqALWGgEHuhaH96sBW1vGKF0SZPA8JEf%2FhnbI%2B0LPups6efXBWO1aKfnU6HWjkfPemBseL1V1dBRWh3MHmM%2BS0wsfJmaYaFWTcHaaVraOkFgeuvtIGQt%2FnimlcxYe9MQMOeYiPnm5yfGLPkCnGOvHZGDepzR19xj6ZjRi9QZRcX734o%2BDyXGZIQOACA5etqDAXBP7fbW8udzjKK9cHEGPamT7IlaTLAWBWnDjL2T9zw8ZCXoeolYzeaw3DTEK0tdnm9pXjkPGWxrNUNlecD6CiG7M3gr2Lm8eody0o9pj5Ec9BIcJ09i9djYQp%2F9Qt3RQagWspbZEm0RIM98ukXmmQO%2Br5NwFl2BqP7h04EVZMR4%2FxuoUkyMfNGipkzTdDAKGOV2wo7LqFqinznpZBKbguQ%2Fg6wWy40kCNyoPguBPf8GYzzIvusP5x6bf1QHvMlXyb8ElzS4lScRKm7v5GhARAJq%2BveXuxU3aR2lwL84TF9HIpOZ6loG5wOggyMO5KqHtaunl14wjKV3M6InDmt6%2F5NLTGbL6N0P36MOn92tYGOqYB%2B%2FclWb0gxQJ%2FmXWJ%2FID3oboHUM%2BqcRMUObsJl1yosDN9kekbLFwXPJS0wjujDnQuGXhjbEK0yPyr8Dgq35lBDPMlxWRQ8upAxHpp%2F0cruekkJPyONG5qpVv25DqS52YxtylQ0pbERuY6rX1AFi5Hwd5%2FQJgU8TuncKO5FjbC0OPiim0nrZjMnv3t4R3u06MVK4%2FRj2yhCS4qwhYGeOsJdP79%2BCqZqQ%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=2d77a18679f1e00728c184a75ac296cdaf10b92f8ba58f1c6ff59af4af5e4475", "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/03d7e2f03b4d93b6f9bd8dcadf40461a45f4ef53.png" ], "primary_partner": { "name": "Gustaf Alstromer", "url": "https://www.ycombinator.com/people/gustaf-alstromer" }, "founders": [ { "user_id": 136341, "full_name": "Stefan Seltz-Axmacher", "title": "Founder", "founder_bio": "Robots that are buildable and valuable are the most magical.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/stefanseltzaxmacher", "x_url": "https://twitter.com/stefanesa", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3a976b8c7814c0a2985a3b21bdfae3f0e1cf5fd0.jpg" }, { "user_id": 920849, "full_name": "Ilia Baranov", "title": "Founder/CTO", "founder_bio": "There is a certain enchanting magic that comes from working with robots;\nBy waving our hands, we can make machines seem alive.", "is_active": true, "linkedin_url": "http://linkedin.com/in/iliabaranov", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c64fc8a7f715908988488379782f0e67bf6d8b88.jpg" } ], "jobs": [ { "id": 90535, "title": "Associate, Customer Program Management", "url": "https://www.ycombinator.com/companies/polymath-robotics/jobs/M1qC4rT-associate-customer-program-management", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Product", "role_type": null, "salary_range": "$100K - $125K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 94200, "title": "Senior Verification & Validation (V&V) Engineer - San Francisco", "url": "https://www.ycombinator.com/companies/polymath-robotics/jobs/izf2VxL-senior-verification-validation-v-v-engineer-san-francisco", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$150K - $200K", "equity_range": "0.02% - 0.05%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 112649, "title": "Account Executive", "url": "https://www.ycombinator.com/companies/polymath-robotics/jobs/8sCtro4-account-executive", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$125K - $250K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 113108, "title": "Applications Engineer - San Francisco", "url": "https://www.ycombinator.com/companies/polymath-robotics/jobs/Ii1hnp1-applications-engineer-san-francisco", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$113K - $150K", "equity_range": "0.03% - 0.06%", "experience": "1+ years", "visa": "Will sponsor", "skills": [] }, { "id": 116081, "title": "Autonomous Vehicle Test Specialist – San Francisco", "url": "https://www.ycombinator.com/companies/polymath-robotics/jobs/KcVZbqK-autonomous-vehicle-test-specialist-san-francisco", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "QA engineer", "salary_range": "$70K - $100K", "equity_range": "0.01% - 0.03%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "The 30 Early-Stage Startups in 2026 Most Likely to Become Tech's Next Unicorns - Business Insider", "url": "https://www.businessinsider.com/the-30-early-stage-startups-in-2026-most-likely-to-become-techs-next-unicorns-2026-3#polymath-robotics-21", "date": "Mar 03, 2026" }, { "title": "Case Study: Polymath Robotics and ConGlobal Collaboration", "url": "https://www.polymathrobotics.com/blog/case-study-polymath-and-conglobal", "date": "Feb 12, 2025" }, { "title": "CES 2025: Self-driving cars were everywhere, plus other transportation tech trends | TechCrunch", "url": "https://techcrunch.com/2025/01/12/ces-2025-self-driving-cars-were-everywhere-plus-other-transportation-tech-trends/", "date": "Jan 12, 2025" }, { "title": "Autonomous vehicles are having a moment and other vibes from CES 2025 | TechCrunch", "url": "https://techcrunch.com/2025/01/09/autonomous-vehicles-are-having-a-moment-and-other-vibes-from-ces-2025/", "date": "Jan 09, 2025" }, { "title": "Polymath Robotics to share lessons learned as a SaaS-based startup at RoboBusiness - The Robot Report", "url": "https://www.therobotreport.com/polymath-robotics-to-share-lessons-learned-at-a-saas-based-startup-at-robobusiness/", "date": "Aug 14, 2024" } ], "launches": [ { "id": 65002, "title": "📍 Polymath Robotics: plug & play autonomy for industrial vehicles", "tagline": "Polymath lets you easily add autonomy to any industrial vehicle, and build your own applications via Rest API.", "body": "Hey, we’re Stefan & Ilia, founders of Polymath Robotics.\n\n**Polymath makes autonomy software for industrial vehicles.**\n\nToday we’re excited to introduce **Caladan**: a set of simulated vehicles & environments you can use to build autonomous industrial vehicle applications via Rest API.\n\n### **The problem**\n\nBuilding an autonomous industrial vehicle is a multiyear, multimillion-dollar project. Just getting a vehicle to drive itself reliably takes 6 months of engineering effort at a minimum. Even then, the autonomy itself is often buggy and unreliable, and impossible to transfer across different vehicle types.\n\nAs a result, many autonomy companies run out of steam before they even have a reliably-working product … let alone product-market fit.\n\n### **Our solution: general autonomy software**\n\nPolymath software handles generalized basic autonomy — in other words, **driving around and not hitting stuff** — for any industrial vehicle in a controlled environment. That means autonomous industrial vehicle companies can have an autonomous vehicle on day 1.\n\nSince we’re _just_ working on autonomy, we can focus on making the system **stable and reliable.** No more battling with weird controls issues or buggy obstacle detection.\n\n### Caladan: build on Polymath autonomy in simulation\n\nCaladan makes it easy to build autonomous vehicle behaviors in sim. The tool is a set of simulated vehicles & environments you can use to interact with Polymath autonomy via Rest API.\n\nThat means you can program custom autonomous vehicle behaviors and applications using Python, not ROS. And when you’re ready, whatever you’ve built in Caladan can seamlessly transition to a physical vehicle.\n\n### Product demo / example application\n\nHere’s an example application you could build with Caladan (you can download the example code [here](https://github.com/polymathrobotics/caladan_examples)).\n\n\n\nThe free version of Caladan comes with a simulated farm environment or simulated port environment. You can also reach out to us about a custom environment & vehicle in Caladan.\n\n### **Does Polymath autonomy work for my vehicle/use case?**\n\nPolymath autonomy is limited to industrial vehicles in closed environments, since the system will stop immediately if there’s trouble, and fall back on teleoperation in hazardous situations.\n\n**Use case examples:** ⛏ Mining trucks • 🚜 Agricultural vehicles • 🌲 Logging trucks • ✈️ Ground support equipment at airports • 🚢 Yard trucks\n\n### **Can I try it?**\n\nHeck yeah. You can try Caladan for free by signing up [here](http://polymathrobotics.com/product).", "url": "https://www.ycombinator.com/launches/GuQ-polymath-robotics-plug-play-autonomy-for-industrial-vehicles", "votes": 67, "created_at": "2022-07-29T15:56:38.207Z" } ] }, { "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": 27828, "name": "Scanbase", "slug": "scanbase", "former_names": [ "Scanbase", "Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c9177f461ffabba1796fab772ec9f376ad91bf4e.png", "website": "https://www.scanbase.com", "all_locations": "Los Angeles, CA, USA", "long_description": "Scanbase makes it easy for medical companies to convert photos of rapid diagnostic tests into results. We do this by providing a simple API that any medical company can access.\r\n", "one_liner": "The API for Diagnostic Test Analysis (COVID-19, FLU, RSV, STD,…", "team_size": 10, "industry": "Healthcare", "subindustry": "Healthcare -> Diagnostics", "launched_at": 1670991822, "tags": [ "Machine Learning", "Computer Vision", "Health Tech", "Telemedicine", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Diagnostics" ], "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/scanbase", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/scanbase.json", "year_founded": 2022, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/scanbases/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c9177f461ffabba1796fab772ec9f376ad91bf4e.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": 1162081, "full_name": "Jeffrey Lange", "title": "Founder", "founder_bio": "Jeffrey is currently the CEO of Scanbase, a seed stage diagnostic scanning API used by at-home test manufacturers. He is a graduate of the Rhode Island School of Design.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/langejeff/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/88486d8d8917eb91776d16c6f3b5540f5e751853.jpg" }, { "user_id": 1150701, "full_name": "Steve Roger", "title": "Founder", "founder_bio": "CTO of Scanbase with 10+ years building enterprise level APIs, and products. Steve holds a degree in CS from UC Berkeley. Ex-LinkedIn.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/steveroger/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2ef49967996d38086a75f694673b3814fe366c7b.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator’s Most Recent Winter 2023 Batch Shows A Shift To AI, DevTools And B2B ", "url": "https://news.crunchbase.com/venture/y-combinator-winter-2023-shift-ai-devtools-b2b/", "date": "Apr 12, 2023" }, { "title": "Our favorite startups from YC’s Winter 2023 Demo Day — Part 2 | TechCrunch", "url": "https://techcrunch.com/2023/04/06/y-combinator-demo-day-2023-favorites-part-two/", "date": "Apr 06, 2023" } ], "launches": [ { "id": 68414, "title": "Scanbase API - Converting Diagnostic Tests to Results, Next Steps, and Treatment", "tagline": "COVID-19, Flu, Pregnancy, RSV, STD/STI, HIV, CKD, UTI, and many more...", "body": "![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/724cd23c-f4df-4100-954e-379c4f248340)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/047c2be5-f90d-4a0b-acc6-693a32527879)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/d6a6fc05-b35f-4382-8d04-5a1c04c5bec0)\n\n[Book a demo](https://www.scanbase.com/schedule-a-demo) and learn how Scanbase can enhance your diagnostic program.\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/9151ad3b-aee8-469f-85a8-865f70e98b45)\n\nWatch a demo of a Scanbase’s algorithm interpreting a **low positive** test!\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/bb4bb5c4-0806-4417-a3e9-a05347115195)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/c31413ed-9102-4eb6-acfd-f4016a843ab4)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/b9e1ac86-a480-48f7-9663-57c6dc724723)\n\nInterested in digitizing your diagnostic tests and don’t have an app? Reach out to us at [info@scanbase.com](mailto:info@scanbase.com) to learn how we’re helping diagnostics companies digitize from the ground up.\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/4378e1d7-15cd-4dfa-88af-66f0c64babeb)\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/821f585b-f832-4df7-8b91-21ca677a0d14)\n\nDon’t see the test you’re looking for? Let know at [info@scanbase.com](mailto:info@scanbase.com).\n\n![uploaded image](/media/?type=post&id=68414&key=user_uploads/1162081/2809d76e-b3e0-405f-925c-82af759f4115)\n\n[Book a demo](https://www.scanbase.com/schedule-a-demo) and learn how Scanbase can enhance your diagnostic program.\\\n\\\n**We believe that everyone deserves access to the best healthcare technology has to offer.** Founded in 2022 and based in San Francisco, California, Scanbase is an API that allows medical companies to enable smartphone-based computer vision quickly so anyone anywhere can analyze their at-home test results with confidence.\\\n\\\nIf you are interested in learning more or joining our team, shoot us an email at [info@scanbase.com](mailto:info@scanbase.com) and we’ll get back to you right away.", "url": "https://www.ycombinator.com/launches/HnS-scanbase-api-converting-diagnostic-tests-to-results-next-steps-and-treatment", "votes": 109, "created_at": "2023-01-13T09:08:29.284Z" } ] }, { "id": 27839, "name": "Cosine", "slug": "cosine", "former_names": [ "Buildt" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e00745e7a3c3ee63862572e35ef2d37f8197af5.png", "website": "http://www.cosine.sh", "all_locations": "London, England, United Kingdom", "long_description": "Cosine is a fully agentic SWE that allows you to instantly work on every single ticket at once, asynchronously. ", "one_liner": "Fully Agentic SWE", "team_size": 12, "industry": "B2B", "subindustry": "B2B", "launched_at": 1670878872, "tags": [ "Developer Tools", "Generative AI", "Machine Learning", "NLP", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B" ], "regions": [ "United Kingdom", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/cosine", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/cosine.json", "year_founded": 2022, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/cosine-ai/", "x_url": "https://twitter.com/cosine_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/buildt", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4e00745e7a3c3ee63862572e35ef2d37f8197af5.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/60/1680797178891-4qhyk9nyuys-27c597e2affd95cfd8f38b3c40eeda54/3fd49c4420776ec98fa2f57aad85e673.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022711Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=42a9581ed5379811c6db9c55e35e93d5dc27a31888836c1b171467980d699f97", "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 963616, "full_name": "Alistair Pullen", "title": "Founder", "founder_bio": "Full-stack Software Engineer with 15 years experience with deep applied knowledge of LLMs and Embeddings. Currently building an AI dev tool. Experienced an exit with Fancy (YC S20, acq. GoPuff).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alistair-pullen-616129226/", "x_url": "https://twitter.com/AlistairPullen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4e8de35fef1d788299b579cfccdaf09b39a8948e.jpg" }, { "user_id": 797223, "full_name": "Yang Li", "title": "Founder", "founder_bio": "1 IPO, 2 Acquisitions, 3 Unicorns. Ex VP of Europe for Digital Currency Group's consumer arm, Luno. Launched Ziglu (acquired by Robinhood) and scaled to 100,000 customers in the first year. Previously Commercial Director for Mobike (acquired by Meituan) growing it to 220M MAUs until the $55B IPO. Yang is one of the 2021 Forbes 30 under 30 cohort.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yangli92/", "x_url": "https://twitter.com/yangli_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ddbf8b30a5bb46a144a3b3d7c6683037a5d087ad.jpg" } ], "jobs": [ { "id": 85658, "title": "Software Engineer, Cloud Infrastructure", "url": "https://www.ycombinator.com/companies/cosine/jobs/NbfI6HD-software-engineer-cloud-infrastructure", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Devops", "salary_range": "£50 - £90 GBP", "equity_range": "", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Amazon Web Services (AWS)", "Go", "Google Cloud", "Kubernetes", "Python", "Linux", "Docker" ] }, { "id": 85769, "title": "Senior Platform Engineer", "url": "https://www.ycombinator.com/companies/cosine/jobs/dUj3uLp-senior-platform-engineer", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "£75K - £100K GBP", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Kubernetes" ] }, { "id": 85770, "title": "Senior Infrastructure Engineer", "url": "https://www.ycombinator.com/companies/cosine/jobs/yBOX3vI-senior-infrastructure-engineer", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Devops", "salary_range": "£75K - £100K GBP", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Kubernetes" ] }, { "id": 85772, "title": "Product Engineer", "url": "https://www.ycombinator.com/companies/cosine/jobs/9KEolFw-product-engineer", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$70K - $90K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 88029, "title": "Senior Developer Evangelist", "url": "https://www.ycombinator.com/companies/cosine/jobs/12jJ07r-senior-developer-evangelist", "location": "London, England, GB / Remote", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "£60K - £90K GBP", "equity_range": "0.10% - 0.20%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [ { "title": "Move over, Devin: Cosine's Genie takes the AI coding crown | VentureBeat", "url": "https://venturebeat.com/programming-development/move-over-devin-cosines-genie-takes-the-ai-coding-crown/", "date": "Aug 12, 2024" } ], "launches": [ { "id": 99817, "title": "Cosine 3.0 - one coding agent across every surface your team uses", "tagline": " (use your existing Claude, OpenAI, Copilot subscription)", "body": "\n![uploaded image](/media/?type=post&id=99817&key=user_uploads/797223/28afdae8-76ad-49c1-a761-3e89100fea74)\n\nTLDR: Cosine is built for the point where coding agents usually start to break down: longer tasks, multiple threads of work, team handoff, and code you still need to maintain later.\n\nPS. you can use your Claude Code, OpenAI and Copilot subscriptions (Don’t tell OpenCode or OpenClaw :P )\n\n```\nFor OpenAI and Copilot subscriptions:\nLaunch Cosine CLI \nCtrl+p and choose ChatGPT or Copilot subscription model \nYou'll get redirected to ChatGPT or Copilot website for auth\n\n_____\n\nFor Claude: \nMake sure you have a Claude Pro or Max subscription and have it installed on your local environment\n\nInstall the Cosine CLI — cosine.sh/cli (mac, windows and Linux supported)\n\nCtrl+p and choose the Claude Max Subscription (it works with both Claude pro and max)\n\nYour Claude Code subscription is recognised automatically — Cosine uses the actual Claude Code binary, so no API key needed\n\nRun cos in any repo — your Claude subscription quota is used directly, no extra Cosine billing for model usage)\n```\n\n\n\n**More info:**\n\nIf you’re already deep into AI coding tools, you probably know the feeling:\n\nYou start with quick wins in terminal, then pretty soon you want the same speed on bigger tasks, across more than one thread of work, without losing track of what the agent is actually doing or ending up with code your team has to clean up later.\n\nThat’s basically why we built Cosine 3.0\n\nCosine is one coding agent across CLI, VS Code, desktop, and cloud. So you can start with a quick local task, pick it up somewhere else later, or spin up parallel agents when the job gets bigger, without hopping between separate tools or losing context.\n\nWhat we kept caring about as we built it was pretty simple:\n\nWe wanted something that still feels fast and direct, but gives you more visibility and more control once the work stops being a toy problem.\n\nSo you can inspect changes properly.\n\nSo you can follow what happened.\n\nSo you can undo things cleanly.\n\nSo you can run multiple threads of work when one agent is not enough.\n\nSo the whole thing still makes sense when other people on your team have to touch the code.\n\nA lot of tools feel great right up until you want them to do more than one thing, in more than one place, on code that actually matters.\n\nWe wanted to build the version that keeps working after that point.", "url": "https://www.ycombinator.com/launches/Pxx-cosine-3-0-one-coding-agent-across-every-surface-your-team-uses", "votes": 5, "created_at": "2026-04-16T15:03:34.648Z" } ] }, { "id": 27867, "name": "DataSuite", "slug": "datasuite", "former_names": [ "Extensional AI", "Extensional", "Extensional AI", "Anarchy AI", "Anarchy", "Anarchy Labs", "TruthSuite" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/98dc541fc5fbbef1e6c9baee61264602e4e64531.png", "website": "https://truthsuite.com", "all_locations": "New York City, NY, USA; Remote", "long_description": "DataSuite provides a way to manage and collect multi-terabyte multi-media datasets, and train generative models (diffusion, gan, interactive video) models based on them. \r\n\r\nWe make it trivial for non-technical builders to create their own networks by providing an instant way to collect with AI without worrying about data formats, unzipping power, caching, or data locality.", "one_liner": "Dataset Management for AI Trainers", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Legal", "launched_at": 1669080392, "tags": [ "Developer Tools", "Generative AI", "Machine Learning", "DevOps", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B", "Legal" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Developers need an easy way to collaborate on the step-by-step process of gathering data and training AI models. Out of the box, LLMs can be **>100b** parameters and require **trillion** token datasets. This is more than most organizations are equipped to handle: a few years ago, few had heard of LLMs, and now organizations are scrambling to get ahold of GPUs to train and run them. The famous models are furthermore _proprietary_. Privacy, quality and efficiency needs are leading organizations to _train their own models._ \\\\While training these models, developers are constantly adjusting their datasets. As their projects grow, it becomes harder to keep track of the changes, and figure out what data and models to use and track these things in production." } ], "url": "https://www.ycombinator.com/companies/datasuite", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/datasuite.json", "year_founded": 2022, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/datasuite", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/anarchy-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/98dc541fc5fbbef1e6c9baee61264602e4e64531.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 129728, "full_name": "Matthew Mirman", "title": "Founder", "founder_bio": "Before founding DataSuite, Matt received his PhD in AI from ETH Zurich in 2022 where he scaled verified machine learning from working on 10 neuron toy nets in hours to 100m neuron nets in milliseconds and invented differentiable abstract interpretation and provable training. Before this, he worked at apple on JS compiler optimizations that saved the world a billion dollars in time wasted waiting for pages to load, and before that got an MS from CMU on meta-programming CoC logic languages.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/matthewmirman/", "x_url": "https://twitter.com/mmirman", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0f8c7b0dfce84c3fc75c64878dd889bf64439c0d.jpg" }, { "user_id": 1151451, "full_name": "James Spoor", "title": "Founder", "founder_bio": "A Generalist, with a passion for learning! After living in multiple countries hustling, building and creating, I ended up in Switzerland diving into the deep tech world where I worked at DeepCode, ml on static analysis which was then acquired by Snyk a cyber security unicorn. A fun fact about me is I was once a professional climber. My superpower is caring deeply and understanding through questions... there will be many questions!\n", "is_active": false, "linkedin_url": "https://linkedin.com/in/james-spoor", "x_url": "https://twitter.com/Spoor2709", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5578b222bbf26bfc932bfdd143434fe50587bbf5.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 69294, "title": "Anarchy 🧪 - LLM infrastructure for building conversational applications 🏗️", "tagline": "Anarchy provides companies with their own LLM endpoints that can call any APIs", "body": "Greetings YC community! We’re Matt and James, the founders of Anarchy. We’ve been working in AI for a long time (Matt just finished a PhD working on formal methods in deep learning) and have become experts in taming LLMs. We started Anarchy to share these advances with devs who want to use LLMs in production.\n\n### **Problem**\n\nWhile LLMs are beginning to be used as universal text interfaces, developers and prompt engineers are beginning to want consistent and reliable LLMs with agency: LLMs which can search through their ever-changing databases, be able to book meetings, and generally make calls to their APIs. However, coaxing LLMs to behave consistently is black magic requiring constant vigilance, and relying on a single provider can be a risky point of failure.\n\n### **Solution**\n\nOur platform abstracts away the unpredictable work of researching ideal prompts so developers can focus on building business logic: Provide your API and its description, and your LLM-keys, and anarchy will provide you an LLM endpoint (or web-interface chatbot) that can answer questions and solve problems using your API and load-balance between LLMs.\n\n[Here's a short product demo](https://www.youtube.com/watch?v=Qcv_wrCYRds)\n\n![uploaded image](/media/?type=post&id=69294&key=user_uploads/129728/2ecd3d46-6642-49b8-bdf7-8f655f59078e)\n\n### **Ask**\n\nIf this excites you (or anybody you might know, YC or not) and you’d like to try our product, please contact us by [signing up.](https://forms.gle/5rqU282gUKmKPmjm9)\n\nIf you want to have a chat about LLM tech or ask us questions directly, feel free to join our discord: \n\nWe’re also in the process of building up our [twitter](https://twitter.com/anarchy_ai_inc) and [linkedin](https://www.linkedin.com/company/extensional) and would appreciate a follow 🙂!", "url": "https://www.ycombinator.com/launches/I1e-anarchy-llm-infrastructure-for-building-conversational-applications", "votes": 28, "created_at": "2023-02-15T01:33:56.036Z" } ] }, { "id": 27906, "name": "1stCollab", "slug": "1stcollab", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/38bfe8c1abadf254cda7e353f68089f57109eaec.png", "website": "https://1stcollab.com/", "all_locations": "San Francisco, CA, USA", "long_description": "1stCollab is the first influencer platform that fully automates and optimizes every aspect of your influencer marketing program. We’ve helped hundreds of startups launch their first campaigns and enabled larger brands to scale to thousands of influencers, while saving them over $1M annually in influencer spend.\r\n\r\nWhat we offer:\r\n- Smart search & recommendations – Instantly connect with thousands of relevant influencers.\r\n- Full campaign automation – From outreach and negotiations to contracts, payments, and taxes, we handle it all.\r\n- Real-time performance tracking – Monitor conversions, revenue, and run A/B tests with detailed reporting.", "one_liner": "Performance-Optimized Influencer Marketing at Scale", "team_size": 11, "industry": "B2B", "subindustry": "B2B -> Marketing", "launched_at": 1671641116, "tags": [ "Machine Learning", "Marketing", "Advertising", "Creator Economy", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "B2B", "Marketing" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "After working on creator-related products for three years at Pinterest—and talking with dozens of creators about the challenges they faced—Varun and Leon began brainstorming different ways creators could better monetize the enormous value they delivered to their audiences and to brands. After testing a number of concepts, they became excited by the idea of building better discovery experiences to help brands find creators for their campaigns. A mutual friend connected them with Carina, who was independently building something similar. Andrew had also previously worked with Varun and Leon on discovery at Pinterest, and joined the team as well. Soon after, 1stCollab was born!" }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "We’re focused on solving a two-sided marketplace problem, for brands and creators. For brands, their biggest pain point with influencer marketing is discovering and sourcing creators. Platforms that exist today not only cost >$30k each year, but have very limited discovery capabilities. For instance, [Grin.co](http://Grin.co), one of the top two influencer marketing platforms today, does not surface any Instagram influencers as part of their sourcing tools. We’re going to be the first influencer marketing platform that prioritizes discovery and create the best discovery experience for brands by growing a network of authenticated creators and surfacing those creators through industry leading discovery algorithms and experiences. For creators, 99% of creators don’t get brand deals today, because creators don’t have a way to actively apply to relevant campaigns. By creating an open platform that has a broad set of brands, we can help creators apply to campaigns by showcasing those brands and their campaigns to relevant creators. Creators know their audience and content the best, and through encouraging creator participation, we can facilitate better matchmaking between brands and creators than any platform out there." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "From working at social media companies, we know that users engage at much higher rates with sponsored creator content than organic ads. But the market for sponsored influencer content is being held back, because it’s hard to run influencer marketing campaigns and the tools out there today don’t make it much easier. 1stCollab will simplify and automate influencer marketing the same way that Facebook and Google transformed the digital ads market. If we’re successful, we’ll enable a new category of ad spend—helping more brands to leverage influencer marketing and shift more of their budgets towards it. That’s money that goes directly to creators, enabling orders of magnitude more people to make a living as full-time creators. We have the opportunity to make content creation a genuinely feasible career for many, many talented folks out there—and we’re excited to make it happen." } ], "url": "https://www.ycombinator.com/companies/1stcollab", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/1stcollab.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/1stcollab", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/38bfe8c1abadf254cda7e353f68089f57109eaec.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": 1168808, "full_name": "Leon Lin", "title": "Founder/CEO", "founder_bio": "Currently working on 1stCollab, the best platform for brands and creators to collaborate on influencer marketing campaigns. I was previously at Pinterest for over nine years, where I watched the company grow from 100 to 3k employees. Most recently, I was Head of Discovery product there where I led our Home, Search, and Pins teams and was responsible for user engagement. Before that, I was a tech lead on the Recommendations team.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/leonqlin/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e613d2a09dfafba1eedd23a45f5dc7c892ea74a9.jpg" }, { "user_id": 1168810, "full_name": "Varun Bansal", "title": "Founder", "founder_bio": "Co-founder at 1stCollab, a platform for brands and creators to connect and run marketing campaigns together. Before 1stCollab, I worked at Pinterest, where I was a product manager for the homefeed recommendations team, the international growth team, and—most recently—the creator engagement and community teams.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bansalvarunb/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3251e49676ec33cca771662e36818914ead68d75.jpg" }, { "user_id": 1176814, "full_name": "Andrew Liu", "title": "Founder", "founder_bio": "Currently working on building a network for brands and creators to connect and launch marketing campaigns. Previously, I was a Tech Lead on search and recommendations at Coupang and Pinterest.\n\n\nIf you're interested in search, recommendations, or creators, please reach out :)!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zeyuliu/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1270d66ac8965363670c2cc98caf2d245db2f2f7.jpg" } ], "jobs": [ { "id": 71585, "title": "Customer Success Manager (CSM)", "url": "https://www.ycombinator.com/companies/1stcollab/jobs/atezDBY-customer-success-manager-csm", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "$70K - $110K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 94796, "title": "1stCollab - Fully-automated, ROI-optimized influencer marketing", "tagline": "Build a community of thousands of creators for your brand–instantly", "body": "**tl;dr**: 1stCollab is the fastest way to test, optimize, and scale your influencer program. Instantly source thousands of creators, and let our platform handle everything — outreach, negotiations, contracting, payments, and taxes — all while maximizing ROI.\n\n**Product Demo:** https://www.youtube.com/watch?v=0KI3LN-xqdM\n\n**Ask**: Introduce [leon@1stcollab.com](mailto:leon@1stcollab.com) to companies interested in influencer marketing!\n\n## The Problem\n\nWe’ve spoken with hundreds of startups running influencer programs, and nearly all hit the same roadblocks:\n\n* **Low response rates.** They reach out to about 10-20 influencers and don’t get any responses. \n* **Time-consuming processes.** Even if some creators reply, sourcing, negotiating, contracting, and paying them quickly turns into a full-time job (or an agency retainer).\n* **Scaling is hard.** Early success is exciting, but optimizing and expanding campaigns takes costly tools, headcount, and process.\n\n## What 1stCollab does\n\n1stCollab helps you test out and scale your influencer marketing program to hundreds of creators with an hour of setup work. We do this by automating the exact same influencer process that the world’s best marketing teams run. \n\nHere’s how it works:\n\n**Step 1: Instantly source thousands of influencers**\n\nFree to try out! We’ve indexed all of TikTok, Instagram, and YouTube so you’ll quickly be able to find relevant creators using our search and recommendation tools.\n\n![uploaded image](/media/?type=post&id=94796&key=user_uploads/1168808/7c914441-b10e-4367-99c6-253a39f48bf6)\n\n**Step 2: We take care of outreach, negotiations, contracting and payments.** \n\nWe handle everything to bring the right creators on board at the right price. You’ll see the full negotiation history and campaign pipeline. Our pricing engine predicts ROI using real-time engagement signals and proprietary data from hundreds of thousands of deals.\n\n![uploaded image](/media/?type=post&id=94796&key=user_uploads/1168808/05e49138-75eb-4fdc-b941-e24044207a7d)\n\n**Step 3: (Optional) Review content**\n\nCreators submit concepts and draft videos for your approval. All revisions stay organized in one place. Once you give the green light, we take care of payouts and collect performance data.\n\n![uploaded image](/media/?type=post&id=94796&key=user_uploads/1168808/fc46b925-16b3-4ef8-82df-1380a85dec64)\n\n\\\n**Step 4: Analyze your metrics**\n\nAs creators are going live, we’ll report out on real time engagement and link click metrics. We’ll cut things by ad groups as well so you can experiment with different briefs and creator populations to understand what works well. \n\n![uploaded image](/media/?type=post&id=94796&key=user_uploads/1168808/90bbd692-5064-454d-a9c4-a20748bd411e)\n\n## Some of our campaigns\n\nWe’ve helped hundreds of startups start their influencer campaigns and scale up to **some of the largest campaigns on social media**. Some interesting case studies to check out include [Framer](https://brands.1stcollab.com/customers/framer), [Locket](https://brands.1stcollab.com/customers/locket), [Gamma](https://brands.1stcollab.com/customers/gamma) and [SnapCalorie](https://brands.1stcollab.com/customers/snapcalorie)!\n\n## How to get started\n\nStart [sourcing creators for free](https://brands.1stcollab.com/) or schedule [some time with Leon](https://calendar.app.google/Zd88eFtzJF3TKs3k6) so we can discuss campaign strategy and we can share with you best practices\n\nAlready running influencer campaigns? A/B test us against your current program — we’re confident we can improve your performance and efficiency!\n\n![uploaded image](/media/?type=post&id=94796&key=user_uploads/1168808/96f60d67-2c55-48e9-9d78-35dcdddb8e28)\n\n", "url": "https://www.ycombinator.com/launches/Oey-1stcollab-fully-automated-roi-optimized-influencer-marketing", "votes": 140, "created_at": "2025-10-21T19:13:55.295Z" }, { "id": 70145, "title": "1stCollab — Run an influencer marketing campaign in less than 30 minutes 🎉", "tagline": "1stCollab is an AI-powered influencer marketing platform that automates the process of running an influencer marketing campaign.", "body": "### **TL;DR**\n\nInfluencer marketing is one of the most efficient ways to reach and acquire new customers, but it’s tedious to run campaigns. By leveraging machine learning and our prior campaign data, 1stCollab lets you run a campaign in under 30 minutes. Visit [1stcollab.com](http://1stcollab.com) to get started with your next influencer marketing campaign!\n\n### **Why is influencer marketing hard?**\n\nFrom our time working at large social media platforms, we know that influencer marketing consistently outperforms organic ads. Unfortunately, launching an influencer campaign is a manual and lengthy process. _Launching an influencer campaign takes weeks_, while launching Google ads takes just minutes. \n\nThe first step of every influencer campaign is to find the right creators to work with—which often takes over 50 hours per campaign. Existing solutions cost tens of thousands of dollars and operate off inaccurate data. As a result, the majority of influencer marketers spend hours manually searching on YouTube, TikTok, or Instagram. From there, they have to manually calculate stats, vet a creator’s audience, evaluate past partnerships, and negotiate rates and contract terms, review creatives, report out campaign metrics, and finally submit payments. This whole process often takes 2+ weeks for a single campaign.\n\n### **How does it work?**\n\n1stCollab lets you launch an influencer marketing campaign in minutes—not weeks. All you have to do is find a single influencer who's relevant to your brand and target customers. Then:\n\n1. Use our [influencer look-alike discovery tool](https://www.1stcollab.com/) to identify, vet, and save additional influencers you'd like to work with. We do what the best influencer marketers do: identify a single relevant creator, dig through that creator’s community, identify additional relevant creators, and repeat the process—but automatically.\n2. Send us your list of saved influencers and tell us about your campaign goals and budget.\n3. We'll take care of everything from there. All you have to do is approve the content that the influencers will post.\n\nWe support campaigns on all platforms. Our self-serve discovery tool currently supports YouTube, and we’re launching for Instagram and TikTok soon.\n\n![uploaded image](/media/?type=post&id=70145&key=user_uploads/1168808/a5397e58-9fda-4820-8ec8-945d2930f80d)\n\n### **Our Team**\n\n![uploaded image](/media/?type=post&id=70145&key=user_uploads/1168808/214846e7-6c27-47c6-9213-4ecac24313be)\n\nOur team has built some of the largest and most-used discovery and ML systems in the world, and has led industry-leading influencer marketing programs.\n\n* [Leon](https://www.linkedin.com/in/leonqlin/) spent nine years at Pinterest, where he was most recently Head of Discovery Product. \n* [Varun](https://www.linkedin.com/in/bansalvarunb/) was at Pinterest for five years, where he led product for both the Creator Engagement and Recommendations teams. \n* [Carina](https://www.linkedin.com/in/carina-cater/) led influencer marketing at Whatnot, Robinhood, and Curology.\n* [Andrew](https://www.linkedin.com/in/zeyu-liu/) was the tech lead for the Recommendations and Search team at Coupang and the Recommendations team at Pinterest.\n* [Kevin](https://www.linkedin.com/in/kevinlau93/) was a Founding Engineer and tech lead for the Machine Learning team at Abnormal Security and earlier, an engineer on the Recommendations team at Pinterest.\n\n### **Our Ask**\n\n* **Want to give influencer marketing a try?** We'd love to help! We're the easiest and fastest way to do it. Our discovery tool is free to try and we’re happy to help you run the rest of your campaign.\n* **Know someone doing influencer marketing?** We'd love to be connected. Even if our product isn't the right fit, we’d love to know how we can make their process faster and improve their performance.\n* **No plans to run an influencer marketing campaign?** Feel free to use our discovery tool and explore creators you might want to work with in the future! We'd love to get feedback on the discovery experience.\n\nIf you have any questions, shoot us an email at [hello@1stcollab.com](mailto:hello@1stcollab.com)!", "url": "https://www.ycombinator.com/launches/IFN-1stcollab-run-an-influencer-marketing-campaign-in-less-than-30-minutes", "votes": 28, "created_at": "2023-03-10T18:04:24.429Z" } ] }, { "id": 27925, "name": "222", "slug": "222", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e693471ebf0087cdb9edcdba2657ef0066b1a88.png", "website": "https://222.place", "all_locations": "New York City, NY, USA", "long_description": "an IRL marketplace facilitating social experiences at local venues/events through AI recommendations.", "one_liner": "the AI social facilitator for offline human to human interactions", "team_size": 28, "industry": "Consumer", "subindustry": "Consumer", "launched_at": 1668648558, "tags": [ "Machine Learning", "Marketplace", "Consumer", "Social", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Consumer" ], "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/222", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/222.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/222place/", "x_url": "https://twitter.com/222place", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e693471ebf0087cdb9edcdba2657ef0066b1a88.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": 358820, "full_name": "Keyan Kazemian", "title": "Founder", "founder_bio": "accelerating chance social encounters @ www.222.place", "is_active": true, "linkedin_url": "https://linkedin.com/in/kaaazemian", "x_url": "https://twitter.com/keyankazemian", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d8f95a34db333932504c444ea1dc29a1216c4543.jpg" }, { "user_id": 821983, "full_name": "Danial Hashemi", "title": "Founder", "founder_bio": "Iranian immigrant building a new solution to social", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/danialhashemi/", "x_url": "https://twitter.com/danialhashemi4", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/447db14f2a4eb34a08dc0b78a2198aec842543af.jpg" }, { "user_id": 485819, "full_name": "Arman Roshannai", "title": "Founder", "founder_bio": "engineering serendipity @ 222\n-\nexcited about tech that improves the human experience", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/arman-roshannai/", "x_url": "https://twitter.com/armanroshannai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c6b68ebd5b85fb9003bf1fa5fa2de4d0db26b10a.jpg" } ], "jobs": [ { "id": 108551, "title": "Backend Engineer", "url": "https://www.ycombinator.com/companies/222/jobs/ixxLfJT-backend-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$165K - $275K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] } ], "news": [ { "title": "In an Age of AI, This Startup Is Betting on IRL", "url": "https://www.wsj.com/articles/in-an-age-of-ai-this-startup-is-betting-on-irl-69608cd4?st=yG7Yiw", "date": "Dec 23, 2025" }, { "title": "Social platform, 222 looks to connect strangers through AI - Fast Company", "url": "https://www.fastcompany.com/91356813/222-aims-to-end-loneliness-by-engineering-chance", "date": "Jun 27, 2025" }, { "title": "222 Startup Wants to Bring Social Back to Real Life, Launches App", "url": "https://www.businessinsider.com/startup-shifting-social-to-real-life-funding-raise-app-launch-2024-2", "date": "Feb 05, 2024" }, { "title": "It's hard to make friends in L.A. Can 222's AI help? - Los Angeles Times", "url": "https://www.latimes.com/lifestyle/story/2023-10-18/can-ai-curate-a-better-night-out-in-los-angeles", "date": "Oct 18, 2023" }, { "title": "222 wants to match perfect strangers for bespoke, real-life experiences • TechCrunch", "url": "https://techcrunch.com/2022/11/14/2440702/", "date": "Nov 14, 2022" } ], "launches": [] }, { "id": 27938, "name": "Boundary", "slug": "boundary", "former_names": [ "Gloo", "Gloo Chat", "Gloo" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7d66bb51ef2922c834b2fbb6de2b175e7a85b8f8.png", "website": "https://www.boundaryml.com", "all_locations": "Seattle, WA, USA", "long_description": "Boundary is building BAML -- a programming language to build AI agents.\r\n\r\nWe used to code in assembly, and moved on to C.\r\nFrom C, we moved on to higher level languages like Python.\r\nNow we are going from Python to natural language.\r\n\r\nWhat does the code look like when 50% of the business decisions is decided by an AI agent / prompts? How do you test these AI components? We made BAML to address these problems.\r\n\r\nBAML helps has built-in tools to test, observe, and work with structured LLM outputs / tool calling natively in the language 10x faster. With BAML you can also instantly parallelize LLM Calls, or react to certain events or changes in your pipelines, without having to write any boilerplate. It's like using 'React' for AI.\r\n\r\nBAML code can used from any other language -- providing a standardized way to organize and declare any LLM or AI code.", "one_liner": "The programming language for AI", "team_size": 10, "industry": "B2B", "subindustry": "B2B", "launched_at": 1678219068, "tags": [ "Developer Tools", "Generative AI", "Machine Learning", "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/boundary", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/boundary.json", "year_founded": 2023, "city": "Seattle", "country": "US", "linkedin_url": "https://www.linkedin.com/company/boundaryml", "x_url": "https://twitter.com/boundaryml", "fb_url": null, "cb_url": null, "github_url": "https://github.com/boundaryml/baml", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7d66bb51ef2922c834b2fbb6de2b175e7a85b8f8.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": 15777, "full_name": "Vaibhav Gupta", "title": "Founder", "founder_bio": "Vaibhav is a software engineer with over 7 years of experience building innovative products. At Microsoft, he worked on realtime 3D reconstruction for HoloLens, gaining expertise in computer vision and 3D graphics. At Google, he led performance optimization on ARCore and Face ID, significantly improving latency and quality. Now he's bringing that same experience to help bring better quality and speed to Generative AI technology. For talking anything Computer Vision/AI/Performance, reach out!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vaigup/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dd752abccdf1bb33cfe3214f5dd358d0950a8f48.jpg" }, { "user_id": 855521, "full_name": "Aaron Villalpando Gonzalez", "title": "Founder", "founder_bio": "Aaron has worked for the better part of a decade scaling out distributed systems at AWS and launching full-stack consumer facing products at Prime Video. He is now passionate about building scalable ML infrastructure and the best developer experience at Gloo. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/aaron-villalpando-99284576/", "x_url": "https://twitter.com/aaronnstuff", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dc2f2802ccc1a0e9e00e06a91218ae60fe6ebb67.jpg" } ], "jobs": [ { "id": 80382, "title": "Engineers that enjoy hard things (like compilers)", "url": "https://www.ycombinator.com/companies/boundary/jobs/f31pAPu-engineers-that-enjoy-hard-things-like-compilers", "location": "Seattle, WA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $180K", "equity_range": "0.15% - 0.75%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Rust" ] } ], "news": [], "launches": [ { "id": 70071, "title": "Gloo: Empower LLM’s with domain-specific knowledge", "tagline": "We help LLMs like ChatGPT connect to knowledgebases and add mechanisms to stop them from hallucinating..", "body": "**tl;dr** LLMs are awesome, but they have no knowledge about your data, and when you give it to them, they’re prone to make stuff up. We help developers build a searchable knowledgebase that LLMs can understand, and validate their responses are legit.\n\nHey everyone! We’re Aaron and Vaibhav from Gloo and we’re on a mission to help LLMs understand your data and get them to stop hallucinating all over the place.\n\n![uploaded image](/media/?type=post&id=70071&key=user_uploads/855521/fefa49cf-6a22-4913-ac9e-da9550da9e69)\n\n### **The problem**\n\nBuilding a knowledge graph isn’t as easy as calling OpenAI embeddings API, storing them in a VectorDB and calling it a day. Developers have to choose from a myriad of parameters like chunking algorithm, embedding type (even based on the content), vectorDB, whether to fine-tune or not, how to securely store the data — and the list goes on and on.\n\nOnce you feed everything into the LLM, you’re only halfway there, as getting rid of hallucinations is tricky, and confidently incorrect answers to customer questions will give you headaches in the long-term.\n\nOn top of that, you still have your actual customer problems to still fix: getting the customer data, building flows on top of the LLM, writing the right prompts, getting more customers.\n\n### **The solution**\n\nGloo is the managed solution to building your knowledge graph — built for LLM context-windows from the ground-up. **We stand up a search API for your data that supports both keyword and semantic search**, and we smartly index and compute embeddings for you based on the content type. Once that’s setup, you can call our **_Check-_GPT API to check LLM answers** against your knowledgebase to ensure trustworthiness.\n\n![uploaded image](/media/?type=post&id=70071&key=user_uploads/855521/4c3b7bd2-6924-427b-a1f6-6aa194ae500f)\n\nWe built our search with a security-first approach. Your data is always server-side encrypted, and never actually stored in 3rd party Vector DBs. We are also supporting new transformations search engines couldn’t do before, like generating document summaries (powered by LLMs!), so you don’t have to spend precious time computing this at query time.\n\nAll of your data is viewable in your own personal dashboard where you can track embedding jobs, play around with different search parameters, and search query performance and analytics.\n\n![uploaded image](/media/?type=post&id=70071&key=user_uploads/855521/3e191b7a-73b1-4b36-983c-8852002a646e)\n\n### **Asks**\n\nReach out to us at **founders@gloo.chat**\n\n* If your AI keeps making shit up ☠️\n* If you want your LLM to stop saying “Sorry I dont know 😵‍💫”\n* If you’re worried about data security and VectorDBs / LLMs 🔒", "url": "https://www.ycombinator.com/launches/IEB-gloo-empower-llm-s-with-domain-specific-knowledge", "votes": 54, "created_at": "2023-03-08T22:57:42.876Z" } ] }, { "id": 27966, "name": "CombineHealth", "slug": "combinehealth", "former_names": [ "Oodles.ai", "upTrain AI", "UpTrain", "UpTrain AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3300df884c68b249e62977103a165c1536ec5e0e.png", "website": "https://www.combinehealth.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Preventing revenue leakage in health systems using AI", "one_liner": "Automating Healthcare Revenue Cycle Management with AI Workforce", "team_size": 15, "industry": "Healthcare", "subindustry": "Healthcare -> Healthcare IT", "launched_at": 1675744580, "tags": [ "Machine Learning", "Healthcare", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Healthcare IT" ], "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/combinehealth", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/combinehealth.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/combine-health/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/uptrain-ai/uptrain", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3300df884c68b249e62977103a165c1536ec5e0e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 588096, "full_name": "Sourabh Agrawal", "title": "Founder", "founder_bio": "Sourabh has been building and deploying machine-learning models for the last 8 years. Previously he co-founded Insane.ai (an AI-powered fitness app) - a seed-funded startup where he built state-of-the-art computer vision models, scaled the product to 150k+ users, and grew the team to 20+ members. Before starting up, Sourabh worked as an AI consultant at Bosch (autonomous driving) & a quant associate at Goldman Sachs. He graduated from IIT Bombay, where he studied Electrical Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sourabh-agrawal-62932b175/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/24492d8f269bdb121604a7a0991458c40d838bfe.jpg" }, { "user_id": 500862, "full_name": "Shikha Mohanty", "title": "Founder", "founder_bio": "Shikha was a venture builder and SaaS investor in the past. She has worked with several deeptech companies in her previous work experience and has contributed to their growth journeys by providing support on their GTM startegy", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shikhamohanty/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8a59dd62220d49df3b92feaa3bfabd8a04ab4040.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 77918, "title": "UpTrain AI: Enterprise-grade tooling to ship LLM applications confidently", "tagline": "Full-stack LLMOps platform for all your production needs 🔧", "body": "**TLDR:** Hi, everyone. We are Shikha & Sourabh, and we help companies ship LLM applications confidently. We provide enterprise-grade tools to evaluate, experiment, monitor, and test LLM changes easily. We provide seamless collaboration, and [being OSS](https://github.com/uptrain-ai/uptrain), we provide tons of customization and self-hosting capabilities to satisfy your data governance needs.\n\n### **PROBLEM 🤔:**\n\n**“Building LLM prototypes is easy, but productionising them is damn hard.”** (You might have guessed it given we are building an LLMOps platform 😉). So, LLMs are unpredictable and dealing with them is complex and messy:\n\n1. You spend countless hours manually reading through your chatbot responses or analyzing the actions of your automation agents.\n2. Fixing the prompt to solve one failure case opens a can of worms and can make previous answers go bad.\n3. Improvements are always reactive - users complain, and then you go back and fix them, eroding their trust and your credibility. \n4. Collaborating with your team is messy - countless Google Docs and Slack channels are created to debug failure cases.\n\n### **SOLUTION 💡:**\n\nUpTrain provides the most advanced LLMOps platform to streamline the complete process. It goes beyond just giving scores or monitoring logs but provides a single platform to identify, debug, and collaborate to improve the accuracy of your LLM applications. \n\n* We give reliable scores over various metrics (both LLM-based and classical NLP ones) as well as allow you to define custom ones to evaluate your full LLM pipeline comprehensively.\n\n![uploaded image](/media/?type=post&id=77918&key=user_uploads/588096/c9c6e03f-be80-4799-a783-05074f0d72ea)\n\n* We run root cause analysis to identify if it is a retrieval issue, if your context requires reranking, if the LLM is unable to utilize the context, or if certain instructions are not being followed.\n\n![uploaded image](/media/?type=post&id=77918&key=user_uploads/588096/117c15f7-c03b-4821-b033-8ab4b0d6e3df)\n\n* State-of-the-art techniques for automated prompt optimization, automatic eval generation, finding common topics among failure cases, etc., to act as your LLMOps co-pilot.\n\n![uploaded image](/media/?type=post&id=77918&key=user_uploads/588096/e190b97e-49f9-45be-9dba-ec8233e3f2fe)\n\nBest of all, UpTrain is open-source under Apache 2.0 with more than 1700 GitHub stars and adoption among developers across multiple Fortune 500 companies. Being OSS, UpTrain has tons of customization options and can be deployed in your secure cloud environment, fulfilling your data governance requirements.\n\n### **OUR ASK 🙌:**\n\n1. If you are building LLM applications, we would love to get feedback on [our platform](https://uptrain.ai/).\n2. If you are doing evaluations with home-grown solutions, let us know what they are. We would be happy to add them to [our OSS](https://github.com/uptrain-ai/uptrain).\n3. Join [our Slack community](https://join.slack.com/t/uptraincommunity/shared_invite/zt-1yih3aojn-CEoR_gAh6PDSknhFmuaJeg) to stay updated on our progress.\n\nIf you want a demo or want to chat more, please [book a call here.](https://calendly.com/uptrain-sourabh/30min)", "url": "https://www.ycombinator.com/launches/KGk-uptrain-ai-enterprise-grade-tooling-to-ship-llm-applications-confidently", "votes": 40, "created_at": "2024-01-29T15:19:58.364Z" }, { "id": 69166, "title": "UpTrain - Open-source tool to observe your ML models ⚙️ 🔧", "tagline": "Open-source Datadog for machine learning models ", "body": "**TL;DR:** UpTrain is an open-source ML observability and refinement tool. It helps data scientists observe how their ML models are performing in production and continuously improve them over time by automatically retraining them on cases where the model accuracy was low.\n\n**Hi everyone!** Sourabh, Vipul, and Shikha here, and we’re the team behind UpTrain. \n\nSourabh (CEO), previously founder at fitness tech, where he spent most of his time improving their AI models rather than, well, working out 🏋️\n\nVipul (CTO), previously worked at Meta and Bytedance, where he was busy applying his doctorate knowledge (yes, the Ph.D. kind) to improve their ML models 🔭\n\nShikha (CMO), an ex-VC, where she witnessed the magnanimity of AI problems from the inside of her portfolio companies and this is where she got her eureka moment 🔔\n\n![uploaded image](/media/?type=post&id=69166&key=user_uploads/588096/a21aa066-6198-48c0-8c27-354f6e4ba73a)\n\n### **Problem (The Origin Story 📖)**\n\nDuring the last 7+ years of building and deploying ML models, we have been constantly frustrated due to a lack of good tools that could answer one simple question - “Are our models working as expected in production?“\n\nSourabh first experienced this issue while working on self-driving cars at a Fortune 500 company. Then again he faced similar issues at his startup. The only way the model issues were highlighted was through reading loads of customer complaints, and it was excruciating to see their (hard-earned) revenue churn due to model degradation **😩.** \n\nCoincidentally, Vipul was solving the same problem at the big techs, where he built internal observability tools and optimized model retraining processes. The three founders spoke to 100+ ML practitioners to validate their hypothesis and got a unanimous yes! In fact, during one of the user interviews, we learned that an innocuous bug in the model deployment code led to 3+ weeks of wasted debugging efforts and losses of millions of revenue dollars 🤯\n\nWe are building UpTrain to prevent you from experiencing the same agony!\n\n### **The Solution 💡**\n\nUpTrain is built specifically for Machine Learning. We natively support all the things an ML practitioner requires to observe and improve their models.\n\n✅ Complete visibility into your model’s online health via real-time dashboards \n\n✅ Automatically collects outliers and edge cases \n\n✅ Identifies data distribution shifts \n\n✅ Monitors quality of object embeddings\n\n✅ Model explainability \n\n✅ Continuously retrains and improves your model \n\nCherry on the cake, we are Open-source! Some of the biggest AI-first startups are adopting UpTrain to fulfill their ML observability needs.\n\n### **How to use UpTrain 🤔**\n\nInstall UpTrain from pip, just tell it what all you want to observe, and UpTrain handles all the complexities in the background. Something as simple as below:\n\n![uploaded image](/media/?type=post&id=69166&key=user_uploads/588096/3f9f9f08-df84-480d-af42-62a6af0950c2)\n\nWe DO NOT require you to change any part of your production pipelines and readily integrate with all major ML libraries and tools. In fact, we are running a limited beta program where we provide a no-strings-attached pilot and do all the heavy lifting to ensure seamless integration of UpTrain in your systems. Ping us to know more!\n\n### **Our Ask 🙌**\n\n* Interested? Try us [out](https://uptrain.ai/) or book a demo [here](https://calendly.com/uptrain-ai/30min?month=2023-02) 🔥\n* To follow our progress, do star our [project](https://github.com/uptrain-ai/uptrain) on Github (show us some love - it’s free ❤️) \n* Know someone who needs ML observability, connect us 🔗\n\nFeel free to reach out to any of us with questions, bugs, requests, etc. at [sourabh@uptrain.ai](mailto:sourabh@uptrain.ai)", "url": "https://www.ycombinator.com/launches/Hza-uptrain-open-source-tool-to-observe-your-ml-models", "votes": 169, "created_at": "2023-02-09T18:18:08.592Z" } ] }, { "id": 28037, "name": "Diffuse Bio", "slug": "diffuse-bio", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/961a0aa4b25ffd0049a0526af8ad2406763125f4.png", "website": "http://diffuse.bio", "all_locations": "San Francisco, CA, USA", "long_description": "Diffuse is building generative AI for protein design. Our mission is to build AI systems that engineer new and useful proteins with unprecedented control and accuracy. Our team has been behind breakthroughs in AI protein design for the past 7 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence. ", "one_liner": "Generative AI for protein design", "team_size": 10, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1673220633, "tags": [ "AI-powered Drug Discovery", "Deep Learning", "Generative AI", "Machine Learning", "Biotech" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/diffuse-bio", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/diffuse-bio.json", "year_founded": 2022, "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/961a0aa4b25ffd0049a0526af8ad2406763125f4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 459396, "full_name": "Namrata Anand", "title": "Founder", "founder_bio": "I’m a bioengineer / computer scientist who’s worked on problems at the intersection of machine learning and biology. For the past several years, I’ve been working on re-envisioning the computational protein design toolkit with AI. Some highlights are the first crystal structures of AI-designed proteins and diffusion models for protein structure generation. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/namrata-a-427807188/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c568c0115ac04f5eeda9ed9ac55bb03c82d123dc.jpg" } ], "jobs": [ { "id": 57697, "title": "ML Research Engineer", "url": "https://www.ycombinator.com/companies/diffuse-bio/jobs/xqlE8GS-ml-research-engineer", "location": "Menlo Park, CA, US / Remote (US; CA)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$90K - $130K", "equity_range": "0.10% - 0.60%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "Deep Learning", "Machine learning" ] }, { "id": 57699, "title": "Software Engineer", "url": "https://www.ycombinator.com/companies/diffuse-bio/jobs/vOf4HTO-software-engineer", "location": "Menlo Park, CA / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$90K - $130K", "equity_range": "0.05% - 0.60%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "C++", "Google Cloud", "Python", "SQL", "Google App Engine" ] } ], "news": [ { "title": "Scientists use new A.I. tech to fight diseases - YouTube", "url": "https://www.youtube.com/watch?v=bJ_Zw99TZ_w&t=168s", "date": "Feb 14, 2023" }, { "title": "A.I. Turns Its Artistry to Creating New Human Proteins - The New York Times", "url": "https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html", "date": "Jan 09, 2023" } ], "launches": [ { "id": 69997, "title": "🧪 ⚛️ Diffuse Bio: Generative AI for protein design", "tagline": "At Diffuse, we build new therapeutics, vaccines, and enzymes with AI!", "body": "Hi 👋🏾 I’m Namrata, founder of ⚛️⚛️⚛️ **Diffuse Bio!** ⚛️⚛️⚛️\n\nAt Diffuse, we computationally design therapeutics, vaccines, and enzymes better, faster, and cheaper — but most importantly, we generate **molecules that simply can’t be designed with existing methods today**.\n\n### The Problem:\n\nProteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. \n\nSo far, computational protein design methods have had some success — but have enormous limitations, the primary one being **low success in downstream validation experiments**. These methods are also painfully slow and scale poorly.\n\n### What we do:\n\nFor the past several years, I’ve been working on **re-envisioning the computational protein design toolkit with AI** in my PhD and beyond, with the goal of overcoming these challenges. Some highlights are the first crystal structures of [AI-designed proteins](https://www.nature.com/articles/s41467-022-28313-9) (i.e. experimental validation) and the **first diffusion models** for [protein structure and sequence generation](https://nanand2.github.io/proteins). Below you can see one of our early models producing structures unconditionally from noise!\n\n![uploaded image](/media/?type=post&id=69997&key=user_uploads/459396/08cd90de-6450-4ebe-8873-a77d625e0ed1)\n\nNow, we’re **scaling up these methods** and **applying them to grand challenge problems in molecular design**. We’re able to handle a whole array of protein engineering tasks at the _push of a button_ — from loop redesign and sequence engineering, all the way to binder design and _de novo_ structure generation. \n\n### Curious to learn more?\n\nCheck out our coverage in the [**New York Times**](https://www.nytimes.com/2023/01/09/science/artificial-intelligence-proteins.html#:\\~:text=Namrata%20Anand%2C%20a%20former%20Stanford%20University%20researcher.%20She%20is%20now%20building%20a%20company%20in%20generative%20A.I.%20protein%20design) and [**NBC news**](http://bit.ly/3JjgVKL), and get in touch at [info@diffuse.bio](mailto:info@diffuse.bio)!\n\n### We’d love your help!\n\n* If you’re working on any protein or molecular engineering problem, we’d love to hear from you!\n* We’d love **intros to pharma and biotech companies** working on problems in protein therapeutics, antigen design, and enzyme design. \n* We’d also love intros to any companies (preferably early stage) doing **high-throughput protein characterization** **assays** (yeast display, phage display, etc).\n* Sign up [here](https://docs.google.com/forms/d/e/1FAIpQLSdamv0S3LB2cyVLJXT6QdCG2g71Lobwkw59qTYGullYgqg7bw/viewform?usp=sf_link) to **beta test** our first-gen protein design software!", "url": "https://www.ycombinator.com/launches/ICz-diffuse-bio-generative-ai-for-protein-design", "votes": 65, "created_at": "2023-03-07T05:47:09.229Z" } ] }, { "id": 28112, "name": "Persist AI", "slug": "persist-ai", "former_names": [ "Persist AI", "Persist AI Formulations Corp" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/eeb1e38a6ca6e6061c101deae73f22c28c142596.png", "website": "http://www.persist-ai.com", "all_locations": "Sacramento, CA, USA", "long_description": "It takes 5 years for pharma to develop long lasting drug injections for chronic diseases like cancer and diabetes. Persist uses AI-driven automation to reduce formulation development time down to 2 years, a ~50% reduction.", "one_liner": "Developing long-lasting drug formulations 50% faster", "team_size": 6, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1671420729, "tags": [ "Machine Learning", "Robotics", "Microfluidics", "Nanotechnology", "Therapeutics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": true, "demo_day_video_public": true, "app_answers": [ { "question": "Describe what your company does in 50 characters or less.\n", "answer": "Help pharma develop slow release drugs faster" }, { "question": "What is your company going to make? 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To do this we will screen formulations in high throughput using automation and train ML which can make better predictions based on data (rather than guesses).\r\n\r\nThis will enable pharma companies to get drugs to market faster and earn revenue sooner." } ], "question_answers": null, "url": "https://www.ycombinator.com/companies/persist-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/persist-ai.json", "year_founded": 2022, "city": "Sacramento", "country": "US", "linkedin_url": "https://www.linkedin.com/company/persist-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/eeb1e38a6ca6e6061c101deae73f22c28c142596.png", "app_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/bf-public_profile/28167/app_video.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIAQC4NIECAOMAGS7ZB%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022712Z&X-Amz-Expires=60&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLXdlc3QtMiJHMEUCIG5%2FEuPdHreXKRdGLYlyC2AjaYHPccvwucNqtFtDJEpNAiEAs4s5lSDOcogMQrwGhSKvVJXgimMvlIc1KK%2Fa7hojYD4q5QMIOhAAGgwwMDYyMDE4MTEwNzIiDMPa%2BQzgV8CSsX3XwyrCA9yWuwhv7VPDGJqPM%2BDoevlHz47k5lLRdgsGITLyqtSgmLV07z1Y86XDlb4dZMfcWmY5u5FLutDaUy2PMJ92q6vIq41n2jI5tC6SNSed8Qx%2Frdr9Wotko6WcUJWENt0Rs1K4pKZ8ZHCjH22BoWJkWLaTQ8fDKsmgWu0C8NMhnc8i44rKtbZLOvQwKZIlI546tkfyyliSiXPWdZ4XhGxEegateXp4TnM4yWfebpv22hrRHRotmFYX8ANU3B8yiEQL9yrU1y10N8c42JlHR%2FXHZNxaFQqX6eENp2aa2yewya2kPgdiQ3efnk5d0LiuPYxk7ZBa6uOug4fCBPHurDSGZqOlYX9D4h6PAMyUN2kZw2IcUR9QWKM2BmEsGorqokH5Oe34gs0DmnOGrGbhANOh9LKlKDu%2B6Pc4U%2F8L3%2Bmxug0q%2B3JBUGE7Wf6ySj%2B%2BbW8eOteynPrtIEUsD0pcfnSFNzQepsmnoTP5xc7qG%2FEn4MNWO3TviPmcIdg9qyW7bwOWKSyly4BuerWbZWVxIVolnUdqKu2PxDrgnb41QrRtObgKLlbhdehgPlqhIVqZ1cWH9s2uGHoiWlN5FRY8vYYodKzLGjDn%2FdrWBjqlAdXY8WsF5AC2X79ObZ%2FmZD1gjSCNFddCxk1e7RhQVznesCZJDMryJDWqJGSqI7lLEk62aPqOUbquB9Ghba%2BJXI%2FUV%2BHgkJZAPy7wzbnv8vLB8WccujudilaH29xgpqopxweKHuQzlcRMMeq0SZHCo3PizMnbhtLrqB%2BiK9IdfwMp8c15fBnQEe2RAGCsOWYNCrhsPiR6yT5V9698E5KnRPYRfOeFQw%3D%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=c66d7d91c1532ade147f60b6506c8084f0b87596e1d90190def0886579506001", "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/60/1680881229067-tn5frveent9-fbed84ada9379d7720a00b6d13e1f184/ba402ea2758b4d68f29d058ba99cd408.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022712Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=69e456b3a0e858aa28c02639034baef60514627411b140d9fa05f425a7d41c39", "company_photos": [], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 1094851, "full_name": "Karthik Raman", "title": "Founder", "founder_bio": "When mom got brain cancer, I used to take her for her \"infusions\". \n\nFor 4 hours, we would sit there while drug was being infused into her veins and she had horrible nausea and hair loss.\n\nI built Persist to eradicate this horrible experience for future patients. \n\nWe package drugs so that patients take 1 injection per month or year, without an infusion. This lowers the overall dose the patient receives (reducing side-effects), improves compliance, and quality of life. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/karthik-raman-scientist/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ecc2a524e445309696a536435506c40bc98010ac.jpg" }, { "user_id": 1114143, "full_name": "Christopher Shelner", "title": "Founder", "founder_bio": "Career program manager, first time founder. Focus has been in electronics, automation, and manufacturing. Super excited to be part of the YC batch! ", "is_active": true, "linkedin_url": "http://www.linkedin.com/in/christophershelner/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/25ae4aac51df1a910d1efc90c169f83830fc6913.jpg" } ], "jobs": [], "news": [ { "title": "Beyond the $12M: Revolutionizing Drug Formulation Development at Persist AI", "url": "https://www.persist-ai.com/post/beyond-the-12m-revolutionizing-drug-formulation-development-at-persist-ai", "date": "May 19, 2025" }, { "title": "Persist AI Raises $4M in Seed Financing to Revolutionize Drug Formulation ", "url": "https://www.persist-ai.com/post/persist-ai-raises-4m-in-seed-financing-to-revolutionize-drug-formulation", "date": "Aug 04, 2023" } ], "launches": [ { "id": 69239, "title": "Persist - We build long-lasting drugs 50% faster 💊", "tagline": "Drug formulations made by robotics and designed by AI", "body": "### **TLDR**\n\nPersist helps pharma make drug injections that last up to 1 year. \n\nUsing robotics & AI, we reduce a 5-year drug formulation process to \\~2 years.\n\n**Contact Us (**[**founders@persist-ai.com**](mailto:founders@persist-ai.com)**) for a case-study formulation**\n\nTo make injections we encapsulate drugs inside injectable microspheres made with FDA-approved materials.\n\n![uploaded image](/media/?type=post&id=69239&key=user_uploads/1094851/19542a20-d5f0-4ccd-9450-d6b8427a6b94)\n\n**_Left:_** _Image of a \\~37 um microsphere made by our robotics_\n\n**_Right:_** _A Schematic showing how encapsulated drugs are released over 1 year so patients don’t take as many injections._\n\n### 🤔**THE PROBLEM**\n\nFor chronic diseases, most patients prefer 1 injection/yr rather than multiple injections per week.\n\nHowever, making a long-lasting injection takes a lot of trial and error and the time between iterations is long.\n\nKey challenges include:\n\n* **Using manual work & intuition is slow**: Typical teams manually mix and match chemicals to optimize the amount of drug that’s loaded into the spheres and the rate of release of drug\n* **Trial and error is cumbersome:** Because changing one variable may have multiple effects \n* **Scale-up is unpredictable:** Even if something works at a small scale, batch-to-batch consistency during scale-up is uncertain because production is done in large reactors\n\n### 💡**OUR SOLUTION**\n\nWe utilize custom robotics and AI to solve the problems with building microspheres. \n\n* **We automatically mix & optimize formulations:** We use a closed-loop robotics + AI approach so there’s minimal manual work\n* **We reduce trial & error with AI predictions:** We generate 50x more data than most formulation teams and use that data to train AI. This unique data enables us to make better predictions than others.\n* **We build small batches, faster:** So we can QC batches more frequently and scale up predictably\n\n### ❤️**OUR ASKS**\n\nIntroduce us to friends who work in Pharma. Email [founders@persist-ai.com](mailto:founders@persist-ai.com) \n\n“Follow” us [on LinkedIn](https://www.linkedin.com/company/persist-ai/)", "url": "https://www.ycombinator.com/launches/I0l-persist-we-build-long-lasting-drugs-50-faster", "votes": 36, "created_at": "2023-02-13T16:43:58.890Z" } ] }, { "id": 28129, "name": "Luca", "slug": "luca", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c1a38009f747692e81bd13516e12a87a399aead1.png", "website": "https://www.askluca.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Pricing Strategy is one of the most powerful levers that retailers have at their disposal to create growth, yet it is underleveraged. Most retail pricing teams settle for making decisions in spreadsheets, shooting in the dark, and working backward from a cost-plus margin target, leaving a LOT of money on the table.\r\n\r\nOur founders experienced these problems at scale when they built pricing tech at Uber that made Uber a billion dollars in profit a year. They realized that retail was lacking the same quality and sophistication of price tooling. So, they built Luca.\r\n\r\nLuca is an AI-powered co-pilot for retail operators, which constantly identifies revenue and profit headroom, makes recommendations for price adjustments and saves countless work hours along the way. \r\n\r\nLuca is backed by Y Combinator, Menlo Ventures, and others.", "one_liner": "The Modern Pricing Engine for Retail", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1670277293, "tags": [ "Machine Learning", "SaaS", "Retail" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Inactive", "industries": [ "B2B", "Retail" ], "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/luca", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/luca.json", "year_founded": 2022, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/lucasoftware/", "x_url": "https://twitter.com/LucaPricingHq", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/luca-d9f8", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c1a38009f747692e81bd13516e12a87a399aead1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/6ea43cf216352c2915d1c75e8bec867f98a5fec1.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 814509, "full_name": "Tanvi Surti", "title": "Founder", "founder_bio": "CEO and Co-Founder at Luca. Before Luca, I spent a decade building product teams at Uber and Microsoft. At Uber, I led the pricing team that created ~$1B in margin improvements on our ridesharing business. ", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/tanvisurti/", "x_url": "https://twitter.com/tanvisurti", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0e16ea269e3764d19d1fb8c69573553088238d5c.jpg" }, { "user_id": 1201150, "full_name": "Yonah Mann", "title": "Founder", "founder_bio": "Co-founder and CPO of Luca. Previously led pricing teams at Uber Eats.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/yonah-mann/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d30f3e0b8db5035b419486244415d420df81ee14.jpg" } ], "jobs": [], "news": [ { "title": "Y Combinator-backed Luca aims to optimize retail prices at enterprise scale | TechCrunch", "url": "https://techcrunch.com/2023/04/27/luca-raises-cash-for-pricing-optimization/", "date": "Apr 27, 2023" } ], "launches": [ { "id": 69842, "title": "Luca - Meet the pricing co-pilot for e-commerce businesses.", "tagline": "We help retailers set prices and discounts that create 10% more profits and revenue, with 10% of the effort.", "body": "**tl;dr**\n\nLuca is a machine learning-powered co-pilot for retail, which constantly identifies headroom in your store, makes recommendations for price adjustments, and helps you manage decisions across all sales channels. \n\nWe are an ex-Uber Pricing team that is already helping retailers manage prices for over $50mill of revenue. [Get in touch](https://forms.reform.app/pqNBb1/early-access-1/kZx3w2) with us!\n\n**Our Story**\n\nWe are Yonah Mann and Tanvi Surti. We met while working at Uber, where we both worked on pricing technology. Our work cumulatively created over $1 billion in margin gains for Uber – and this experience made us realize that configurable revenue tools in the hands of operators could be incredibly powerful!\n\nSo, we are now on a mission to bring revenue optimization tools to retail, and build a fun company as we go along!\n\n![uploaded image](/media/?type=post&id=69842&key=user_uploads/814509/5422d569-a5ce-4f8a-bbc2-1cf3c2a98268)\n\n**The Problem**\n\nE-commerce teams have to react to large swathes of data from multiple channels to build a pricing and discount strategy, such as  –\n\n1. ⛅ Seasonal and Market trends, with changing customer price elasticities\n2. 👯 Competitor price changes or promotional campaigns\n3. 🚛 Inventory availability \n4. 🚢 Changing manufacturing and shipping costs\n\n_And_, they have to do this across 1000s of SKUs and across multiple stakeholder teams such as Merchandising, Marketing, Revenue, and Inventory. This level of complexity makes changing prices time-consuming and inefficient, while still leaving money on the table.\n\n**Our Solution**\n\n![uploaded image](/media/?type=post&id=69842&key=user_uploads/814509/004e0bac-15fd-49c9-a3af-d7d20e6bc40d)\n\nWe replace fragmented decision making in e-commerce teams with our pricing system that organizes, recommends and measures outcomes, ensuring that every product is priced in a way that optimizes revenue and profits. Here’s how –\n\n1. **👀 Observe:** We connect with the customer’s sales and inventory data sources (Amazon and Shopify to start, more to come soon), and create a real-time view of customer elasticities and conversion rates. \n2. **👉 Recommend**: We feed historical sales, competitor and market data into our machine learning model, combine it with inventory availability and make recommendations for price and discount adjustments.\n3. **🖐 Act:** We bubble up the recommendations over email and Slack, allowing the business to quickly act upon the data and push the changes to production across all their channels.\n4. **⚙ Configure**: Lastly, we allow the business to configure custom rules, such as competitor-based pricing guardrails or wholesale-price-based constraints.\n\n**Asks**\n\n* If you work in retail and would like to try our product, please sign up [here](https://forms.reform.app/pqNBb1/early-access-1/kZx3w2) and we will get in touch with you.\n* If you know someone in your network who would be interested in this, please drop us a note at [founders@askluca.com](mailto:founders@askluca.com)", "url": "https://www.ycombinator.com/launches/IAU-luca-meet-the-pricing-co-pilot-for-e-commerce-businesses", "votes": 52, "created_at": "2023-03-01T20:37:56.184Z" } ] }, { "id": 28214, "name": "Atmeto", "slug": "atmeto", "former_names": [ "Keeling Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9903b97190d67a01064a67c7522d3703402c8f0c.png", "website": "https://www.atmeto.com", "all_locations": "Los Angeles, CA, USA", "long_description": "", "one_liner": "Building a frontier world model for power", "team_size": 2, "industry": "Industrials", "subindustry": "Industrials -> Energy", "launched_at": 1674416288, "tags": [ "Machine Learning", "Climate", "Energy", "ClimateTech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2023", "status": "Active", "industries": [ "Industrials", "Energy" ], "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/atmeto", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2023/atmeto.json", "year_founded": 2022, "city": "Los Angeles", "country": "US", "linkedin_url": "https://www.linkedin.com/company/atmeto/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9903b97190d67a01064a67c7522d3703402c8f0c.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": 310129, "full_name": "Jack O'Grady", "title": "Founder", "founder_bio": "I build applied machine learning for energy systems. Currently building Atmeto, previously at Rivian building battery data science from the ground up, where I ultimately focused on reinforcement learning for battery optimization in the R1T/R1S.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jackogrady/", "x_url": "https://mobile.twitter.com/jack_ogrady", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/044029ca7972f026b79274f3a4bf14405f53265e.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 68703, "title": "Keeling Labs: Applied AI for batteries in the grid", "tagline": "We help grid-scale battery operators adapt to changes in the grid with energy management software that automatically adjusts its behavior to maximize revenue", "body": "### **Meet the Team:**\n\nI’m [Jack](https://www.linkedin.com/in/jackogrady/), the founder of [Keeling Labs](https://www.keelinglabs.com/)! I started the company because I believe that batteries are crucial to enabling more renewable energy from solar and wind, and I wanted to bring applied AI to the table to help scale the deployment, efficiency, and performance of these assets.\n\nPrior to this, I worked on battery algorithms, data, and optimization for 3+ years at Rivian, where I started the company’s Battery Data Science Team and pioneered the application of Reinforcement Learning to fast charging in the R1T/R1S.\n\n![uploaded image](/media/?type=post&id=68703&key=user_uploads/310129/7bf230b1-f162-4080-967d-104572029552)\n\n### **Let’s Talk About Grid-Scale Batteries…**\n\n**What’s Happening**\n\nThe U.S. is deploying batteries in the grid at an insane pace, but managing them is unlike any other power asset:\n\n* They don’t make their own power\n* They don’t have contracts for selling power at a fixed price\n\nBattery storage assets are constantly buying and selling power at dynamic prices, and doing that well or poorly leads to massive differences in project returns. With every new battery and renewable energy system added to the grid, though, the right thing to do shifts. Looking at planned battery additions across nearly every ISO, we can see that changes to grid dynamics will only be intensifying.\n\n![uploaded image](/media/?type=post&id=68703&key=user_uploads/310129/2997b0e7-f7be-4efa-8f8a-4ab56d9462fd)\n\n**How Our Product Handles This**\n\nRather than rely on human experience or hard-coded instructions, we make AI that learns the best way to buy and sell power specific to each battery asset – all on its own. This type of AI is called Reinforcement Learning, and it’s a paradigm shift in how we optimize hardware systems. Our product builds on the founding team’s years of experience with RL-based battery controls and optimization in the EV space.\n\nYou can read more about our technology here: \n\n![uploaded image](/media/?type=post&id=68703&key=user_uploads/310129/dd18161f-2f67-49d8-8f32-0f4cbc2b3cc8)\n\n### **One ask**\n\nWe’re looking for tech-forward industry partners to work with us as we develop an amazing product. If you have connections to the following groups, we’d love the introductions!\n\n* asset managers / battery owners\n* utilities\n\nFeel free to send me a DM on LinkedIn to get in touch: \n\n### **Quick Links:**\n\nWebsite: [https://www.keelinglabs.com](https://www.keelinglabs.com/)\n\nContact Us: \n\nJoin Our Team: ", "url": "https://www.ycombinator.com/launches/Hs7-keeling-labs-applied-ai-for-batteries-in-the-grid", "votes": 27, "created_at": "2023-01-25T16:33:06.343Z" } ] }, { "id": 28808, "name": "PropRise", "slug": "proprise", "former_names": [ "River AI", "River", "Introscopic", "River AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/099267e4ee22818f2d0af078193f368735107d1e.png", "website": "https://www.proprise.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "PropRise is the AI platform for institutional CRE investment teams. We automate the tedious, Excel-heavy workflows that slow down dealmaking: from sourcing to underwriting to due diligence. Our two live products serve 50+ customers including Public Storage: Beacon automates site selection for developers, and Primer extracts financial data from deal packages into structured, model-ready formats, cutting hours of analyst work to minutes. We're expanding across asset classes to become the operating system for CRE dealmaking.", "one_liner": "The AI platform for CRE investment teams", "team_size": 3, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction", "launched_at": 1690588132, "tags": [ "Machine Learning", "SaaS", "Real Estate", "B2B", "Analytics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Real Estate and Construction" ], "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/proprise", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/proprise.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/proprise/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/099267e4ee22818f2d0af078193f368735107d1e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/87716158f2af30f25f48c75d3dec63f35411c1ea.png" ], "primary_partner": { "name": "Jared Friedman", "url": "https://www.ycombinator.com/people/jared-friedman" }, "founders": [ { "user_id": 959155, "full_name": "Jean-Michael Diei", "title": "Founder", "founder_bio": "Big fan of applying machine learning to real-world problems. \n\nAt PropRise, we're building the AI platform that automates dealmaking workflows for institutional CRE investment teams.\n\nPrior to this, I led applied ML efforts across various industries at companies such as Stripe, Embark Trucks and Microsoft. \n\nStudied Machine Learning/Computer Vision at Carnegie Mellon University.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jeanmichaeldiei", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f8530be72b98145014f829b651bd9973da199a7d.jpg" }, { "user_id": 353992, "full_name": "Matthew Krager", "title": "Founder", "founder_bio": "Heyo! I'm Krager. \n- Currently working in PropTech.\n- Long-time iOS and Full-Stack Dev.\n- Worked on FB Recruiting and Stripe Terminal", "is_active": true, "linkedin_url": "https://linkedin.com/in/matthew-krager-60b360102", "x_url": "https://twitter.com/kragerDev", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2ca54e3bf2fd8ad7a804502870b6a54e31659771.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 28811, "name": "Casca", "slug": "casca", "former_names": [ "CORE Ai", "Cascading AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/db0a05cec73fa1efa5d096d33cfcc210c8930955.png", "website": "https://www.cascading.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Casca is building AGI for Banking.\r\n\r\nOur first product is an AI-native platform transforming small business lending by enabling banks and lenders to originate 10x more loans with 90% less manual effort. We’ve found product market fit already serving top 10 US banks and are scaling our team very quickly.\r\n\r\nSmall businesses are the heart of the American economy. Many banks shy away from providing funding because of the manual effort in pursuing those deals. With us, that changes. We unlock affordable, quick bank funding for the 30M+ small businesses in the US who would otherwise be subject to the high interest rates from predatory online lenders.\r\n\r\nWe are a world-class team of banking & AI experts from Stanford, MIT & Y Combinator. We like to win and we know that the only thing between us and the title is our own ability to improve every day.", "one_liner": "Make Banking Magical", "team_size": 40, "industry": "Fintech", "subindustry": "Fintech", "launched_at": 1691481257, "tags": [ "Conversational Banking", "Fintech", "Machine Learning", "Finance", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Fintech" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "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": "**Banking IT is broken**: 30 year old legacy systems, failing RPA projects and millions of people doing manual repetitive work. Over years in the industry, we saw how the financial industry was struggling to deal with mountains of unstructured data and failed to live up to customer expectations. Loan applicants have to go through humiliatingly slow application processes when they really needed someone to count on (no wonder look-to-book churn rates are at 65-80%) - the mania of manual workflows has to end\\\\Money runs the world. Banks are critical and we need fresh talent to shake up a $25T industry." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "**Unlocking $1T in value for global banks** and making banking magical for billions of customers" } ], "url": "https://www.ycombinator.com/companies/casca", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/casca.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cascading-ai/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/db0a05cec73fa1efa5d096d33cfcc210c8930955.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": 1361486, "full_name": "Lukas Haffer", "title": "Founder/CEO", "founder_bio": "Lukas is the CEO & Co-founder of Cascading AI. He previously served as Chief of Staff for the European Market at Avaloq (a globally leading provider of core banking software with $4 Trillion in Assets managed on the platform. Lukas holds an MBA from Stanford University, where he graduated as an Arjay-Miller Scholar (top 10%).", "is_active": true, "linkedin_url": "https://linkedin.com/in/lukashaffer", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2a855142800aa28353a0259b59092eec00fa218b.jpg" } ], "jobs": [ { "id": 79577, "title": "Don't See the Perfect Fit?", "url": "https://www.ycombinator.com/companies/casca/jobs/b5IKSoC-don-t-see-the-perfect-fit", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$100K - $200K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 97521, "title": "Technical Recruiter", "url": "https://www.ycombinator.com/companies/casca/jobs/7mHU8Y2-technical-recruiter", "location": "San Francisco", "type": "Full-time", "role": "Recruiting & HR", "role_type": "Recruiter", "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 97523, "title": "Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/casca/jobs/uDbuq7M-forward-deployed-engineer", "location": "San Francisco", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 97524, "title": "Head of Security & Compliance", "url": "https://www.ycombinator.com/companies/casca/jobs/of4eYL1-head-of-security-compliance", "location": "San Francisco", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 97525, "title": "Director of Customer Support", "url": "https://www.ycombinator.com/companies/casca/jobs/KAmSBwh-director-of-customer-support", "location": "San Francisco", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "", "equity_range": "", "experience": "5+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Series A announcement", "url": "https://www.cascading.ai/company/series-a-announcement", "date": "Aug 19, 2025" }, { "title": "FIs Explore GenAI in SMB Lending Despite Customer Concerns", "url": "https://www.pymnts.com/news/artificial-intelligence/2024/financial-institutions-explore-generative-ai-potential-small-business-lending-despite-customer-concerns/", "date": "Feb 12, 2024" }, { "title": "Cascading AI Secures $3.9 Million in Pre-Seed Funding to Revolutionize the Banking Industry with its AI-native Loan Origination System", "url": "https://www.prnewswire.com/news-releases/cascading-ai-secures-3-9-million-in-pre-seed-funding-to-revolutionize-the-banking-industry-with-its-ai-native-loan-origination-system-302058481.html", "date": "Feb 09, 2024" }, { "title": "Bankwell Bank pilots generative AI in small-business lending | American Banker", "url": "https://www.americanbanker.com/news/bankwell-bank-pilots-generative-ai-in-small-business-lending", "date": "Feb 08, 2024" } ], "launches": [ { "id": 73899, "title": "🤖 Cascading AI - Automate manual banking processes", "tagline": "Lending, account opening, customer service, back-office processing ... ", "body": "Watch our launch video [here](https://youtu.be/NFJvgmm4lIs).\n\n**tl;dr**: _Banks, credit unions, and fintechs spend billions to train and employ staff to handle complex systems and ever-growing service portfolios. Our AI agents make your teams 10x faster by navigating existing systems, surfacing the right information, and even communicating compliantly with your customers._\n\n### **🏆THE TEAM**\n\n![uploaded image](/media/?type=post&id=73899&key=user_uploads/1361486/1a6a8344-6014-46dd-81da-a861bd539b7e)\n\n**Lukas Haffer** **(CEO, Stanford GSB 23)** spent the past 4 years deploying core banking systems and outsourcing banking processes as Chief of Staff for Europe at Avaloq (one of the world’s leading core banking system providers).\n\n**Isaiah Williams (CTO, Stanford CS 22)** spent the past 4 years building conversational AI and production ML applications as a Senior ML engineer at EliseAI (one of the world’s leading conversational AI companies).\n\n### \n\n### **WHAT CAN AI DO FOR BANKING?**\n\n**Lending & Onboarding**: 300,000+ loan officers spend hours on the phone or e-mail getting applicants to complete their applications before they churn to competitors - our AI agent Leo does not give up until he has all the documents he needs.\n\n> Even if we pre-qualify more applicants, we don’t close more loans - because my loan officers can only have one conversation at a time.\n\n![uploaded image](/media/?type=post&id=73899&key=user_uploads/1361486/3b366876-5474-45ab-ba82-ffd1d6a6d59b)\n\n**Customer service**: 30,000+ daily service requests present an insane challenge to the staff of a mid-sized bank, trying to navigate multiple outdated systems and product categories - our AI agent Tom navigates their systems, surfaces the right information, and drafts responses to your clients.\n\n> Customer support has become an insane job - my teams need to navigate 15 different systems just to help 1 customer.\n\n![uploaded image](/media/?type=post&id=73899&key=user_uploads/1361486/4cc6b719-7ac1-4321-95d1-e88f98d160d0)\n\n**Back-office**: at one bank 50,000+ payments are halted every day for reasons as simple as a typo in the recipient account ID - our AI agent Anna analyzes unstructured financial information to recommend resolutions.\n\n> Even with 99% STP-rates (straight through processing) in payments, we have to manually handle thousands of exceptions.\n\n![uploaded image](/media/?type=post&id=73899&key=user_uploads/1361486/c4385ccb-a65f-4e51-9c54-0f7b8ca54a54)\n\n### **❌ THE PROBLEM**\n\nAntiquated systems and manual exception handling slow down crucial banking processes, costing banks billions. Traditional robotic process automation (RPA) could not handle unstructured information. Traditional chatbots quickly reach their limits of usefulness. And custom ML applications take months (if not years) to develop from scratch.\n\n### **✅ OUR SOLUTION**\n\n_Cascading AI agents integrate with the bank's existing IT infrastructure (in their private cloud or on-premise), learn to use their systems, and work with unstructured financial data to automate core processes._\n\n### **HOW IT WORKS**\n\n1. **Our Agents have eyes**: we use computer vision to read documents & integrate with existing systems on a UI level in addition to your APIs\n2. **Our Agents can talk**: we use conversational AI to communicate with customers or colleagues via SMS, E-Mail, Slack, Teams, secure messaging, or ticket management systems\n3. **Our Agents know banking**: we monitor agents in human-in-the-loop systems and align them with banking regulation and compliance policies\n\n### **OUR ASK**\n\n* Connect us with Banks, Credit Unions, or Fintechs looking to automate manual processes and improve customer communication.\n* Book a [demo](https://calendly.com/lhaffer/30-min-meeting?month=2023-08) or email us at [lukas@cascading.ai](mailto:lukas@cascading.ai)\n* Follow Cascading AI on [LinkedIn](https://www.linkedin.com/company/cascading-ai)", "url": "https://www.ycombinator.com/launches/JDv-cascading-ai-automate-manual-banking-processes", "votes": 21, "created_at": "2023-08-08T15:24:50.943Z" } ] }, { "id": 28837, "name": "Subsets", "slug": "subsets", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef940e88fb60e59b177570256531952d8e4b1322.png", "website": "https://www.subsets.com", "all_locations": "Copenhagen, Denmark", "long_description": "Subsets is the leading lifecycle growth platform for consumer subscription teams.\r\n\r\nWe are building the AI decisioning layer that unifies and powers the existing growth stack, enabling teams to automate and scale lifecycle experimentation, orchestrate personalized user journeys, and measure impact across retention, engagement, and LTV without engineering support.\r\n\r\nTrusted by consumer subscription businesses like The Atlantic, NBCUniversal, Daily Mail, Matas Group, and Hearst.\r\n\r\nSubsets has raised $6.1m and launched out of Y Combinator’s Summer 2023 batch in San Francisco, and is now based in Copenhagen.", "one_liner": "AI-driven Retention Automation for subscription media businesses.", "team_size": 7, "industry": "B2B", "subindustry": "B2B", "launched_at": 1689562154, "tags": [ "Machine Learning", "B2B", "Enterprise Software", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B" ], "regions": [ "Denmark", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/subsets", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/subsets.json", "year_founded": 2023, "city": "Copenhagen", "country": "DK", "linkedin_url": "https://linkedin.com/company/subsets", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef940e88fb60e59b177570256531952d8e4b1322.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": 783105, "full_name": "Martin Johnsen", "title": "Founder", "founder_bio": "Martin is the co-founder and CEO of Subsets (S23). Previously worked in management consulting helping large organizations scale AI initiatives across different business functions and industries. Holds an MSc in Mathematical modeling from The Technical University of Denmark. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/martin-johnsen-subsets", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0dc8255451fa83b84ca36787d84578cde1f78842.jpg" }, { "user_id": 868323, "full_name": "Oliver Brandt", "title": "Founder", "founder_bio": "Oliver is the co-founder and CTO of Subsets.com (S23).", "is_active": true, "linkedin_url": "https://dk.linkedin.com/in/oliver-brandt-93b1b8110", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e0abfb84be9b3b8a10eaf29f8fcd222a31499bf5.jpg" }, { "user_id": 1190102, "full_name": "Nikolai Skelbo", "title": "Founder", "founder_bio": "Co-Founder @ Subsets. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/nikolaiskelbo", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fd789fcfa94adf5207c56104494a1fd553091e75.jpg" } ], "jobs": [ { "id": 105383, "title": "Account Executive - Enterprise", "url": "https://www.ycombinator.com/companies/subsets/jobs/rCbPOAE-account-executive-enterprise", "location": "New York City", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$180K - $300K", "equity_range": "0.05% - 0.10%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Subsets helps subscription businesses reduce churn with ‘retention experiments’ and explainable AI | TechCrunch", "url": "https://techcrunch.com/2024/02/27/subsets-uses-explainable-ai-to-help-subscription-media-companies-reduce-customer-churn/", "date": "Feb 27, 2024" } ], "launches": [ { "id": 73123, "title": "Subsets – The AI retention co-pilot for subscription businesses", "tagline": "We help consumer subscription businesses improve retention and make subscribers stay longer.", "body": "⌛️ **TL;DR**: Subsets makes it simple to understand, engage, and retain subscribers by enabling personalized and proven retention initiatives based on AI. Our solution helps leading subscription businesses increase CLV by +20% and extend subscriber lifetime up to 6 months.\n\nHi everyone! We are Oliver, Martin, Matias, and Nikolai - the co-founders of [Subsets](http://www.subsets.com).\n\n![](https://lh5.googleusercontent.com/Yuh27xND-dD2BFgP7TFtBnzQLiE540bgly9YdC26PzZ0LSep6C6K3t4sm4TnxdQWM2mKwH0ZxlWjaTzWgaeHm3chv-cOZa9_0L6Rv2GxjEM5_MFG0KnIVK3uA26Qj_N9pioUE949cuSSLTyD7VyvacY)\n\n_from left to right._\n\n**_Oliver_** _leads machine learning and AI development; graduate in machine learning, previously worked as an ML engineer._\n\n**_Martin_** heads the company; graduate in machine Learning, worked in management consulting for two years helping companies scale AI applications. \n\n**_Matias_** carries engineering at Subsets; CS graduate, software engineer in both venture scale-up and big tech (Amazon and Microsoft). \n\n**_Nikolai_** _drives growth and operations; experience with growth (ABM & marketing automation) and revenue operations at a fast growing SaaS scale-up._\n\n**🫠 The Problem**\n\n* Healthy consumer subscription businesses lose 20-40% of their subscribers annually\n* Think about a subscription you have personally - subscribers are flooded with impersonal and irrelevant information which does not encourage active engagement\n* CRM teams do not have solutions available to figure out what the subscriber needs right now to make the most out of their subscription\n* Current AI retention solutions are built for engineers, not commercial people\n\n**💡 The Solution**\n\n_Subsets is The AI retention co-pilot for subscription businesses_\n\nWe enable CRM teams to understand, engage, and retain subscribers by enabling personalized and proven retention initiatives based on AI. Initiatives that work get automated and run continuously on dynamic subscriber audiences. By knowing what initiatives work across different subscription businesses, Subsets can recommend initiatives that give ROI fast.\n\n_Understand, engage, retain by learning from experimentation_\n\n![](https://lh4.googleusercontent.com/m_beqEsY3ABmu53JrkAj5YYaHcCNvJBwRDmlGqS6ofA6PLJecveNU8n5u0-_nUQ1fMHRmXyuzcsOdPtletoYUnsOgTyIEMvfVYmpUxzTEay1BEgMRvFmOYeRCpn9hXhU-Gu_jFqq74HuzZa6xaj6FLk)\n\n![](https://lh6.googleusercontent.com/ZzbJxXa23lKZAWO1Wioh6UHlU834Sf9__lovSz1ZxjZOaDSwEpwLFwVHGJoV0Xs1TeTRRb-Lf4vIxWEHNGy7CtD5zZWlBKhZvduP2kkYt98pobuvi078zfgZbgfokIfUaSK-mNY6kNo54Se7woMtjj4)\n\n![](https://lh5.googleusercontent.com/YnhYcAnQdgkZlMn_tzyB2QyHZkQBcv2sIHcxO3vLat6IPP602dPNlUmVHcujl7erjzHRk1syzYhPzMdu4IE1bF2kS_5m_6LXaRCzRAojnAItcCXvX8piINPg2KzOLaSVLtp4tEB8mYFI4qUkyhRuqBM)\n\nOur solution helps leading subscription businesses increase CLV by +20% and extend subscriber lifetime up to 6 months.\n\n**🙌 Our Ask:** Intros to consumer subscription businesses\n\n* **Users:** If you know anyone who works in consumer-focused subscription businesses, we would like to talk to them and share what we are building! You can introduce us at [martin.johnsen@subsets.ai](mailto:martin.johnsen@subsets.ai)\n* **Pilots:** If you are interested in using Subsets, feel free to sign up on [www.subsets.com](http://www.subsets.com) or reach out directly to the founders at [founders@subsets.com](mailto:founders@subsets.com)\n* Follow [Subsets on LinkedIn](https://www.linkedin.com/company/subsets/) if you find it interesting\n\n \n\nThanks a lot for reading!", "url": "https://www.ycombinator.com/launches/J1P-subsets-the-ai-retention-co-pilot-for-subscription-businesses", "votes": 34, "created_at": "2023-07-17T03:32:48.291Z" } ] }, { "id": 28838, "name": "GreenTally", "slug": "greentally", "former_names": [ "LogUnify", "DSensei", "Empower" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/673f3845b8ea0c788142227e93c082ac8e4ff91c.png", "website": "https://www.greentally.ai", "all_locations": "San Francisco, CA, USA", "long_description": "GreenTally is a carbon accounting platform built for all businesses that transforms environmental reporting from a complex, expensive ordeal into a streamlined, affordable process. Using LLM, we help companies measure their carbon footprint in hours instead of months.\r\n\r\nGreenTally automates emissions calculations and reporting, enabling businesses to meet regulatory requirements and track reduction targets without the overhead of consultants or dedicated sustainability teams. By making enterprise-grade carbon accounting accessible and practical, GreenTally empowers growing businesses to take control of their sustainability journey.", "one_liner": "Affordable carbon tracking solution for all businesses", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1690923306, "tags": [ "Generative AI", "Machine Learning", "SaaS", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Inactive", "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/greentally", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/greentally.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/greentally-ai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/673f3845b8ea0c788142227e93c082ac8e4ff91c.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": 52811, "full_name": "Yulong Liu", "title": "Founder", "founder_bio": "Co-founder at GreenTally. Previously machine learning engineering manager at Snap, and senior software engineer at Google Research", "is_active": false, "linkedin_url": "https://linkedin.com/in/yulong-liu-b4b96b16", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b325468b4fda71929fa7eda1a1d992d08c2c5779.jpg" }, { "user_id": 52812, "full_name": "Daiyi Yang", "title": "Founder", "founder_bio": "Co-founder at Empower, previous uber TL in Meta across multiple areas including Lead Ads, News Feed Experience and Metaverse Avatar. Director of engineering at Revinate before Meta leading the product development and infrastructure.", "is_active": false, "linkedin_url": "https://linkedin.com/in/daiyi-yang-ba2a2849", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d2c7c91f83a617c698715d33b03bc52887b1f7a0.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 28891, "name": "Numen", "slug": "numen", "former_names": [ "Cleancard" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/801ff758c035f5849acfdcd35329ec161bd30cef.png", "website": "https://www.numen.com", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Engineering a future without cancer deaths.", "team_size": 18, "industry": "Healthcare", "subindustry": "Healthcare -> Diagnostics", "launched_at": 1691571179, "tags": [ "Machine Learning", "Biotech", "Healthcare", "Diagnostics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Diagnostics" ], "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/numen", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/numen.json", "year_founded": 2021, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/76116332", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/801ff758c035f5849acfdcd35329ec161bd30cef.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 863380, "full_name": "Luca Springer", "title": "Founder", "founder_bio": "Luca is an expert in network science and computational game theory. He also has a strong policy background with a focus on healthcare systems. Luca previously worked at Stockdale Street, where he gathered years of experience analyzing, investing in, and helping grow healthcare, consumer, and technology companies. He holds an MSc in Computer Science and an MSc in Global Governance from the University of Oxford where he studied as a Rhodes Scholar. Luca is a Schmidt Futures ISF Fellow.", "is_active": true, "linkedin_url": "https://linkedin.com/in/luca-springer-b3801b8a", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d3953e4f6336e741bb8cfd4657d9d3b3622852aa.jpg" }, { "user_id": 863406, "full_name": "Thomas Carroll", "title": "Founder", "founder_bio": "Clinical cancer researcher with a specialty in bioinformatics, biomarkers, and methods development. Passionate about building better diagnostics and putting a dent in cancer mortality.", "is_active": true, "linkedin_url": "https://linkedin.com/in/thomas-carroll-62a60367", "x_url": "https://twitter.com/_tom_dot_com", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6730b7b4c46770d90b1cc88259531421c45248dc.jpg" } ], "jobs": [ { "id": 99608, "title": "Visual Designer", "url": "https://www.ycombinator.com/companies/numen/jobs/AaAkTx0-visual-designer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Design", "role_type": "UI / UX", "salary_range": "$90K - $140K", "equity_range": "0.05% - 0.10%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 73949, "title": "💧🧬Cleancard - Making cancer screening as easy as a pregnancy test", "tagline": "Combining SynBio and AI to make rapid and reliable at-home cancer testing possible", "body": "**Tl;dr:** We’re Cleancard, a pair of Rhodes Scholars on a mission to reinvent cancer diagnostics. We combine synthetic biology + AI to create powerful at-home cancer tests, with results from urine in just 30 minutes. **Check out our **[**website**](https://www.cleancard.bio/)** for more!**\n\nHey everyone - we’re Tom and Luca, the co-founders behind Cleancard!\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/9ea23e71-c6d5-4715-8236-7ed89e1d6a90)\n\n👨🏻 [Tom](https://www.linkedin.com/in/thomas-carroll-62a60367/): Clinical cancer researcher, expert in bioinformatics and methods development. No stranger to designing [better biomarker tests](https://www.cell.com/cancer-cell/fulltext/S1535-6108(23)00216-7). 👨🏻‍🦰 [Luca](https://www.linkedin.com/in/luca-springer-b3801b8a/): Computer scientist with a strong healthcare policy background. Years of experience growing healthtech and consumer businesses.\n\n### **Early cancer detection saves lives 🕑**\n\nMore than 18M Americans have cancer today. Unfortunately, this is a growth market (26M projected by 2040). 1 in 3 have a close family member with a history of cancer, and thus have a higher risk for cancer themselves (2x). We know these realities all too well from our personal lives, dealing with hereditary cancer syndromes and family histories of cancer.\n\nCancer will come for many of us. To give ourselves drastically better odds of surviving this disease, we need to diagnose it before it reaches an advanced stage.\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/10066d6b-22e6-4e98-a5cf-4fc34047d766)\n\n### **Problem: Cancer screening is failing us 🤬**\n\n* Cancer symptoms are often unnoticeable until the cancer is too advanced to cure\n* Current lab-based screening tests are **time-consuming** and **costly** - they fail to get people to screen before they are symptomatic (even for PSA, only [1 in 3 participate](https://jamanetwork.com/journals/jamaoncology/fullarticle/2797845))\n* At-home rapid tests would increase rates of asymptomatic screening but **just don’t perform well enough** to be trusted for cancer diagnostics\n* Current options are **not sufficient** to give people their best chance at surviving cancer\n\n### **Solution: Cleancard’s rapid testing platform 😍**\n\nWe’ve built a new rapid-test method enabling highly sensitive biomarker-guided diagnostics from the urine in just 30 minutes. Key advantages of our proprietary technology:\n\n* **10x quicker/cheaper** than lab-based tests\n* **10x more sensitive** than lateral flow\n* **No expensive machinery** or **complicated sample manipulation**.\n\nSee how we stack up:\n\n![uploaded image](/media/?type=post&id=73949&key=user_uploads/863380/ed2218af-7627-4f5b-9d17-7adec46c6632)\n\nCleancard technology combines synthetic biology, machine learning, and computer vision into a multiplexable, machine-readable format which allows us to detect multiple cancer biomarkers from a single sample.\n\nOur first rapid at-home screens will identify prostate, bladder, and ovarian cancers. Our method is highly agile - we’ve successfully targeted 10 new cancer biomarkers during this YC batch alone, with more to come soon. \n\nCleancard’s platform is the one-stop shop for reliable detection and tracking of multiple cancer biomarkers in the comfort of your own home.\n\n### **Our asks 🙏**\n\n* **Share this post!** Please help us spread the word with your networks.\n* **Join our team!** We are always looking for highly motivated scientists and engineers to join us in our mission to reinvent cancer diagnostics. If that’s you or someone you know, please get in touch ([contact@cleancard.bio](mailto:contact@cleancard.bio)).", "url": "https://www.ycombinator.com/launches/JEj-cleancard-making-cancer-screening-as-easy-as-a-pregnancy-test", "votes": 46, "created_at": "2023-08-09T15:57:45.669Z" } ] }, { "id": 28936, "name": "Empirical Health", "slug": "empirical-health", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/dd8134cbe5b8ea696157c320672eb55cc8649540.png", "website": "https://empirical.health", "all_locations": "New York City, NY, USA", "long_description": "Don't die of heart disease. While heart disease is the #1 killer worldwide, 80% of heart disease is preventable.\r\n\r\nEmpirical's program starts with an advanced lab test with 100+ biomarkers, including ApoB, Lp(a), inflammation, and more. Then, we help you model your lifetime risk of heart disease and generate a plan to reduce your risk to an optimal level. Then we help you achieve that plan.\r\n\r\nEmpirical is licensed, registered, and insured to deliver real medical care in 30+ US states covering 200m+ people. We're growing >25% monthly.", "one_liner": "Don't die of heart disease. Empirical is the first AI-native heart…", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Consumer Health and Wellness", "launched_at": 1689658487, "tags": [ "Generative AI", "Machine Learning", "Consumer Health Services", "Healthcare", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Consumer Health and Wellness" ], "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/empirical-health", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/empirical-health.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/empiricalhealth/", "x_url": "https://twitter.com/EmpiricalHlth", "fb_url": "https://www.facebook.com/EmpiricalHLTH", "cb_url": "https://www.crunchbase.com/organization/empirical-health", "github_url": "http://github.com/EmpiricalHealth", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/dd8134cbe5b8ea696157c320672eb55cc8649540.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 11792, "full_name": "Brandon Ballinger", "title": "Founder", "founder_bio": "AI in medicine.\n\nCo-founder at\n * Empirical Health (YC S23) - full stack preventive heart health scaled with AI\n * Cardiogram (acquired) - deep learning for heart disease\n * Sift - top 50 YC company, machine learning for fraud detection.\n\nAlso:\n * HealthCare.gov rescue team\n * Google tech lead on machine learning for speech recognition\n * Head of Risk Engineering at Brex", "is_active": true, "linkedin_url": "https://linkedin.com/in/brandonballinger", "x_url": "https://twitter.com/bballinger", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5a29f270bcb83b372b9bdb5cdc4e58a0ba6c376b.jpg" }, { "user_id": 1360101, "full_name": "Raquel Rodriguez Martinez", "title": "Founder", "founder_bio": "Physician | Health Technology | Co-founder at Empirical Health | Previously Assistant Clinical Professor at UCSF & Family Medicine at Kaiser", "is_active": true, "linkedin_url": "https://linkedin.com/in/raquel-rodriguez-martinez", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a95a90089a93018c4a6bc76e79402beaefdca047.jpg" } ], "jobs": [], "news": [ { "title": "This new health protocol combines 40 smartwatch biomarkers and blood tests to give you a health score | TechRadar", "url": "https://www.techradar.com/health-fitness/this-new-health-protocol-combines-40-smartwatch-biomarkers-and-blood-tests-to-give-you-a-health-score", "date": "Mar 11, 2025" }, { "title": "This app turns your smartwatch data into a personal health assistant, and it just launched on Android | TechRadar", "url": "https://www.techradar.com/health-fitness/this-app-turns-your-smartwatch-data-into-a-personal-health-assistant-and-it-just-launched-on-android", "date": "Feb 24, 2025" }, { "title": "Empirical Health is launching to 47 million midwesterners", "url": "https://data.empirical.health/p/midwest-primary-care", "date": "Dec 12, 2023" }, { "title": "Empirical Health app brings proactive primary healthcare to the Apple Watch", "url": "https://bgr.com/tech/empirical-health-app-brings-proactive-primary-healthcare-to-the-apple-watch/", "date": "Jul 18, 2023" } ], "launches": [ { "id": 90204, "title": "Empirical: don't die of a heart attack", "tagline": "Measure, predict, and prevent your risk of heart disease", "body": "Hi! We’re Brandon, Raquel, Harish, and Wyatt—we’re a team of doctors and engineers here to help you improve your heart health. We previously worked at Kaiser, Google, and Apple, and ran one of the first studies to use deep learning to detect abnormal heart rhythms using the Apple Watch's heart rate sensor.\n\n**Heart disease kills more people than all cancers combined**—but it’s also the area of health most in your control. **80% of heart attacks** can be avoided, and your risk is predictable using [statistical models up to 30 years in advance](https://www.empirical.health/blog/heart-attack-risk-calculator/).\n\nToday, we’re **launching Empirical heart health**. We help you measure key heart biomarkers (including ApoB), predict your personal heart disease risk, and build a tailored action plan—covering nutrition, medication, and exercise.\n\nHere’s how it works. Or just [get started now](https://www.empirical.health/product/comprehensive-health-panel).\n\n# **Measure: 85+ biomarkers for $190**\n\n![uploaded image](/media/?type=post&id=90204&key=user_uploads/11792/753c7d44-dc07-427d-a094-f96ae817412c)\n\nEmpirical heart health starts with a $190 test measuring 85+ biomarkers—available at 2,200+ locations nationwide.\n\nYou’ll get insights into key heart markers like:\n\n* **ApoB**: every [10 mg/dL drop cuts heart disease risk by 9%](https://www.empirical.health/blog/apob-blood-test/)\n* **Lp(a)**: up to [6x more atherogenic](https://www.empirical.health/blog/lipoprotein-a-blood-test/) than regular LDL\n* **Cholesterol & Triglycerides**: core inputs into [heart attack risk predictors.](https://www.empirical.health/blog/heart-attack-risk-calculator/)\n* 80+ others: inflammation (hs-CRP), cell counts, electrolytes, creatinine, glucose, ketones, and more.\n\n# **Predict: model your heart attack risk**\n\n![uploaded image](/media/?type=post&id=90204&key=user_uploads/11792/c6b20b30-392f-4d5f-9586-8440b6b782f6)\n\nNext, we show how your heart attack risk could change by age 70—\n\n* If you do nothing\n* If you follow a tailored plan with medication, diet, and exercise\n\nMany people cut their risk by 50% or more with the right changes.\n\n# **Prevent: personalized action plans**\n\n![uploaded image](/media/?type=post&id=90204&key=user_uploads/11792/0a3741e8-1518-41cc-afda-ca50302a3d83)\n\n_“Eat less red meat.” “Eat more greens.”_ You’ve heard it all before.\n\nThis is different. We build a biomarker-driven nutrition plan tailored to your bloodwork—targeting the **exact** nutrients that matter for your heart. These tailored plans have been shown in the medical literature to be about twice as effective as a simple \"eat more fruits and vegetables\" diet.\n\nAnd tracking? Just snap a photo of your meal. We’ll use AI to analyze nutrients like fiber, potassium, sodium, saturated fat, and more.\n\n# **Help prevent 1 million heart attacks**\n\nHelp us save one million people from heart disease by 2027. Empirical Heart health is available today across 30+ states, reaching over 200 million people.\n\n[Get started yourself](https://www.empirical.health/product/comprehensive-health-panel), or share with a loved one.", "url": "https://www.ycombinator.com/launches/NSu-empirical-don-t-die-of-a-heart-attack", "votes": 18, "created_at": "2025-05-08T14:04:54.403Z" }, { "id": 81886, "title": "Empirical: Your personal healthcare assistant", "tagline": "Transform your biomarkers into a personalized care plan, reviewed by a real human doctor", "body": "Hi! We’re Raquel and Brandon. Last year we launched Empirical Health in four states — today we’re launching a major update.\n\nEmpirical Health 2.0 transforms your biometric data and personal goals into a customized health plan. To ensure safety, your care plan must always be reviewed with a real human doctor, who can order necessary labs, referrals, or prescriptions. Medical care through Empirical is now available to 200 million people across 27 states.\n\n![uploaded image](/media/?type=post&id=81886&key=user_uploads/11792/0dd4da20-de1b-4eb7-968b-bc7d01daf62e)\n\n## Here’s how Empirical can help you live a healthier life:\n\n* Let’s say you have **low deep sleep**. Empirical will analyze your sleep stages and bedtime consistency to design a protocol to assess sleep hygiene, risk of sleep apnea, and recommend further testing, reviewed by a doctor.\n* **Want to increase VO2Max?** Empirical crafts an exercise protocol with Norwegian 4x4s, Zone 2 training, and strength training to improve VO2Max.\n* **Suffering from Long Covid, Dysautonomia, or POTS**? Empirical creates a plan to evaluate you for POTS. It starts with taking orthostatic vitals using a heart rate monitor and a blood pressure cuff (essentially, an [at-home version of the $1,900 tilt table test).](https://turquoise.health/services/evaluation-of-heart-function-using-tilt-/) Then, we use your ECG and blood test results to rule out other causes of tachycardia. If positive, a personalized care plan with medication, low-intensity exercise (based on the science behind the CHOP protocol), and hydration strategies are designed (and unlike the VO2Max program above, we avoid high-intensity training to prevent crashes).\n\n# **Why does this matter?**\n\nWe’re facing a **massive primary care shortage**. Simply providing recommended preventive health [would require 27 hours per day from the average primary care doctor](https://www.fiercehealthcare.com/providers/primary-care-docs-need-27-hours-day-provide-guideline-based-care-study-finds)—clearly an impossible task.\n\nWhile [LLMs have already passed medical licensing exams](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931230/), one missing piece has been grounding in objective, physical biomarkers. We think the combination of broadly available consumer devices like Apple Watch (which generates 240,000 data points per year and has multiple FDA clearances) and health information exchanges (which offer objective outcomes, such as hospitalizations or ER visits) makes it possible to ground medical AI in objective physical data.\n\nWant to try it? Book an appointment, or download the app (currently available on iOS and compatible with Apple Watch) at [https://empirical.health.](https://empirical.health)\n\n![](https://media-cldnry.s-nbcnews.com/image/upload/t_fit-1500w,f_auto,q_auto:best/newscms/2016_10/749406/baymax.jpg)\n\n", "url": "https://www.ycombinator.com/launches/LIk-empirical-your-personal-healthcare-assistant", "votes": 19, "created_at": "2024-07-17T04:19:44.857Z" }, { "id": 73170, "title": "🩺 Empirical Health: Proactive primary care", "tagline": "We use AI to scale great primary care for everyone.", "body": "**TL;DR:** [Empirical Health](https://empirical.health/) is proactive primary care. You can get medical care for heart health, POTS / long covid, sleep apnea, and other primary care needs directly through our app, starting today.\n\nDownload [Empirical Health on the App Store](https://apps.apple.com/app/apple-store/id6449271489). \n\n## **The Problem**\n\n* **Healthcare is expensive and wasteful**. We spend $1.1T per year reacting to the complications of chronic illness and a fraction of that on prevention. While the best boutique primary care models [reduce hospitalizations by 51%](https://brandonmb.substack.com/p/what-oak-street-health-got-right) and overall medical spend by [15-30%](https://www.healthcaredive.com/news/medicare-direct-contracting-aco-reach-oak-street-clover/637854/), they're labor intensive, expensive, and don’t scale.\n* **Terrible patient experience.** Did you know the average doctor has a net promoter score of -1.2, and that it takes 26 days on average to get an appointment?\n* **Not enough doctors**. It would take a primary care physician [27 hours _per day_](https://www.fiercehealthcare.com/providers/primary-care-docs-need-27-hours-day-provide-guideline-based-care-study-finds) just to deliver the currently-recommended set of preventive screenings, and things are about to get worse: [1 in 4](https://jamanetwork.com/journals/jama/fullarticle/2790791) doctors plan to leave primary care in the next 3 years.\n\n## **Our Solution**\n\nEmpirical Health is **proactive primary care that scales. Using AI, we triage healthcare resources to efficiently manage chronic illness** and succeed in value-based care payment models.\n\n![uploaded image](/media/?type=post&id=73170&key=user_uploads/11792/a630f1a3-c94a-4aad-bd67-21aed986f07b)\n\n* We've built a **fully licensed, registered, insured virtual medical practice**. Within the Empirical Health app, we can help you with annual preventive visits, sick virtual visits, chronic disease management, labs, prescriptions, referrals, and more. Medical care is initially available in California, Texas, Florida, and Hawaii and the app is available globally.\n* **No wait times**. Just start chatting with a doctor immediately.\n* Behind the scenes, our **AI-enabled EMR** software helps our in-house physicians turn raw patient data (e.g, symptoms, past medical records, sensor data from Apple Watch and similar devices) into clinically-useful insights.\n* **Affordable and transparent pricing:** $9/month subscription, with video visits covered by major health insurance.\n\nWe think this is the first step toward building the primary care of the future, making use of both new technologies (AI, consumer health sensors, virtual modalities) and new business models (value-based care).\n\n## **Who are we?**\n\nThe stars aligned to bring our team together — we're a doctor and an engineer who share a deep passion for improving healthcare by bridging a data-driven approach with emerging technologies. We first met at Cardiogram (deep learning for heart health), Brandon’s prior company, where Raquel was a visiting scholar. [Raquel Rodriguez Martinez](https://www.linkedin.com/in/raquel-rodriguez-martinez/) was previously a Clinical Assistant Professor at UC San Francisco and a physician at Kaiser. [Brandon Ballinger](https://www.linkedin.com/in/brandonballinger/) co-founded Cardiogram and also Sift (machine learning for fraud detection, top 50 YC company), and worked on Risk Engineering at Brex, the [HealthCare.gov](http://HealthCare.gov) rescue team, and machine learning at Google.\n\n## **You can help us make healthcare better!**\n\n* Do you use an Apple Watch or need a primary care doctor? Download [Empirical Health on the App Store](https://apps.apple.com/app/apple-store/id1623615785) or check us out on [Product Hunt](https://www.producthunt.com/posts/empirical-health).\n* Do you or someone you know work for a health insurer on value-based payment models? Email [founders@empirical.health](mailto:founders@empirical.health).", "url": "https://www.ycombinator.com/launches/J2A-empirical-health-proactive-primary-care", "votes": 57, "created_at": "2023-07-18T14:38:51.523Z" } ] }, { "id": 28940, "name": "Guac", "slug": "guac", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3ffff2e450f7c22939b75b50d62a49acbce05716.png", "website": "https://www.guac.com/", "all_locations": "New York City, NY, USA", "long_description": "Guac accurately forecasts grocery demand to help supermarkets order and produce the right amount of inventory — reducing food waste and increasing availability.", "one_liner": "AI-powered forecasting & replenishment for grocery", "team_size": 7, "industry": "B2B", "subindustry": "B2B -> Supply Chain and Logistics", "launched_at": 1688056061, "tags": [ "Grocery", "Machine Learning", "Climate", "Supply Chain" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "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/guac", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/guac.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/guac-ai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3ffff2e450f7c22939b75b50d62a49acbce05716.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": 1328192, "full_name": "Euro Wang", "title": "Founder", "founder_bio": "Co-Founder & CEO of Guac. Passionate about tackling food insecurity & waste with technology!", "is_active": true, "linkedin_url": "https://linkedin.com/in/eurowang", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b14c1e630ea2d7c23cae28383bd412bf53ab2aee.jpg" }, { "user_id": 1361004, "full_name": "Jack Solomon", "title": "Founder", "founder_bio": "Co-Founder and CTO of Guac. ML engineer working to make grocery retail more efficient.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jackasolomon", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/995482d8fecb6fd97f6aaffd15da4536953da04c.jpg" } ], "jobs": [ { "id": 95055, "title": "Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/guac/jobs/qTXMy4i-forward-deployed-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Google Cloud", "Python", "AI Agents" ] }, { "id": 95386, "title": "Fullstack Engineer", "url": "https://www.ycombinator.com/companies/guac/jobs/s9SJAAT-fullstack-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "React", "TypeScript", "Next.js" ] }, { "id": 95387, "title": "Backend Engineer", "url": "https://www.ycombinator.com/companies/guac/jobs/NECdEjo-backend-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $250K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Google Cloud", "Python" ] }, { "id": 100833, "title": "Strategy & Operations", "url": "https://www.ycombinator.com/companies/guac/jobs/IctDRvB-strategy-operations", "location": "New York, NY, US", "type": "Full-time", "role": "Product", "role_type": null, "salary_range": "$150K - $180K", "equity_range": "0.30% - 0.80%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "How the Produce Aisle Inspired a Grocery Tech Startup", "url": "https://progressivegrocer.com/exclusive-how-produce-aisle-inspired-grocery-tech-startup", "date": "Jan 02, 2024" }, { "title": "Y Combinator-backed Guac trains algorithms to predict grocery demand | TechCrunch", "url": "https://techcrunch.com/2023/12/19/y-combinator-backed-guac-trains-algorithms-to-predict-grocery-demand/", "date": "Dec 19, 2023" }, { "title": "Our favorite startups from YC's Summer 2023 Demo Day, Day 1 | TechCrunch", "url": "https://techcrunch.com/2023/09/06/ycombinator-demo-day-one-summer-2023/", "date": "Sep 06, 2023" } ], "launches": [ { "id": 72798, "title": "🥑 Guac: Ending grocery food waste with AI", "tagline": "Hyper-accurate demand forecasting for supermarkets", "body": "**TLDR:** We tell grocery stores exactly how many avocados they’ll sell next Tuesday.\\\n\\\n[Guac](http://tryguac.co) uses machine learning to accurately predict how much grocery retailers will sell each day. This helps them order the perfect amount of inventory: enough to meet customer demand without sending tons of leftover food to landfill.\n\n### **🗑️ PROBLEM**\n\n* **Predicting future demand is hard**: product-level demand drastically fluctuates each day based on hundreds of factors like weather, road closures, and sports games. Yet, even the largest supermarket chains still use basic regression models or Excel formulas, which means … \n* **Tons of food waste and empty shelves**: which costs grocery retailers in Europe/US $188B and costs our planet 15 million metric tons of greenhouse gas emissions each year.\n\n### **🥑 OUR SOLUTION**\n\n* **Industry-leading algorithm** that predicts order quantities for each item, each day, each store location. Our average forecast error (MAE) for our current customers is 0.95 units, which translates to a 38% reduction in food waste.\n\n![](https://lh6.googleusercontent.com/ib8L-X2brvq2yDx0i7QlNLakpS-1x79zlJSzMI5phoohO6II0KB9q3GhWEilhqDc6sXoBUm24rj4NXCX4O5sTa9susviyZFku6vb0YzC9_qIVa0Nbq-6YMHvuNaBx_P8_30I8wteFfbGtJu_3g5vdqo)\n\n* How?\n * **More than just ML**: Grocery demand is driven by real world events that ML models don’t know exist. So we inject 230+ external variables to actually capture people’s buying behavior …  \n * **_For example,_** _we found that sports betting odds help us predict beer sales. When a sports match is predicted to be a close game, it turns out more people watch the game, driving up beer sales!_\n * **Customized** for each store location: grocery stores located just 5 miles apart have different customer behavior.\n* **Inventory ordering optimization**: We then turn our hyper-accurate predictions into useful inventory order quantities, based on individual SKU-specific requirements: minimum order quantities, promotions, unknown loss estimates, shelf life, supplier lead times, and more.\n* **Modular integration:** We integrate our ordering recommendations right into the retailer’s existing inventory ordering softwares and workflow. Whether inventory ordering is done at the HQ-level vs. store-level, we fit into the retailer’s system, instead of forcing them to uproot their system to fit into ours.\n\n### **🙏 OUR ASK**\n\nWe’d love introductions to grocery chains (anywhere in the world), especially:\n\n1. Discount grocery chains, with tighter margins\n2. Grocery chains in more remote locations (e.g. Hawaii), with longer supplier lead times and inflexible supply chain\n3. Asian grocery chains in the US, who import a lot of their products from abroad\n\nAnd if you know anyone in the general food/grocery space (POS providers, suppliers/distributors, grocery food waste charities, etc), please let us know at [founders@tryguac.co](mailto:founders@tryguac.co).", "url": "https://www.ycombinator.com/launches/IwA-guac-ending-grocery-food-waste-with-ai", "votes": 85, "created_at": "2023-07-06T00:47:22.192Z" } ] }, { "id": 28958, "name": "Rimward", "slug": "rimward", "former_names": [ "Quack AI", "Sourcepulse" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd215af97ab581709a04ff6284c6091f29c2058e.png", "website": "https://rimward.ai/", "all_locations": "Paris, Île-de-France, France", "long_description": "Rimward helps solar asset managers detect intrusions using the cameras already installed across their sites. It flags camera failures and coverage gaps, and gathers video evidence from multiple cameras to help their team or monitoring provider filter out false alarms and decide when to act.", "one_liner": "Solar farm security software for existing cameras", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1691807496, "tags": [ "Machine Learning", "Computer Vision", "B2B", "Security", "Enterprise" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Security" ], "regions": [ "France", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rimward", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/rimward.json", "year_founded": 2023, "city": "Paris", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/rimward", "x_url": "https://twitter.com/rimward_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/rimward", "github_url": "https://github.com/rimward", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd215af97ab581709a04ff6284c6091f29c2058e.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": 1390247, "full_name": "F-G Fernandez", "title": "Founder", "founder_bio": "Founder and CEO of Rimward. FG co-founded Pyronear, an open-source nonprofit, helping turn a Raspberry Pi, his laptop GPU and entry-level camera into a wildfire detection network across Europe and South America. Running AI locally on affordable hardware, it serves French fire services and detected a wildfire 32 km away.\n\nA computer vision researcher by background, he led research at Mindee (YC W21), published conference papers without a PhD and was formerly a top PyTorch contributor.", "is_active": true, "linkedin_url": "https://linkedin.com/in/frgfm", "x_url": "https://twitter.com/FrG_FM", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eed0d8708a06aa60a305c5d378c1355472e2e065.jpg" } ], "jobs": [ { "id": 92922, "title": "Security / IoT Deployment Engineer", "url": "https://www.ycombinator.com/companies/rimward/jobs/j4L5SKW-security-iot-deployment-engineer", "location": "Paris, IDF, FR / Paris, Île-de-France, FR", "type": "Full-time", "role": "Engineering", "role_type": "Embedded systems", "salary_range": "€50K - €70K EUR", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Linux", "Docker", "Information Security", "Internet of Things (IoT)" ] }, { "id": 92923, "title": "Platform Engineer", "url": "https://www.ycombinator.com/companies/rimward/jobs/Ocflvow-platform-engineer", "location": "Paris, IDF, FR / Paris, Île-de-France, FR", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "€50K - €70K EUR", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [ "Node.js", "TypeScript", "Docker" ] } ], "news": [ { "title": "Here are this year’s top 40 startups at Station F | TechCrunch", "url": "https://techcrunch.com/2023/11/15/here-are-this-years-top-40-startups-at-station-f/", "date": "Nov 15, 2023" } ], "launches": [ { "id": 74260, "title": "🦆 Quack AI - AI companion for developer onboarding", "tagline": "Bridging open-source efficiency to drive enterprise excellence.", "body": "Hello there! I'm FG (short for François-Guillaume 🇫🇷), the founder of [Quack AI](https://www.ycombinator.com/companies/quack-ai).\n\n**tl;dr:** _We help large software companies run like the most efficient open-source projects. In the future, top-tier developers in those organizations will be able to contribute to pivotal projects, regardless of what teams they're from._\n\n### **💬** The Problem\n\nSuccessful OSS projects are well-organized communities where information is shared and valued. Recognizing this gold standard, corporate giants like Microsoft and IBM are shifting towards 'innersource' - a strategy that fosters ownership, augments communication, and bolsters productivity. However, like many cluttered OSS projects, the vast array of solicitations and varying expertise easily gets overwhelming.\n\n### 💻 Our Solution\n\nOur Dual-Fold Approach:\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/195e8fa1-3b7d-4327-9507-c05a168f1531)\n\n* **Clarity for maintainers** (Web interface): we empower maintainers with a framework to streamline contribution processes. Through our intuitive interface, maintainers can design, curate, and sequence the optimal steps for smooth project contributions.\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/94e7fe10-fc64-4df9-893a-8f20fd16e2ea)\n\n* **Guidance for developers** (VSCode extension): with our IDE extension, developers aren't just compliant; they're efficient. It aids in identifying the right issues, ensures alignment with contribution flows, and keeps a pulse on project priorities.\n\nWhile maintaining popular OSS projects and being an active contributor to PyTorch, I’ve often faced the frustration of seeing Pull Requests from newcomers and thinking “If only that person had the reflex to do XYZ.” Those unwritten norms, which often evade CI restrictions, only sink in after repeated code reviews and numerous trials and errors. This uneasy feeling that “it would be so easy if they were to do ABC” highlights the absence of a solid framework for collaborative development.\n\n### 🦆 The Team\n\n![uploaded image](/media/?type=post&id=74260&key=user_uploads/1390247/153b4727-f3fd-4ba1-9020-45fbc7e19d2b)\n\nI tend to describe myself as a _\"Deep learning engineer by day, open source contributor by night 🦇\"_ which is fairly accurate! On top of being among the top contributors of PyTorch, I’ve led the development of the open-source OCR library of devtool company Mindee (YC W21). I experienced first-hand how some open-source communities are structured to achieve high engagement & productivity, which now appeals to software behemoths.\n\n### 🙏 How you can help\n\n* **🤝 Refer**: know someone in a major corporation looming over innersource or part of a substantial OSS project? Please connect us.\n* **⭐ Engage**: star us on [GitHub](https://github.com/quack-ai/companion) & hop into our [Discord](https://discord.gg/E9rY3bVCWd) for the latest updates!\n* **👍👎 Experiment & feedback**: mold your unique coding insights and take our [VSCode extension](https://marketplace.visualstudio.com/items?itemName=quackai.quack-companion) for a whirl.\n* **📣 Share**: share this post with your network, you can find us on [Twitter](https://twitter.com/quack_ai) or [LinkedIn](https://www.linkedin.com/company/quackai)\n\nUp to revolutionize developer collaboration? Let’s get quacking then!", "url": "https://www.ycombinator.com/launches/JJk-quack-ai-ai-companion-for-developer-onboarding", "votes": 157, "created_at": "2023-08-21T14:34:11.158Z" } ] }, { "id": 28968, "name": "Trainy", "slug": "trainy", "former_names": [ "AB Labs" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/78fb6a9f034f5447cc3472b66f6c660fdf0cfa60.png", "website": "https://trainy.ai/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Goodbye Slurm, Hello Konduktor.\r\n\r\nTrainy Konduktor is a software platform for AI teams to schedule workloads with priority, control resource allocation, and improve GPU reliability. With Konduktor, teams submit jobs to a healthy pool of GPUs, assign job priority with a simple user interface, and never worry about hardware faults again.", "one_liner": "Infrastructure for managing GPU clusters for training/serving.", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1687891811, "tags": [ "Developer Tools", "Machine Learning", "SaaS", "Infrastructure" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "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": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/trainy", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/trainy.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/trainy-ai/about/", "x_url": "https://twitter.com/TrainyAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Trainy-ai/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/78fb6a9f034f5447cc3472b66f6c660fdf0cfa60.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": 509361, "full_name": "Roanak Baviskar", "title": "Founder", "founder_bio": "Studied CS & Mathematics at UC Santa Cruz. Led Audio team at Hive AI, where we trained and deployed 500M parameter-scale models to production.", "is_active": true, "linkedin_url": "https://linkedin.com/in/roanakb", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5b4b5bdb3a6c92540c471e0ac581790dcb5bba8a.jpg" }, { "user_id": 1404133, "full_name": "Andrew Aikawa", "title": "Founder", "founder_bio": "Co-founder and CTO at Trainy building a training platform to make deep learning go faster. Previously a lead Machine Learning Engineer for Hive AI's object detection products. I completed my Physics Ph.D. UC Berkeley '22 where my thesis focused on applying computer vision and deep learning on nanoscience. Physics & Computer Science B.A. UC Berkeley '17.", "is_active": true, "linkedin_url": "https://linkedin.com/in/asai", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/870dd300be109c4ae6d065c20bc6114a5a646b21.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 97424, "title": "🪐 Pluto – OSS experiment tracker for Neptune users", "tagline": "Neptune-compatible experiment tracking, built for teams running AI workloads", "body": "[**→ Try the live playground**](https://demo.pluto.trainy.ai/o/dev-org/projects/my-ml-project) – explore Pluto with real sample experiments, no signup required.\n\nNeptuneAI is shutting down on March 5th after being acquired by OpenAI. We built Pluto so you can migrate safely without rushing.\n\n---\n\n### **👋 Who we are**\n\nHey YC, we're Roanak and Andrew! We founded Trainy to help AI teams efficiently manage and utilize GPU infrastructure. When we heard Neptune was shutting down, our customers started panicking about losing years of experiment history and having to rewrite their logging code on a tight deadline.\n\nSo we built Pluto.\n\n![uploaded image](/media/?type=post&id=97424&key=user_uploads/509361/6cbcdb3e-6f5f-4f7f-8855-58001d921c66)\n\n### **🔥 The problem**\n\nNeptune's March 5th shutdown is forcing research teams into one of the worst kinds of migrations: rushed, high-stakes, and hard to verify.\n\nMost alternatives require you to:\n\n* Rewrite your logging code\n* Lose your historical runs (or spend days exporting and reformatting)\n* Hope the new tool handles your multi-GPU workloads the same way\n\nAnd you have to do all of this before March 5th or your data gets deleted.\n\n### **🛠 How Pluto solves it**\n\n**1. Dual-log to both platforms simultaneously**\n\n[Add one import](https://docs.trainy.ai/pluto/neptune-migration), keep your existing Neptune code running, and Pluto logs side-by-side. Validate that everything matches on your real training workloads before you cut over.\n\n**2. Export your Neptune history**\n\nPluto includes [a Neptune exporter](https://docs.neptune.ai/transition_hub/migration/to_pluto) that brings your old runs into Pluto. Years of experiment data, preserved.\n\n**3. Built for the workloads Neptune users actually run**\n\nWe're optimizing for what Neptune users loved: UI responsiveness at scale, reliable logging throughput under multi-node/multi-GPU workloads, and the ability to track thousands of per-layer metrics without lag.\n\n**Deployment options:**\n\n* **Self-hosted** – for sensitive data or air-gapped environments, instructions in [server readme](https://github.com/Trainy-ai/pluto-server)\n* **Hosted** – $250/seat/month (free for existing Trainy customers)\n\n---\n\n### **🙏 Our ask**\n\n**If you're a Neptune user, please try Pluto with dual-logging before the March shutdown.**\n\nWe're specifically looking for feedback on:\n\n* Anything that would block your cutover\n* Features you rely on that we're missing\n* UI/performance differences at scale\n\n**Early Bird offer:** Start dual-logging in the next 2 weeks → **3 months hosted free**.\n\n👉 Once you’ve made an account on [pluto.trainy.ai](http://pluto.trainy.ai), email [roanak@trainy.ai](mailto:roanak@trainy.ai) or [grab a time to chat](https://calendly.com/roanak/trainy-demo) and we’ll add seats/storage.\n\n---\n\n### **🔗 Links**\n\n* [**Playground**](https://demo.pluto.trainy.ai/o/dev-org/projects/my-ml-project) – try it now\n* [**Quickstart**](https://docs.trainy.ai/pluto/quickstart) – get running in 5 minutes\n* [**GitHub**](https://github.com/Trainy-ai/pluto) – Apache-2.0 (client), AGPL-3.0 ([server](https://github.com/Trainy-ai/pluto-server)) ⭐\n\nWe're listed on[ **Neptune's official transition hub**](https://docs.neptune.ai/transition_hub/migration/to_pluto).\n\nThank you!", "url": "https://www.ycombinator.com/launches/PLM-pluto-oss-experiment-tracker-for-neptune-users", "votes": 10, "created_at": "2026-02-04T22:26:06.748Z" }, { "id": 72587, "title": "🚅 Trainy - Identify bottlenecks, boost training", "tagline": "Dashboards to help ML engineers training large models isolate performance bottlenecks and boost training speed.", "body": "**tl;dr** We help ML engineers training large models isolate performance bottlenecks during training. Trainy summarizes profiling information during large distributed training so that you know exactly what is limiting the speed of your model training.\n\nHello everyone, we are Andrew and Roanak, ML engineers from the bay. We’ve experienced firsthand the challenges of distributed training and getting the most out of your compute resources. That’s why we are building tools to help ML engineers training large models optimize training speed and take the guesswork out of estimating the time and cost of training.\n\n![uploaded image](/media/?type=post&id=72587&key=user_uploads/1404133/cde44cc9-7436-422f-b0fd-c7c3f2a94ff2)\n\n# **The Problem**\n\nDistributed training has enabled the training of ever-growing generative AI models. However, gains in speed are not simply proportional to the number of GPUs recruited and can quickly have diminishing returns depending on infrastructure and the model being trained. Tools now mainly focus on profiling a few GPUs, but become unwieldy the moment you scale beyond the 10 GPU mark.\n\n# **Our Solution**\n\nWe created a dashboard to display timing information across many GPUs. It’s a graph interface built on top of Tensorboard and PyTorch Profiler, existing tools familiar to ML engineers.\n\n![uploaded image](/media/?type=post&id=72587&key=user_uploads/1404133/729377af-3ca9-499b-acf7-1e2f1f569940)\n\n# **How it works**\n\nWe summarize profiling information over many GPUs in a few key views that help ML developers identify where in the ensemble of GPUs there are inefficiencies. Statistics about computation, communication, and memory operations across many GPUs isolate straggling GPUs. Distributed training can only proceed as fast as the slowest GPU participating. By isolating slow outliers, an ML developer can zoom in on the operations running on the straggling GPU and make optimizations to their code to balance timings across GPUs and lower resource idling.\n\n# **The Ask**\n\nWe’d love for you to try out our profiler if you are training your own models. \n\nAlso join our community if you want to stay up to date with the development of our tools. ", "url": "https://www.ycombinator.com/launches/Isl-trainy-identify-bottlenecks-boost-training", "votes": 21, "created_at": "2023-06-27T19:06:48.265Z" } ] }, { "id": 29008, "name": "Andromeda Surgical", "slug": "andromeda-surgical", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ad29152e507992c17de56b3d0a62d1fd6769fd3b.png", "website": "http://www.andromedasurgical.com", "all_locations": "San Francisco, CA, USA", "long_description": "We're building autonomous robots to make surgery safe, easier and more efficient. Robots currently perform about 1/4 of surgeries but only help with the physical aspects. We use AI to make surgery cognitively easier. This has far greater potential to improve outcomes and reduce costs. Founded by 3x founders from medtech and autonomous vehicles, we're on track to be the fastest surgical robot to market of all time. First indication is prostate enucleation.", "one_liner": "Autonomous surgical robots", "team_size": 12, "industry": "Healthcare", "subindustry": "Healthcare -> Medical Devices", "launched_at": 1684515035, "tags": [ "Hard Tech", "Machine Learning", "Medical Robotics", "Medical Devices", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "Healthcare", "Medical Devices" ], "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/andromeda-surgical", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/andromeda-surgical.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "http://linkedin.com/company/andromeda-surgical", "x_url": "https://twitter.com/andromedasurg", "fb_url": "http://facebook.com/andromedasurgical", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ad29152e507992c17de56b3d0a62d1fd6769fd3b.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 4171, "full_name": "Nick Damiano", "title": "Founder", "founder_bio": "First YC medtech founder and only 2x YC medtech founder; \nCo-founder & CEO @ Andromeda Surgical S23;\nFounder & CEO @ Zenflow W15 - treated > 300 patients so far, Class 3 FDA approval, raised > $100M;\nCo-founder & CTO @ Avail MedSystems (f.k.a. Nurep, acq. by Mendaera) - launched in 1000 hospitals, raised $120M;\nVenture Partner @ Pioneer Fund;\nStanford Biodesign Innovation Fellow & Stanford MS/BS;\nGeneralist/SW engineer;\nDad to two daughters;\nSemicolon enthusiast;", "is_active": true, "linkedin_url": "https://linkedin.com/in/nickdamiano", "x_url": "https://x.com/nickdamian0", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d66722f33e37245caa7dd8f7da2e5ab43158e9a5.jpg" }, { "user_id": 37368, "full_name": "Kartik Tiwari", "title": "Founder", "founder_bio": "Background in robotics\n3x technical founder, 2x YC founder\nLed the team at Starsky Robotics to become the first in the world to have a driverless truck on a public road", "is_active": true, "linkedin_url": "https://www.linkedin.com/profile/public-profile-settings?trk=prof-edit-edit-public_profile", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a6e5d0daa20f47f66ea411a42df32eb0df8fac6a.jpg" } ], "jobs": [ { "id": 62175, "title": "Robotics Controls Engineer", "url": "https://www.ycombinator.com/companies/andromeda-surgical/jobs/EiC4MLO-robotics-controls-engineer", "location": "South San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$150K - $250K", "equity_range": "0.10% - 0.50%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [ "C++", "Python" ] }, { "id": 66541, "title": "Senior Software engineer", "url": "https://www.ycombinator.com/companies/andromeda-surgical/jobs/0NF6Ie3-senior-software-engineer", "location": "South San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $250K", "equity_range": "0.10% - 0.50%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 80395, "title": "UX Designer", "url": "https://www.ycombinator.com/companies/andromeda-surgical/jobs/dTs6gll-ux-designer", "location": "South San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$150K - $250K", "equity_range": "0.10% - 0.50%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 83839, "title": "Lead Hardware Engineer", "url": "https://www.ycombinator.com/companies/andromeda-surgical/jobs/93sD4oD-lead-hardware-engineer", "location": "South San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$140K - $200K", "equity_range": "0.10% - 0.50%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Arduino", "Python", "Linux", "CAD Design" ] }, { "id": 108088, "title": "Machine Learning Engineer", "url": "https://www.ycombinator.com/companies/andromeda-surgical/jobs/l9lWEvX-machine-learning-engineer", "location": "South San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $250K", "equity_range": "0.10% - 0.50%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Python", "Torch/PyTorch", "Machine Learning", "Data Modeling" ] } ], "news": [], "launches": [ { "id": 74646, "title": "Andromeda Surgical: Autonomous surgical robots 🦾🏥", "tagline": "Autonomous robots that use AI to elevate every surgeon to world expert level and deliver consistently great patient outcomes", "body": "tl;dr: We’re [Andromeda Surgical](https://andromedasurgical.com/) and we’re building autonomous surgical robots. We’re a team of two 3x founders and 2x YC founders from medtech and autonomous vehicles. Our AI-assisted robots will empower every surgeon to operate at world expert level for every procedure, every time, with minimal training. We’re starting with a proven urologic procedure that can treat over half a million patients per year in the US alone.\n\n \n\n**About Us**\n\nHi everyone, we’re Nick and Kartik, the co-founders of Andromeda Surgical. Nick has founded medtech companies collectively worth over $1 billion whose products are used in over 1,000 medical facilities. Kartik led the team that built and launched the world’s first autonomous truck to operate driverless on US highways. We were both YC alums before this batch, Nick with Zenflow (W15) and Kartik with Starsky Robotics (S16). We met through [YC Co-Founder Matching](https://www.ycombinator.com/cofounder-matching) and realized we could apply the same principles underlying self-driving vehicles to fundamentally change how surgery is performed.\n\n![uploaded image](/media/?type=post&id=74646&key=user_uploads/4171/baf84b2c-4724-43aa-b88d-fc1980e4a389)\n\n\n**Problem**\n\nHuman mental and physical capabilities limit surgical outcomes. Existing surgical robotics companies focus almost exclusively on solving the physical challenges, making surgical movements more ergonomic and precise. These improvements alone have revolutionized surgery and spawned a large and fast-growing industry, led by [Intuitive Surgical](https://www.intuitive.com/en-us) ($108 billion market cap). Surgeons love to use these products and patients eagerly sign up for robotic procedures.\n\nDespite the proliferation of robotics in surgery, the clinical need to reduce complication rates remains largely unmet. Almost one third of patients undergoing surgery experience complications, and 8% experience major complications \\[1\\]. Many surgeons have told us they blame themselves for sub-optimal outcomes, and this adds significant stress to their lives. As AI becomes more powerful, it offers tremendous potential to improve surgical decision making and reduce complication rates.\n\n**Our Solution**\n\nAndromeda uses AI to augment surgeons’ mental and cognitive faculties. We’re starting with a common prostate procedure that can treat over half a million patients per year and has better outcomes than all alternatives. Its steep learning curve limits the number of urologists who can perform it. The procedure is algorithmically simple, but it’s hard for urologists to know exactly where they are and what they’re seeing, resulting in mistakes and delays. We’ve designed a GPS-like location system to help surgeons accurately orient themselves and perform the best possible action in every moment.\n\n![uploaded image](/media/?type=post&id=74646&key=user_uploads/4171/e37dbeac-932f-42bc-b896-954c0c4c0fcb)\n\nAfter launching, we’ll expand into other urologic procedures and then beyond. Whereas most surgical robots focus on multi-port laparoscopic procedures, we’re starting with natural orifice procedures that tend to be less mechanically complex. We’re a hardware-lite company and built our simple yet versatile robot in just three months. The main value we’ll provide is in our software. Our long-term vision is to enhance all surgeons’ abilities with AI, empowering them to perform a broad range of procedures at expert level and deliver consistently optimal outcomes to patients.\n\n**Asks**\n\n·      We’re hiring robotics and HRI/industrial design engineers\n\n·      We’re looking to meet urologists who can partner with us on data collection and future clinical trials\n\nPlease reach out at [info@andromedasurgical.com](mailto:info@andromedasurgical.com) if you’re interested to help us build the future of surgery.\n\n\\[1\\] ", "url": "https://www.ycombinator.com/launches/JPy-andromeda-surgical-autonomous-surgical-robots", "votes": 46, "created_at": "2023-09-06T13:49:39.308Z" } ] }, { "id": 29154, "name": "Catamaran", "slug": "catamaran", "former_names": [ "Ultravity", "CatX" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b0af944f6ad96c6f8c2095e8c485de560a528833.png", "website": "https://www.trycatamaran.com", "all_locations": "London, England, United Kingdom", "long_description": "Catamaran uses AI to help people make clearer, faster decisions about risk. Our platform brings all internal and external risk data into one easy-to-understand workspace. By automating data gathering, checking, and scenario testing, we help insurers, reinsurers, brokers, and investors quickly understand and manage their risks. Catamaran also provides a regulated marketplace to easily buy, sell, and manage insurance coverage.", "one_liner": "The AI-powered risk decision platform ", "team_size": 5, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1687023200, "tags": [ "Machine Learning", "Finance", "Analytics", "Insurance", "Investing" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Acquired", "industries": [ "Fintech", "Insurance" ], "regions": [ "United Kingdom", "Europe", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/catamaran", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/catamaran.json", "year_founded": 2023, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/catamaranrisk/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b0af944f6ad96c6f8c2095e8c485de560a528833.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": 635059, "full_name": "Benedict Altier", "title": "Founder", "founder_bio": "Benedict has advised more than 10 leading insurers and asset managers working across insurance, reinsurance, and risk modeling. He is also a front-end developer with a decade's worth of experience designing and building web applications. He graduated with a 1st class degree in Chinese Studies from the University of Oxford. As a visiting student at Peking University, he advised leading Chinese technology companies including a spin-out from Baidu. ", "is_active": true, "linkedin_url": "https://uk.linkedin.com/in/benedict-altier-0041748b", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1dab89abad066a8fdb09e97fb62b1a097a4d29d6.jpg" }, { "user_id": 1360323, "full_name": "Lucas Schneider", "title": "Founder/CTO", "founder_bio": "Building CatX to revolutionize risk transfer for insurers. Bringing my expertise in financial markets and investing to the insurance world. \n\nStarted my career in Credit Securitisation at Barclays in London where I structured and executed multiple transactions in excess of $10bn+ in value. Oxford PhD Candidate in Machine Learning for Gene Editing. Previously, leading the research team at a web3 hedge fund, focusing on infrastructure and decentralized finance investments.\n\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/lucas-sch", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/358ede5a7b0831d4ff15db816976f97505db9b0f.jpg" } ], "jobs": [], "news": [ { "title": "CatX Raises US$2.7 Million to Bring More Alternative Capital into the Insurance Sector | Insurtech Insights", "url": "https://www.insurtechinsights.com/catx-raises-us2-7-million-to-bring-more-alternative-capital-into-the-insurance-sector/", "date": "Nov 24, 2023" }, { "title": "Our favorite startups from YC's Summer 2023 Demo Day, Day 1 | TechCrunch", "url": "https://techcrunch.com/2023/09/06/ycombinator-demo-day-one-summer-2023/", "date": "Sep 06, 2023" }, { "title": "Risk exchange start-up CatX accepted into Y Combinator accelerator - Artemis.bm", "url": "https://www.artemis.bm/news/risk-exchange-start-up-catx-accepted-into-y-combinator-accelerator/", "date": "Jun 23, 2023" } ], "launches": [ { "id": 88573, "title": "Catamaran: Intelligent Catastrophe Risk Analysis and Underwriting 🌊", "tagline": "The smartest way to analyze natural catastrophe risks for any property, location, or business.", "body": "Hey YC! We’re the CatX team, and we are excited to announce the launch of [Catamaran 2.0](https://trycatamaran.com/)—the smartest way to analyze natural catastrophe risks for any property, location, or business.\n\n🔍 **Problem**\n\nOrganizations today struggle with outdated, slow, and expensive processes for assessing natural catastrophe risks. Traditional tools lack comprehensive insights, require significant manual effort, and leave critical gaps in understanding how disasters like wildfires, hurricanes, earthquakes, and severe weather could impact their assets and portfolios.\n\n💡 **Our Solution**\n\nCatamaran makes catastrophe risk analysis effortless and powerful. Just upload your property details or any relevant documents, and Catamaran instantly generates comprehensive risk insights using extensive historical data across all major catastrophe perils—including wildfires, hurricanes, earthquakes, typhoons, weather, hail and more.\n\n\n\nPowered by advanced AI agents, Catamaran quickly identifies errors, provides clarity from complex datasets, and delivers insights previously only available through costly, manual underwriting.\n\nWe spent the past two years helping institutional investors understand, model, and invest in catastrophic risk. Frustrated by outdated workflows in underwriting and risk assessment amidst modernizing processes everywhere else, we built Catamaran as our solution.\n\n⚡ **What Catamaran Can Do:**\n\n* 📚 **Historical Risk Analysis:** Evaluate location-specific risk profiles based on hundreds of years of historical data.\n* 🛡️ **Optimal Insurance Protection:** Automatically identify the best insurance structures—parametric or traditional—tailored to your unique risk exposure.\n* 📄 **Advanced Document Extraction:** Effortlessly process complex, unstructured documents to rapidly extract key insights.\n* 📈 **Calculate Loss & Scenario Analysis:** Instantly conduct sophisticated scenario analyses to quantify potential losses for proactive risk management.\n\n![uploaded image](/media/?type=post&id=88573&key=user_uploads/635059/e8343eea-4ad1-4a95-8674-d2a4e1d2bfb8)\n\n⚙️ **Use Cases**\n\n* 🏦 **Insurers:** Accelerate underwriting, cut costs by up to 90%, and precisely manage portfolio risks.\n* 📊 **Brokers:** Transform your quoting process with faster, more accurate risk assessments.\n* 🏢 **Businesses & Investors:** Confidently evaluate and manage catastrophe risk exposure across investments, portfolios, and operations.\n* ⚖️ **Regulators:** Enhance regulatory oversight and compliance with transparent, data-driven risk analytics.\n* 🛠️ **Developers:** Easily integrate catastrophe risk analytics into your apps using our developer-friendly API\n\n![uploaded image](/media/?type=post&id=88573&key=user_uploads/635059/de65f188-e565-4c35-99e7-4a149dfc73fb)\n\n👥 **The Team**\n\nCatamaran is built by the CatX technology team led by:\n\n**Benedict Altier**, insurance consultant with experience modeling programs for leading insurers like AXA, Beazley, and MS Amlin, ex-Oxford.\n\n**Lucas Schneider**, Oxford PhD candidate in Machine Learning, ex-Barclays Investment Banker, ex-McKinsey.\n\n**Jon Wood**, formerly on the retro leadership team at top insurance broker AON and previously at Guy Carpenter (Marsh) and Willis.\n\n**Matthew Salamon**, experienced software developer formerly at AON, PwC and Balancer.\n\n🙏 **Our Ask**\n\nIf you manage or insure properties at risk, we'd love to show you how Catamaran can streamline your process and enhance your decision-making:\n\n\\- Email us directly: [catamaran@catxglobal.com](mailto:benedict@catx.re)\n\n\\- Schedule a quick demo here: \\[[trycatamaran.com](http://trycatamaran.com)\\]()\n\nCatamaran: The future of intelligent catastrophe risk analysis.\n\n– Benedict, Lucas & the CatX Team\n\n![uploaded image](/media/?type=post&id=88573&key=user_uploads/635059/5ec96af3-9467-4beb-9127-d6aa336ed3af)\n\n![uploaded image](/media/?type=post&id=88573&key=user_uploads/635059/a70b0e67-95fe-4e9a-aaf4-c5f74b643729)\n\n", "url": "https://www.ycombinator.com/launches/N2b-catamaran-intelligent-catastrophe-risk-analysis-and-underwriting", "votes": 45, "created_at": "2025-03-13T17:17:13.085Z" }, { "id": 72510, "title": "CatX - the digital marketplace for insurance risk", "tagline": "We are the best way for insurance companies to offload risk and for asset managers to invest in an attractive, uncorrelated asset class. ", "body": "CatX is a digital marketplace connecting insurance carriers with institutional investors to trade and transfer insurance risk efficiently. We help to make insurance risk models understandable and accessible for asset managers so they can unlock new sources of return. In addition, we streamline and digitalize the entire risk transfer process, so insurers can get faster, cheaper access to capital from our partner funds.\n\n**❌ The Problems:**\n\n1. **Rising hedging and funding costs for insurers,** US property rates are up 265% over a six-year period\n2. **Highly inefficient risk transfer markets,** with average cost ratios of 40% remaining largely unchanged despite recent technological advancements\n3. **Slow and expensive set-up and structuring processes** when issuing new insurance-linked products, involving multiple parties\n4. **Little secondary market liquidity for insurance-linked securities** due to their complex economics and limited availability of risk models\n5. **Poor analytics or data-drive insights** for understanding novel areas of risks\n\n**💡Our Solution**\n\nCatX addresses these challenges by simplifying the market and providing access to world’s most advanced modeling technologies directly within our platform. In addition, we streamline the origination process, including legal setup and pricing, and offer an electronic secondary trading venue coupled with sophisticated analytics.\n\nCatX helps:\n\n* **Insurers** to access capital at **better rates** through direct capital markets access,\n* **Investors** to unlock new investment opportunities in an uncorrelated asset class,\n* **Protection buyers** find more efficient structures to offload risks, reducing the protection gap for novel and emerging risks.\n\n ![uploaded image](/media/?type=post&id=72510&key=user_uploads/1360323/ca917659-8d2c-4371-a236-a1fc95a58903)\n\n\n**🙏Our Ask**\n\nConnect us with\n\n* **Investors** interested in accessing new sources of uncorrelated returns,\n* **(Re)insurers** looking for access to more flexible capital at better rates,\n* **Businesses** looking to more efficiently transfer risks (property, weather, energy, cyber)\n* **Risk model providers** seeking new distribution opportunities.\n\n**✉️ Contact**\n\nIf you have any questions or suggestions, feel free to reach out to [Ben](https://www.linkedin.com/in/benedict-altier-0041748b/) or [Lucas](https://www.linkedin.com/in/lucas-sch/) directly or email [founders@catxglobal.com](mailto:founders@catxglobal.com).", "url": "https://www.ycombinator.com/launches/IrW-catx-the-digital-marketplace-for-insurance-risk", "votes": 23, "created_at": "2023-06-24T19:31:35.892Z" } ] }, { "id": 29253, "name": "Andon Labs", "slug": "andon-labs", "former_names": [ "Vectorview" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7330cbaf802a2b7ccf2d046a09809940eb0d1050.png", "website": "https://andonlabs.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Safety from humans in the loop is a mirage. We evaluate, research, and apply AI control in our own real-world deployments of autonomous organizations.\r\n\r\nWe are building the Safe Autonomous Organization. We iteratively launch and scale autonomous organizations, while bridging AI control research with real-world testing.", "one_liner": "Autonomous organizations without humans in the loop", "team_size": 11, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1699380174, "tags": [ "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "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/andon-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/andon-labs.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/andonlabs/", "x_url": "https://x.com/andonlabs", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7330cbaf802a2b7ccf2d046a09809940eb0d1050.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": 1308973, "full_name": "Lukas Petersson", "title": "Founder/CTO", "founder_bio": "Breaking capitalism @Andon Labs", "is_active": true, "linkedin_url": "https://linkedin.com/in/lukas-petersson-181a83172", "x_url": "https://twitter.com/lukaspet", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d7aa6fed23dfa2a5eb3369d5ff801bab7ea63053.jpg" } ], "jobs": [], "news": [ { "title": "Anthropic's office launched an AI-run vending machine. It had AI-run stores and cafes within a year | Fortune", "url": "https://fortune.com/2026/06/02/anthropic-office-vending-machine-ai-agents-vendo-andon-lukas-petersson/", "date": "Jun 02, 2026" }, { "title": "The café run by AI just ordered 3,000 pairs of gloves", "url": "https://www.thetimes.com/world/europe/article/life-in-an-ai-cafe-f6khbhq99", "date": "May 24, 2026" }, { "title": "Andon Labs’ AI radio stations show why Grok and Gemini can’t be trusted | The Verge", "url": "https://www.theverge.com/ai-artificial-intelligence/931479/andon-labs-ai-radio-companies", "date": "May 15, 2026" }, { "title": "What Happens When A.I. Runs a Store in San Francisco? - The New York Times", "url": "https://www.nytimes.com/2026/04/21/us/san-francisco-store-managed-ai-agent.html?searchResultPosition=1", "date": "Apr 21, 2026" }, { "title": "We Let AI Run Our Office Vending Machine. It Lost Hundreds of Dollars.", "url": "https://www.wsj.com/tech/ai/anthropic-claude-ai-vending-machine-agent-b7e84e34", "date": "Dec 18, 2025" } ], "launches": [] }, { "id": 29296, "name": "Simplex", "slug": "simplex", "former_names": [ "Simplex", "Pansimulate" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/556d5a66ef0386d859db3ad9f500ceeadf756032.png", "website": "https://simplex.sh", "all_locations": "San Francisco, CA, USA", "long_description": "Simplex builds AI agents to handle provider enrollment end-to-end. We automate pulling provider data from CAQH, filling out enrollment forms + uploading licenses via payor portals/email/phone, updating applications when returned for corrections, and performing effective date follow-ups until a final determination is made, all autonomously.", "one_liner": "AI agents to enroll providers with payors", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1723709594, "tags": [ "Machine Learning", "Robotic Process Automation", "B2B", "AI" ], "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" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/simplex", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/simplex.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/usesimplex", "x_url": "https://x.com/usesimplex", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/556d5a66ef0386d859db3ad9f500ceeadf756032.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/023924cfc3ff92ee7ef40347a383d95811798be6.png" ], "primary_partner": { "name": "Aaron Epstein", "url": "https://www.ycombinator.com/people/aaron-epstein" }, "founders": [ { "user_id": 1588677, "full_name": "Shreya Karpoor", "title": "Founder/CEO", "founder_bio": "Shreya is the co-founder and CEO of Simplex. She holds a BS and MEng in Electrical Engineering and Computer Science from MIT. She previously built software at Tesla and researched dexterous robotic manipulation at MIT.", "is_active": true, "linkedin_url": "https://linkedin.com/in/shreya-karpoor", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2ea7b8584bb0c1fd567102ce20a5cf745915ce09.jpg" }, { "user_id": 910356, "full_name": "Marco Nocito", "title": "Founder/CTO", "founder_bio": "Marco is the co-founder and CTO of Simplex. He holds a BS and MEng in Computer Science from MIT. He previously built machine learning models to generate synthetic data at Waymo and built data infrastructure tooling at Viam and Bloomberg.", "is_active": true, "linkedin_url": "https://linkedin.com/in/marco-nocito/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dba39276e5b4d649b475457eb63e059283e2a78e.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90728, "title": "Simplex - enterprise-grade web agents", "tagline": "AI agent companies automate legacy portals using our SDKs.", "body": "Simplex builds enterprise-grade web agents that companies use to integrate with legacy portals. We handle everything from building the web agents for you to integrating directly with your existing software stack like forward deployed engineers.\n\nCustomers use Simplex in production right now to:\n\n* submit information from outbound calls into freight software\n* edit and download their customers’ invoices\n* fetch websites’ internal APIs\n* and more.\n\nhttps://youtu.be/97gX3I9PMZ0\n\n# The Problem\n\nLegacy portals aren’t well suited for web agent automation — portals are unintuitive to navigate and functionality is often broken.  Existing web agents like Operator lack fine-grained control over agent behavior and can’t consistently perform these critical legacy workflows.\n\nA fully autonomous AI might fail to reach the desired page, get stuck pressing a broken button, or mistype critical information like an invoice amount or a shipping address. Without control over agent behavior and clear observability during workflows, web agents are blocked from being used during production in these critical scenarios.\n\n# The Solution\n\nSimplex is the production-grade web agent platform that enables AI agents to reliably do work on legacy portals at scale. You can see a live demo of Simplex below – the demo is at 1x speed.\n\nhttps://youtu.be/4VSexxYptV4\n\nWe’ve engineered Simplex to give AI agent developers control, transparency, and reliability. With Simplex you can:\n\n* easily **integrate with your existing stack** – pass the results from LLM calls, databases, your existing agents into Simplex\n* **cache element actions** for speed and consistent performance\n* build **strong evals** to stress-test your workflows at scale and emulate production conditions\n* scale up to **100s of concurrent browser sessions** while handling CAPTCHAs, 2FA, and website authentication on your customers’ portals\n* ensure **no incorrect actions** are taken — if you prompt our agents to click on elements that don’t exist on the page, we'll raise an exception\n* debug with shareable **session replays** and **detailed agent logs**\n\n# The Team\n\n[Shreya Karpoor](https://www.linkedin.com/in/shreya-karpoor/) and [Marco Nocito](https://www.linkedin.com/in/marco-nocito/) are MIT grads who’ve spent years doing computer vision work at Waymo, Tesla, and within MIT labs. They’re excited to use their expertise to bring reliable web agents into widespread production.\n\n# \n\n# Our Ask\n\n* [Book a call here](https://cal.com/marco-nocito-kon7ng/30min) if you want to request API access.\n* [Visit our website here](https://www.simplex.sh/) to see a live demo of Simplex.", "url": "https://www.ycombinator.com/launches/NbM-simplex-enterprise-grade-web-agents", "votes": 17, "created_at": "2025-05-27T14:42:32.436Z" }, { "id": 87190, "title": "Simplex - Web automation for developers", "tagline": "API to automate manual workflows on the web", "body": "**TL;DR:**\n\n* Simplex is an API that allows you to automate manual workflows on the web. For example, a customer uses us to autonomously purchase materials from Amazon, poll their email inbox for tracking information, then update their internal documentation with that tracking info every six hours.\n* Book [time with us here](https://calendly.com/simplex-interest/marco-chat) if we can help you automate your business.\n\nHi everyone, we’re [Shreya](https://www.linkedin.com/in/shreya-karpoor/) and [Marco](https://www.linkedin.com/in/marco-nocito/), two MIT grads building Simplex.\n\nTeams are often burdened by manual tasks that have to be repeated thousands of times per month — for example, populating spreadsheets with information from recent emails. From our experience, we found that asking AI agents to solve these problems just didn’t work. They often didn’t follow the same path when we asked them to do the same thing twice, and we couldn’t control any of the actions they took when they made a mistake.\n\nSo we built Simplex to inject intelligence into repeatable, known workflows to help you automate your manual tasks.\n\nWith Simplex, writing a function to pull tracking information from your email is as easy as this:\n\n![uploaded image](/media/?type=post&id=87190&key=user_uploads/910356/27f7171e-7a04-408e-84f9-d7596b9a2e5b)\n\n**Watch a quick demo here:**\n\n\n\nA few reasons customers love Simplex:\n\n* **Straight-forward, reliable intelligence:** We break up your web workflows into smaller, more atomic actions (click, type, extract) to enforce while allowing you to leverage the intelligence of AI.\n* **Extract anything on the web:** We’ve built our own vision system to more accurately extract small icons, text, images, or even entire sections of a page.\n* **Run on any site, even internal tooling:** We store your credentials securely to handle website authentication, meaning you can use Simplex on any website or private tooling you have access to.\n* **No need to deal with browser infrastructure yourself:** We handle all the infrastructure, hosting, and scheduling, letting you run these workflows asynchronously at scale.\n\nPeople have requested Simplex to:\n\n* **act as procurement software:** automatically order parts and update internal spreadsheets with delivery information across multiple vendors (Amazon, DigiKey, etc.)\n* **scrape financial information for their hedge fund clients:** pull daily headlines and perform sentiment analysis across hundreds of financial websites for quantitative analysis research\n* **autonomously update their internal database of products:** aggregate manufacturer marketing information for over 2,000,000+ retail products\n* **do the work of an entire data collection team:** collect grant and award information from over 700 government websites\n* and more…\n\n## Our Ask\n\n* We’re looking to onboard teams, especially those that are burdened by manual processes or need to scrape hundreds of thousands of websites. Book a [meeting with us](https://calendly.com/simplex-interest/marco-chat) for a demo and API access. We’re happy to write your first automation for you.", "url": "https://www.ycombinator.com/launches/MgI-simplex-web-automation-for-developers", "votes": 15, "created_at": "2025-01-27T15:36:17.454Z" }, { "id": 83077, "title": "Simplex: on-demand photorealistic vision datasets", "tagline": "Receive millions of high quality images and labels for training your AI models.", "body": "**TL:DR;** Simplex creates photorealistic vision datasets rendered from 3D scenes for AI model training. Submit a request [on our website](https://simplex.sh/#data-request-form) to receive high-quality data and labels.\n\n![uploaded image](/media/?type=post&id=83077&key=user_uploads/1588677/826c7bea-8936-451d-97ff-895bc8ca6c38)\n\n**Data request for the above sample:** _“Generate images and labels of a home kitchen with household objects on a center table. I need a variety of household objects in a variety of lighting conditions. Our desired labels are semantic segmentation and depth maps.”_\n\nHi everyone, we’re [**Shreya**](https://www.linkedin.com/in/shreya-karpoor/) and [**Marco**](https://www.linkedin.com/in/marco-nocito/), two MIT grads building Simplex.\n\nCollecting vision data for model training is time-consuming, costly, and often unsafe. Shreya spent over 200 hours physically operating a robot to collect image training data during her research at MIT. Marco worked on machine learning for synthetic data at Waymo to solve this exact problem.\n\nWe realized data scarcity wasn’t just an issue in robotics – it affects any company training vision models. When fine-tuning foundation models or building a new dataset from scratch, teams must curate existing data or label and collect data themselves. \n\nWe resolve the data scarcity problem by generating photorealistic ground truth labeled datasets for **any scenario**. We can generate **millions of varied images** **from 3D scenes** using our physics engine pipeline.\n\nHere’s how you’d use Simplex:\n\n1. Fill out our data request form [here](https://simplex.sh/#data-request-form) – it takes less than a minute. \n2. Give us feedback on a few sample image/label pairs that we generate. Repeat if necessary.\n3. Once you’re satisfied, download your complete dataset.\n\nWe support semantic segmentation, captions, simulated LiDAR, depth maps, and bounding boxes. You can generate large volumes of randomized scenes or provide a CAD/phone scan model for more specific scenes.\n\n# **Our Ask**\n\n* If you or someone you know needs vision data, fill out our 30-second data request [form.](https://www.simplex.sh/#data-request-form) We’re taking a limited number of early customers.\n* If you have a more complicated request or would otherwise like to contact us, email [shreya@simplex.sh](https://mailto:shreya@simplex.sh).\n\n# **The Team**\n\n![uploaded image](/media/?type=post&id=83077&key=user_uploads/1588677/d2e5778c-86c2-4c7b-bc75-3c73aefff883)\n\n[**Shreya**](https://www.linkedin.com/in/shreya-karpoor/): Computer science (BS and MEng) at MIT, software engineer at Tesla and Viam. Built simulation pipelines for locomotion and dexterous manipulation research at MIT. \\\n[**Marco**](https://www.linkedin.com/in/marco-nocito/): Computer science (BS and MEng) at MIT, software engineer at Waymo, Bloomberg, and Viam. Built machine learning models to generate synthetic data at Waymo.", "url": "https://www.ycombinator.com/launches/Lbx-simplex-on-demand-photorealistic-vision-datasets", "votes": 407, "created_at": "2024-08-15T14:29:52.537Z" } ] }, { "id": 29365, "name": "Ellipsis", "slug": "ellipsis", "former_names": [ "BitBuilder", "Ellipsis", "ellipsis" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1fa4719de38384c762164e3dcd13cfaa014c5ba3.png", "website": "https://ellipsis.dev", "all_locations": "New York City, NY, USA", "long_description": "Ellipsis is a cloud platform that allows engineering teams to deploy fleets of coding agents. Each agent gets an isolated sandbox, scoped permissions, and hard budget caps. Full observability included. Get started with a $100 credit at www.ellipsis.dev.\r\n\r\nYou can deploy agents for feature development, code review, and workflow automation. Creating an agent is simple: simply commit a YAML file to Github. Spawn agents from Github/Linear/Slack/etc., or via a REST API or our CLI. \r\n\r\nOrganizations get full observability and governance of their cloud coding agents. Agent session logs are downloadable so companies own their own data.\r\n\r\nFull documentation: https://www.ellipsis.dev/docs", "one_liner": "Cloud platform for coding agents", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1706288452, "tags": [ "Developer Tools", "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2024", "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/ellipsis", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/ellipsis.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ellipsis-dev/", "x_url": "https://twitter.com/ellipsis_dev", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/bitbuilder-91a2", "github_url": "https://github.com/ellipsis-dev", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1fa4719de38384c762164e3dcd13cfaa014c5ba3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/815be6dcb94c4c84b15bf95c9e73fd5d55148771.png" ], "primary_partner": null, "founders": [ { "user_id": 1449858, "full_name": "Hunter Brooks", "title": "Founder/CEO", "founder_bio": "Building www.ellipsis.dev, a Managed Cloud Agents platform.\n----\n\nPreviously, I created a code generation product that dynamically created programming challenges used in technical interviews. I've worked as an ML Engineer at AWS and Amazon Ads, in addition to filling various engineering roles at companies like Brigit and Capital One.\n\nBefore that, I published in the field of Astrophysics.", "is_active": true, "linkedin_url": "https://linkedin.com/in/seanhunterbrooks", "x_url": "https://twitter.com/hunterfromnyc", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/717c6b351e12433dc0220a43bc4f5478573447ef.jpg" } ], "jobs": [ { "id": 73519, "title": "Founding AI Engineer", "url": "https://www.ycombinator.com/companies/ellipsis/jobs/hOLJHSp-founding-ai-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$100K - $200K", "equity_range": "0.50% - 5.00%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] } ], "news": [ { "title": "AI Coding Startups That Have Raised the Most From VCs Like A16z - Business Insider", "url": "https://www.businessinsider.com/ai-code-startups-that-have-raised-the-most-vcs-a16z-2024-10#ellipsis-raised-a-2-million-seed-round-in-april-2024-as-part-of-y-combinators-w24-batch-12", "date": "Oct 13, 2024" }, { "title": "Ellipsis raises a $2M Seed Round - Ellipsis.dev | Automated code reviews", "url": "https://www.ellipsis.dev/blog/ellipsis-raises-a-2m-seed-round?utm_source=ycseedround", "date": "Jun 19, 2024" } ], "launches": [ { "id": 78871, "title": "🤖 Ellipsis: Automated code reviews & bug fixes", "tagline": "We review pull requests and convert GitHub comments into working, tested code.", "body": "**TL;DR**: [**Ellipsis**](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc) is a devtool that converts technical instructions into working, tested code. Customers like PromptLayer and Warp use us to review code and fix bugs, [**and you can too**](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc)!\n\n---\n\n---\n\n—\n\nHello world, [**Hunter**](https://www.linkedin.com/in/seanhunterbrooks/) and [**Nick**](https://www.linkedin.com/in/nsbradford/) here! We’re building [**Ellipsis**](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc), an AI devtool that allows engineering teams to ship faster. Today, our customers use us to review pull requests and convert GitHub comments into working, tested code.\n\nBut that’s just the beginning; we’re on a mission to build the AI software engineer. Specifically, we’re automating tasks for the Full Stack Engineer role, starting in the Python and JavaScript/TypeScript ecosystem.\n\n![uploaded image](/media/?type=post&id=78871&key=user_uploads/1449858/918bc808-84a8-4fd5-b106-d4db0ced57ad)\n\n### **How does it work? 🧐**\n\nWorking with Ellipsis is like working with a remote colleague; simply tag it in Slack, GitHub, or Linear. \n\nYou can ask it to:\n\n* Create multi-file code changes from natural language requirements, such as a **bug reports** or **feature requests**\n* Review a pull request, checking for **best practices** and **custom rules**\n* Answer questions about a codebase during onboarding, development, and bug triaging\n* Create daily or weekly digests of the most important changes in your codebase\n* Automate tedious changes, such as **library upgrades** and **refactors**, to a large batch of files\n\nThe best part is that Ellipsis works in the cloud, not on your local laptop. This means developers can have Ellipsis working on multiple tedious tasks in parallel, while they handle the more complex work themselves.\n\nThe question we get most often is “does it work?”, so we invite you to start a [**7 day free trial**](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc). Or [check](https://github.com/hyperdxio/hyperdx/pull/326) [out](https://github.com/jxnl/instructor/pull/467) [some](https://github.com/relari-ai/continuous-eval/pull/43) [examples](https://github.com/ion-design/numi/pull/1) from the open source community.\n\n### **About the team 🧙‍♂️**\n\n* [**Hunter**](https://www.linkedin.com/in/seanhunterbrooks/) previously created a code generation product that dynamically generated programming challenges used in technical interviews. He's worked as a ML Engineer at AWS and Amazon Ads, in addition to filling various engineering roles at companies like Brigit and Capital One. Before teaching himself to code, he published in the field of Astrophysics.\n* [**Nick**](https://www.linkedin.com/in/nsbradford/) most recently took an AI developer tool for codebase migrations from 0 to 1 as founding engineer of Grit. Previously, he researched LLM interpretability at Redwood Research, managed several teams at ML startup Hyperscience, and worked on trading algorithms at Bridgewater Associates.\n\n### **Our ask 🙏**\n\n* [**Try it out!**](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc) Ellipsis will review your pull requests (and do a good job!) for 7 days **for free**.\n* Share this post with Engineering leaders interested in enforcing best practices or shipping faster.\n\nOur inbox is always open at [**team@ellipsis.dev**](mailto:team@ellipsis.dev). Here’s a blurb you can copy/paste:\n\n> [Ellipsis](https://ellipsis.dev?utm_source=launchyc&utm_medium=blog&utm_campaign=yc) is an AI devtool that reviews pull requests and converts GitHub comments into working, tested code. Customers like [PromptLayer](https://promptlayer.com/) and [Warp](https://www.joinwarp.com/) use them to ship faster because they spend less time reviewing changes and fixing bugs. You can get started with a 7 day trial immediately, or [schedule time](https://cal.com/hunter-brooks/15min) for a 15 minute demo.", "url": "https://www.ycombinator.com/launches/KW7-ellipsis-automated-code-reviews-bug-fixes", "votes": 81, "created_at": "2024-02-28T22:06:45.566Z" } ] }, { "id": 29375, "name": "Preloop", "slug": "preloop", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/62ebbbabf82f9c9badd0bbcb60f8749d391d5a78.png", "website": "https://www.preloop.com", "all_locations": "Seattle, WA, USA", "long_description": "Only 2 out of 10 ML models make it from experiment to production. Preloop helps automate the process of deployment, helping companies realize more value from their machine learning teams, while focusing teams' attention on science instead of engineering.", "one_liner": "Translate your experimental scripts into production ML services", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1702654646, "tags": [ "Deep Learning", "Developer Tools", "Machine Learning", "Data Science", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "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/preloop", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/preloop.json", "year_founded": 2023, "city": "Seattle", "country": "US", "linkedin_url": "https://www.linkedin.com/company/preloop/", "x_url": "https://twitter.com/preloop", "fb_url": "https://facebook.com/preloop", "cb_url": "https://www.crunchbase.com/organization/preloop", "github_url": "https://github.com/preloop", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/62ebbbabf82f9c9badd0bbcb60f8749d391d5a78.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": 1310033, "full_name": "Tejas Hosangadi", "title": "Founder/CEO", "founder_bio": "Tejas is the co-founder and CEO of Preloop, a product that automatically translates ML experimental scripts into production services. Before Preloop, he worked at Amazon where he scaled a data science team from 0-1, delivering 4 projects in the first year and leading the expansion of the team. He also worked as a senior MLE at EvolutionIQ, where he made significant improvements to a predictive model in his first month, helping them land a long term contract with Prudential.", "is_active": false, "linkedin_url": "https://linkedin.com/in/tejashosangadi", "x_url": "https://www.twitter.com/tejastmh", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/02c87a1b7a20ffb5d7fdff8f05417310d0a049b5.jpg" }, { "user_id": 1660688, "full_name": "Nikith Hosangadi", "title": "Founder", "founder_bio": "Technical founder experienced in working with distributed systems and machine learning. Before Preloop, he worked as a software engineer at AWS, writing code in C and Java for the VPC team.", "is_active": false, "linkedin_url": "https://linkedin.com/in/nikithhosangadi", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9d2b3aed1c122e5c4788fc71373bfc4dd8e5ad4d.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 78894, "title": "➿ Preloop - Deploy your ML models in hours instead of weeks", "tagline": "Preloop automatically translates your experimental scripts into production ML workflows", "body": "**Tl;dr:** [Preloop](https://www.preloop.com/) automatically translates your ML training scripts into production services, handling the creation of the training pipeline and REST endpoints. This means that science teams can focus on developing new models while cutting deployment times from weeks to just a few hours (or less). We’re building Vercel for model deployments.\n\n—\n\nHey everyone, we’re [Tejas](https://linkedin.com/in/tejashosangadi) and [Nikith](https://linkedin.com/in/nikithhosangadi) and we’re building Preloop. Tejas previously worked as a data scientist and software engineer at several companies, including Amazon and most recently EvolutionIQ, often leading 0-1 projects on newly established teams. Nikith has experience as a software engineer building multi-tenant distributed systems, most recently working at AWS on the networking team.\n\n![uploaded image](/media/?type=post&id=78894&key=user_uploads/1310033/6f2948a3-73ad-43eb-829c-0b23006cd26b)\n\nOur goal is to unshackle scientists from the repetitive tasks that accompany their model deployments and empower science teams to move faster. \n\n## **😫 The problem**\n\nData scientists hate the work associated with deploying their models. Science teams spend anywhere from a couple of weeks to over two months deploying their models. Assuming that a team deploys 10 models a year and each model takes 2 weeks on average to deploy, this is 20 weeks of science time spent just on deployments. \n\nThe biggest bottlenecks to quick deployment are a lack of easy-to-use tools and delays caused by handing off deployments to a separate team.\n\n## **🪄 Our solution**\n\nPreloop automates the tedious and repetitive tasks associated with deploying and using your ML models, including creating the training pipeline and inference endpoints, autoscaling based on demand and retraining. We provide an easy-to-use CLI in addition to a dashboard to monitor and track your models. It works with major ML packages like xgboost, torch, and scikit-learn, with broader support on the roadmap.\n\n![uploaded image](/media/?type=post&id=78894&key=user_uploads/1310033/3a401fe4-e051-45c2-a3b9-98bd8b492abd)\n\n## **🔍 How it works**\n\nWe scan through your existing script, identifying key information about the data, transformations, and the model being trained. This is used to construct both the training and data pipelines, as well as the inference endpoints that serve your model. \n\n![uploaded image](/media/?type=post&id=78894&key=user_uploads/1310033/976dd0ad-2492-43a4-a674-eb3618536144)\n\nWe also handle auto-scaling, observability, and versioning, so that you can train and deploy, but just as easily roll back unintended model changes. We have support for on-prem deployments for teams that have stronger security requirements.\n\n## **🤔 Why are we building this?**\n\nAs a data scientist, Tejas noticed that scientists (including himself) loved experimenting and building models but hated deploying them. More importantly, he witnessed that it often took multiple weeks before they were in production. There was a lack of a more automated solution to handle the deployment process, and even tools like Sagemaker require tons of boilerplate to work.\n\nWe want to make it easier to deploy models, so that teams can move quicker, and scientists can focus more on science. \n\n## **🙏 Our ask**\n\n* Intros to data science and ML managers at mid to large-size startups as well as large enterprises. \n* If your science team struggles with lengthy deployment times, please reach out to us by emailing [founders@preloop.com](mailto:founders@preloop.com), or by booking a meeting [here](https://outlook.office.com/bookwithme/user/27ea63c6cc9c4d398ba6525a9c0e6b25@preloop.com/meetingtype/ZKpbyV0QyU2cya_WNaNuJw2?anonymous&ep=mlink).", "url": "https://www.ycombinator.com/launches/KWU-preloop-deploy-your-ml-models-in-hours-instead-of-weeks", "votes": 209, "created_at": "2024-02-29T16:10:32.259Z" } ] }, { "id": 29442, "name": "Ocular AI", "slug": "ocular-ai", "former_names": [ "AutoflowAI (Zapier for AI Copilots)", "AutoflowAI", "Ocular" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2d1b75bdedcefb73d59cc0a2f122bdc71abfcc6e.png", "website": "https://useocular.com", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Applied AI Data Research Lab Encoding Human Expertise into Machines…", "team_size": 8, "industry": "B2B", "subindustry": "B2B", "launched_at": 1706216304, "tags": [ "Machine Learning", "Speech Recognition", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 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": [ { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Having worked at Microsoft and Google, we experienced first-hand how difficult it is to access and find information and perform actions across multiple Saas tools both in the workplace and engineering. We’ve interviewed numerous employees and engineers at Appian, Uber, Pinecone, Brex, Coda, Microsoft, Google among others and learned that as companies grow it gets harder to: 1\\. **Find what you need at work:** Company knowledge lives in multiple, isolated Saas applications and employees spend over a quarter of their workday looking for information, documents, apps, and people they need to do work or get permissions. 2\\. **Manage complex engineering tools:** As engineering teams scale and adopt microservices, they onboard multiple tools for documentation, development, release, monitoring and alerting. \\* Tools (such as CircleCI, DataDog, New Relic, etc) all live in different places which makes it very difficult to have a unified view of your techstack.\\* \\* \\* Critical data, metrics, logs, and dashboards are all in isolated platforms, complicating discovery, correlation, multitool workflow coordination, and usage—especially during incidents.Siloed tooling and data introduces operational tax to the life of employees and engineers, slowing down productivity, development cycles, and incident management." } ], "url": "https://www.ycombinator.com/companies/ocular-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/ocular-ai.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/use-ocular", "x_url": "https://x.com/OcularHQ", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/ocular-ai", "github_url": "https://github.com/OcularEngineering", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2d1b75bdedcefb73d59cc0a2f122bdc71abfcc6e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/aad644def34ae3d517719e693ef92a82868a185a.png" ], "primary_partner": null, "founders": [ { "user_id": 928098, "full_name": "Michael Moyo", "title": "Founder/CEO", "founder_bio": "CEO & Co–Founder of Ocular AI. Computer and Biomedical Engineer.\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/michaelmoyo", "x_url": "https://twitter.com/SonofMushabati", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6959040840b8fd81769bb3f59506b26293892c8a.jpg" }, { "user_id": 1313975, "full_name": "Louis Murerwa", "title": "Founder", "founder_bio": "Louis is the Co-founder and CTO of Ocular AI. He is from Zimbabwe and studied Computer Science at Dartmouth College. Previously Louis worked at Google NYC as a Software Engineer where he built Distributed Architecture that powers Google Cloud. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/louismurerwa/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3ae4ac166c25ea521a78371102958f1076540542.jpg" } ], "jobs": [ { "id": 71525, "title": "Member of Technical Staff, Founding Backend Engineer", "url": "https://www.ycombinator.com/companies/ocular-ai/jobs/BFBHWQd-member-of-technical-staff-founding-backend-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$150K - $200K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "PostgreSQL", "Python", "React", "Torch/PyTorch" ] }, { "id": 71623, "title": "Member of Technical Staff, Founding Design Engineer", "url": "https://www.ycombinator.com/companies/ocular-ai/jobs/GWPuqvr-member-of-technical-staff-founding-design-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Frontend", "salary_range": "$130K - $200K", "equity_range": "0.30% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "TypeScript", "Next.js" ] }, { "id": 71633, "title": "Member of Technical Staff, Founding Frontend Engineer", "url": "https://www.ycombinator.com/companies/ocular-ai/jobs/Cjr9AlQ-member-of-technical-staff-founding-frontend-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Frontend", "salary_range": "$130K - $200K", "equity_range": "0.30% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "JavaScript", "React", "TypeScript", "Computer Vision", "Figma", "Next.js" ] }, { "id": 73015, "title": "Member of Technical Staff, Founding Machine Learning Engineer", "url": "https://www.ycombinator.com/companies/ocular-ai/jobs/9UVGCDr-member-of-technical-staff-founding-machine-learning-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$130K - $200K", "equity_range": "0.30% - 1.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python", "TensorFlow", "CUDA", "OpenCV" ] } ], "news": [], "launches": [ { "id": 79032, "title": "Ocular AI - Bringing enterprise search, gen AI, and actions to the workplace", "tagline": "Ocular AI lets teams within organizations search, visualize, and perform critical cross tool actions all on one unified platform.", "body": "We’re [Michael](https://www.linkedin.com/in/michaelmoyo/) and [Louis](https://www.linkedin.com/in/louismurerwa/) and are excited to officially launch [Ocular AI](https://www.useocular.com/)!\n\nBook demos [here.](https://calendly.com/use-ocular/30min?month=2024-03#utm_source=launchyc&utm_medium=Launch+YC+Blog+Post&utm_campaign=launch&utm_id=launchyc&utm_content=launch+yc)\n\n**💡 Tl;Dr:**\n\n[**Ocular AI** ](https://www.linkedin.com/company/use-ocular)makes it easy for employees in mid-market and enterprise companies to find information and perform cross-tool actions using Generative AI-powered Search. We do this by providing a beautiful Google-like interface and a co-pilot that connects to all your company apps and tools. \n\n[ ](https://youtu.be/gAHGlKwBQJg)\n\n**❌ Problem**\n\nHaving worked at Google and Microsoft, we experienced first-hand how difficult it is to access and find information and perform actions across multiple SaaS tools both in the workplace and in engineering. Through our experience and conversations with people at Appian, Boeing, General Electric, Uber, Pinecone, Brex, Coda, Unity, Microsoft, and Google, we learned that as companies grow it gets very challenging to:\n\n1. **Find what you need at work:** Company knowledge lives in multiple, isolated SaaS applications, and employees spend over a quarter of their workday looking for information (think of documentation, past slack threads, etc), finding the people needed to complete tasks, and getting the required permissions.\n2. **Manage complex engineering tools:** As engineering teams scale and adopt microservices, they onboard multiple tools for documentation, development, release, monitoring, and alerting. Critical data, metrics, logs, and dashboards are all in isolated platforms such as CircleCI, DataDog, and New Relic and this makes it very difficult to have a unified view of company data, correlation data across these tools, and complete multitool workflow coordination - especially during incidents.\n\n \n\nSiloed tooling and data introduce an operational tax to the lives of employees like engineers, slowing down productivity, development cycles, and incident management. Workers spend an average of a quarter of their day searching for information. \n\nAll of this time can be used to write more code, do more deals, and talk to customers. With the rise of AI and Copilots, the value of the time spent on doing actual work has never been so high.\n\n**🚀🚀🚀 Our Solution**\n\n🔎 **Enterprise Search for everyone in your company:** Integrate all of your company’s applications, tooling, and data sources to power organizational and team-level search. Organization members only see the information they have access to based on their roles.\n\n![uploaded image](/media/?type=post&id=79032&key=user_uploads/928098/2acb388c-05d6-4ccc-bbb7-60a0a979fbf0)\n\n\\\n🤖 **Ocular Copilot- your work assistant that knows everything about your company:** Power cross-tool workflows and actions using our generative AI-powered Ocular Copilot. Create workspaces, upload files (structured or unstructured), create collections, and build usage-specific assistants for your day-to-day tasks. \n\n![uploaded image](/media/?type=post&id=79032&key=user_uploads/928098/71f00356-86e2-46b0-b8f9-8f2521f83b09)\n\n**App MarketPlace:** Integrate the tools (Notion, Jira, Google Drive, Slack) you already love and use for your day-to-day tasks.\n\n![uploaded image](/media/?type=post&id=79032&key=user_uploads/928098/b72de7e6-e224-4695-9eac-f934add88a6f)\n\n**Admin Portal:** \n\nOcular AI provides search-driven insights such as trending searches, number of searches, etc., which allows execs and managers to understand their organizations better. With Ocular, you can now see which applications are used the most and which ones are underutilized, allowing organizations to make reliable SaaS spend decisions. In addition, add members, teams, and applications to your Ocular instance.\n\n[ ](https://youtu.be/9Y9ex6e_vqE)\n\n## **📈 Maximizing Generative AI ROI for Enterprise**\n\nEnterprises are eager to adopt Generative AI in the workplace to enhance operational efficiencies. We’re partnering with organizations to implement secure, compliant, and company-specific Generative AI.\n\n**🔐 Enterprise security**\n\nComing from Microsoft and Google, we understand the importance of data privacy and security. For that reason, you can self-host Ocular AI on-premise or on bring your own cloud (BYOC), so all your data stays within your control- we don’t have access to it. Ocular AI is also getting SOC2 compliant and enforcing end-to-end data encryption AES 256. We’ll also be adding more security layers to ensure your data is 100% safe.\n\n**💯 The Team**\n\n[Dartmouth College](https://home.dartmouth.edu/) graduates with software engineering experience building large-scale and compliant applications at Google and Microsoft.\n\n![uploaded image](/media/?type=post&id=79032&key=user_uploads/928098/63025954-cc5d-4297-90fc-57ea63c5491f)\n\n**🫡 Our Ask**\n\nPlease introduce us to managers, CEOs, CIOs, and CTOs. We can help unify their tools and data together to make their teams more efficient. \n\nSchedule a demo at [www.useocular.com](https://www.useocular.com#utm_source=launchyc&utm_medium=Launch+YC+Blog+Post&utm_campaign=launch&utm_id=launchyc&utm_content=launch+yc) or [here.](https://calendly.com/use-ocular/30min?month=2024-03#utm_source=launchyc&utm_medium=Launch+YC+Blog+Post&utm_campaign=launch&utm_id=launchyc&utm_content=launch+yc)\n\nJump on the [waitlist!](https://form.asana.com/?k=kIT2oNFOWol6_RptDc_vpQ&d=1206400736295884) 🚀\n\nReach us at [founder@useocular.com](mailto:founder@useocular.com)! ✉️", "url": "https://www.ycombinator.com/launches/KYi-ocular-ai-bringing-enterprise-search-gen-ai-and-actions-to-the-workplace", "votes": 58, "created_at": "2024-03-04T20:04:01.162Z" } ] }, { "id": 29460, "name": "Ryse", "slug": "ryse", "former_names": [ "RYSE" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b3127fdf5e5c4be37c6ea79ff2812b08ce513d40.png", "website": "https://www.rysemarket.com/", "all_locations": "New York City, NY, USA", "long_description": "Ryse is the only marketplace where investors who want to buy leases can trade with real estate operators who want to sell leases.", "one_liner": "The secondary market for real estate leases", "team_size": 13, "industry": "B2B", "subindustry": "B2B", "launched_at": 1711502260, "tags": [ "Fintech", "Machine Learning", "Marketplace", "B2B", "Proptech" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 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/ryse", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/ryse.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ryse-market/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b3127fdf5e5c4be37c6ea79ff2812b08ce513d40.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 159369, "full_name": "Sean Mitchell", "title": "Founder/CEO", "founder_bio": "Sean Mitchell went to Florida A&M University where he received a bachelor's in Business Administration and minor in math and computer programming. He started his career in 2009 at JP Morgan's Chief Investment Office, where he was part of a 5-person team that managed a $30B structured mortgage backed security portfolio. Sean began working for Corbin Capital Partners in 2012, where he managed a $300M in a structured credit portfolio. Sean co-founded REZI (YC W17), a tech-enabled property manager.", "is_active": true, "linkedin_url": "https://linkedin.com/in/seanfmitchell", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/453fa43391e3a5ecef0592c14d8faff4c7e00562.jpg" }, { "user_id": 166772, "full_name": "Harsh Yadav", "title": "Founder/CTO", "founder_bio": "Harsh earned his MS, Computer Engineering from Rutgers, New Brunswick, New Jersey in 2010. He also has a Bachelor of Technology degree from the National Institute of Technology in Allahabad, India. Harsh began his career at Consumer Reports in 2010; helping transitioning Consumer Reports print media to web first world. In 2013, Harsh joined Torrential Inc., a digital media company as their VP, Engineering. Harsh co-founded REZI (YC W17), a tech-enabled property manager.", "is_active": true, "linkedin_url": "https://linkedin.com/in/harsh-yadav-a382b911", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b2c98e5368dbb11722190484334e7e3ea3e8d97c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 79735, "title": "Ryse: The secondary market for real estate leases", "tagline": "Ryse enables the trading of $1.5T in leases between banks/asset managers and real estate operators", "body": "Hello everyone! We’re [Harsh](https://www.linkedin.com/in/yadav-harsh/), [Sean](https://www.linkedin.com/in/seanfmitchell/), [Tom](https://www.linkedin.com/in/thomasleonardsmith/) and [Seva](https://www.linkedin.com/in/seva-levitski-4107053/). We’re building Ryse: the secondary market for real estate leases.\n\nSee it here: [Ryse | Innovative Growth Capital For Modern Real Estate Operators (rysemarket.com)](https://www.rysemarket.com/)\n\n**_TL;DR_**\\\nRyse _is the only marketplace where investors who want to buy leases can trade with real estate operators who want to sell leases._\n\n**The Problem**\\\nFinancial institutions like banks, asset managers and insurance companies want to invest directly into the $1.5T in real estate leases originated in the US each year, but they can’t.  Why?\n\n* There's no marketplace for buyers and sellers to meet\n* There's no access to or standardization of the data required to underwrite opportunities\n* There's no existing infrastructure to support trading, reporting, or payment processing\n\n**The Solution**\\\nRyse is the first marketplace where lease buyers and sellers can transact seamlessly. We're building a marketplace where:\n\n* Buyers have access to underwriting data sourced directly from custom API integrations with Sellers\n* We standardize that data into a format that makes it easy to understand and compare to other investments (loans, bonds, t-bills, etc.)\n* We automate trade execution, reporting, and payment processing\n* We enable real estate experience providers to innovate and scale without balance sheet risk.\n\n**See Who Our Customers Are:**\n\n![](https://lh7-us.googleusercontent.com/BZE6zMCgR0w_OqsVfyOkTsSPdlM6_QTiZhPdSUfJH9CxogpJusXtp6p-IweULkz7rnvuJ-5ttOEnn3D_D5TQhIq7w1vCtXCSXxUBR7r4ie8MD0orOyNRYqBj6UTuaEYEIcujpPkGVRLjgIRFye59xF8)\n\n**User Experience**\\\nSee how our customers interact with Ryse:\n\n* Buyers:\n\n ![uploaded image](/media/?type=post&id=79735&key=user_uploads/166772/de468896-0660-4dd1-a091-1ccce3044664)\n\n ![uploaded image](/media/?type=post&id=79735&key=user_uploads/166772/40bff86c-434f-4c2d-a473-6bfe9a8b018b)\n\n* Sellers:\n\n ![uploaded image](/media/?type=post&id=79735&key=user_uploads/166772/8f3f7f4f-50a9-47fe-a8a6-b48553daff75)\n\n ![uploaded image](/media/?type=post&id=79735&key=user_uploads/166772/69969c5e-ce23-4bb8-a0ed-472113fc0cbe)\n\n\n**Traction**\\\nSince launching 10 weeks ago, we've signed a $100M term sheet with our first buyer, a $3B+ AUM asset manager. We are also engaged in late-stage negotiations on our first $50M seller term sheet, which is expected to close in Q2 2024.\n\n**An Experienced Team**\\\nOur founding team worked together at REZI (YC W17), a prop-tech company whose software aimed to make renting an apartment as simple as ordering on Doordash. When building REZI, we learned that financial institutions had an enormous appetite to buy leases as an asset class.\n\n**Our Ask**\\\nIf you’re a real estate operator or financial institution interested in using Ryse, please email us at [hello@rysemarket.com](mailto:hello@rysemarket.com)", "url": "https://www.ycombinator.com/launches/Kk3-ryse-the-secondary-market-for-real-estate-leases", "votes": 14, "created_at": "2024-03-27T13:56:19.649Z" } ] }, { "id": 29523, "name": "K-Scale Labs", "slug": "k-scale-labs", "former_names": [ "dpsh" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9a76d2d8803d634b3e622a584f55e531a3e72f45.png", "website": "https://kscale.dev/", "all_locations": "San Francisco, CA, USA", "long_description": "We're building humanoid robots to do most of what you find boring or tedious. We have an open-source design which we are releasing to the public, which is capable of walking, talking and manipulating objects.", "one_liner": "Open-source humanoid robots", "team_size": 10, "industry": "Consumer", "subindustry": "Consumer -> Consumer Electronics", "launched_at": 1711580332, "tags": [ "Machine Learning", "Robotics", "Consumer", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2024", "status": "Inactive", "industries": [ "Consumer", "Consumer Electronics" ], "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/k-scale-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2024/k-scale-labs.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/kscale/", "x_url": "https://twitter.com/kscalelabs", "fb_url": null, "cb_url": null, "github_url": "https://github.com/kscalelabs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9a76d2d8803d634b3e622a584f55e531a3e72f45.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": 216598, "full_name": "Benjamin Bolte", "title": "Founder", "founder_bio": "AI researcher @ OpenAI, fmr. Tesla, Meta, Google and Amazon", "is_active": false, "linkedin_url": "https://linkedin.com/in/benjaminbolte", "x_url": "https://twitter.com/benjamin_bolte", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8a2681a62183e56fe3557cdbbbf57fc2e3eed5b8.jpg" }, { "user_id": 1963405, "full_name": "Matthew Freed", "title": "Founder", "founder_bio": "MechE + AI", "is_active": false, "linkedin_url": "", "x_url": "https://twitter.com/mattfreed", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bf71707e2370d20249f9a01a3806c01a878ce0fc.jpg" } ], "jobs": [], "news": [ { "title": "The Open-Source Robotics Startup That Caught OpenAI’s Eye — The Information", "url": "https://www.theinformation.com/articles/open-source-robotics-startup-caught-openais-eye", "date": "May 16, 2025" }, { "title": "X", "url": "https://twitter.com/Scobleizer/status/1766365088129368113", "date": "Mar 08, 2024" } ], "launches": [ { "id": 79758, "title": "K-Scale Labs 🤖 Open-source humanoid robots for everyone", "tagline": "Meet Stompy, the world's first open-source general-purpose humanoid robot", "body": "At [K-Scale Labs](https://kscale.dev/), we are building the world’s first open-source general-purpose humanoid robot.\n\n### The Problem\n\nIn recent years, domains like NLP and computer vision have benefited enormously from the [bitter lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) that general methods that are able to leverage computation are ultimately the most effective. The ability to scrape the internet for large, in-the-wild datasets has brought these fields from working well on academic benchmarks to becoming useful real-world products.\n\nHowever, although [there is evidence](https://robotics-transformer-x.github.io/) that the same scaling laws likely apply to robotics as well, the field is stymied by lack of access to the same large, in-the-wild datasets that work well in other domains, resulting in foundation models with poor cross-embodiment and out-of-distribution generalization. While there has been a [surge of investment](https://agilityrobotics.com/news/2023/opening-robofab-worlds-first-factory-for-humanoid-robotsnbsp-xxna2-g9mhr-rnk52) in trying to grow these datasets, we view the [capital-intensive](https://www.therobotreport.com/figure-raises-70m-to-commercialize-humanoids/), [factory-first](https://www.prnewswire.com/news-releases/figure-announces-commercial-agreement-with-bmw-manufacturing-to-bring-general-purpose-robots-into-automotive-production-302036263.html) approach as unlikely to generate the scale and diversity of data required to make these robotics foundation models match the state-of-the-art seen in other fields.\n\n### Our Solution\n\nWe designed and built a fully-featured humanoid robot that anyone with a 3D printer and some time can build on their own. We are releasing it for anyone to build themself, along with an operating system that will enable the robot to perform basic task and learn from experience.\n\n![uploaded image](/media/?type=post&id=79758&key=user_uploads/216598/4ba0e510-77a0-4703-8df4-4d04593c537a)\n\nOur robot, Stompy, is a 4-foot humanoid robot featuring the claw gripper from the recent [Universal Manipulation Interface](https://umi-gripper.github.io/) paper, designed so that each part fits on a 256 × 256 3D printer bed, with a total bill of materials costing less than $10,000. We’re using carbon fiber PAHT, but in principle, any reasonably strong plastic could be used instead. Our actuators are quasi-direct drives with between 6:1 and 8:1 reduction ratios, meaning that all the joints on our robot are low-inertia and back-drivable, with nominal torque values from 3-12 Nm. Each robot features a hot-swappable 48V 15Ah battery pack, which can power it for over an hour.\n\nWe started designing Stompy from scratch shortly after the start of the YC batch. In the last three months, we built five different iterations of the hardware as we’ve ironed out issues with assembly, part reliability, motor torque, and gripper design. Having worked with a number of robots in my professional career, including the Stretch from Hello Robot, the Spot from Boston Dynamics, and the early prototypes of the Optimus from Tesla, I feel confident saying that we have the best general-purpose hardware design for our BOM cost - and we are going to give it away for free.\n\n![Image](https://pbs.twimg.com/media/GILolqbaEAAzDUv?format=jpg&name=large)\n\nAdditionally, we have built the software tooling to train policies in simulation, using simulators like Isaac from Nvidia, and translate those policies to the robot. This enables anyone to get started developing models for simple tasks for free. We will also release the VLM we’ve trained in-house as a foundation model for the robot perception stack, enabling developers to learn task-specific policies in a data-efficient manner.\n\n![uploaded image](/media/?type=post&id=79758&key=user_uploads/216598/2379dd19-b364-4990-958c-007b258bc1d7)\n\n### About Us\n\nOur CEO ([Ben](https://bookface.ycombinator.com/user/216598/linkedin_redirect?key=f2e912c331dc16e3f6c704008aeced6a0055e4ec)) is a robotics researcher and engineer who has worked on robotics foundation models at Tesla and Meta AI. At Tesla, he wrote the CUDA kernel behind the voxel occupancy network, which served as the first perception foundation model to run in the Optimus. At Meta, his papers on speech and robotics representation learning have been cited over 2000 times.\n\n### Our Ask\n\nIf you are interested in either building, buying, or developing a humanoid robot, please send an email to [ben@kscale.dev](mailto:ben@kscale.dev) with your request. We’re working towards a future where anyone with a few 3D printers and some scrappiness can start a robot factory, and anyone who wants a humanoid robot will be able to get one.", "url": "https://www.ycombinator.com/launches/KkQ-k-scale-labs-open-source-humanoid-robots-for-everyone", "votes": 82, "created_at": "2024-03-27T23:01:52.242Z" } ] }, { "id": 29573, "name": "Undermind", "slug": "undermind", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e44c4453098a5caed0988fdf2dff5b898cdcac4.png", "website": "https://www.undermind.ai", "all_locations": "San Francisco, CA, USA", "long_description": "At Undermind, we're building a search engine that can handle extremely complex questions. It’s geared at experts, like research scientists and doctors, who need to find very specific resources to solve high-stakes problems. \r\n\r\nWe’ve rebuilt search from the ground up to address this. Our new approach employs high-quality LLMs to adaptively explore a database, mimicking how a human researcher carefully discovers information. This approach dramatically outperforms (by 10-50x) traditional keyword search and other modern AI-based retrieval methods.\r\n\r\nOur first target users are the 50 million researchers searching for scientific literature on PubMed and Google Scholar every month. We’ve have paying users across fields like medicine, ML, biotech, finance, and more.", "one_liner": "An AI agent for scientific research", "team_size": null, "industry": "B2B", "subindustry": "B2B", "launched_at": 1718632251, "tags": [ "Machine Learning", "Biotech", "Search", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 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/undermind", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/undermind.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/undermind-ai", "x_url": "https://twitter.com/UndermindAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2e44c4453098a5caed0988fdf2dff5b898cdcac4.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": 1394068, "full_name": "Joshua Ramette", "title": "Founder/CEO", "founder_bio": "Josh is the co-founder and CEO at Undermind. He has research experience in gravitational-wave astrophysics (LIGO), particle physics (CERN), atomic physics, and quantum information science. He did his PhD at MIT studying quantum computing architecture design and inventing techniques for generating entangled multi-particle states using atom-light interactions, with 3 patents pending and 6 peer-reviewed publications.", "is_active": true, "linkedin_url": "https://linkedin.com/in/joshua-ramette-50984791", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b83cecf8c968f61bbefc70b6d5c28c2a7d512460.jpg" }, { "user_id": 1862556, "full_name": "Tom Hartke", "title": "Founder/CTO", "founder_bio": "Tom is the co-founder and CTO at Undermind. He studied physics at Princeton, then did his PhD in quantum physics at MIT. His thesis was recognized by the American Physical Society as one of the top 4 theses in the field of atomic physics in 2022, and he has published 10 papers in the field, including two in Nature and Science. Tom has deep expertise in building and deploying complex systems.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/thomas-hartke-229a0679/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7a52405c32e607c04ab8d9ec083887ee8c51205f.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 82132, "title": "🔍 Undermind - Radically better search for scientific research", "tagline": "Imagine sending a smart colleague to find exactly what you need", "body": "# **📝 TL;DR:**\n\n* [Undermind](https://undermind.ai/1) is Google for scientific research. It finds exactly what you need, no matter how complex, using an AI agent.\n* **Try it for free -** [www.undermind.ai](https://undermind.ai/1)\n\n# **———**\n\n---\n\n---\n\n---\n\n---\n\n# **❌ The Problem:**\n\nFinding the right resources to solve problems is crucial for doing impactful research. However, researchers often need to find really complex information, and tools like PubMed and Google Scholar simply aren’t up to the task. They only work for simple, short keyword phrases, and it often takes hours to sift through the (mostly irrelevant) results.\n\n# **✨ Our Solution:**\n\nWe rebuilt search from the ground up to make it work really well for complex questions. Our AI agent mimics how a human researcher carefully discovers information. It spends a few minutes carefully looking through hundreds of papers for you and adapts based on what it finds to uncover everything. Ultimately **Undermind’s results are 10-50x better** than traditional keyword search engines.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/4ce1e368-67de-47c7-af78-860c088daf7e)\n\n# **⚙️ How to use it**\n\nJust write down your complex research topic, describing exactly what you want, as if to a colleague. After 2-3 minutes, Undermind will give you the precisely relevant articles you need.\n\n![uploaded image](/media/?type=post&id=82132&key=user_uploads/1394068/8b54d81a-70b5-4001-b0a8-2235cf27e4b2)\n\n# **Why we’re doing this**\n\nWe’re scientists ourselves, and we’ve spent a lot of time looking through scientific literature. We’ve personally experienced those “aha” moments - when you realize you could have saved 3 months of work if only you knew about that one paper earlier! So, our goal isn’t to save you a few minutes of searching yourself; it’s to make it easy to find the solutions that can drastically improve your overall research plan and trajectory.\n\n# **——**\n\n# **🙏 Asks**\n\n* Try Undermind now - [www.undermind.ai](https://undermind.ai/1)\n* **Share** this post with anyone you know who reads research - it’s helpful for anyone, from machine learning to biotech, medicine, and engineering!", "url": "https://www.ycombinator.com/launches/LMi-undermind-radically-better-search-for-scientific-research", "votes": 50, "created_at": "2024-07-15T15:37:14.292Z" } ] }, { "id": 29665, "name": "Cloudglue", "slug": "cloudglue", "former_names": [ "Aviary", "CloudGlue" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/83e6dd01545303c123ca60f1dcf8bdc7566497c5.png", "website": "https://cloudglue.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Cloudglue is the video context layer for AI. We make it easy for your AI to understand video.\r\n- Tinycloud - your AI agent for video, now open for beta: https://tinycloud.cloudglue.dev\r\n- Developer API Platform: https://cloudglue.dev\r\n", "one_liner": "The video context layer for AI.", "team_size": 4, "industry": "B2B", "subindustry": "B2B", "launched_at": 1723146818, "tags": [ "Developer Tools", "Machine Learning", "Video", "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/cloudglue", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/cloudglue.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cloudgluedev/", "x_url": "https://x.com/cloudgluedev", "fb_url": null, "cb_url": null, "github_url": "https://github.com/cloudglue", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/83e6dd01545303c123ca60f1dcf8bdc7566497c5.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": 473205, "full_name": "Amy Xiao", "title": "Founder/CEO", "founder_bio": "Co-founder & CEO at Cloudglue. Ex ML & infrastructure @ Snapchat, AWS, Arize AI, math/physics @ UofT", "is_active": true, "linkedin_url": "https://linkedin.com/in/amyxiao", "x_url": "https://x.com/amyxst", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c735026b5678820f760811967dd225533d1d4713.jpg" } ], "jobs": [ { "id": 89577, "title": "Founding Engineer, Full Stack", "url": "https://www.ycombinator.com/companies/cloudglue/jobs/j9Xc587-founding-engineer-full-stack", "location": "San Francisco, CA, US / Remote (Seattle, WA, US; Los Angeles, CA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $250K", "equity_range": "0.50% - 2.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Node.js", "PostgreSQL", "React", "TypeScript", "Serverless", "Prototyping" ] }, { "id": 89578, "title": "Founding Engineer, Infrastructure", "url": "https://www.ycombinator.com/companies/cloudglue/jobs/pqyktS0-founding-engineer-infrastructure", "location": "San Francisco, CA, US / Remote (Seattle, WA, US; Los Angeles, CA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$120K - $250K", "equity_range": "0.50% - 2.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Go", "Node.js", "PostgreSQL", "Python", "Distributed Systems", "Docker", "Serverless", "Search" ] }, { "id": 90063, "title": "Research Engineer", "url": "https://www.ycombinator.com/companies/cloudglue/jobs/7Rf6hB8-research-engineer", "location": "San Francisco, CA, US / Remote (Seattle, WA, US; Los Angeles, CA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$120K - $250K", "equity_range": "0.50% - 2.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Torch/PyTorch", "Machine Learning", "Reinforcement learning (RL)", "Natural Language Processing", "Computer Vision", "Search", "Prompt Engineering" ] } ], "news": [], "launches": [] }, { "id": 29673, "name": "Storia AI", "slug": "storia-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b303aec8b08b8ee5b7c074ef02f910506fd73654.png", "website": "https://storia.ai", "all_locations": "San Francisco, CA, USA", "long_description": "With AI increasingly automating away code generation, software engineers will spend more time reading, judging, and architecting code rather than writing it. Storia is building an open-source copilot that knows a company's codebase and its context.\r\n\r\nWe are starting with Sage, a Perplexity-like agent for helping developers understand, judge, and generate software. Given an existing codebase, developers can ask Sage questions such as:\r\n1) Given my project’s SLA and latency constraints, what is the appropriate underlying vector database to use? How would I incorporate it into my existing codebase?\r\n2) Why should I pick Redis over Milvus as my underlying vector store?\r\n3) Does this codebase in our organization still work and what steps are required for a complex integration with another library?\r\n\r\nSage’s answers are directly supported by documentation and external references like GitHub, Stack Overflow, technical design documents, and project management software, preventing hallucinations. Today, Sage has up-to-date knowledge about open-source repositories (indexed daily). Tomorrow it will have a deep understanding of every line of code on the Internet. For teams, Sage will know about your private codebase too.\r\n\r\nNo group has yet solved how to build an AI system that comprehends a codebase and its context and can empower every developer to architect better code, faster. This requires new research advances because vanilla RAG and out-of-the-box LLMs aren’t going to cut it. \r\n\r\nWe have 20+ years of software engineering and AI research experience. Julia worked on precursors of Gemini using contextual neural techniques before they were called “RAG” (and applied it to products like Google Keyboard and Pixel phones). Mihail built the earliest LLMs at Amazon Alexa and launched the first contextual deep learning conversational AI system in production at Alexa.\r\n", "one_liner": "Open source AI copilot that knows your company's code and its context", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1724629645, "tags": [ "Developer Tools", "Machine Learning", "SaaS", "AI" ], "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" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/storia-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/storia-ai.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/storia-ai/", "x_url": "https://x.com/StoriaAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Storia-AI", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b303aec8b08b8ee5b7c074ef02f910506fd73654.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/faa659cfff4b26d1fc08977958554918d71fbace.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/44a4714aa8eb81008f443a15fb6e5c75b9f3a7e8.png" ], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 916267, "full_name": "Julia Turc", "title": "Founder/CTO", "founder_bio": "Cofounder @ Storia & previously at Google Research.\n\n10+ years in NLP. Built the first bidirectional RNN for machine translation at Oxford. At Google, I helped build pivotal technologies like federated learning, the AI layer on top of Android, and RAG for precursors of Gemini. Excited about how NLP is now redefining software engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/iulia-raluca-turc/", "x_url": "https://twitter.com/juliarturc", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f5db934f711d17168c390835067d38e60b8e84fc.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83405, "title": "Storia: A contextual AI pair programmer for developer teams", "tagline": "We help engineering teams understand, analyze, and generate software with their codebases.", "body": "![uploaded image](/media/?type=post&id=83405&key=user_uploads/812126/ce70880c-852a-4e80-a573-88f6742298ed)\n\nHi everyone. We’re Julia and Mihail, the team behind [Storia](https://storia.ai/?utm_source=yc&utm_medium=social&utm_campaign=launch-yc). We have 20+ years of software engineering and AI research experience. [Julia](https://www.linkedin.com/in/iulia-raluca-turc/) worked on precursors of Gemini using contextual neural techniques before they were called “RAG” (and applied it to products like Google Keyboard and Pixel phones). [Mihail](https://www.linkedin.com/in/mihaileric/) built the earliest LLMs at Amazon Alexa and launched the first contextual deep-learning conversational AI system in production at Alexa.\n\n**👎 The Problem: <5% of software teams use AI code understanding and generation systems**\n\nDespite all the hype around AI coding following the release of GitHub Copilot, a disappointingly small percentage of engineering teams have actually adopted AI assistants into their developer workflows. Why?\n\nWhat developers don’t like about systems today:\n\n1. **Code search is not accurate enough**, slowing dev velocity.\n2. **Code assistants are not grounded in their team’s codebase**, so they lack the necessary context to produce useful and high-quality answers.\n3. **Code outputs can be incorrect** due to hallucination which strains dev patience and trust.\n\n**🚀 The Solution: A contextual AI pair programmer built for any team’s codebase**\n\n![uploaded image](/media/?type=post&id=83405&key=user_uploads/812126/0e093af4-4352-4550-a123-be44cf3dc747)\n\nWe’re building [Sage](https://sage.storia.ai/?utm_source=yc&utm_medium=social&utm_campaign=launch-yc), a Perplexity-like agent for helping developers understand, analyze, and generate software. Given an existing codebase, developers can ask Sage questions such as:\n\n* _For my project’s SLA and latency constraints, what is the appropriate underlying vector database to use? How would I incorporate it into my existing codebase?_\n* _Why should I pick Redis over Milvus as my underlying vector store?_\n* _Does this codebase in our organization still work, and what steps are required for an integration with another library?_\n\nA few of the features Sage supports:\n\n* **We index repos daily**, providing users with the most up-to-date info about how to use a library (no stale, deprecated API definitions),\n* We have a Perplexity-like focus on **citing the source of every generated line of code**, enhancing developer trust.\n* We've developed algorithms grounded in research for **syntactic and semantic understanding of a codebase** so that we can provide more relevant context and use it effectively given a user query.\n\n We are now actively working with seed to series C partner companies to help them integrate Sage into their developer workflows. \n\n**🌅 The Opportunity: Making every engineer on a software team a 10x developer**\n\nThere are \\~28M developers worldwide. Github Copilot is the frontrunner in AI assistant tooling and reached $100M ARR in 2 years, which is only a small percentage of the $913B spent on software development in 2023.\n\n**🙏 Our Ask**\\\nWe’re still onboarding a closed group of beta partners to integrate with Sage. We would appreciate connecting with engineering leaders (VP of engineering, Head of Engineering, CTO)  at Seed-Series C companies. \n\nAdditionally, if you want your open-source repository indexed reach out to us at [mihail@storia.ai.](mailto:founders@storia.ai)\n\n![uploaded image](/media/?type=post&id=83405&key=user_uploads/812126/dd520900-8415-4ee5-8707-554b7197037b)\n\n", "url": "https://www.ycombinator.com/launches/LhF-storia-a-contextual-ai-pair-programmer-for-developer-teams", "votes": 38, "created_at": "2024-08-26T16:32:20.077Z" } ] }, { "id": 29687, "name": "MinusX", "slug": "minusx", "former_names": [ "minusone.ai", "minusx.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/92dbd9d186e22d922d985755f90407e21859378a.png", "website": "https://minusx.ai", "all_locations": "San Francisco, CA, USA", "long_description": "MinusX is a state-of-the-art data agent that can build the best dashboards for your data, and notify you when something's up. You can interrogate all your data on our open source BI platform via Slack or MCP. Connect your data, and put agents to work.", "one_liner": "Open source agentic BI for founders", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1722024736, "tags": [ "Machine Learning", "Analytics", "Data Science", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "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/minusx", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/minusx.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/minusx-ai", "x_url": "https://twitter.com/minusxai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/minusxai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/92dbd9d186e22d922d985755f90407e21859378a.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": 164724, "full_name": "Vivek Aithal", "title": "Founder/CEO", "founder_bio": "Co-founder & CEO of MinusX. Spent the last 3 years as a research engineer building the world's best open-source autonomous driving agent at comma.ai. Currently building a data science agent that can sit in your browser and use all your data tools. UC Berkeley and IIT Kharagpur grad. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/vivek-aithal-575b207b/", "x_url": "https://twitter.com/nuwandavek", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/430c252ee108b6b83096cba330f4b1b46f7c26ab.jpg" }, { "user_id": 1798297, "full_name": "Sreejith Puthanpurayil", "title": "Founder", "founder_bio": "Co-Founder & CTO of MinusX. Previous stints include Ads Delivery & Ranking at Meta, Ride-hailing backend & infra at Gojek, & full stack at Elanic, a social c2c marketplace (acquired by Share Chat)", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ppsreejith", "x_url": "https://twitter.com/ppsreejith_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/51ad324025788a0b7a4961ab7b419a0b20b734ff.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 82787, "title": "MinusX: AI Data Scientist for Jupyter and Metabase", "tagline": "An AI data scientist that is a scientist, not yet-another-new-analytics-platform.", "body": "# **TL;DR**\n\nMinusX is a Chrome Extension that adds a side chat to Jupyter and Metabase. Given an instruction, our agent operates the apps— via clicking and typing— to analyze data and answer queries. We strongly believe that you already have all the tools you need; you just need someone like MinusX to use them! Checkout [**minusx.ai**](http://minusx.ai) for more info.\n\n\n\n# **Asks**\n\n1. Install our [**Chrome extension**](https://minusx.ai/chrome-extension).\n2. Take MinusX for a spin in our [**playground**](https://minusx.ai/playground)!\n3. We’re working on supporting more tools. Want us to hurry up, or don't see your favorite tool in our list? Here’s a [**google form**](https://minusx.ai/tool-request) you can fill out so that we can notify you when we support your tool!\n\n# **Features**\n\n### 1\\. Generate Hypotheses and Explore data\n\n![uploaded image](/media/?type=post&id=82787&key=user_uploads/164724/53f4faaf-4dae-4226-9037-4142ffea5462)\n\n### 2\\. Interop with MinusX to modify existing Jupyter notebooks or Metabase Questions\n\n![uploaded image](/media/?type=post&id=82787&key=user_uploads/164724/259d0be2-0f20-4440-90af-84cae7297264)\n\n### 3\\. Select a region and ask questions, or ask for modifications\n\n![uploaded image](/media/?type=post&id=82787&key=user_uploads/164724/40ed2aeb-29c3-42c8-9e9b-3b1d96b866ae)\n\n# **Pain**\n\n* Lack of data analyst/data scientist bandwidth is a certified pain.\n* If you’re a programmer, you just want answers. If you’re a product manager, you just want answers. If you’re an analyst/scientist, you want 10 clones of yourself.\n* Any new fancy “talk-to-data” platform is a whole thing. You have to migrate your data, and you have to convince your whole team to move— just to start using it.\n* Working in ChatGPT / Claude requires constant copy-pasting. Also, you want to inter-operate with AI, make corrections when it messes up, and guide it rather than just admonish it in chat.\n* More importantly, they’re still just chat! No AI system today can really use analytics software - click and type - to get you that data you needed 5 minutes ago.\n\n# **Our Approach**\n\n> ### **_An AI Data Scientist is a Scientist, not yet-another-new-analytics-platform_**\n\n* Advanced agents should just work with tools you already use and get out of the way.\n* MinusX integrates seamlessly into workflows. You can invoke it only when you need it to do something.\n* Whether you’re a data analyst, programmer, or product manager, you can use MinusX to speed up your workflows instantly.\n* We believe the path to advanced agents runs through specialized, useful intermediaries.\n\n# **Team**\n\n![uploaded image](/media/?type=post&id=82787&key=user_uploads/164724/8f305720-54ba-4af7-9303-8125b9f02f2a)\n\nWe love to build stuff. We met about a decade ago in the drowsy hinterlands of IIT Kharagpur and have been building things together at work, on side projects, and at hackathons ever since. ​​[**Vivek**](https://x.com/nuwandavek) was most recently a Research Engineer working on self-driving cars at [**comma.ai**](http://comma.ai/), [**Sreejith**](https://x.com/ppsreejith_) was a Senior Engineer in the Meta shops ads ranking team optimizing Facebook Shops Ads delivery & core infrastructure, and [**Arpit**](https://x.com/arpit_tarang) was building a warehouse robot fleet as a Product Engineer at Udaan. We have extensive experience in machine learning, data infrastructure, and systems engineering needed to build the best, most delightful class of Data Science agents you'll ever use.\n\n# **Ask(ing again!)**\n\n1. Install our [**Chrome extension**](https://minusx.ai/chrome-extension).\n2. Take MinusX for a spin in our [**playground**](https://minusx.ai/playground)!\n3. We’re working on supporting more tools. Want us to hurry up, or don't see your favorite tool in our list? Here’s a [**google form**](https://minusx.ai/tool-request) you can fill out so we can notify you when we support your tool!\n\nIf you have any questions or comments, you can join our [**discord**](https://minusx.ai/discord) or ping me anytime at [**vivek@minusx.ai**](mailto:vivek@minusx.ai). Hope you enjoy using MinusX as much as we enjoy building it!\n\n### ❤️\n\n![uploaded image](/media/?type=post&id=82787&key=user_uploads/164724/a35b579f-ea8a-4bf4-a0d8-ad65bd607b47)\n\n", "url": "https://www.ycombinator.com/launches/LXH-minusx-ai-data-scientist-for-jupyter-and-metabase", "votes": 57, "created_at": "2024-08-06T20:14:50.518Z" } ] }, { "id": 29698, "name": "AutoPallet Robotics", "slug": "autopallet-robotics", "former_names": [ "Anteatr Robotics" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/d2f0c4ca0e1ee62abec263fe32fc601c69e08139.png", "website": "https://autopallet.bot", "all_locations": "San Francisco, CA, USA", "long_description": "We’re building the next generation of warehouse robotics.\r\n\r\nIn the US today, retailers spend approximately $10B per year paying human laborers to pick up and move cardboard boxes in warehouses. Existing solutions for automating this are expensive and difficult to install, which is why manual operation is still so prevalent.\r\n\r\nOur solution is different. We make swarms of small mobile robots that install into existing warehouses to provide a low-cost and robust automation solution for case picking and mixed-SKU palletization. Our novel technology allows these robots to be installed and operate at significantly lower cost than existing solutions while being both flexible and robust.", "one_liner": "We make robots that move boxes in warehouses", "team_size": 6, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1723496700, "tags": [ "Hard Tech", "Machine Learning", "Warehouse Management Tech", "Swarm Robotics", "Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Eric and Nathan met at Olin College, where they worked on 15 group projects together. " } ], "url": "https://www.ycombinator.com/companies/autopallet-robotics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/autopallet-robotics.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/autopallet-robotics/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/AutoPallet/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/d2f0c4ca0e1ee62abec263fe32fc601c69e08139.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": 242906, "full_name": "Nathan Yee", "title": "Founder/CEO", "founder_bio": "Nathan is the co-founder and COO of AutoPallet Robotics.\n\nAt 18, he graduated as the valedictorian of College of Alameda and then studied Engineering with Computing at Olin College where he met Eric. He has since built ML models for MBARI, consulted for the DIU (xView2) and WashU (drug discovery), and built core software for lab automation robotics at Trilobio.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nathanielyee/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6e1472399a54fec10ff193ce2453753f7c5170c4.jpg" }, { "user_id": 1979497, "full_name": "Eric Miller", "title": "Founder/CTO", "founder_bio": "Eric is the co-founder and CEO of AutoPallet Robotics.\n\nAfter graduating from Olin College in 2019, he led the Subject Tracking and Autonomy Infrastructure teams at Skydio, rapidly growing from individual contributor to lead a team of 6 engineers.\n\nEric is a multidisciplinary robotics engineer with significant experience building modern robotics stacks, from hardware and electronics through perception and high-level software.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/olin-eric/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4fa34c4d02dcef337053aafb9f45b11b7a7c2531.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 83238, "title": "🤖 AutoPallet Robotics: The future of case picking in warehouses", "tagline": "AutoPallet Robotics uses swarms of hundreds of mobile robots to do case picking in existing warehouses.", "body": "# TL;DR\n\n* AutoPallet Robotics unlocks automated case picking for existing warehouses. Our novel system delivers rapid ROI by leveraging the current infrastructure to deploy swarms of hundreds of small, agile, mobile robots.\n* Want to get in touch? [**connect@autopallet.bot**](mailto:connect@autopallet.bot) or [send us an inquiry](https://forms.gle/YT1XLadSpWaKAa668)\n\n![uploaded image](/media/?type=post&id=83238&key=user_uploads/242906/61c0e05b-c2d2-470a-8248-dcb394bbaeaa)\n\n# The Team\n\n![uploaded image](/media/?type=post&id=83238&key=user_uploads/242906/0e93dfda-da4f-43f2-8170-dcbef1c80b90)\n\n**Nathan and Eric** met at Olin College of Engineering 9 years ago, where they collaborated on over 15 projects.\n\n**COO** - [Nathan](https://www.linkedin.com/in/nathanielyee/) brings four years of experience in machine learning. In his previous position, he worked on robot software and explored heuristic-based and RL-based methods for multi-robot scheduling.\n\n**CEO** - [Eric](https://www.linkedin.com/in/olin-eric/) has extensive experience creating hardware, electronics, and software for modern robotics systems, including five years at Skydio, founding and growing the Autonomy Infrastructure team.\n\n# The Problem\n\n**Case picking for order selecting**, a critical task in the supply chain for grocery stores, retail, and restaurants, remains largely manual in U.S. warehouses. We estimate that **over 30 billion cases** are manually moved between pallets annually, resulting in labor costs exceeding **$10 billion** each year.\n\nCurrent automation solutions are prohibitively expensive and fall into two main categories:\n\n1. **Build a new warehouse** with extensive conveyor belt systems, industrial robot arms, and high-density case storage. Costs range from **$100M to $350M per warehouse**.\n2. **Install large mobile robots** that directly replace humans in existing warehouses at a cost of **$300k to $500k per unit**. This high capital expenditure and long ROI period make them infeasible for many businesses.\n\n## Our Solution Is Fundamentally Different\n\nWe've developed a way to retrofit existing warehouse infrastructure with swarms of affordable, agile robots to automate the order-selecting process. This approach eliminates the need to rebuild warehouses from scratch or invest in expensive large mobile robots. We are designing our system to:\n\n* **Meet or exceed** your current throughput—our robots work in swarms, easily picking tens of thousands of cases per shift.\n* Ensure **resilience** through a decentralized swarm that continues operating even if several robots fail.\n* Optimize **multi-stop deliveries** and create **maximally dense pallets** using an **AI-powered** pallet-building algorithm.\n* Deliver a typical ROI in **less than** **12 months**.\n\n## How You Can Help\n\nWe’re actively seeking **partnerships and collaborations** to propel our mission forward. If you or anyone in your network is interested in exploring opportunities or learning more, we’d love to connect! We’re especially interested in engaging with:\n\n* Retailers and 3PLs operating distribution centers\n* Grocery and Broadline Foodservice Distributors\n\nIf this resonates with you or someone you know, please don’t hesitate to reach out! [**connect@autopallet.bot**](mailto:connect@autopallet.bot) or [send us an inquiry](https://forms.gle/YT1XLadSpWaKAa668)", "url": "https://www.ycombinator.com/launches/LeY-autopallet-robotics-the-future-of-case-picking-in-warehouses", "votes": 27, "created_at": "2024-08-20T18:44:31.152Z" } ] }, { "id": 29735, "name": "FINNY AI", "slug": "finny-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f2d0e659d5d03f52a35513c7fe53a3701c771832.png", "website": "https://www.finny.com/", "all_locations": "New York City, NY, USA", "long_description": "FINNY is the AI-native organic growth engine for financial advisors, focused on helping them grow their practices\r\n \r\nOur goal is to empower financial advisors to reach the right potential clients at the right time, with the right message. We are building a world where anyone who wants financial advice can connect with the right advisor, while helping advisors ensure organic growth, the most existential problem advisors face. \r\n \r\nThe organic growth process has historically been highly inefficient and full of noise. Advisors often waste 60+ hours of business development to convert 1 new client. \r\n\r\nAt FINNY, we are certain we can do better with AI. With our tool, advisors can:\r\n- Identify prospects within their target niche, aggregating thousands of data points per lead\r\n- Prioritize prospects based on their predicted likelihood of converting using F-Score, a score unique to each advisor and prospect pair\r\n- Automate the outreach and meeting scheduling with their high priority prospects\r\n\r\nIn essence, FINNY does all the rote work for the advisors, letting them focus on what actually matters: the client relationship.", "one_liner": "The organic growth engine for financial advisors ", "team_size": 35, "industry": "Fintech", "subindustry": "Fintech -> Asset Management", "launched_at": 1716852989, "tags": [ "Fintech", "Machine Learning", "Finance", "Sales Enablement", "Automation" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Fintech", "Asset Management" ], "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/finny-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/finny-ai.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/finnyai/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/finny-6acf", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f2d0e659d5d03f52a35513c7fe53a3701c771832.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": 1755270, "full_name": "Eden Ovadia", "title": "Founder/CEO", "founder_bio": "Eden is the Co-founder and CEO of FINNY.\n\nShe studied Software Eng. at McGill University with a focus on ML. Prior to starting FINNY, Eden was at BCG where she worked in Private Equity & Financial Services. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/edenovadia", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1cb89e9ce9be37246ecb1f3e0e66556756e6db01.jpg" }, { "user_id": 1754854, "full_name": "Theodore Janson", "title": "Founder/CTO", "founder_bio": "Theo is the co-founder and CTO at FINNY. \nBefore FINNY, Theo worked as an applied ML engineer in an early Fintech startup, leading the development of statistical models and LLMs for automated financial analysis. He has also held research roles in LLM explainability and particle physics. Theo has degrees in electrical engineering and CS from Ecole Polytechnique (X) and McGill University.\n\n\n\n", "is_active": true, "linkedin_url": "https://linkedin.com/in/theodore-janson", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b30d06543a1a116e358a9ec48f753d7e759411e2.jpg" }, { "user_id": 1630058, "full_name": "Victoria Toli", "title": "Founder", "founder_bio": "Victoria is the co-founder and CPO of FINNY, an AI-driven growth engine for financial advisors. \n\nBefore FINNY, she was a Product Manager at Uber, where she led growth and international expansion for Uber One. She holds a BS in Symbolic Systems, Artificial Intelligence from Stanford. Victoria grew up in Athens, Greece. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/victoria-toli/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9493c48a901d6d10732682c7f6b2b657f49c8953.jpg" } ], "jobs": [ { "id": 92443, "title": "Product Engineer ", "url": "https://www.ycombinator.com/companies/finny-ai/jobs/diUrCMS-product-engineer", "location": "New York City", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$180K - $200K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 110017, "title": "Senior Account Executive, Enterprise ", "url": "https://www.ycombinator.com/companies/finny-ai/jobs/ZpW4jSD-senior-account-executive-enterprise", "location": "New York, NY, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$150K - $200K", "equity_range": "", "experience": "6+ years", "visa": "Will sponsor", "skills": [] }, { "id": 110018, "title": "Growth Marketing Manager ", "url": "https://www.ycombinator.com/companies/finny-ai/jobs/tVk0vEd-growth-marketing-manager", "location": "New York, NY, US", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$140K - $170K", "equity_range": "", "experience": "6+ years", "visa": "Will sponsor", "skills": [] }, { "id": 110019, "title": "Growth Strategist, Enterprise ", "url": "https://www.ycombinator.com/companies/finny-ai/jobs/woHEz5n-growth-strategist-enterprise", "location": "New York, NY, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$130K - $150K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] } ], "news": [ { "title": "WealthTech Roundup: FINNY Partners With Osaic for AI", "url": "https://www.wealthmanagement.com/artificial-intelligence/wealthstack-roundup-finny-partners-with-osaic", "date": "Jan 29, 2026" }, { "title": "FINNY closes $17M Series A to help RIAs fight organic growth squeeze - InvestmentNews", "url": "https://www.investmentnews.com/fintech/finny-closes-17m-series-a-to-help-rias-fight-organic-growth-squeeze/263644", "date": "Dec 18, 2025" }, { "title": "FINNY Adds Multi-Channel, Automated Prospect Outreach Feature", "url": "https://www.wealthsolutionsreport.com/finny-adds-multi-channel-automated-prospect-outreach-feature/", "date": "Aug 07, 2025" }, { "title": "YC-backed Finny is using AI to match financial advisors with prospects | TechCrunch", "url": "https://techcrunch.com/2024/12/11/yc-backed-finny-helps-financial-advisors-find-new-clients/", "date": "Dec 11, 2024" }, { "title": "Finny and the Great AdvisorTech Acceleration | Wealth Management", "url": "https://www.wealthmanagement.com/technology/finny-and-great-advisortech-acceleration", "date": "Aug 12, 2024" } ], "launches": [ { "id": 82467, "title": "⚡FINNY - Supercharge organic growth for Financial Advisors", "tagline": "We help independent financial advisors find and convert new clients, using AI. ", "body": "Hey everyone, we are Theo, Eden, and Victoria — the founders of [FINNY](https://www.finnyai.com/). We help independent financial advisors find and convert new clients, using AI.\\\n\\\nIf you’re confused as to why the three of us are so excited about building for financial advisors, keep reading.\n\n![uploaded image](/media/?type=post&id=82467&key=user_uploads/89898/9cd181f2-59e6-4458-b5c9-250b9399ee43)\n\n# 🧑‍🔬 **The Team**\n\n**🏦The Sales Whisperer**  \n\n[Eden](https://www.linkedin.com/in/edenovadia/) was most recently at BCG NYC, where she primarily worked in the Tech, Financial Institutions & Private Equity practices. She holds a BEng in Software Engineering from McGill (ML focus). \n\n**👾The Model Man** \n\n[Theo](https://www.linkedin.com/in/theodore-janson/) worked as a ML engineer at an early Fintech startup and AI researcher in Explainable ML. He holds a BEng in EE and Math from McGill and MS in AI from Ecole Polytechnique, Paris.\n\n**👩🏼‍🎨The Visionary** \n\n[Victoria](https://www.linkedin.com/in/victoria-toli/) came from Uber, where she led membership growth for Uber One as a Product Manager. She holds a BS from Stanford in Symbolic Systems (AI focus). \n\n# **🫶🏻 Why are we obsessed with Financial Advisors?**\n\n* **The Great Wealth Transfer** - $80T of wealth will be transferred from the Boomer/Silent Gen to the next generation over the next 20 years. \n* **Retiring advisors** - 45,000 financial advisors are over the age of 65 in the US, managing $3T in wealth that will change management when they retire.\n* **Wealth creation** - Wealth is being created in unprecedented ways (E.g., Innovation, Entrepreneurship, Social media, Racial wealth gain), but the institution serving the wealth has not changed\n* **Consolidation** - Private equity driving consolidation of a historically long tail of independent advisors\n* **Price Inertia:** Financial Advisors are used to paying 25 bps annually IN PERPETUITY for referrals. That’s $200B annually from the wealth transfer alone.\n\n \n\n# 💀 **The Problem FINNY is Solving**\n\nFinancial advisors manage >$100T in assets globally. Yet, bringing on new clients is highly inefficient. Their best bet is to repurpose horizontal solutions - like Linkedin, ZoomInfo, and Pitchbook - to create long and noisy prospect lists. However, without a proper way to qualify these leads or even reach their particular niche at scale, advisors end up spending an average of **58 hours** going after the wrong, often unqualified prospects before converting one new client.\n\n![uploaded image](/media/?type=post&id=82467&key=user_uploads/1755270/ea1b3592-dc3a-4581-86ad-447709eec5bf)\n\n# 💡 **The Solution**\n\nFINNY is reimagining organic growth for financial advisors with AI. Our first product, a multi-channel Sales agent, allows users to sit back and relax while it goes “out in the wild” (aka our massive database of high-net-worth individuals) and prospects for them. It’s like your intern, except it’s always on, running in the background.\n\nFINNY’s goal is for advisors to wake up in the morning to meetings with prospects on their calendars, entirely booked by FINNY. Our value proposition is three-fold: \n\n1. **_Prospect identification:_**\n * _We identify prospects within their target niche, aggregating thousands of data points per lead and constantly monitoring for life triggers_\n\n  \n2. **_Prospect prioritization_:**\n * We prioritize prospects based on their predicted likelihood of converting, a score unique to each advisor and prospect pair\n3. **_Prospect outreach & engagement:_**\n * _We automate the outreach and meeting scheduling with the high-priority prospects, intelligently selecting the optimal channel for the specific prospect_\n\n![uploaded image](/media/?type=post&id=82467&key=user_uploads/1755270/2df42888-af79-4d31-a06f-a153ff0487e7)\n\n# ✅ **Progress so far**\n\nIn just 3 months, we have:\n\n* Released the **1st** and **2nd iteration** of the product to 15 advisory firms.\n* Grown revenue by >**11x** MoM.\n* Got **70** firms to join our waitlist, without any acquisition efforts or outbound.\n* **Won** Morningstar’s Fintech Showcase last month.\n* Have been getting some **buzz**! [Here](https://www.businesswire.com/news/home/20240724776742/en/Future-Proof-Announces-Groundbreaking-Lineup-of-Fintech-Demo-Drop-III-Finalists) and [here](https://www.downtownjoshbrown.com/p/future-financial-advice-already-993f).\n\n \n\n![uploaded image](/media/?type=post&id=82467&key=user_uploads/1755270/5a150faf-0c65-424a-96f1-90f6e4c79b94)\n\n# **❓One Ask for You (If you made it this far)**\n\nIf you ever thought about working with a financial advisor, evaluated one, currently work with one _(or want to get matched with one_ **😉**_),_ we’d love to talk to you. Book 15 min with us [here.](https://calendly.com/eden-finny/finny-intro-call) If you’re in SF, we’ll buy you a coffee. If not, let’s call it an IOU.", "url": "https://www.ycombinator.com/launches/LS7-finny-supercharge-organic-growth-for-financial-advisors", "votes": 62, "created_at": "2024-07-26T15:05:12.600Z" } ] }, { "id": 29754, "name": "Conductor Quantum", "slug": "conductor-quantum", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f87b0dddb05a92646b51780f21648d12f3019bf.png", "website": "https://www.conductorquantum.com", "all_locations": "San Francisco, CA, USA", "long_description": "Conductor Quantum is building quantum superintelligence: AI that operates quantum computers to make scientific discoveries beyond human reach.\r\n\r\nQuantum computers let us understand the world at its most fundamental level, the path to new drugs, new materials, and discoveries no human can reach alone. \r\n\r\nA quantum computer is the perfect simulator of nature: it encodes the logic of reality into a programmable machine, atom by atom, electron by electron. Give AI that simulator and you open the door to discovery.\r\n\r\nThe bottleneck is operating the hardware. Today, engineers spend days or weeks by hand to bring a chip to operating conditions for just two qubits. A qubit is the information-carrying unit of a quantum computer, the equivalent of a bit in a classical one, and a useful machine needs billions. \r\n\r\nRemoving the human from that loop is only half the problem. Every command an AI sends to a quantum computer must be optimised for the specific hardware it runs on.\r\n\r\nWe build the AI that operates quantum computers and tunes every machine it runs on. That is the path to quantum superintelligence.", "one_liner": "AI that operates quantum computers for scientific discovery", "team_size": 4, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1718219316, "tags": [ "Hard Tech", "Machine Learning", "Quantum Computing", "Semiconductors", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "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": [ { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "Everyone will have access to a quantum computer from their desk." } ], "url": "https://www.ycombinator.com/companies/conductor-quantum", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/conductor-quantum.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/conductorquantum/", "x_url": "https://twitter.com/conductorquant", "fb_url": null, "cb_url": null, "github_url": "https://github.com/conductorquantum", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6f87b0dddb05a92646b51780f21648d12f3019bf.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": 1977732, "full_name": "Brandon Severin", "title": "Founder/CEO", "founder_bio": "Building quantum superintelligence.\n\nDuring my PhD at Oxford, I worked with 4 quantum institutions across the globe (IST Austria, Basel, UNSW, Diraq), developing AI for semiconductor quantum device control and published 4 papers on the topic including one in Nature. \n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/brandon-severin/", "x_url": "https://twitter.com/BrandonSeverin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/87d24eacb09bbfa920e690defd179aa0cfe720b2.jpg" }, { "user_id": 947594, "full_name": "Joel Pendleton", "title": "Founder/CTO", "founder_bio": "Co-founder of Conductor Quantum, building software for quantum computers. I’ve worked at many deep tech startups and research labs, exploring various quantum computing technologies — from carbon nanotubes to superconducting transmon qubits. I left my PhD at Oxford to start Conductor Quantum.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/joelpendleton/", "x_url": "https://twitter.com/joelpendleton", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eec48af10146303ada5db62e00483a97d82d86a9.jpg" } ], "jobs": [ { "id": 76007, "title": "Quantum Engineer (Spin)", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/mvfbgvb-quantum-engineer-spin", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Electrical", "salary_range": "$120K - $180K", "equity_range": "0.25% - 1.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python", "Design systems" ] }, { "id": 90538, "title": "Quantum Engineer (Superconducting)", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/Gcnhe4e-quantum-engineer-superconducting", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Electrical", "salary_range": "$120K - $180K", "equity_range": "0.25% - 1.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] }, { "id": 92445, "title": "AI Systems Engineer", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/bVWYarJ-ai-systems-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$120K - $180K", "equity_range": "0.25% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Microsoft Azure", "Google Cloud", "Python", "Redis", "Machine Learning", "Reinforcement learning (RL)", "Design systems", "Next.js", "FPGAs" ] }, { "id": 102431, "title": "Quantum Engineer (Neutral Atom)", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/SjZPBMm-quantum-engineer-neutral-atom", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Electrical", "salary_range": "$120K - $180K", "equity_range": "0.25% - 1.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] }, { "id": 102432, "title": "Quantum Engineer (Ion Trap)", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/eV4F5ew-quantum-engineer-ion-trap", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Electrical", "salary_range": "$120K - $180K", "equity_range": "0.25% - 1.50%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python" ] }, { "id": 103103, "title": "Executive Assistant / Lab Operations Manager", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/1vOd00H-executive-assistant-lab-operations-manager", "location": "Albuquerque, NM, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$55K - $75K", "equity_range": "0.05% - 0.15%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Python" ] }, { "id": 104396, "title": "AI Systems Engineer (NM)", "url": "https://www.ycombinator.com/companies/conductor-quantum/jobs/OjWoAtF-ai-systems-engineer-nm", "location": "Albuquerque, NM, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$75K - $125K", "equity_range": "0.25% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 97009, "title": "Coda — Natural language quantum computing", "tagline": "Describe your problem and execute it on real quantum computers.", "body": "![uploaded image](/media/?type=post&id=97009&key=user_uploads/1977732/6b78d7ee-645f-420a-8e65-86a4e4da0982)\n\n**_TL;DR Quantum computing will not scale if every program is a hand-written circuit. The future is describing what you want a quantum computer to do and letting the system handle the rest._**\n\nHi - We are Brandon and Joel from Conductor Quantum.\n\nWe’re announcing Coda - our natural language interface for quantum computing.\n\nQuantum computers offer a new computing paradigm for simulating the world at the atomic level. This opens up new possibilities in chemistry, materials science, and drug discovery that push beyond what the largest classical supercomputers can handle.\n\n## Problem\n\nUsing quantum computers today requires deep domain expertise and low-level tooling.\n\n## Solution\n\n\n\nType your problem into a box, Coda builds the quantum circuit, verifies the code and runs it on current state-of-the-art quantum computers.\n\nIf you’re completely new to quantum, we’ve included a learn mode to get you started. One of the first quantum computers we’re offering on our platform is [Rigetti’s (S14)](https://www.ycombinator.com/companies/rigetti-computing) 84-qubit quantum computer. We also support simulations up to 34 qubits supported by NVIDIA cu-Quantum.\n\n## Why?\n\nWe’re building Coda because we want everyone to be able to access a quantum computer from their desk, as soon as possible.\n\n## What we’ve achieved so far\n\n* Built the first API for low-level silicon quantum chip control.\n* Partnered with quantum chip maker SemiQon and ran our software across 64 of their quantum devices.\n* Shipped quantum control software for companies including Quobly and EeroQ.\n\n## What’s next?\n\n* End-to-end GPU + QPU orchestration, so classical and quantum computation work together in a single workflow.\n* Connecting Coda to our lower-level quantum control and tune-up software, closing the loop from high-level intent to device-level execution.\n* Pushing our control software to larger numbers of qubits as hardware scales.\n\n## Team\n\n![uploaded image](/media/?type=post&id=97009&key=user_uploads/1977732/fca09063-084c-4734-b9ec-0edde6ec9dd3)\n\n[Brandon](https://www.linkedin.com/in/brandon-severin/), CEO, Previously a postdoc at Oxford, with multiple Nature journal publications on silicon quantum computing. Developed early AI systems for automated qubit formation. PhD, Oxford.\n\n[Joel](https://www.linkedin.com/in/joelpendleton/), CTO, Built machine-learning–based control software for superconducting qubits at QuantrolOx, with prior roles at Quantum Motion and C12. MSc Physics, UCL. Left machine learning and quantum computing PhD at Oxford to start Conductor.\n\n[Raymond](https://www.linkedin.com/in/raymond-jow-190654145/), Founding Engineer, Former SpaceX guidance, navigation, and control software engineer whose code flew on 100+ Falcon 9 missions. Previously worked on robotics at NASA and quantum machine learning at Cornell. BS Physics and Computer Science, Harvard.\n\n## Asks\n\n* Join our team. We’re looking for hardcore full-stack quantum engineers who want to work across the entire stack - from device fabrication, fridge wiring to qubit operation. As well as engineers focused on quantum algorithms and applications.\n* Intros to companies based on hard science - think pharma, biotech, materials, chemistry, engineering who are interested in leveraging quantum computing in their workflows.\n* Email us at [founders@conductorquantum.com](mailto:founders@conductorquantum.com) \n* Try Coda here: [https://coda.conductorquantum.com/](https://coda.conductorquantum.com/login)", "url": "https://www.ycombinator.com/launches/PEf-coda-natural-language-quantum-computing", "votes": 10, "created_at": "2026-01-21T17:45:59.744Z" }, { "id": 83306, "title": "Conductor Quantum - Quantum computers on silicon chips using AI", "tagline": "Our AI software creates qubits 1000x faster than current methods.", "body": "**Tl;dr:**\n\n* [Conductor Quantum](https://www.conductorquantum.com) is using **AI software** to **create qubits** in **semiconductor chips** to **build quantum computers** at scale.\n* The two founders met during their **PhDs at Oxford**. They have experience at 5 quantum companies and institutions, plus 4 publications between them.\n* Conductor Quantum is launching **the first publicly available API** to **classify quantum transport features in semiconductors**.\n\n# **🧑‍🔬🧑‍💻The Team**\n\nHi - We are Brandon and Joel! We are building Conductor Quantum.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdZn6_SJC2SQu80WOKYWfJsFE9MCQt2R9PXG6mXNMb3co5B6iBYNZ7ckJ8i8Mgd01fTALt1oZeeLzN19Lc68mTVIqnZZZeJjPQVt_I6ilRcz1m8J-WB-L3xn-zcN4Hci1MBDC6UggpvjN13o_5EMAk3hCWM?key=M7s5FyBBuze--OVSs9y0AQ)\n\nCEO, [Brandon](https://www.linkedin.com/in/brandon-severin/) – Brandon developed **_AI for Quantum Computing in Silicon_** during his **PhD** at **Oxford**. Over the past four years, Brandon has worked with four top quantum research institutions on **software for quantum device control** and has **four publications**, including one in **Nature**.\n\nCTO, [Joel](https://www.linkedin.com/in/joelpendleton/) – Joel has shipped software at **four deep tech companies** and has worked with a series of quantum devices ranging from **carbon nanotubes to superconducting circuits**. Joel studied **Physics at UCL** and **dropped out of his PhD** at **Oxford** to start Conductor Quantum.\n\nDuring Joel’s PhD at **Oxford**, he launched Feynman.ai— an AI science research assistant— with Brandon, which they bootstrapped to **300 signups within a month** of launch.\n\n# **😯 The Problem**\n\n* **Quantum computers** will **revolutionize drug discovery** and **material development** as we will, for the first time, be able to accurately **simulate nature at its most fundamental level**.\n* Leveraging the **trillion-dollar semiconductor** industry, silicon chips offer a **scalable architecture** for building quantum computers.\n\n→ Currently, quantum engineers **spend days, sometimes weeks,** manually configuring silicon chips to **create a single quantum bit (qubit)**, analogous to a bit in a classical computer.\n\n**→ We need millions, if not billions, of qubits** to make a **useful quantum computer.**\n\nAutomation software to create qubits at scale is vital to realize this goal.\n\n# **✅ Our Solution - AI software to create qubits**\n\n* We are building **leading AI software** that creates qubits by **learning and understanding the principles of quantum transport** in semiconductor chips.\n* Our AI software will **replace the human** in the loop, **unlocking rapid qubit generation** and **fabrication feedback iteration cycles**.\n* Automatic qubit creation and control are the foundation of **the first quantum operating system** on **semiconductor chips**.\n\n[Our AI Playground](https://youtu.be/FBeTFudh-Jc)\n\n# **🤝 How You Can Help**\n\n* Sign up for our [**waitlist**](https://www.conductorquantum.com) (Announcements soon!)\n* Quantum engineers who want to get back to building and save themselves 100s of hours per qubit created - Email us at [founders@conductorquantum.com](mailto:founders@conductorquantum.com)\n* We’d love intros to:\n * Chip designers/verifiers/manufacturers\n * Anyone who wants to **bring chip manufacturing back to the USA**", "url": "https://www.ycombinator.com/launches/Lfe-conductor-quantum-quantum-computers-on-silicon-chips-using-ai", "votes": 100, "created_at": "2024-08-22T17:05:04.031Z" } ] }, { "id": 29791, "name": "Cartage", "slug": "cartage", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3d0afccc0962e52bb6440d323cf8b43f42b8a268.png", "website": "https://cartage.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Cartage is the future of freight coordination. Transparent, tech-driven and eliminating the need for human coordinators.", "one_liner": "Autonomous freight coordination", "team_size": 12, "industry": "B2B", "subindustry": "B2B -> Supply Chain and Logistics", "launched_at": 1716915052, "tags": [ "Machine Learning", "Workflow Automation", "Logistics", "Supply Chain", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Supply Chain and Logistics" ], "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/cartage", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/cartage.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cartage-ai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3d0afccc0962e52bb6440d323cf8b43f42b8a268.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/016bceb345b673eb7a6146a8648f6fae229d760b.png" ], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 1567005, "full_name": "Abdul Basharat", "title": "Founder/CEO", "founder_bio": "Abdul is cofounder and CEO of Cartage. He started his career training as a military pilot. He was the product lead for network and PLG at Rose Rocket (YC S16 trucking ERP) and helped Pathstream launch a B2B platform from 0-1. Prior to tech he worked in management consulting. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/abashara/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9c376ce0fbd19db66106190530c91c82235a0006.jpg" }, { "user_id": 1434656, "full_name": "Josh Lampen", "title": "Founder/CTO", "founder_bio": "Josh is cofounder and CTO of Cartage. At Rose Rocket (YC S16 trucking ERP), he was a founding engineer on their Platform team, where he led the development of their first workflow engine. He was also an engineer at Together (YC S19). Prior to tech, Josh was a management consultant, and played water polo for Canada's National Team.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/joshlampen", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/538e12fe798236176632daab0ef4a2e99faeb3c8.jpg" } ], "jobs": [ { "id": 77197, "title": "Sales Development Representative", "url": "https://www.ycombinator.com/companies/cartage/jobs/1QP2BxS-sales-development-representative", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$60K - $90K", "equity_range": "0.30% - 0.70%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 82372, "title": "Full Stack Engineer", "url": "https://www.ycombinator.com/companies/cartage/jobs/xVIZlS8-full-stack-engineer", "location": "San Francisco, CA, US / Vancouver, BC, CA / Vancouver, British Columbia, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "0.50% - 2.00%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 87895, "title": "Autonomy Operations Architect", "url": "https://www.ycombinator.com/companies/cartage/jobs/LDjKBtk-autonomy-operations-architect", "location": "Vancouver, BC, CA / Vancouver, British Columbia, CA / San Francisco, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$50K - $80K", "equity_range": "0.02% - 0.10%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 93487, "title": "Autonomy Manager", "url": "https://www.ycombinator.com/companies/cartage/jobs/u04pgWD-autonomy-manager", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$100K - $150K", "equity_range": "0.20% - 0.50%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 116077, "title": "Growth Lead", "url": "https://www.ycombinator.com/companies/cartage/jobs/zRsevOC-growth-lead", "location": "Vancouver, BC, CA / Vancouver, British Columbia, CA / San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$80K - $180K", "equity_range": "0.20% - 0.60%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Growth design" ] } ], "news": [ { "title": "Cartage secures $3.3M to support shippers and carriers with automation - FreightWaves", "url": "https://www.freightwaves.com/news/cartage-secures-3-3m-to-support-shippers-and-carriers-with-automation", "date": "Oct 10, 2024" } ], "launches": [ { "id": 83370, "title": "Cartage: Autonomous freight operations 🚚", "tagline": "Service-as-software that replaces freight brokers and logistics teams.", "body": "**_Tl;dr:_** You don’t need people to coordinate freight. We replace brokers and logistics teams, cut costs by 30%, and embed visibility into shipping.\n\n![uploaded image](/media/?type=post&id=83370&key=user_uploads/1567005/0930f62f-8507-486b-a01b-846b7bceea4e)\n\n**🫠 Problem**\\\nManufacturers and distributors in North America spend $400 billion every year on third-party vendors to coordinate freight shipments for them. These vendors are inefficient and opaque and inflate freight spend by up to 50%.\n\n![uploaded image](/media/?type=post&id=83370&key=user_uploads/1567005/029b60ed-1218-4b77-ab82-d9e67e073503)\n\n**😁 Solution**\\\nCartage is a service-as-software system that coordinates freight shipments between shippers and trucking companies. We replace freight brokers and internal logistics teams. By requiring minimal headcount to coordinate freight, we afford margins traditional vendors can’t, reducing freight costs for shippers by up to 30%.\n\nIn addition to cheaper shipping costs, we embed technology into the shipping experience, providing all stakeholders visibility and transparency on shipments.\n\n![uploaded image](/media/?type=post&id=83370&key=user_uploads/1567005/3986ca96-2699-4343-9aa8-03854af6c159)\n\n**😁 Impact**\\\nWe started by selling software to freight brokers, growing to $150k ARR in under 4 months full-time, but since pivoting to replacing brokers, we’ve received immediate love from shippers.\n\nWe’ve signed 8 shipper customers since launching in early August, and have acquired [Westcore Logistics](https://westcorelogistics.com/), [Canada’s fastest-growing logistics company](https://www.newswire.ca/news-releases/westcore-logistics-places-no-3-on-the-globe-and-mail-s-fifth-annual-ranking-of-canada-s-top-growing-companies--867532237.html) in 2023. We are on pace to coordinate more than $2m in freight shipments over the next six months.\n\n**👨‍💻 Team**\\\n[Abdul Basharat](https://www.linkedin.com/in/abashara/) (CEO) and [Josh Lampen](https://www.linkedin.com/in/joshlampen/) (CTO) were early employees at [Rose Rocket (YC S16)](https://www.roserocket.com/), a leading ERP solution for trucking companies. [Harman Sahota](https://www.linkedin.com/in/harmansahota/) (COO) founded Westcore Logistics, scaling it from 0 to $50M+ in revenue within four years.\n\nFun facts:\n\n* Abdul trained as a military pilot\n* Josh played water polo for Team Canada\n* Harman started brokering freight at 14\n\n![uploaded image](/media/?type=post&id=83370&key=user_uploads/1567005/773960d0-c363-46a0-aca9-7b09af81e61b)\n\n", "url": "https://www.ycombinator.com/launches/Lgg-cartage-autonomous-freight-operations", "votes": 47, "created_at": "2024-08-24T19:24:05.802Z" } ] }, { "id": 29957, "name": "Anglera", "slug": "anglera", "former_names": [ "Angler" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2dfb2adf611e11737fa14a1b9b4be581858cfc3.png", "website": "https://www.anglera.com/", "all_locations": "San Francisco, CA, USA", "long_description": "At Anglera, we're developing a suite of AI agents to help e-commerce companies run their operations more efficiently. Our flagship agent helps our customers onboard, enrich, and manage their product data, reducing time per product from 15 mins down to 5 seconds.\r\n\r\nWe previously developed ML to automatically enrich millions of products at Uber Eats, and we're now on a mission to automate the most common manual workflows for every e-commerce business.", "one_liner": "AI-Powered Product Data Enrichment", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1719875304, "tags": [ "Machine Learning", "SaaS", "B2B", "E-commerce", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "B2B", "Retail" ], "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/anglera", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/anglera.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/anglera/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/anglera", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a2dfb2adf611e11737fa14a1b9b4be581858cfc3.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": 1164027, "full_name": "Amay Aggarwal", "title": "Founder/CEO", "founder_bio": "Amay is the Co-Founder of Anglera. \nHe graduated from Stanford with Bachelor's & Master's degrees, specializing in AI/ML.\nPreviously, he led Catalog AI efforts to onboard, enrich, & standardize millions of products sold on Uber Eats.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/amayaggarwal/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3b3efa8fd2d4d81bb68515971d014bc0e51d048d.jpg" }, { "user_id": 497318, "full_name": "Ray Iyer", "title": "Founder/CTO", "founder_bio": "Ray is the Co-Founder of Anglera. He graduated from Stanford with a BS and MS in Computer Science, specializing in Systems & AI. Previously, he launched the first CPG Ads products at Uber Eats, and worked in engineering roles at Meta, Verkada, & Microsoft.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/~iyer", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/556924594c0533b9f4e05f5ef281f52bb996f327.jpg" } ], "jobs": [ { "id": 83916, "title": "Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/anglera/jobs/zm2yMzn-forward-deployed-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$125K - $160K", "equity_range": "0.25% - 0.75%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Python", "SQL" ] }, { "id": 96228, "title": "Growth Lead", "url": "https://www.ycombinator.com/companies/anglera/jobs/0EcFRvd-growth-lead", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Marketing", "role_type": null, "salary_range": "$80K - $120K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 91364, "title": "Anglera: AI Product Data Enrichment", "tagline": "We're building the product data infrastructure for the future of AI-driven commerce", "body": "**TLDR:** Anglera is the foundational AI layer that transforms messy product data into structured, searchable, enriched content at scale.\n\n🎥 **See our AI agents in action**: https://youtu.be/zlLaT-Uv9Xs\n\n---\n\n### **🔥 The Problem**\n\nWhile everyone's building AI apps, there's a massive infrastructure gap underneath: **product data is broken at scale.**\n\n* **Poor Discoverability** - 20% of searches return zero results due to missing/inconsistent information\n* **Low Conversion** - Data errors cause 23% loss in impressions and 14% drop in conversions\n* **Slow Time-to-Market -** Weeks of delays getting new products live due to manual data entry\n\n**Result:** Billions in lost revenue from broken infrastructure.\n\nThe tech stack looks like this today:\n\n```\nBeautiful frontend shopping UI/UX\n↓\nIntelligent search & discovery algorithms\n↓\nMessy, unstructured product data ← ✨ We fix this\n```\n\n### **🧠 Our Approach**\n\nWe've built AI agents that automatically transform any product data source into clean, structured, enriched content:\n\n* **Input:** Messy spreadsheets, PDFs, images, brand websites, supplier feeds \n* **Output**: Complete, enriched product catalogs, continuously optimized for AI discoverability\n* **Scale:** Process thousands of SKUs in seconds, not weeks.\n\n---\n\n### **🎯 Why Now?**\n\nTwo massive shifts are happening simultaneously:\n\n1. **AI Search Explosion:** ChatGPT, Perplexity, and others are becoming primary product discovery channels\n2. **Agentic Commerce:** AI agents are starting to make purchases autonomously\n\n---\n\n### **🤝 The Founding Story**\n\n![uploaded image](/media/?type=post&id=91364&key=user_uploads/1164027/c4bc63d9-8268-43bc-8c95-118fbac2588b)\n\n**Amay** and **Ray** - Stanford AI grads who lived this pain at Uber Eats scale:\n\n* **Amay:** Led Catalog AI, enriching millions of SKUs\n* **Ray:** Led CPG Ads, scaling product-driven ad campaigns\n\nNow we're bringing that same level of sophistication to every retailer.\n\n---\n\n### **🔮 The Vision**\n\nImagine a world where:\n\n* Every product is instantly discoverable by AI agents\n* Product information flows seamlessly between systems\n* New products go to market in hours, not months\n\n---\n\n### **🙏 Our Ask**\n\nWe're looking for connections to:\n\n* **Technical leaders** at retailers/marketplaces dealing with product information challenges\n* **AI shopping, search, or marketplace founders** who might benefit from what we're building\n\n\\- Amay, Ray & the Anglera Team\n\nWebsite: [www.anglera.com](https://www.anglera.com)\n\nEmail: [founders@anglera.com](https://founders@anglera.com)", "url": "https://www.ycombinator.com/launches/Nlc-anglera-ai-product-data-enrichment", "votes": 13, "created_at": "2025-06-17T13:40:54.115Z" }, { "id": 82584, "title": "🛍️ Anglera: The smartest way to manage e-commerce products", "tagline": "Turn complex product data into high-converting listings in seconds", "body": "**TLDR**: [Anglera](https://www.anglera.com) is an AI-powered platform designed to help e-commerce companies manage their product data. We empower brands, retailers, and marketplaces to effortlessly onboard, enrich, and list hundreds of product SKUs. This saves time and improves sales. Contact us to learn more: [amay@anglera.com](mailto:amay@anglera.com)\n\n### **👋 Who we are**\n\nHello! We’re [Amay](https://www.linkedin.com/in/amayaggarwal/) & [Ray](https://www.linkedin.com/in/\\~iyer), and we’re on a mission to redefine how e-commerce companies manage their product data.\n\n![uploaded image](/media/?type=post&id=82584&key=user_uploads/1164027/42a17226-77c0-4c1d-8fb9-cab72b34aaf6)\n\nWhether you’re a brand, retailer, or marketplace, we know that dealing with e-commerce products can be challenging. We faced this problem first-hand at Uber Eats, where we built tech to enhance millions of listings uploaded by our restaurant and retail partners.\n\n### **🔍 Problem**\n\nMost rapidly growing e-commerce companies spend several hours each week managing their products across sales channels. Teams drown in spreadsheets, manually adding descriptions, images, and attributes for hundreds of items. This time-consuming process diverts focus away from actually growing their core business.\n\n### **💡 Solution**\n\nWe’re tackling the $20B Product Information Management (PIM) industry by building the smartest platform for managing e-commerce product data.\n\n\n\nAnglera empowers every e-commerce business with their own AI catalog ‘expert’ trained on thousands of top-converting product listings. We not only help our customers onboard new products faster, but also improve all of their listings to drive better sales.\n\n**Key Features:**\n\n* **✅ Faster Time-To-Market**: Instantly onboard products from any source (Website URL, Spreadsheet, PDF, etc.)\n* **​✅ Better Sales**: Automatically enrich descriptions, images, & attributes to drive conversion\n* **✅ Wider Reach**: Start selling on new channels with just one click (Shopify, Amazon, etc.)\n\nWe measure success for our customers with two simple metrics: 1) $ increase in sales, and 2) # of hours saved.\n\n### **🙏 Asks**\n\n1. **Let’s chat** if you're a brand/retailer/marketplace looking to improve how you manage your product listings. Happy to share data-driven learnings from years of experience working on this at Uber Eats.\n2. **Introduce us** to any e-commerce companies in your network. Whether it's a friend running their own brand or someone who works at a larger retailer or marketplace, we’d love to connect.\n\nContact: [amay@anglera.com](mailto:amay@anglera.com) | Website: [www.anglera.com](http://www.anglera.com) | Calendar: [Book Time](https://calendly.com/amay-anglera/15-min)", "url": "https://www.ycombinator.com/launches/LU0-anglera-the-smartest-way-to-manage-e-commerce-products", "votes": 45, "created_at": "2024-07-30T21:50:52.321Z" } ] }, { "id": 30016, "name": "Zeroframe", "slug": "zeroframe", "former_names": [ "Andoria AI", "Andoria" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/3e936e66c47aaa18d5b9994edfdeb20a8146b9b4.png", "website": "https://zeroframe.ai", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Pushing the boundaries of AI", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1730323819, "tags": [ "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Inactive", "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/zeroframe", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/zeroframe.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/andoriaai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/3e936e66c47aaa18d5b9994edfdeb20a8146b9b4.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": 1079966, "full_name": "Daryl Budiman", "title": "Founder", "founder_bio": "Co-Founder @ Andoria. Previously Founding Team @ MultiOn & Stanford", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/dbudiman/", "x_url": "https://x.com/dbudimane", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4931057cab90a4b109a1408a2fef5bd1c95bd509.jpg" }, { "user_id": 1002589, "full_name": "Anirudh Ramprasad", "title": "Founder", "founder_bio": "Co-Founder @ Andoria", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/anirudh-ramprasad-7aa7b8215/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4e2c339cc5343909abf0e57c2deb71a924fe011c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 85184, "title": "Andoria - AI customer agent that can interact with the user's interface", "tagline": "When your users get stuck, Andoria steps in and helps them use your software", "body": "[Andoria](https://andoria.ai/) is an **AI customer agent that can interact with your user’s interface**. The AI agent learns how your web application works and generates a custom walkthrough whenever a user needs help. When a user gets stuck, Andoria can step in, and show them exactly what to do by performing such actions.\n\n### **Problem**\n\n* Today, AI customer support solutions only give back text responses\n* Most companies today put the burden of learning how to use their software on the user, whether it be through documentation, video tutorials, or simply leaving them to learn by themselves. Help happens out of the platform\n* People don’t know how to use the software → they don’t get value or use the software wrongly → churn\n\n### **Solution**\n\n* Imagine giving each one of your users their dedicated user assistant that can show them how to use the application whenever they need help\n* Our AI agent learns how your web application works, gathers data about your user (e.g. background, goal), and generates the right custom walkthrough based on their goal\n* The best part: setup is only pasting a script tag into your HTML & Andoria takes it from there\n\n### **Asks**\n\n* If you’re interested in setting up Andoria for your users, feel free to schedule a call with us [here](https://andoria.ai/)\n* Warm intros to customer success + support teams who are looking to go beyond text-based chatbots\n\n### **Our Team**\n\n[Daryl](https://www.linkedin.com/in/dbudiman/) and [Anirudh](https://www.linkedin.com/in/anirudh-ramprasad-7aa7b8215/) were part of the early team at MultiOn (backed by General Catalyst and Amazon) where we built autonomous AI web agents that can interact with your browser. We scaled the business to a $XXXM valuation in under 9 months. We previously designed and built agentic systems and interfaces at Amazon & Actively AI.\n\n![uploaded image](/media/?type=post&id=85184&key=user_uploads/1079966/da263644-0bbf-45fc-b5f2-abeea65084cf)\n\n", "url": "https://www.ycombinator.com/launches/M9w-andoria-ai-customer-agent-that-can-interact-with-the-user-s-interface", "votes": 40, "created_at": "2024-10-31T16:57:51.567Z" } ] }, { "id": 30040, "name": "supercontrast", "slug": "supercontrast", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/790820337661ae1bc9c065bae89b47e0ad126432.png", "website": "https://supercontrast.com/", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "At Hive we worked on a product called Gencraft, an ai art generator we ramped from 0 to $1M ARR in 6 months. This inspired us to build supercontrast, an AI Copilot which empowers anyone to create and refine high quality designs and assets. ", "one_liner": "AI Co-Pilot for Design", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1727282803, "tags": [ "Machine Learning", "Design", "Design Tools" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Inactive", "industries": [ "B2B", "Engineering, Product and Design" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/supercontrast", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/supercontrast.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/supercontrast/?viewAsMember=true", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/790820337661ae1bc9c065bae89b47e0ad126432.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 1571215, "full_name": "Carter McClellan", "title": "Founder", "founder_bio": "CEO at supercontrast. Previously MLE/ Tech Lead at Hive AI", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/carter-mcclellan/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/62c1452599da597d8c56651c350b4c204c30e2b7.jpg" }, { "user_id": 1572927, "full_name": "Shravan Kumaran", "title": "Founder", "founder_bio": "CTO at supercontrast, an AI copilot for creating designs and assets. Previously worked on AI asset generation for video games at Google Stadia, and developed Gencraft at Hive, an AI art generation tool that went from 0 to over $1M ARR in 6 months.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/shravan-kumaran-7b2b80123/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/86a4896216e3973d6f7a017acc5cee68a31661cf.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30045, "name": "Archil", "slug": "archil", "former_names": [ "Neptune Storage", "Regatta Storage" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0695554aaa43f831a6d970c73486aad8f7a9101f.png", "website": "https://archil.com", "all_locations": "San Francisco, CA, USA", "long_description": "Archil transforms S3 buckets into a 30x faster, unlimited, local disk. Archil enables AI, analytics, and serverless applications to instantly access massive data sets without waiting for data transfer. Researchers use Archil for shareable, local storage of data set and model versions that never runs out of capacity.", "one_liner": "The high-performance file system that connects AI to data", "team_size": 11, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1727196277, "tags": [ "Developer Tools", "Machine Learning", "Big Data", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/archil", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/archil.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/archil", "x_url": "https://x.com/archildata", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0695554aaa43f831a6d970c73486aad8f7a9101f.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": 2542, "full_name": "Hunter Leath", "title": "Founder/CEO", "founder_bio": "I'm building Archil, a cloud file system with unlimited capacity, local-like performance, and instant access to massive data sets in your S3 buckets. I've spent the past 9 years building cloud storage products at AWS and Netflix, and I'm thrilled that teams can finally stop managing disks.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hleath/", "x_url": "https://x.com/jhleath", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/211cb23fe3bd004ef4434c9069d5d0694c5b647e.jpg" } ], "jobs": [], "news": [ { "title": "Archil - All of your data, on every instance, instantly", "url": "https://archil.com/post/archil-file-system-to-data-company", "date": "Jun 02, 2025" }, { "title": "The Quest for a Distributed POSIX-Compatible Filesystem", "url": "https://materializedview.io/p/the-quest-for-a-distributed-posix-fs", "date": "Dec 05, 2024" } ], "launches": [ { "id": 85093, "title": "Archil: Transform S3 into an infinite, local file system", "tagline": "AI and analytics teams use Archil to instantly access massive S3 data sets, without waiting for data transfer or worrying about capacity.", "body": "_TL;DR:  Archil transforms S3 buckets into infinite, local file systems. It’s 30x faster than accessing S3 directly, and 90% cheaper than manually managing EBS disks. Archil provides instant access to data in S3, and it powers AI applications that can’t wait for massive data sets to download. Archil doesn’t require code changes because it’s just a file system. You can try it today at [archil.com](https://archil.com)._\n\nI’m Hunter, the founder of Archil. I’ve spent the past 9 years building cloud storage products on the frontier of what’s possible – leading both Amazon’s Elastic File System product and Netflix’s cloud storage team. During this time, I worked with thousands of application developers, cloud teams, and software providers to deeply understand how they use storage.\n\nHere's the problem: I found that AI and analytics teams love storing their data in S3 because it’s low-cost and scalable, but their applications can only use this data once it’s in a local file system. As a result, these teams are constantly moving data back and forth from their local instance file systems to S3. For larger data sets, applications can wait for hours to download and decompress data before starting. Unlike S3, local file systems have a limited amount of capacity that you can’t easily change after creation, they don’t support sharing data, and they’re 10x more expensive.\n\nArchil transforms your existing S3 buckets into a new kind of infinite, local file system. With Archil, your applications can instantly use petabyte-scale data sets in S3 without waiting for manual data download. Archil automatically scales with your data, and you only pay for the data you’re actively using – 90% cheaper than using EBS. With Archil’s shared, high-speed cache, all your instances and containers can take advantage of 30x faster, consistent reads and writes. When you’re done editing data, it automatically flows back to S3 within a few minutes, so you can easily share results with teammates. Archil has full POSIX compatibility (including renames, appends, and locking), so it’s already compatible with your applications – no code changes needed. Teams move faster when they use Archil because they don’t have to build custom data movement infrastructure.\n\n[Check out how easy Archil is to set up and use.](https://www.tella.tv/video/cm2num964000e03jng6jl5d9c/view)\n\nWe are really excited about how Archil will help teams simplify their data management. We are currently working with: AI teams who want to start model inference without waiting for downloads to complete, researchers who want to experiment on models and create new versions without running out of disk capacity, analytics teams who want to speed up their batch pipelines, and cloud teams who want lower-cost local storage without worrying about utilization. We’d love to work with you, too. If you have any questions, feel free to email me at [hleath@archil.com](https://mailto:hleath@archil.com). You can try Archil today by signing up at [archil.com](https://archil.com).\n\nLet’s build the future of the cloud together.\n\n![uploaded image](/media/?type=post&id=85093&key=user_uploads/2542/df85a282-87df-4986-b3c7-9cb58acc84ab)", "url": "https://www.ycombinator.com/launches/M8T-archil-transform-s3-into-an-infinite-local-file-system", "votes": 65, "created_at": "2024-10-28T17:59:23.946Z" } ] }, { "id": 30048, "name": "Matcha", "slug": "astrix-health", "former_names": [ "Astrix Health" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/03a2402a95d35a5f582628b40db3166e9aba2e26.png", "website": "https://www.matcha-health.com", "all_locations": "New York City, NY, USA", "long_description": "We're building Matcha, the faster, smarter way to hire clinical talent—helping hospitals find active, qualified candidates at a fraction of the time and cost of job boards or headhunters.\r\n\r\nMatcha engages candidates at scale, matching them to your organization by qualifications and cultural fit. Recruiters save time, hospitals save money, and candidates get a better experience—a radically better hiring model for everyone involved.", "one_liner": "The best way to hire clinical talent.", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare", "launched_at": 1728327995, "tags": [ "Machine Learning", "Marketplace", "Recruiting", "Healthcare", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "Healthcare" ], "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/astrix-health", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/astrix-health.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/matcha-health", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/astrix-health", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/03a2402a95d35a5f582628b40db3166e9aba2e26.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 457535, "full_name": "Abhinav Swaminathan", "title": "Founder/CEO", "founder_bio": "Co-founder / CEO @ Astrix Health.\n\nBackground in economics and computer science. Previously led healthcare distribution and AI projects at Palantir. Before that was at various healthcare organizations working on big data projects, namely Duke Health, MedStar Health, and on the VBC side, Evolent Health.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/abuswami/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0c95fff2d738e72fbf7c4689767342d866ac0ef3.jpg" }, { "user_id": 659147, "full_name": "Thomas Blake", "title": "Founder/CTO", "founder_bio": "Founder / CTO @ Astrix Health. Studied Applied Maths at Oxford with a focus on Healthcare and Machine Learning. Spent a couple of years at Palantir, building out some of the earliest generative AI workflows in healthcare. Now focusing on using AI to reduce the administrative load on clinics and hospitals.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/thomas-blake-abbba0151/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0b946a940132f93bb2909c8a14a53640d38a6e41.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 85809, "title": "Astrix: AI purchasing agents for healthcare providers", "tagline": "Automated purchasing of the right healthcare supplies, at the right time.", "body": "Hi everyone, we are Abu and Tom, co-founders of [Astrix Health](https://www.astrix.health/)\n\n# **TL;DR**\n\nAstrix builds AI purchasing agents for healthcare providers.\n\nOur agents automatically purchase the right products at the right time, helping eliminate the $25 billion in wasted spend on medical supplies annually in the US.\n\n# **Why is this important?**\n\nThe US spends roughly $4.5 trillion per year on healthcare, with wasteful spending accounting for up to a quarter of that amount. On average, hospitals in the US waste at least $12 million each on medical supplies. Adding to the problem, nurses can spend anywhere between **20 minutes to one hour per shift** searching for medical supplies.\n\nFor clinics, this problem is even worse. Often, the same people responsible for treating patients are also in charge of purchasing supplies. They’re already stretched on time to care for patients, let alone shop around for the best deals.\n\nWhile shadowing an imaging center, we observed a nurse doubling as an office manager, simultaneously managing inventory, searching for deals, placing orders, and treating patients. When we got home we started building.\n\n![uploaded image](/media/?type=post&id=85809&key=user_uploads/659147/266d9b6a-1150-42bb-9fd1-5a79f9640753)\n\n# **Our solution**\n\nOur product has three main functions:\n\n1. **Analyze:** The agents comb through purchasing data (such as invoices) to identify high-spend areas and cost drivers.\n2. **Search:** Deployed AI agents search for better deals, considering provider-specific arrangements like preferred vendor discounts, GPO contracts, rebates, and other incentives.\n3. **Purchase:** Where appropriate, Astrix place orders when more cost-effective options are found, saving providers time and money.\n\nIn under three weeks, Astrix has started working with 10 healthcare clinics, **identifying an average 20% cost savings** across our first users’ recurring supply purchases. Our AI agents deliver immediate value, reducing expenses and freeing up providers to focus on patient care.\n\n![uploaded image](/media/?type=post&id=85809&key=user_uploads/659147/e7f5971c-af42-4f86-9f25-420e42710daf)\n\n# **Why now?**\n\nUntil recently, it was impossible to efficiently scan numerous websites and compare the myriad factors influencing purchasing decisions—such as per-unit costs, availability, expiration dates, device sizes, shipping, and more—at scale to find the best deals. Automating ERP and website navigation for purchases or negotiating with vendors was also previously unfeasible.\n\nAdditionally, we're frustrated by the poor quality and outdated nature of the incumbent supply chain software and strongly believe providers deserve better. AI offers a unique opportunity to fundamentally rethink how healthcare professionals interact with computer systems.\n\n# **Moving forward**\n\nPurchasing is just the beginning. We aim to become the comprehensive optimization layer for hospital supply chains, integrating inventory, purchasing, and patient data to put procurement operations on autopilot—ultimately reducing that **$25 billion waste to zero.**\n\nHealthcare professionals should never feel stressed about ordering or obtaining medical supplies, nor should patient care be delayed due to supply chain inefficiencies. That’s the problem we’re committed to solving.\n\n# **The team**\n\nOur team has extensive experience building AI/ML systems for major healthcare institutions. Abu and Tom, who met at Palantir, developed some of the first generative AI projects for the UK's National Health Service.\n\nAbu led Palantir's medical and pharmaceutical distribution vertical in the US and its healthcare generative AI team in the UK. Previously, he managed data projects at Duke Health and MedStar Health across clinical science projects and hospital operations.\n\nTom has experience solving optimization problems with AI from his time as an Engineer at Palantir and his Maths degree from Oxford.\n\n# **Our ask**\n\nEmail us at [founders@astrix.health](mailto:founders@astrix.health). We're seeking introductions to:\n\n* Hospitals\n* Private practices\n* Ambulatory surgical centers\n\nWe'd be incredibly grateful for any connections. As a bonus, if they share their last month's purchasing data with us, we'll provide a detailed analysis of their potential cost and time savings.", "url": "https://www.ycombinator.com/launches/MK1-astrix-ai-purchasing-agents-for-healthcare-providers", "votes": 36, "created_at": "2024-11-22T19:31:39.742Z" } ] }, { "id": 30103, "name": "Metreecs", "slug": "metreecs", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0896c8d6a3292048c3275f782ddd6f5a282d3e4a.png", "website": "https://www.metreecs.com", "all_locations": "Paris, Île-de-France, France", "long_description": "Metreecs helps retailers plan, buy, and allocate products using AI-demand forecasting. We prevent overstock and out-of-stock situations, allowing clients to eliminate waste, free up capital, and drive higher sales.", "one_liner": "AI-powered demand forecasting for retail", "team_size": 20, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1729012958, "tags": [ "Machine Learning", "Retail Tech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B", "Retail" ], "regions": [ "France", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/metreecs", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/metreecs.json", "year_founded": 2024, "city": "Paris", "country": "FR", "linkedin_url": "https://www.linkedin.com/company/metreecs/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0896c8d6a3292048c3275f782ddd6f5a282d3e4a.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": 1934075, "full_name": "Thibaut Pellegrin", "title": "Founder/CEO", "founder_bio": "Co-Founder and CEO of Metreecs. Previously co-founded a leather bag company before moving to consulting and growth equity specialised in B2B SaaS.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/thibaut-pellegrin-0b4353171/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4caa56c627d03c56ae128279655917c9d408bd16.jpg" }, { "user_id": 1951235, "full_name": "Martin Dimitrov", "title": "Founder/COO", "founder_bio": "Co-Founder and COO of Metreecs. Previously led the international expansion of Lydia in Italy, before moving into early stage venture capital where I invested in AI and B2B SaaS startups.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mardimitrov/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eca40fb99a0b6e96d223cd4d010f08a8f348d59c.jpg" }, { "user_id": 1980546, "full_name": "Elie Dufeu", "title": "Founder/CTO", "founder_bio": "Co-Founder and CTO of Metreecs. Previously graduated from École Polytechnique in Applied Mathematics and Columbia in Data Science, and developed forecasting models for financial institutions.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/elie-dufeu-goldenberg", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9b313a0036c6ce726490ac390f4081f357a0694e.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 85641, "title": "Metreecs - AI-Powered Demand Forecasting For Retailers", "tagline": "Smart inventory management with AI", "body": "**TL;DR:** Metreecs helps retailers plan, buy, and allocate products using AI-demand forecasting. We prevent overstock and out-of-stock situations, allowing clients to eliminate waste, free up capital, and drive higher sales.\n\n### **Problem 📉**\n\n* **Financial Loss**: Retailers worldwide lose over $1.8 trillion annually due to poor inventory planning. This issue stems primarily from two main problems: overstock and out-of-stock.\n* **Outdated Practices**: Retailers rely on outdated tools like Excel, causing inefficiencies, forecasting errors, and missed opportunities.\n* **Environmental Cost**: Fashion is the 3rd most polluting industry due to massive overproduction: 73% of textiles are burned or landfilled which contributes to 900 million tons of CO2 annually.\n\nIn the world of retail, inventory planning can often feel like a guessing game. One of our prospects, a company with a whopping $250M in revenue, humorously encapsulated this challenge during a recent call:\n\n> **_I quickly review our data and then check my crystal ball to pull a number out of thin air._**\n\nThis playful take highlights a serious issue: without the right tools for accurate demand forecasting, retail planners resort to guesswork.\n\n![uploaded image](/media/?type=post&id=85641&key=user_uploads/1951235/e07d224f-ffef-470b-9a82-2a12ad344d58)\n\n### **Solution 📈**\n\nMetreecs is an **AI demand forecasting software for retailers**. We analyze large sets of internal and external data to predict consumer demand. This helps our clients align their inventory with actual market trends, reducing overproduction, stockouts, and markdowns, all while automating 90% of manual tasks.\n\nOur demand forecasting models drive 3 different modules:\n\n* **Buying:** We ensure that our clients buy the right products, in the right quantities for the right locations.\n* **Reordering:** We anticipate stockouts before they occur, and suggest product replenishments to prevent them.\n* **Rebalancing:** We provide inventory reallocation suggestions to eliminate waste and meet customer demand.\n\n### **Our Team 👬**\n\n* [**@Thibaut Pellegrin**](https://www.linkedin.com/in/thibautpellegrin/) experienced the challenge firsthand—after co-founding a leather bag company and talking with industry experts, he realized there had to be a better way to manage inventory in retail.\n* [**@Elie Dufeu**](https://www.linkedin.com/in/elie-dufeu-goldenberg/), a graduate from École Polytechnique in Applied Mathematics and Columbia in Data Science, developed forecasting models for financial institutions before joining Metreecs.\n* [**@Martin Dimitrov**](https://www.linkedin.com/in/mardimitrov/) led the international expansion of Lydia in Italy, before moving into early stage venture capital where he invested in AI and B2B SaaS startups.\n\n![uploaded image](/media/?type=post&id=85641&key=user_uploads/1951235/1b4e5323-2dcf-4fb3-9c1a-88ca376ece07)\n\n### **Our Ask 🫂**\n\nIf you’ve worked in retail, sold software to retailers, or have connections in the industry, we’d greatly appreciate an introduction!\n\nFeel free to reach out at [**martin@metreecs.com**](mailto:martin@metreecs.com) or book a slot directly: [**https://calendly.com/martin-metreecs**](https://calendly.com/martin-metreecs)", "url": "https://www.ycombinator.com/launches/MHJ-metreecs-ai-powered-demand-forecasting-for-retailers", "votes": 136, "created_at": "2024-11-18T06:15:29.630Z" } ] }, { "id": 30240, "name": "Bezel", "slug": "bezel", "former_names": [ "Replenish" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6fff4fb87d06a4a76e786b38906a07e576147ff8.png", "website": "https://www.trybezel.com/", "all_locations": "New York City, NY, USA", "long_description": "Bezel helps fashion brands create virtual photo/video shoots with AI. \r\n\r\nUpload images of clothes, select the human you want to model it, and Bezel generates pictures and videos that rival a full studio production. \r\n\r\nEvery detail of the clothing is rendered flawlessly. Try it for yourself.", "one_liner": "Digital AI humans that model clothes for e-commerce brands.", "team_size": 1, "industry": "B2B", "subindustry": "B2B", "launched_at": 1755619154, "tags": [ "Generative AI", "Machine Learning", "Marketing" ], "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/bezel", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/bezel.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/bezelai", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6fff4fb87d06a4a76e786b38906a07e576147ff8.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": 717333, "full_name": "Kashyap Achar", "title": "Founder", "founder_bio": "Kashyap is the Founder and CEO of Bezel. He previously led engineering efforts at Bilt scaling a $30 billion rent rewards platform and built Americas first auto HSA payment platform ($XXM in volume in first 3 months). While at Clearco he built tech that funded $2.5 billion to e-commerce companies.\n\nHe's now working on shaking up the e-commerce marketing industry at Bezel.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kashyapachar/", "x_url": "https://x.com/_kashrocks", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/77d784f83d8550a909cbd38cfd19369100b62fe6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 93362, "title": "Bezel: AI human fashion models for e-commerce brands.", "tagline": "Stop hiring production agencies, and start cranking out creatives with AI", "body": "**Bezel turns days-long apparel photo and video shoots into studio-quality images and videos in seconds—with AI and a few clicks.** What once costed $5,000 a shoot and required a full production team can now be created for a fraction of the price, instantly.\n\n**_Promo:_** All new signups this month get **10,000 free credits** on Bezel—enough to generate hundreds of stunning images for your brand.\n\n# [https://youtu.be/PKG8REHmbNM\\\\](https://youtu.be/PKG8REHmbNM%5C*%5C*%5C%5C)\n\n# **The Problem**\n\nApparel brands invest over $100k annually on photo and video shoots to produce marketing content. Yet, even after this significant spend, marketing teams often lack a sufficient variety of high-quality imagery and video to develop fresh, high-performing ads and creatives at scale.\n\nDue to a constant need to remix visuals, teams quickly exhaust their original shoot assets, creating a bottleneck that limits growth and stifles ad performance.\n\nThis gap between content needs and available assets slows experimentation, increases acquisition costs.\n\n# **Our Solution**\n\nBezel makes it easy for e-commerce teams (small and large) to create realistic, studio-quality images and videos of their apparel on AI-generated human avatars.\n\n![uploaded image](/media/?type=post&id=93362&key=user_uploads/717333/b7ddc2ed-00bb-4fd3-8a5b-da5e30443a38)\n\n![uploaded image](/media/?type=post&id=93362&key=user_uploads/717333/d868f009-c760-47c1-ab79-a1992b799112)\n\nJust upload your clothing images and Bezel quickly produces the visuals you need for ads, websites, and social media. No photo shoots, complex AI prompts, or creative delays. Bezel gives you the ready-to-use assets to power your next campaign.\n\n![uploaded image](/media/?type=post&id=93362&key=user_uploads/717333/dd2765d8-23af-454f-a4f4-3ad91209ada1)\n\n# **Key Features**\n\n* **Preset Human Models:** Choose from a wide variety of realistic male and female AI models in multiple ethnicities.\n* **Instant Studio Shoots:** Generate studio-quality, static images of any apparel in seconds—no photo shoots needed.\n* **Effortless AI Video Shoots:** Instantly create photorealistic videos of your apparel on human avatars, perfect for ads and social channels.\n* **No AI Prompting Required:** Just upload your clothing image and Bezel handles the rest, with no technical skills needed.\n* **Flexible Formats:** Download assets in both vertical and horizontal aspect ratios for websites, ads, and social use.\n* **Self-Serve Platform:** Designed for fast, easy content creation meaning no waiting for bulk processing or outside support.\n\n![uploaded image](/media/?type=post&id=93362&key=user_uploads/717333/c79768da-8bae-4fe8-9875-cb224f3ac365)\n\n# **Our Ask**\n\nIf you know any apparel brands put us in touch with them [founders@trybezel.com](https://mailto:founders@trybezel.com)\n\n# **The Team**\n\n[Kashyap ](https://www.linkedin.com/in/kashyapachar)- Previously built technology at Clearco to fund $2.5 billion dollars of marketing spend to e-commerce brands. He also scaled a $30 billion dollar consumer rent payment platform at Bilt and built America’s first automatic HSA payment platform.", "url": "https://www.ycombinator.com/launches/OHq-bezel-ai-human-fashion-models-for-e-commerce-brands", "votes": 78, "created_at": "2025-08-27T14:03:14.046Z" } ] }, { "id": 30247, "name": "Osmosis", "slug": "osmosis", "former_names": [ "Gulp" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0e0e653e5da051b7c87a56070249ad76479b0594.png", "website": "https://osmosis.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Osmosis is a post-training platform that helps companies fine-tune language models using reinforcement learning. We work with fast-growing AI companies to train task/domain-specific models that beat foundation models on performance, cost, and latency. Our platform handles compute orchestration, reward modeling, and training run observability as a CLI-based product usable by developers and agents. ", "one_liner": "Reinforcement Learning (RL) for AI Agents", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1737656598, "tags": [ "Machine Learning", "Reinforcement Learning", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "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/osmosis", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/osmosis.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/osmosis-ai/", "x_url": "https://x.com/osmosis_ai", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/gulp-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0e0e653e5da051b7c87a56070249ad76479b0594.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": 695591, "full_name": "Kasey Zhang", "title": "Founder/CEO", "founder_bio": "Currently: osmosis.ai \n\nPreviously: gaming founder, VC", "is_active": true, "linkedin_url": "https://linkedin.com/in/kasey-zhang", "x_url": "https://x.com/_WEEXIAO", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/89bfeaa626b0c7c6444e4634610ad1c228392c9f.jpg" }, { "user_id": 1479747, "full_name": "Andy Lyu", "title": "Founder/CTO", "founder_bio": "Defining how real time systems should be built. Previously worked on data architecture and ecosystem at TikTok recommendations as the 5th and youngest US hire", "is_active": true, "linkedin_url": "https://linkedin.com/in/baiqingl", "x_url": "https://x.com/viewerisland", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4acaad1754ca9e5bf80ca3d25cdc86b8226565d6.jpg" } ], "jobs": [ { "id": 88480, "title": "Machine Learning Engineer ", "url": "https://www.ycombinator.com/companies/osmosis/jobs/N17l8oV-machine-learning-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$180K - $250K", "equity_range": "", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "Reinforcement learning (RL)" ] } ], "news": [], "launches": [ { "id": 87475, "title": "Osmosis: Unlocking Real-Time Learning for AI Agents", "tagline": "Osmosis gives AI agents the ability to learn like DeepSeek R1, all in real-time ", "body": "What if AI agents could immediately learn from their mistakes?\n\n## TL;DR\n\nWhile AI agent capabilities continue to improve, the agentic workflows remain brittle since agents aren’t able to learn from tasks in real-time. Osmosis is a framework that helps your AI agents learn in real-time (similar to DeepSeek R1’s post-training loop) in order to make them better, faster, and more responsive.\n\nOn the first run, we scored 21.5% accuracy, which is 60% better than base 4o performance (16X more expensive!). **This means that even on a first run, we're 25X+ more cost effective than base 4o - and that would be significantly better on a second run.** We also averaged 7.5 steps per task while OpenAI Operator averaged 67 steps per task (faster and cheaper!).\n\nIf you’re building AI agents and want to make them better, faster, and more responsive, we’d love to chat! Email [kasey@gulp.ai](mailto:kasey@gulp.ai) or book time [here](https://calendly.com/kasey-gulp/).\n\n## The Problem\n\nAI agents still struggle with novel and complex (multi-step) tasks. Even OpenAI Operator (current state-of-the-art) just scores 58% accuracy on the WebArena benchmark. The worst part is that agents make the same mistakes again and again!\n\nAnd even when agents can solve a task, there’s generally a lack of optimization in their process. This ends up negatively affecting performance while also driving up inefficient inference costs for users.\n\n## The Solution\n\nOsmosis builds and maintains a ‘library’ of an agent’s previous interactions. It’s a super simple two line addition in a developer’s agent infrastructure that:\n\n* Ingests and understand a user’s prompt\n* Queries a vector database to search for similar past scenarios\n* Recommends a relevant plan of action for the task\n* Quantifies an agent’s performance when it finishes, and embeds the assessment\n\nJust like that, we have a flow that enables agents to learn in real-time with each successive task!\n\nHere’s a head-to-head (Operator versus Claude 3.5 Sonnet + Osmosis):\n\n\n\n**Osmosis was 3X faster and 6X more efficient in the number of steps taken!**\n\n## Our Ask\n\nIf you (or anyone you know) are building AI agents and struggling with accuracy, speed, or cost, we’d love to chat and see how we can help! Please reach out at [kasey@gulp.ai](mailto:kasey@gulp.ai), or book some time directly [here](https://calendly.com/kasey-gulp/).\n\n## The Team\n\nWe’ve (Kasey and Andy) known each other for over a decade!\n\n[Kasey](https://www.linkedin.com/in/kasey-zhang/) (CEO) previously co-founded and ran a gaming startup for two years before it was acquired. From there, he worked at various VC firms focusing on AI and data investments before deciding to jump back into building.\n\n[Andy](https://www.linkedin.com/in/baiqingl/) (CTO) previously worked at TikTok, where he joined as an early member of the data infrastructure team and eventually became a tech lead, running data infrastructure for recommendations across the core TikTok app, Shop, Live, and more.\n\n![uploaded image](/media/?type=post&id=87475&key=user_uploads/695591/d662049a-cf42-4d21-bc5f-08643fd3be5e)\n\n", "url": "https://www.ycombinator.com/launches/Mkt-osmosis-unlocking-real-time-learning-for-ai-agents", "votes": 155, "created_at": "2025-02-06T19:22:40.649Z" } ] }, { "id": 30251, "name": "Mecha Health", "slug": "mecha-health", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e851087b2c25e5bd175d39d8631791d74e04ebb1.png", "website": "https://www.mecha-health.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Mecha Health builds foundation models to automate x-ray analysis for radiologists. We take medical images and process them using proprietary models to produce accurate draft medical reports. Our first model was built in less than two months, and beat Microsoft, Google, and OpenAI on clinical accuracy metrics. On top of that, it’s two orders of magnitude smaller and trained with a quarter of the data.\r\n\r\nWe are partnering with the largest privately owned radiology practice in the US and a multinational tele-radiology company to provide them with their own foundation model, enabling their radiologists to go from reading 1 scan per hour to 1 scan every 5 minutes. By charging on a per scan basis, x-ray report generation represents a 40B+ market opportunity. ", "one_liner": "Foundation models to automate x-ray analysis for radiologists", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare", "launched_at": 1737153298, "tags": [ "Machine Learning", "Computer Vision", "Health Tech", "Healthcare", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "Healthcare" ], "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/mecha-health", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/mecha-health.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mecha-health/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e851087b2c25e5bd175d39d8631791d74e04ebb1.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1653084, "full_name": "Ahmed Abdulaal", "title": "Founder/CEO", "founder_bio": "Medical doctor @Imperial, where I gained entry to the most competitive deanery in the country. Microsoft PhD scholar in machine learning @UCL - I have more than 20 peer reviewed publications in medicine, digital healthcare, and machine learning - including first author publications in top AI venues such as ICLR. 360+ citations on google scholar. Ex Research Scientist at the Center for Artificial Intelligence @AstraZeneca. Building the future of automated radiology @Mecha Health.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ahmed-abdulaal-5b8b6a295/", "x_url": "https://x.com/AAY1993", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/47903da794d7f4d6b321cfcd16ef868b0cfcf586.jpg" }, { "user_id": 2219237, "full_name": "Hugo Fry", "title": "Founder", "founder_bio": "Mathematics (BA) and Physics (MSc) graduate from the University of Cambridge, ranking top 30 in the university. British Physics Olympiad participant, ranking in the top 11 in the UK. Former ML interpretability research scholar (MATS), where I was the first in the world to use Sparse Autoencoders on vision models. My research has been cited by Anthropic and Google DeepMind. Currently working on building state of the art radiology report generation AI in a way that is interpretable and safe.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hugo-fry-825828271/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/79920f2bd30552a4582e64400474702ed96fb8eb.jpg" }, { "user_id": 2209530, "full_name": "Ayodeji Ijishakin", "title": "Founder", "founder_bio": "I am a Machine Learning & Medical Imaging PhD candidate from University College London (UCL). During my PhD I published work at tier-one Machine Learning venues (NeurIPS, ICML & ICLR), and was invited to present my work at international medical imaging conferences, Digitas, & Harvard Medical School. Before this startup I was poached by my Professors from UCL including the Head of Computer Science to be CEO for their spin-out. Currently building Radiology 3.0 by Automating reporting. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ayodeji-ijishakin-b6208b1a9/", "x_url": "https://x.com/AIjishakin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91a8103e7adc247e6c59cde54f1798dbb2c12e3f.jpg" }, { "user_id": 2272824, "full_name": "Nina Montaña Brown", "title": "Founder/CTO", "founder_bio": "Solving the interpretive moment in radiology.\n\nex-ML Engineer across multiple medical device startups, with experience in FDA and CE marked medical devices, as well as LLM safety in healthcare. My PhD in Medical Imaging (UCL) focussed on surgical vision; my work has been patented, cited 100+ times, and published across the world's highest impact scientific venues (Nature, NeurIPS, ICLR).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nina-monta%C3%B1a-brown-6aa24b133/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4399f9dcfff9db94bacec22dd876670a6b70d5cb.jpg" } ], "jobs": [], "news": [ { "title": "Mecha Health Raises $4.1M Seed Round", "url": "https://www.mecha-health.ai/blog/Mecha-raises-seed", "date": "Nov 03, 2025" } ], "launches": [ { "id": 117908, "title": "Mecha Wayfinder: locating any finding, anywhere, in any medical image.", "tagline": "Meet Mecha Wayfinder! It locates any finding anywhere in any medical image by inspecting our foundation models, like giving the model an “MRI.”", "body": "# Mecha Wayfinder\n\nOne of the most important challenges for next generation foundation models in radiology is the ability to locate any type of pathology in across anatomies and modalities.\n\nWe’ve recently had a bit of research breakthrough on this, and today we’re introducing Mecha Wayfinder, a new tool for locating findings in medical images.\n\nTraditional systems like segmentation models are too restrictive for the task we’re trying to solve. Grad-CAM weights and upsamples a late convolutional feature map, which can leave localization spatially coarse and obscure boundaries.\n\nWayfinder takes a different approach. Think of giving the underlying AI an “MRI”: a way to inspect where the model looks, requiring zero human annotations.\n\nThe part we’re so excited about is that this approach scales with only images and reports. As we train better underlying systems, we expect Wayfinder to improve too.\n\nYou can learn all about it in our latest blog post linked below:\\\n\n\n", "url": "https://www.ycombinator.com/launches/Ufk-mecha-wayfinder-locating-any-finding-anywhere-in-any-medical-image", "votes": 3, "created_at": "2026-10-01T18:37:27.462Z" }, { "id": 97529, "title": "Mecha Health - Vision-language foundation models, that scale healthcare.", "tagline": "Today Mecha is launching a new set of capabilities, vision-language foundation models that interpret studies the way radiologists do.", "body": "Today we’re launching a new set of capabilities at Mecha: foundation models that interpret studies the way radiologists do, across anatomy, modality, views, and priors, while fitting into any workflow or software environment.\n\n\\\n\n\n\\\nWhat’s new is less about “one more model”, and more about a human-like feature set for real workflow:\n\n* Multi-anatomy interpretation\n* Multi-modality support\n* Multi-view reasoning with full clinical context\n* Prior-aware comparison: identifies clinically relevant change vs prior exams\n* Environment-agnostic delivery: a radiologist opens the study and finds a high-quality draft report already waiting in their template.\n\nThese new capabilities are designed to help frontier practices scale healthcare, without asking radiologists to relearn their workflow.\n\n### Our Ask\n\n* If you’re evaluating next-generation reporting AI, or know a group that is, we’d love to talk.\n\n**Please reach out directly at [ahmed@mecha-health.ai](mailto:ahmed@mecha-health.ai) - you can also check us out at** [**https://mecha-health.ai**](https://www.mecha-health.ai/)", "url": "https://www.ycombinator.com/launches/PN3-mecha-health-vision-language-foundation-models-that-scale-healthcare", "votes": 9, "created_at": "2026-02-19T18:31:53.538Z" }, { "id": 87081, "title": "Mecha Health 🩻 Trustworthy medical image reporting using AI", "tagline": "Automated analysis of medical images that is accurate and interpretable.", "body": "**TLDR;** \\\nMecha Health helps radiologists save time and effort by producing draft reports of medical images using AI. \\\n\\\n**The Problem: Too Many Scans Not Enough Doctors.** \\\nDue to a shortage of experts, US radiologists have to read 100 - 250+ studies per day, and this means that 55% have reported high levels of burnout. Additionally, patients sometimes have to wait days to receive medical reports for their scans due to backlogs. At times, this can result in preventable patient harm. \\\n\\\n**Our Solution: Accurate & Interpretable Medical Image Analysis.** \\\nWe improve the reporting process by producing draft reports with AI and have invented new science to do this both accurately and safely. Our system can trace every finding to its visual source in the image and evidence each finding with examples from the training dataset. This allows for transparent reporting that doctors can trust.\n\n![uploaded image](/media/?type=post&id=87081&key=user_uploads/2272824/2dd6b41e-c6f1-46a8-b932-678f32885952)\n\n**Our Background: Frontier Scientific Expertise.** \\\nWe are leading researchers in machine learning and medical imaging having published in tier-one AI venues (NeurIPS, ICLR, ICML). [Nina](https://www.linkedin.com/in/nina-monta%C3%B1a-brown-6aa24b133/), [Ahmed](https://www.linkedin.com/in/ahmed-abdulaal-5b8b6a295/) & [Deji](https://www.linkedin.com/in/ayodeji-ijishakin-b6208b1a9/) crossed paths through University College London’s Medical Imaging PhD program. [Hugo](https://www.linkedin.com/in/hugo-fry-825828271/) met the team after he published AI research that was cited by Anthropic. Ahmed, a trained medical doctor from Imperial College London, witnessed firsthand how reporting delays harm patient outcomes—driving Mecha Health’s mission to transform radiology.\n\n![uploaded image](/media/?type=post&id=87081&key=user_uploads/2272824/0785d872-c2c8-4a5f-ae6b-f06069c9ec95)\n\n**Our Ask: Intros to Pioneering Medical Professionals and Healthcare Executives.** \\\nPlease reach out to the team at [ahmed@mecha-health.ai](mailto:ahmed@mecha-health.ai) if you know any tech-forward clinicians, radiologists, radiology practices, or healthcare executives who could benefit from our technology. You can also check us out at .\n\n**Watch our full product video here** \\\n[**https://youtu.be/s5uJS7S8EF4**](https://youtu.be/s5uJS7S8EF4)", "url": "https://www.ycombinator.com/launches/MeX-mecha-health-trustworthy-medical-image-reporting-using-ai", "votes": 37, "created_at": "2025-01-23T03:22:50.784Z" } ] }, { "id": 30271, "name": "Mundo AI", "slug": "mundo-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/31474af1f951c6780af0608ab7ed6a5b739d95bf.png", "website": "https://mundoai.world", "all_locations": "Vancouver, BC, Canada", "long_description": "Mundo develops data, evaluations, and applied research for frontier labs and AI companies to advance perceptual intelligence across audio, video, and emerging modalities.", "one_liner": "The data layer for perceptual intelligence", "team_size": 30, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1739847539, "tags": [ "Machine Learning", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "Canada", "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/mundo-ai", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/mundo-ai.json", "year_founded": 2024, "city": "Vancouver", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/mundoai", "x_url": "https://x.com/hellomundoai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/31474af1f951c6780af0608ab7ed6a5b739d95bf.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": 750534, "full_name": "Jason Liao", "title": "Founder/CEO", "founder_bio": "Hi! I'm Jason. Last year I worked on ML research abroad, where I discovered how impossibly challenging it is to build good multi-lingual AI models. Before that, I was the youngest quant researcher at a $60B hedge fund in Canada. jason@mundoai.world", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jasonliao02/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/be0e99765802b0bf0a763e509b44d2903daf9651.jpg" }, { "user_id": 984513, "full_name": "Naijide Anwaer", "title": "Founder", "founder_bio": "Co-founder at Mundo AI building the largest and highest quality multilingual datasets. Product builder. Lover of languages and culture. Ex-Platform PM at Binance.US.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/naijide-anwaer", "x_url": "https://x.com/naijide_a", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dd3b5a268a63009e488ae677dcc9299168d1066c.jpg" }, { "user_id": 2380741, "full_name": "Garreth Lee", "title": "Founder", "founder_bio": "Co-Founder at Mundo AI. I previously worked on pretraining data at Cohere and tokenization at Hugging Face. I'm interested in all things related to ML/AI and loove playing music.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/garrethlee", "x_url": "https://x.com/garrxth", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2d40f5dde2d4b1edac8ed36994ad2829e70c00e2.jpg" }, { "user_id": 1060697, "full_name": "Kenneth Wu", "title": "Founder", "founder_bio": " doing fun stuff", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/wukenneth/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6133834a74d84163ff8b8e27b188a11e293af174.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 88044, "title": "Mundo AI - High Quality Multilingual Training Data for AI Models", "tagline": "We're building the world's largest and highest-quality multilingual data library.", "body": "# **TL;DR**\n\nAI models are great at English, but struggle with almost every other language. So, we are building the world’s largest and highest quality multilingual data library to help AI labs build better non-English models.\n\n# **The Story**\n\nWhen Jason was working on AI research abroad, he found that it was incredibly difficult to find training data in non-English languages. Because of this, his peers were all working on English models rather than ones in their native language.\n\n# **The Problem**\n\nAfter speaking with researchers and entrepreneurs around the world, it became clear to us that AI usability was dramatically behind in non-English languages - even for major languages like Hindi and Arabic. This is because of the severe shortage of high quality training data in non-English languages. That leaves the 75% of the world that does not speak English out of the AI revolution.\n\nData has been a major bottleneck for researchers and AI labs building multilingual AI models, and the demand for better and larger datasets is only increasing.\n\nCurrent workarounds such as synthetic data and machine translation simply don’t achieve the desired results, and open-source efforts fail to produce datasets in the quantity and quality required.\n\n# **How are we solving this**\n\nWe work directly with native speakers to build and create completely novel and high quality datasets. We do this by setting up end-to-end operations in the country where native speakers of a language reside, and by using our proprietary software platform to streamline data collection, generation, annotation, and quality assurance.\n\n# **Demo Video**\n\n[**https://www.youtube.com/watch?v=zZiilPrhDJs**](https://www.youtube.com/watch?v=zZiilPrhDJs)\n\n# **The Team**\n\n[**Jason Liao**](https://www.linkedin.com/in/jasonliao02/) helped build a record-breaking fraud detection AI model at Tsinghua University. Before that, he led a quant research team at a $60B quant hedge fund.\n\n[**Kenneth Wu**](https://www.linkedin.com/in/wukenneth/) was a quant at Canada’s largest quant fund. Previous roles in SWE at Amazon Web Services and Analyst at the Ontario Teachers’ Pension Plan.\n\n[**Naijide Anwaer**](https://www.linkedin.com/in/naijide-anwaer) was the youngest Platform PM at Binance US. He speaks 4 languages.\n\n[**Garreth Lee**](https://www.linkedin.com/in/garrethlee) was an ML engineer and the first Indonesian at Hugging Face, where he helped build the world’s best open pre-training dataset. Previously a member of technical staff at Cohere.\n\nAlso shoutout to our founding PM @Ahnaf Muqset Haque\n\n# **Our Ask**\n\nDo you know any researchers or data partnership managers at any AI labs? We’d love to get in touch! We’re trying to learn as much as we can about the data bottlenecks that are preventing researchers from making progress.\n\nYou can reach us at [**contact@mundoai.world**](mailto:contact@mundoai.world)\n\n![uploaded image](/media/?type=post&id=88044&key=user_uploads/89898/1f30211a-9e17-45c0-bab2-e5477ece04a1)\n\n", "url": "https://www.ycombinator.com/launches/Mu4-mundo-ai-high-quality-multilingual-training-data-for-ai-models", "votes": 67, "created_at": "2025-02-26T01:50:57.921Z" } ] }, { "id": 30286, "name": "Nitrode", "slug": "nitrode", "former_names": [ "Roach AI", "Inception Technologies" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/514545bed169ad1eb626571d7e0d77375b33f656.png", "website": "https://www.nitrode.com", "all_locations": "San Francisco, CA, USA", "long_description": "Nitrode is a research company focused on improving game development with AI. To advance game development, we believe AI models firstly need to be judged against robust evaluation frameworks that reflect the actual complexity of the field.\r\n", "one_liner": "Frontier AI research to advance game development", "team_size": 8, "industry": "B2B", "subindustry": "B2B", "launched_at": 1738144366, "tags": [ "Machine Learning", "B2B", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "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/nitrode", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2025/nitrode.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/nitrode", "x_url": "https://x.com/NitrodeResearch", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/514545bed169ad1eb626571d7e0d77375b33f656.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 1654263, "full_name": "Ben Kim", "title": "Founder/CEO", "founder_bio": "Co-founder at Nitrode. InfoSci @ Cornell. Previously @Supercell AI Lab, and @WSGR", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ben-yoonsung-kim/", "x_url": "https://x.com/ysungkim_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a5878f4fe3ac86da75a818f47a588a531d134b4b.jpg" }, { "user_id": 1654842, "full_name": "Sejoon Chang", "title": "Founder/COO", "founder_bio": "Co-founder at Nitrode. CS & Product Design @ Stanford. Top 500 GeoGuessr globally in moving and builder of unnecessarily ambitious Minecraft cities.", "is_active": true, "linkedin_url": "https://linkedin.com/in/sejoonchang", "x_url": "https://x.com/sejoonchang", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d96b7380c521c01891b3d1b496f8b4a1347e2fc2.jpg" }, { "user_id": 1654330, "full_name": "Brian La", "title": "Founder/CTO", "founder_bio": "Co-founder at Nitrode. CS @ Cornell. Previously Data Scientist @ Nexon", "is_active": true, "linkedin_url": "https://linkedin.com/in/brianyla", "x_url": "https://x.com/BrianLa0616", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6d63c684f51d17016bfd7ad010bb0b3fb1face7d.jpg" } ], "jobs": [ { "id": 98321, "title": "Member of Technical Staff (Post-Training) ", "url": "https://www.ycombinator.com/companies/nitrode/jobs/flMnYGU-member-of-technical-staff-post-training", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Data science", "salary_range": "$150K - $350K", "equity_range": "1.00% - 3.00%", "experience": "1+ years", "visa": "US citizen/visa only", "skills": [ "C++", "Python", "Distributed Systems", "Reinforcement learning (RL)", "Natural Language Processing", "CUDA", "Data Analytics" ] }, { "id": 98322, "title": "Member of Technical Staff (Evals)", "url": "https://www.ycombinator.com/companies/nitrode/jobs/BrIHVMH-member-of-technical-staff-evals", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Data science", "salary_range": "$120K - $250K", "equity_range": "1.00% - 3.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "C++", "Java", "Python", "Torch/PyTorch", "Machine Learning", "Reinforcement learning (RL)" ] }, { "id": 98323, "title": "Full-Stack Engineer", "url": "https://www.ycombinator.com/companies/nitrode/jobs/Hpc1xDj-full-stack-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$110K - $160K", "equity_range": "0.50% - 2.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "C++", "Java", "JavaScript", "Python" ] }, { "id": 110036, "title": "Game Developer ", "url": "https://www.ycombinator.com/companies/nitrode/jobs/KZbQlmq-game-developer", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$95K - $145K", "equity_range": "", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Unreal Engine", "Unity", "Blender" ] } ], "news": [], "launches": [ { "id": 104921, "title": "Nitrode - Advancing AI for Game Development", "tagline": "Advancing game development through frontier AI research.", "body": "Hey YC,\n\nWe’re Ben, Brian, and Sejoon from **Nitrode.**\n\nNitrode is a research company focused on improving game development with AI. \n\n**The Problem - How do we advance game development with AI?**\n\nWe started by building an AI game engine. It got to [**#3 on Product Hunt**,](https://www.producthunt.com/products/nitrode-ai-game-engine) and we gained several useful insights:  despite recent progress in agentic coding, game development remains uniquely difficult for AI models because it requires **(1) strong spatial reasoning**, **(2) reliable tool use/tool-calling**, **(3) strong agentic coding capabilities** and the **(4) ability to operate in real-time environments for complex games**.\n\n**The Solution** \n\nTo advance game development, we believe AI models firstly need to be judged against robust evaluation frameworks that reflect the **actual complexity of the field.** Over the coming months, Nitrode will open-source a suite of benchmarks designed to measure where AI models truly stand in game development. Our first benchmark, [**GameEngineBench**,](https://arxiv.org/abs/2607.03525) evaluates agentic coding performance on C++ game engine tasks in real runtime environments.\n\n![uploaded image](/media/?type=post&id=104921&key=user_uploads/1654263/a084ce2c-86d5-431b-97a1-d2ccf07b3f04)\n\nOur results are clear: model class and reasoning effort are strong predictors of performance, while model rankings align more closely with spatial reasoning benchmarks than with traditional agentic coding benchmarks. **This suggests that game development is fundamentally different from pure coding.** Success requires not only writing correct code, but also understanding space, tools, runtime behavior, and the interaction between systems.\n\nWe also observe significant complementarity across frontier models, indicating that different models exhibit distinct strengths within the game development task space. Mythos/Fable-class models point toward a potential step change in AI game development capability, where improved spatial understanding and stronger tool use combine to produce emergent behaviors that older model generations struggle to match.\n\nOur benchmark has revealed clear model-specific performance gaps across game development tasks, with outcomes that diverge meaningfully from standard agentic coding benchmarks.\n\nWe are also building [Mint](http://mint.nitrode.com), a platform that onboards game development experts to work on custom datasets. Think engine programmers, technical artists, and developers with years of enterprise/indie game development experience. We already have hundreds of contributors across game development and creative software. Whenever you need a certain type of data, we construct and lead a team to produce:\n\n* Post-training /SFT data captured from real workflows: how developers actually build, debug, test, and iterate inside engines and creative tools.\n* RL environments and executable tasks where models learn through execution and feedback.\n* Custom benchmarks and post-training for gaming enterprises: we pinpoint where models fail on real game development tasks, then post-train a custom model to close those gaps.\n\n**Team** \n\nBen and Brian studied Information Science and Computer Science at Cornell, while Sejoon studied Computer Science and Product Design at Stanford. The trio has been building things together for the last six years, from a sports team analytics tool to Nitrode, and are, unsurprisingly, massive gaming nerds. Sejoon is top 400 in the world at moving GeoGuessr and a certified Civ sweat, Brian is ranked in Tetris, and Ben used to be ranked in Minecraft speedrunning.\n\n**Our Ask:**\n\n1. If you need custom datasets or evals, let us know! Email us [research@nitrode.com](mailto:research@nitrode.com)\n2. Are there any creative or gaming companies you could introduce us to?\n\n**Thank you!**", "url": "https://www.ycombinator.com/launches/RIH-nitrode-advancing-ai-for-game-development", "votes": 10, "created_at": "2026-07-07T15:17:58.603Z" } ] }, { "id": 30400, "name": "Plexe", "slug": "plexe", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4911874a37ff01bfa27bb33e5f83f49e6970068e.png", "website": "https://plexe.ai", "all_locations": "London, England, United Kingdom", "long_description": "Plexe builds predictive ML models from a problem description. It connects to data sources, conducts experiments, evaluates and deploys the models to an API endpoint.", "one_liner": "Open-source agents to build predictive ML models from a prompt", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1745031118, "tags": [ "Machine Learning", "Data Science", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "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/plexe", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/plexe.json", "year_founded": 2025, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/plexe-ai/", "x_url": "https://x.com/PlexeAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/plexe-ai/", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4911874a37ff01bfa27bb33e5f83f49e6970068e.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": 2260416, "full_name": "Vaibhav Dubey", "title": "Founder/CEO", "founder_bio": "I make AI create AI.", "is_active": true, "linkedin_url": null, "x_url": "https://x.com/VaibhavMDubey", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3ef2a9cbf97215a02c0cd71bfe9793e95f7182c3.jpg" } ], "jobs": [ { "id": 78821, "title": "Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/plexe/jobs/eocGfXv-forward-deployed-engineer", "location": "IN / Remote (IN)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "₹1.5M - ₹3M INR", "equity_range": "0.01% - 0.03%", "experience": "3+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [ { "title": "10 Most Exciting AI Agent Startups at YC Demo Day Spring Cohort 2025 - Business Insider", "url": "https://www.businessinsider.com/y-combinator-yc-demo-day-spring-ai-agent-startups-2025-6", "date": "Jun 11, 2025" } ], "launches": [ { "id": 90589, "title": "Plexe - Build ML models from a prompt", "tagline": "Plexe is an open-source agent that turns natural language task descriptions into trained machine learning models.", "body": "We’re Vaibhav and Marcello. We’re building Plexe, an open-source agent that turns natural language task descriptions into trained ML models.\n\nhttps://youtu.be/CwNX0aU9MsE\n\n## **🤔 1. Problem**\n\nLots of great use cases for ML models in businesses never materialize because ML projects are messy and convoluted. You spend months finding the data, cleaning it, experimenting with models and deploying them to production, only to find out that the project has been binned due to taking so long. At a previous employer, we witnessed a team of 10 ML engineers spend 2 years and $3M building models for a project that never saw the light of day. \n\nThere are several tools for “automating” ML, but it still takes teams of ML experts to actually productionize something of value. And we can’t keep throwing LLMs at every ML problem. Why use a generic 10B parameter language model, if a logistic regression trained on your data could do the job better?\n\n## **✅ 2. Solution**\n\nStep 1: Connect your data sources\\\nStep 2: Describe your problem statement\\\nStep 3: Use the deployed model via API\n\nhttps://youtu.be/zByvH0wTSuE\n\n## **🚀 3. How does it work?**\n\nPlexe uses a self-correcting team of ML engineering agents to research, experiment, evaluate, refine, and deploy the best performing model over an API endpoint. It connects to data sources, discovering relevant fields, and autonomously building the model. Think of it like your own ML helper helping you go from idea to deployed models 10x faster.\n\nhttps://youtu.be/bUwCSglhcXY\n\n## **👋 4. Team**\n\n[Vaibhav Dubey](https://bookface.ycombinator.com/user/2260416) (ex-Proofpoint, Expedia, Imperial College London) and [Marcello De Bernardi](https://bookface.ycombinator.com/user/2337737) (ex-AWS, Expedia, Oxford) met 6 years ago while building a chatbot that served 9+ million customers of a large bank. Since then, they’ve built enterprise-scale ML solutions that serve billions of predictions per day for millions of users worldwide.\n\n![uploaded image](/media/?type=post&id=90589&key=user_uploads/2260416/4e5a0513-e013-4adc-9520-ebde07c44981)\n\n## **🙏 5. Our ask**\n\n* We’d love to get in touch with any companies that have a lot of data but have not been able to build ML features on top of them!\n* If you’re an individual who believes that the future of ML development is autonomous and open source, check out our agent: https://github.com/plexe-ai/plexe\n\nGet in touch with us: [founders@plexe.ai](mailto:founders@plexe.ai) ", "url": "https://www.ycombinator.com/launches/NZ7-plexe-build-ml-models-from-a-prompt", "votes": 67, "created_at": "2025-05-21T01:44:48.117Z" } ] }, { "id": 30402, "name": "Kashikoi", "slug": "kashikoi", "former_names": [ "EigenAI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bcf95c28a8d2140be9982bdbaf6aa05199441da7.png", "website": "https://www.getkashikoi.com", "all_locations": "San Francisco, CA, USA", "long_description": "Kashikoi is a simulation engine to benchmark AI agents. We generate CPU friendly world models that autonomously interview agents and generate deep behavioral assessments. We built a similar technology at Moveworks which was used to ship 250+ enterprise agents to customers daily.", "one_liner": "Simulation Engine for Benchmarking AI Products", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1748454120, "tags": [ "Developer Tools", "Generative AI", "Machine Learning", "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/kashikoi", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/kashikoi.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/kashikoi/", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/kashikoi", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bcf95c28a8d2140be9982bdbaf6aa05199441da7.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": 267393, "full_name": "Aaksha Meghawat", "title": "Founder/CEO", "founder_bio": "Founder, Kashikoi. Aaksha led the Simulation & Evaluation stack at Moveworks(ServiceNow) shipping 250+ customized enterprise ready agents to Fortune 500 and federal agencies. Aaksha has done award winning NSF sponsored research in Transformers at CMU (long before OpenAI made them cool). She shipped edge speech recognition models on 1bn+ iPhones (for the most esoteric dialects you can think of) at Apple. The innovation behind this was nominated for a Best Paper at Interspeech 2021.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/aaksham/", "x_url": "https://x.com/aaksham", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ef7784a42a7d61a3bbbf55314d4593da8456d7a1.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90990, "title": "Kashikoi - Simulation Engine for Benchmarking AI Agents", "tagline": "Autonomously interview your Agents!", "body": "Hey YC! \\\nWe are [Tim](https://www.linkedin.com/in/timmichaud/) and [Aaksha](https://www.linkedin.com/in/aaksham/) - cofounders of Kashikoi. [Kashikoi](https://www.getkashikoi.com) is a simulation engine to benchmark GenAI Agents. We generate CPU friendly world models that autonomously interview agents and generate deep behavioral assessments.\n\n# **The Problem**\n\nBuilding high-performing AI agents is becoming increasingly complex. Teams face many challenges:\n\n* Managing prompt bloat and keeping up with endless prompt tuning cycles.\n* Evaluating their agents (or competitors') meaningfully and efficiently.\n* Understanding agent performance in ways that reflect real-world values and expectations—not just public benchmarks.\n\nDespite growing interest and investment, most solutions rely heavily on prompt engineering, public benchmarks, or surface-level observability. These approaches often mislead more than they inform, creating a false sense of progress.\n\n# **The Solution**\n\n![uploaded image](/media/?type=post&id=90990&key=user_uploads/267393/5573f5c1-baf0-4fc5-b58f-d6fc289fb8f2)\n\nToday’s “LLMs” are adaptive systems which run test time adaptation loops behind that tiny “Thinking…” blinking on the screen. We are building a scalable version of test time adaptation and inference scaling a.k.a World Models that bring the power of these techniques to you.\n\nSimply put you can simulate highly customized benchmarks, diverse data and align your evaluations while maintaining all of these for the long run, all without writing prompts! Our world models unlock automatic prompt optimization and detecting regression test staleness as fun side effects.\n\nLLM based Systems are getting smarter and so should you using our World models!\n\nCheck out our simulation engine adaptively interviewing RAG Agents and multi-turn evaluation in action [here](https://www.loom.com/share/7b3bdb899b06433fbc0636df487c6133?sid=6dd87cdc-495e-48e4-bb7a-971b1430cae5).\n\n# Why Us\n\n* Tim and Aaksha used similar world models tech at Moveworks to massively reduce dev cycles for shipping 250+ customized enterprise ready agents.\n* Aaksha has done cutting edge research in Transformers at CMU (long before OpenAI made them cool). She shipped edge speech models on 1bn+ iPhones. The innovation behind these models was published as a paper at Interspeech 2021 and nominated for a Best Paper Award that same year.\n* Tim has found many high impact security vulnerabilities throughout his career. One of his top bug discoveries was in all Qualcomm GPS chips leading to a 50 mile 0-click exploit that had no mitigations. Tim has many [public CVEs](https://www.inulledmyself.com/2023/05/using-ai-to-find-software.html) for a variety of Apple products, including: Safari, MacOS, iOS, tvOS, & iTunes.\n\n# Our Ask\n\nSign up for our waitlist at [getkashikoi.com](http://getkashikoi.com), or email us at [founders@getkashikoi.com](mailto:founders@getkashikoi.com) if you or someone you know wants:\n\n* Instant evals on your agent (or a competitor’s 😉) and we’ll generate [such a report](https://www.notion.so/NewAgent-Benchmark-Report-1db104cde9e88066b62ac3d68fb6c878?pvs=21) for you.\n* Making advanced features—like automatic prompt optimization, world models, and inference scaling—work seamlessly for you.\n* Reliable prompt free evaluations that are aligned with your values and expectations (which we auto-encode in a special edge-friendly world model for you).\n* Don’t come to us if:\n * You love writing prompts\n * You trust public benchmarks\n * You think that good observability is enough to make your agent win\n * You aren’t ready to have an honest conversation about your agents’ performance\n* Jokes aside 😅, if you know enterprises building agents suffering from prompt bloat, please (pretty please 🥺) send them our way!", "url": "https://www.ycombinator.com/launches/Nfa-kashikoi-simulation-engine-for-benchmarking-ai-agents", "votes": 18, "created_at": "2025-06-03T21:48:35.068Z" } ] }, { "id": 30433, "name": "The Robot Learning Company", "slug": "the-robot-learning-company", "former_names": [ "The Robot Learning Company (TRLC)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fe9d24b61e68d5f71ffc72977fe38d21eece7328.png", "website": "https://www.robot-learning.co/", "all_locations": "BY, Germany", "long_description": "", "one_liner": "Tools for the next era of physical automation.", "team_size": 1, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1747554289, "tags": [ "Machine Learning", "Robotics" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "Germany", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/the-robot-learning-company", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/the-robot-learning-company.json", "year_founded": 2025, "city": null, "country": "DE", "linkedin_url": "https://www.linkedin.com/company/the-robot-learning-company/", "x_url": "https://x.com/RobotLearningCo", "fb_url": null, "cb_url": null, "github_url": "https://github.com/robot-learning-co", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/fe9d24b61e68d5f71ffc72977fe38d21eece7328.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/382e706b6ee5904a0c147f93557e4b51c58f4a40.png" ], "primary_partner": null, "founders": [ { "user_id": 2427273, "full_name": "Jannik Grothusen", "title": "Founder/CEO", "founder_bio": "Jannik holds a degree in Mechatronics and spent the past two years conducting robot learning research at UC Berkeley and TU Munich. He is actively contributing to open-source software for robot learning.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jannik-grothusen/", "x_url": "https://x.com/JannikGrothusen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f13baadd68541cb03243f553e8c8050d2014021c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 91187, "title": "TRLC - Affordable robotic workers", "tagline": "AI-powered robot platform that makes automation accessible to businesses.", "body": "Hi 👋 this is [Jannik](https://www.linkedin.com/in/jannik-grothusen/) - Founder of The Robot Learning Company.\n\n**TL;DR:** We make automation accessible to businesses with an affordable, general-purpose robot platform designed to automate repetitive, stationary tasks.\n\n![uploaded image](/media/?type=post&id=91187&key=user_uploads/2427273/0797ea73-9264-46d4-b089-731c1ec76f89)\n\n# The Problem\n\n**Traditional automation is broken** for tasks outside of factories. It’s expensive, rigid, and slow to deploy. Each new use-case demands a custom $100,000+ integration, months of setup, and only works in tightly controlled environments.\n\n**General-purpose robots promise to change that**. The dream: drop-in systems that replace expensive human labor with cheap, flexible robotic labor. But in reality, most general robotic systems don’t justify their cost. The math doesn’t work:\n\n**_Value = Human labor saved – Robot cost_**\n\nToday, robots lose this equation in two key areas:\n\n* Low productivity: they don’t save enough labor\n* Hardware, setup, and training costs are too high\n\nThe future doesn’t need more traditional robotic arms - it needs smarter, cheaper ones that can adapt quickly.\n\n# Our Solution\n\nWe’re building affordable and easy-to-deploy general-purpose robotic workers.\n\n\n\nWe combine:\n\n* **Low-cost hardware**: a pair of stationary robotic arms for under $10,000\n* **Self-learning control software**: we use imitation learning and offline reinforcement learning to adapt the robot to new tasks and environments from just a handful of demonstrations\n\nThis makes robotic automation accessible to a far broader set of businesses - even ones that previously couldn’t justify the investment.\n\n# About Us\n\nI’m [Jannik](https://www.linkedin.com/in/jannik-grothusen/), a former robot learning researcher at UC Berkeley and TU Munich, with a degree in Mechatronics. I’ve contributed to frontier open-source projects in robotic manipulation, and my work in the space has been [featured by TechCrunch](https://techcrunch.com/2024/11/04/built-in-four-days-this-120-robot-arm-cleans-a-spill-with-help-from-gpt-4o/).\n\n# Our Ask\n\nWe’re looking for pilot partners who want to automate repetitive manual tasks in their operations - and help us build the future of low-cost robotic labor.\n\n![uploaded image](/media/?type=post&id=91187&key=user_uploads/2427273/06432ad1-2691-4aac-b7ec-61325aa2c246)\n\n", "url": "https://www.ycombinator.com/launches/Nil-trlc-affordable-robotic-workers", "votes": 21, "created_at": "2025-06-10T19:52:00.513Z" } ] }, { "id": 30465, "name": "Photonium", "slug": "photonium", "former_names": [ "PhotonIQ" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1f9f89a3e11065395408b656b79f12f0fd9868a1.png", "website": "https://www.photoniumoptics.com/", "all_locations": "New York City, NY, USA", "long_description": "Photonium is building software to automate optical system design. We supercharge optics experts with intelligent tooling to reduce costs and deliver faster. We handle the full design stack — from optimization, verification, sourcing, to prototyping — for AR/VR, quantum, biotech, metrology/chip fab, LiDAR, and more. ", "one_liner": "AI-Powered Optical Consulting & Software", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1748388578, "tags": [ "Hard Tech", "Hardware", "Machine Learning", "B2B", "Manufacturing" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "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/photonium", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/photonium.json", "year_founded": 2025, "city": "New York City", "country": "US", "linkedin_url": "https://linkedin.com/company/photonium-optics/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/1f9f89a3e11065395408b656b79f12f0fd9868a1.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": 2467765, "full_name": "Jennifer Song", "title": "Founder/CEO", "founder_bio": "Co-founder of Photonium. Quantum Computing at Harvard/Stanford/QuEra", "is_active": true, "linkedin_url": "https://linkedin.com/in/jennifer-y-song", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/727e50b2591f66ca240e53da3afda54e3a081413.jpg" }, { "user_id": 1865732, "full_name": "Adam Mhatre", "title": "Founder/CTO", "founder_bio": "Co-founder of Photonium. Computational Physics at Stanford", "is_active": true, "linkedin_url": "https://linkedin.com/in/adam-mhatre-5b8aaa230", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d9986f5f0661a450e5b754ef2c49dce90970e9db.jpg" } ], "jobs": [ { "id": 79020, "title": "Founding ML Engineer", "url": "https://www.ycombinator.com/companies/photonium/jobs/JEOfTqj-founding-ml-engineer", "location": "New York, NY, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $200K", "equity_range": "0.50% - 2.00%", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [ "Machine learning" ] } ], "news": [], "launches": [ { "id": 90808, "title": "Photonium: AI-Accelerated Optical Design Automation ⚛️", "tagline": "Design optical systems faster and cheaper.", "body": "Hey YC!\n\nWe’re [Jennifer](https://www.linkedin.com/in/jennifer-y-song) and [Adam](https://www.linkedin.com/in/adam-mhatre-5b8aaa230/) - cofounders of Photonium.\n\n**TL;DR**\n\nPhotonium automates optical system design with AI, replacing costly manual iteration with end-to-end system optimization.\n\n**🚨 The Problem**\n\nOptical systems are mission-critical across many high-growth sectors — including quantum technology, biomedical imaging, LiDAR for defense and autonomous vehicles, and metrology for semiconductor fabrication.\n\nBut designing them is a nightmare:\n\n* Companies pour millions into teams of 20+ optical engineers\n* Projects take months or years to go from R&D to production\n* Tools are fragmented and outdated\n* Optimization is manual and often based on trial-and-error\n\nEngineers spend countless hours on repetitive workflows — constrained by tools that haven’t changed in decades.\n\n**⚙️ Our Solution**\n\nPhotonium is building the electronic design automation (semiconductor EDA) for optics. We work with complex optical setups across industries and handle the entire design process — from system generation and optimization to simulation, tolerancing, and sourcing.\n\nPhotonium enables companies to:\n\n* Generate and optimize designs from product specs\n* Predict tolerance impacts without slow simulations\n* Recommend data-driven alignment and manufacturing tweaks\n* Optimize for cost, size, and performance trade-offs\n* Seamlessly integrate with existing CAD workflows and optics simulation tools\n* Automate the full design-to-sourcing pipeline\n\n[Launch Video](https://youtu.be/YvX3Xr2_Gt8)\n\n**🚀 The Team**\n\nJennifer studied physics, math, and CS at Harvard and built quantum optics experiments across Harvard, Stanford, and QuEra (a Google-backed quantum computing startup).\n\nAdam studied computational physics at Stanford, and worked on plasma simulation for fusion and large-scale astrophysical modeling of black holes.\n\nWe’ve both felt the pain of inefficient engineering design firsthand — and built Photonium to fix it.\n\n⚛️ **Why This Matters**\n\nOptical systems are foundational to cutting-edge technologies, from quantum computing to semiconductor fabrication. Yet, outdated tools and fragmented workflows slow innovation and inflate costs. We hope to eliminate these bottlenecks and accelerate innovation across industries.\n\n📣 **Our Ask**\n\nIf you know anyone working with lasers and optics in any context, we’d love to chat! Contact us at [founders@photonium.ai](mailto:founders@photonium.ai)\n\n![uploaded image](/media/?type=post&id=90808&key=user_uploads/2467765/c27811f3-aa01-4860-9682-d3136973df3e)\n\n", "url": "https://www.ycombinator.com/launches/Nce-photonium-ai-accelerated-optical-design-automation", "votes": 51, "created_at": "2025-05-28T19:10:45.827Z" } ] }, { "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": 30548, "name": "Theorem", "slug": "theorem-2", "former_names": [ "Aletheia" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4462cecf7e86ccbeaa67750a89b934e1dcf2fa9f.png", "website": "https://theorem.dev", "all_locations": "San Francisco, CA, USA", "long_description": "Theorem is training models that make program verification 10,000 times faster. Using verification as a feedback loop, developers have found zero-days in GPU accelerated code and cryptography implementations, and sped up code migration in legacy systems. If you have complicated code that needs to be correct and secure, sign up for our beta!", "one_liner": "Program verification so even your systems engineers can vibecode", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Engineering, Product and Design", "launched_at": 1747681841, "tags": [ "Machine Learning" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2025", "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/theorem-2", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/theorem-2.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/theoremlabs", "x_url": "https://x.com/theoremlabs", "fb_url": null, "cb_url": null, "github_url": "https://github.com/theorem-labs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4462cecf7e86ccbeaa67750a89b934e1dcf2fa9f.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": 2540968, "full_name": "Jason Gross", "title": "Founder/CEO", "founder_bio": "Developed performant, verified code powering trillions of https connections to Chrome and other browsers.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jason-gross-86b72125/", "x_url": "https://x.com/diagram_chaser", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a5fa30f4252b80f30a34aa445733c970884b1f68.jpg" }, { "user_id": 2206829, "full_name": "Rajashree Agrawal", "title": "Founder/CTO", "founder_bio": "Babysitting models. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rajashreeagrawal/", "x_url": "https://x.com/___rajashree___", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4fb6028cdc57abac5fe224dffd96fd588267b46d.jpg" } ], "jobs": [ { "id": 83835, "title": "ML Research Engineer", "url": "https://www.ycombinator.com/companies/theorem-2/jobs/CsReXnw-ml-research-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$150K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [ "Machine Learning" ] }, { "id": 83836, "title": "ML Research Scientist", "url": "https://www.ycombinator.com/companies/theorem-2/jobs/eWwY3Yx-ml-research-scientist", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$300K - $500K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 83837, "title": "Senior SWE", "url": "https://www.ycombinator.com/companies/theorem-2/jobs/I2adiHF-senior-swe", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$300K - $500K", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [] }, { "id": 83838, "title": "Systems Engineer", "url": "https://www.ycombinator.com/companies/theorem-2/jobs/Le5BTc3-systems-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "id": 90592, "title": "Theorem – AI coding that is trustworthy-by-default", "tagline": "Program verification for superhumanly complex code", "body": "### **TL;DR:** **Please give us your most complicated coding problem that _needs_ to be correct. [Sign up!](https://theoremlabs.com/waitlist/)**\n\nHello World! We’re [Jason](https://jasongross.github.io/) and [Rajashree](https://scholar.google.com/citations?user=41ZE3NwAAAAJ&hl=en&oi=ao)! We think AI code generation is going to be bigger than any of us can imagine.\n\n### **MISSION**\n\nTheorem is an AI and programming languages research lab. Our question: as the models get better at generating code, what is the bottleneck to safely deploying vastly more computation in the world? \n\n* _Code has bugs._  AI-enabled attackers will increase the volume and velocity of attacks on software infrastructure. Human code review will not scale.\n* _AIs are untrusted generators._ In order to deploy AIs in critical systems without fully understanding their generalization behavior, we’ll need robust, scalable methods for overseeing their solutions.\n\n### **OPPORTUNITY**\n\nProgramming languages and formal methods is the promise that we can bootstrap roots of trust. Even as complexity of software goes up, we can design simple interfaces for steering with leverage.\n\nPreviously, this technology was only applied to the most mission-critical infrastructure, because of the enormous labor bottleneck — requiring years of PhD level engineering work. For instance, Jason worked on the project that wrote the code generator powering trillions of secure https connections everyday, but it took fifteen man-years to complete!\n\nWith advances in AI, we’re able to speed this development cycle 10,000 times! As a [wild example](https://www.youtube.com/watch?v=O9nKMjjb3Hg) of what’s possible, we spent two hours finding what would have been a zero-day in https code from 2011.\n\n### **OUR APPROACH**\n\nWe want to build workflows for trustworthy-by-default programming by pushing our best ideas till they break. The current candidate to beat is _program equivalence_ driven development. You oversee a simple reference implementation. We give you an optimized implementation, along with a proof that the programs are functionally equivalent. \n\n![uploaded image](/media/?type=post&id=90592&key=user_uploads/2206829/f5c671df-ac60-4b94-8081-36ae8c0aeda4)\n\nWe’re gradually rolling out our programming environment and models. Some use cases from our customers:\n\n* Performance optimization: finding edge cases where hyper-optimized GPU code would fail; getting clear documentation of dependencies that modify behavior in unforeseen ways. \n* Code migration: automating legacy code refactors from Python to Rust while proving no functional change; writing custom kernels to support new services.\n\n**If you work in public infrastructure, finance, hardware, or have other use cases that demand correctness, we’d love to help. Please reach out at [founders@theoremlabs.com](mailto:founders@theoremlabs.com)**", "url": "https://www.ycombinator.com/launches/NZA-theorem-ai-coding-that-is-trustworthy-by-default", "votes": 83, "created_at": "2025-05-21T14:58:50.047Z" } ] }, { "id": 30558, "name": "BitPatrol", "slug": "bitpatrol", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a49803acbbc34d447ce47d0129bde3f02dd6b0ec.png", "website": "https://www.bitpatrol.io/", "all_locations": "New York City, NY, USA", "long_description": "Leverage cutting-edge AI to detect exposed credentials in real time and protect your organization from high-impact data breaches.", "one_liner": "AI-powered code security", "team_size": 1, "industry": "B2B", "subindustry": "B2B -> Security", "launched_at": 1747422859, "tags": [ "DevSecOps", "Machine Learning", "Cybersecurity" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2025", "status": "Acquired", "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/bitpatrol", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2025/bitpatrol.json", "year_founded": 2024, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/bitpatrol/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": "https://github.com/BitPatrol", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/a49803acbbc34d447ce47d0129bde3f02dd6b0ec.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": 2475147, "full_name": "Christopher Lambert", "title": "Founder/CEO", "founder_bio": "Building BitPatrol — AI-powered secret detection that actually works. ex-Stripe, Tesla, Lyft.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/~chris/", "x_url": "https://x.com/theriley106", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/91f2d4b7be340fcad47a8c214790d2be665e9e90.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 90617, "title": "🚨 BitPatrol – Secret Detection That Actually Works", "tagline": "AI-powered security tool that prevents secret leaks in source code.", "body": "**Vibe coding is flooding GitHub with leaked secrets.**\n\nAs writing code becomes increasingly accessible, it's easier than ever to accidentally expose API keys, database credentials, SSH private keys, and more.\n\n**TLDR:** BitPatrol detects secret leaks in your source code that traditional scanners miss, helping you prevent costly data breaches.\n\n🎥 **Launch Video:** [Watch here](https://youtu.be/2qu3B_bX7dw)\n\n---\n\n### **The Problem**\n\nSecret leaks are happening every day, impacting companies across every industry. Traditional scanning tools attempt to catch these leaks using rigid regex patterns and keyword matching.\n\n![uploaded image](/media/?type=post&id=90617&key=user_uploads/2475147/274a7869-42fd-41fc-86f4-668846d4d01e)\n\nHowever, these methods struggle with secrets that don't follow set patterns, allowing critical leaks like these to slip through undetected.\n\n![uploaded image](/media/?type=post&id=90617&key=user_uploads/2475147/e67f96a4-a636-461e-9434-93a92f23f1ff)\n\n### **Our Solution**\n\nUnlike legacy tools that rely on regular expressions, BitPatrol uses our proprietary machine learning model to detect secrets based on code context and developer intent – not just string patterns.\n\n![uploaded image](/media/?type=post&id=90617&key=user_uploads/2475147/243b128a-4438-4e39-bf3b-7c6c1b8e3d32)\n\nOnce our model flags a potential secret, we reference it against our dataset of billions of public commits, docker images, and open source packages. This helps us rapidly assess the secret's exposure frequency, drastically reducing false positives and solving the issue of alert fatigue.\n\nUsing our [GitHub App](https://github.com/apps/bitpatrol), you can trigger actions that occur when a secret is detected, including automated alerts and custom workflows to immediately respond and remediate when a secret is leaked.\n\n### **The Team**\n\nBitPatrol was founded by [Chris Lambert](https://www.linkedin.com/in/~chris), a former Stripe engineer who quickly rose to the top 2% globally on HackerOne after uncovering secret leaks at some of the world’s largest companies—many of which were actively using our competitor’s tools.\n\n### 🙏 **Ask: How You Can Help**\n\n* **Try BitPatrol:** Install our [GitHub App](https://github.com/apps/bitpatrol) and experience immediate, real-time secret detection.\n* **Connect with Us:** If you know a technical leader concerned about protecting their company from secret leaks in code, please connect them to [founders@bitpatrol.io](mailto:founders@bitpatrol.io)\n\n### 🔥 **The Deal**\n\nTo celebrate our launch, we’re offering:\n\n* **Free 2024 Company Secret Leak Audit**\n * External-only scan — requires no internal data access\n * Deep, high-coverage audit of your public code surface (commits, mobile apps, etc.)\n * Request an [audit here](https://docs.google.com/forms/d/e/1FAIpQLSfBwBwk9-tizbOo1y3Aj9aK6jXeX8QNKJIhwEFTp0OuvhcNAg/viewform?usp=header)\n* **Exclusive early-adopter pricing**\n * Just $20/dev/month for real-time secret scanning with our [GitHub App](https://github.com/apps/bitpatrol)\n\nDon’t let vibe coding turn into your next major breach—secure your code with BitPatrol today.\n\n👉 [Get started at BitPatrol.io](https://bitpatrol.io/) or reach out directly: [founders@bitpatrol.io](mailto:founders@bitpatrol.io)", "url": "https://www.ycombinator.com/launches/NZZ-bitpatrol-secret-detection-that-actually-works", "votes": 62, "created_at": "2025-05-21T20:55:32.897Z" } ] }, { "id": 30649, "name": "Lilac", "slug": "lilac", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/18234851431f8da18d1f087abc867a57412f1a97.png", "website": "https://getlilac.com", "all_locations": "San Francisco, CA, USA; Remote", "long_description": "Lilac sells flexible GPU contracts and runs the software layer on top: monitoring, bare-metal provisioning, and managed Kubernetes and Slurm. We also run this layer for GPU cloud providers as their named operations and support partner.", "one_liner": "We sell GPUs.", "team_size": 4, "industry": "B2B", "subindustry": "B2B", "launched_at": 1754345371, "tags": [ "Machine Learning", "Cloud Computing", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": true, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/lilac", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/lilac.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/get-lilac", "x_url": "https://x.com/LilacML", "fb_url": null, "cb_url": null, "github_url": "https://github.com/getlilac", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/18234851431f8da18d1f087abc867a57412f1a97.png", "app_video_url": null, "dday_video_url": "https://bookface-images.s3.us-west-2.amazonaws.com/video_event/73/1757438550-c9621df37029cc122bc2415a49351917/9eadda8ada622d88427020d698f25df7.mp4?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAQC4NIECABPQ2AHNW%2F20261020%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20261020T022728Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=9aaaa0453d588f17ec05b77f091b5d1a5603a8b5cf39b20595f6f0bb5ada7d38", "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/e53628ce1b948c6a0fdc6aa376c679c34368c258.png" ], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 2382454, "full_name": "Lucas Ewing", "title": "Founder/CEO", "founder_bio": "I'm Lucas, born and raised in Stockholm Sweden, and moved to the US for High School and College (Harvey Mudd '23!)\n\nWorking on democratizing GPUs!", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lucasboewing/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4052bd2e866b8460ca058a8fff43ad5c446f355c.jpg" }, { "user_id": 2356063, "full_name": "Ryan Ewing", "title": "Founder", "founder_bio": "I'm Ryan, a software engineer with a background in building cloud and networking services at AWS.\n\nCurrently working on Lilac, a spot GPU marketplace that leverages idle GPUs in data centers and on-prem clusters.\n\nwww.getlilac.com", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rjewing/", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/bb13aa0cdc322cde8ee75db007c6ad439670d73c.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92724, "title": "Lilac - We find you GPUs!", "tagline": "Lilac! The open-source tool that ensures your data scientists always have GPUs", "body": "💬 **TL;DR Lilac is an open-source tool that ensures your data scientists always have enough GPUs for their work. We seamlessly connect compute from any source, on-prem or cloud. [Check out our GitHub!](https://github.com/getlilac/lilac)**\n\n![uploaded image](/media/?type=post&id=92724&key=user_uploads/2382454/b1a41fba-535e-4392-9526-f97e72771387)\n\n❗**The Problem: The GPU Scramble is Slowing Down AI Innovation**\n\nAsk any data scientist and they will likely tell you their biggest frustration is finding GPUs. One team we talked to is spending $7 million dollars a year with a service provider to guarantee GPUs, yet they were still running out. This forces data science and infrastructure teams into a difficult position:\n\n* **Wasted Time:** Data scientists waste hours hunting for available GPUs across different clouds and on-premise servers, distracting them from building models.\n* **Spiraling Costs & Security Risks:** Infrastructure teams lose visibility as desperate data scientists use personal keys on unmanaged instances, leading to security gaps and unpredictable cloud bills.\n* **Slowed Innovation:** The result is a direct trade-off between innovation speed and engineering best practices. Companies either accept a slow model development lifecycle or pay millions for dedicated GPU contracts in a single cloud, losing flexibility.\n\n🏆 **The Solution: Unify Your Compute, Unleash Your Data Scientists**\n\nLilac is an open-source platform that transforms your scattered, multi-cloud, and on-premise GPU resources into a single, unified compute fabric.\n\nInstead of hunting for servers, your data scientists simply submit their training jobs to Lilac. Our intelligent scheduler handles the rest—finding the first available GPU that meets the job's requirements, allocating it, and running the job.\n\n**The Result:**\n\n* **For Data Scientists:** Maximum productivity. They can focus on building models, not managing infrastructure.\n* **For Infrastructure Teams:** Centralized control. Gain full visibility, enforce security policies, and manage costs across your entire fleet of compute resources.\n\nLilac bridges the gap between R&D and infrastructure, turning GPU scarcity into a streamlined, efficient workflow.\n\nHere’s an introduction and example of how it works:\n\n[**https://www.youtube.com/watch?v=n8Ye-8YS0I4&ab_channel=Lilac**](https://www.youtube.com/watch?v=n8Ye-8YS0I4&ab_channel=Lilac)\\\n\\\n🥇 **About Us**\n\nWe're a team of two brothers, [**@Ryan Ewing**](https://bookface.ycombinator.com/user/2356063) and [**@Lucas Ewing**](https://bookface.ycombinator.com/user/2382454), who have spent our careers building the infrastructure that powers the modern web. With experience scaling enterprise infrastructure at **AWS** and building end-to-end data platforms at **Webflow**, we've seen firsthand how critical, and how broken, the tooling for AI development is. We believe the rapid acceleration of AI requires a fundamental rethinking of the infrastructure stack, and we built Lilac to be the foundation of that new ecosystem.\\\n\\\n⭐ **Our Ask**\n\nWe are thrilled to announce that Lilac is now open-source with the release of **v0.1.0**!\n\n* **Check out our GitHub:** [**github.com/getlilac/lilac**](https://github.com/getlilac/lilac)\n* **Try it out:** Star the repo, try the quick start, and give us your feedback. We welcome all contributions, from bug reports to feature requests.\n* **Get in touch:** If the problem we're solving resonates with your company, we'd love to connect. Reach out at [**contact@getlilac.com**](mailto:contact@getlilac.com), [**schedule a 15-minute chat**](https://calendly.com/lucas-getlilac/15-minute-meeting) with us, or checkout our [**website**](https://getlilac.com/).", "url": "https://www.ycombinator.com/launches/O7Y-lilac-we-find-you-gpus", "votes": 23, "created_at": "2025-08-04T22:34:11.492Z" } ] }, { "id": 30652, "name": "OnDeck AI", "slug": "ondeck-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/9fb6ad4cbd4cdfac49ff1b40404d7053d5789883.png", "website": "https://www.ondeckai.com", "all_locations": "Vancouver, BC, Canada", "long_description": "OnDeck is the infrastructure layer that makes Vision Language Models accessible and scalable for enterprise. Our model, Perception-0, let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data.\r\n\r\nThe Pain: Creating vision models usually takes months: collecting training data, training, then deployment. Worse yet: \r\n+ it’s often impossible to get enough data for a specific task, and \r\n+ even the best cv models struggle to generalize across diverse camera setups, workflows and environments. \r\n\r\nTo overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We published a NeurIPS workshop paper showing our new methods with VLMs beat traditional CV even at niche tasks. Now, our model Perception-0 beats the best VLMs including Gemini at long video understanding.\r\n\r\nOur current customers include:\r\n- National Defense Organizations\r\n- Robotics Research\r\n- Security cameras\r\n- Behaviour analysis for port monitoring\r\n- Off-shore oil & gas monitoring", "one_liner": "Analyze any footage, without training a model", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1755488960, "tags": [ "Machine Learning", "SaaS", "Computer Vision", "B2B", "Video" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "B2B", "Analytics" ], "regions": [ "Canada", "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/ondeck-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/ondeck-ai.json", "year_founded": 2025, "city": "Vancouver", "country": "CA", "linkedin_url": "https://www.linkedin.com/company/ondeckai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/9fb6ad4cbd4cdfac49ff1b40404d7053d5789883.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": 1161477, "full_name": "Alexander Dungate", "title": "Founder/CEO", "founder_bio": "Solving vision @ OnDeck. BSc in CS + biology, National Geographic Explorer.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexander-dungate/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2cb92fb1e2f11f9bd040c8681d3875328c486091.jpg" }, { "user_id": 1148628, "full_name": "Sepand Dyanatkar", "title": "Founder/CTO", "founder_bio": "Solving vision. Masters @ Cambridge in multi-agent RL. Research in swarm robotics for space, and built software at Amazon and the European Space Agency. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/sepandd", "x_url": "https://x.com/SepandD", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9b396c35a4599135d95d8b377d1bace2b0ca3453.jpg" } ], "jobs": [ { "id": 81971, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/ondeck-ai/jobs/IQTL9o5-founding-engineer", "location": "Vancouver, BC, CA / Vancouver, British Columbia, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $200K CAD", "equity_range": "0.10% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [], "launches": [ { "id": 93172, "title": "OnDeck AI - Analyze any video, without training a model.", "tagline": "We let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data.", "body": "Hey everyone 👋\n\nWe’re Alexander and Sepand, cofounders of [OnDeck AI](https://ondeckai.com).\n\n### TL;DR\n\nOnDeck lets you analyze any video, without training a model. Instantly find any event, behaviour, or object without needing any training data.\n\n### Ask:\n\n**Do you know anyone who needs to analyze tons of video or imagery? Please introduce us!**  [alexander@ondeck-ai.com](mailto:alexander@ondeck-ai.com)\n\n\\[[https://youtu.be/MafiM94IkV8\\\\\\]](https://youtu.be/MafiM94IkV8%5C%5D)\n\n### The Pain:\n\nCreating vision models can take months of engineering time: collecting training data, training, then deployment.  Worse yet: \n\n* it’s often impossible to get enough data for a specific task, and \n* even the best cv models struggle to generalize across diverse camera setups, workflows and environments.\n\n \n\n### Our Magic:\n\nTo overcome these blockers, we bet early on the power of VLMs and built a vision engine that can generalize across any task and doesn’t need any training data. We **published a NeurIPS workshop paper** showing our new methods with VLMs beat traditional CV even at niche tasks.\n\nWhy this is good:\n\n* We never train on customer data, unlocking sensitive use cases.\n* Deploy instantly and skip months of R&D.\n* Identify things that have never been seen before.\n* Unlike traditional cv, OnDeck can **_understand_** qualitative behaviour and sequences of events.\n\nThis summer we’ve analyzed thousands of hours of footage across:\n\n* Fleets of Autonomous Surface Vessels \n* Robotics Research\n* Security cameras \n* Behaviour analysis for port monitoring\n* Off-shore oil & gas monitoring\n\n### The Team:\n\nSepand (CTO) has a Master in ML from Cambridge and worked at the European Space Agency, Amazon, and swarm robotics research labs. We both studied cs in university together and we’ve been best friends since grade 6. We originally started OnDeck to automate fisheries monitoring (counting fish in video 🐠). We generated six-figure revenue before pivoting in January.\n\n![uploaded image](/media/?type=post&id=93172&key=user_uploads/1161477/796316a1-6848-4ece-a2b3-cc5e56514825)\n\n**Ask: Do you know anyone with loads of video or imagery to analyze? Please introduce us!\\\n\\\nPing us anytime:** [alexander@ondeck-ai.com](mailto:alexander@ondeck-ai.com) / [ondeckai.com](http://ondeck-ai.com) / [Linkedin](https://www.linkedin.com/company/ondeckai/) / [Twitter](https://x.com/ondeckai)", "url": "https://www.ycombinator.com/launches/OEm-ondeck-ai-analyze-any-video-without-training-a-model", "votes": 34, "created_at": "2025-08-20T01:13:05.704Z" } ] }, { "id": 30662, "name": "DeepAware AI (Robotics Center of Silicon Valley)", "slug": "deepaware-ai-robotics-center-of-silicon-valley", "former_names": [ "DeepAware AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/b55e7c4492211c3d35247562da291230d55e6cdb.png", "website": "https://roboticscenter.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "DeepAware (Robotics Center of Silicon Valley, https://roboticscenter.ai/) is the fastest way for enterprises and researchers to get robots and robotics parts in the US — 72-hour delivery or Bay Area pickup.\r\n\r\nBeyond hardware, we help teams collect teleoperation data, build reinforcement learning environments, and deploy robots into production. Customers include AI labs, industrial operations, research teams, and event producers.", "one_liner": "Robots, parts & RL envs & evals - delivered in 48 hrs / pickup in SF.", "team_size": 4, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1753029704, "tags": [ "Machine Learning", "Robotics", "Supply Chain", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/deepaware-ai-robotics-center-of-silicon-valley", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/deepaware-ai-robotics-center-of-silicon-valley.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/roboticscenter/", "x_url": "https://x.com/RoboticsCtr", "fb_url": "https://www.facebook.com/roboticscenterai", "cb_url": "https://www.crunchbase.com/organization/deepaware-ai", "github_url": "https://github.com/roboticscenter", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/b55e7c4492211c3d35247562da291230d55e6cdb.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 2760576, "full_name": "Jerry Huang", "title": "Founder/CEO", "founder_bio": "Building Robotics Center of Silicon Valley https://roboticscenter.ai/", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jerryh01/", "x_url": "https://x.com/jerryhuang01", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1a1c665b9491d7b3b0b21fc7085627a6859b1b85.jpg" }, { "user_id": 2786524, "full_name": "Stela Tong", "title": "Founder/CTO", "founder_bio": "Building DeepAware AI", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/stela-tong-a2583110b/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eec06f27921ccffb2a5407cb05d034193102259a.jpg" } ], "jobs": [ { "id": 79914, "title": "Business Development Associate ", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/Oz49Id3-business-development-associate", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$50K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] }, { "id": 79915, "title": "Robotics Engineer", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/2PyKMig-robotics-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$80K - $180K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] }, { "id": 99767, "title": "Operations Associate", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/iyyRVWT-operations-associate", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$50K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "Will sponsor", "skills": [] }, { "id": 100904, "title": "Business Development Intern", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/a9Gr7BR-business-development-intern", "location": "San Francisco, CA, US", "type": "Internship", "role": "Sales", "role_type": null, "salary_range": "", "equity_range": "", "experience": null, "visa": "Will sponsor", "skills": [] }, { "id": 100905, "title": "Robotics Engineer Intern", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/extm5EN-robotics-engineer-intern", "location": "San Francisco, CA, US", "type": "Internship", "role": "Engineering", "role_type": "Robotics", "salary_range": "", "equity_range": "", "experience": null, "visa": "Will sponsor", "skills": [] }, { "id": 100906, "title": "Operations Intern", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/o5YcLop-operations-intern", "location": "San Francisco, CA, US", "type": "Internship", "role": "Operations", "role_type": null, "salary_range": "", "equity_range": "", "experience": null, "visa": "Will sponsor", "skills": [] }, { "id": 102117, "title": "Marketing Intern", "url": "https://www.ycombinator.com/companies/deepaware-ai-robotics-center-of-silicon-valley/jobs/PHQdrZy-marketing-intern", "location": "San Francisco, CA, US", "type": "Internship", "role": "Marketing", "role_type": null, "salary_range": "", "equity_range": "", "experience": null, "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "id": 92985, "title": "DeepAware - Automation for next-gen AI data centers", "tagline": "AI-driven automation that makes any data center as secure, efficient, and adaptive as Google’s or Amazon’s.", "body": "## **DeepAware - Automation for next-gen AI data centers**\n\n👋 Hello world! Are you ready to embrace a much more efficient AI infrastructure? Check out [DeepAware](http://deepawareai.com/) / [LinkedIn](https://www.linkedin.com/company/deepaware/):\n\nhttps://youtu.be/mywozPhOAnA\n\nTL;DR\n\n1. DeepAware AI brings Google- and Amazon-level technology to any data center, making them more secure, efficient, and autonomous.\n2. Our reinforcement learning scheduler, real-time market integration, and robotics cut energy waste by up to 30% while ensuring 24/7 uptime.\n\n![uploaded image](/media/?type=post&id=92985&key=user_uploads/2760576/4188b3ad-71bd-4af7-a52d-eac733f67211)\n\n**Problem**\n\n1. With AI workloads surging, the data center industry is booming—global capacity is expanding rapidly, and the market is projected to hit **$1 trillion by 2030**\n2. Data centers waste 20–30% of energy due to siloed tooling and manual controls.\n3. Hyperscalers like Google and Amazon solve this with custom AI, but mid-market operators lack access to those technologies.\n\n![uploaded image](/media/?type=post&id=92985&key=user_uploads/2760576/c08c6a9b-22d9-4ffa-830a-c828ce7597a5)\n\n**Solution**\\\nDeepAware brings Google- and Amazon-grade AI controls to any data center:\n\n1. **RL Scheduler** optimizes GPU workload placement for power, performance, and cost.\n2. **Real-Time Market Integration** shifts workloads to low-price windows and supports demand-response programs.\n3. **Unified Dashboard** for one-click policy tuning, automated alerts, and “what-if” scenario testing.\n4. **Autonomous Robotics (coming soon)** for remote “robot hand” inspections, cable swaps, and maintenance—enabling 24/7 physical operations with minimal on-site staff.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeWFdq7APkR0uqewAgWtXeKANKr7EDV1KIz0diikWoGKDlTmA6bqIP_-8vNP0V4Fp9d0il-oEnk8u4uCkO_usPJd4xVHtCOifCEAgMO4muD-Dh7s_sFDGyNnR8YeClKD71vLBoOeA?key=Ok6o8HSu--upaY5wBSe41Q)\n\n**Our Team:** \n\n1. We are both second-time founders. We spent 3 years together building our first company providing second-level energy optimization for dozens of factory.\n\n**Traction:**\n\n1. **6-digit agreement** with a major 30 MW+ data center operator to use our system.\n2. **Simulation results** show 15% energy savings.\n3. **Industry validated** through Green AI Summits at Harvard with industry leaders from Oracle and Meta AI.\n\n**Ask:\\\nWe’re looking for:**\n\n1. **Data center operators** (and people in the AI infra sector) interested in piloting AI-driven optimization.\n2. **AI/ML researchers** tackling real-world control problems.\n\nGot feedback or want to pilot? Reach out: 👉 [jerry@deepawareai.com](mailto:jerry@deepawareai.com) or [LinkedIn](https://www.linkedin.com/in/jerryh01/) visit http://deepawareai.com/.\n\n![uploaded image](/media/?type=post&id=92985&key=user_uploads/2760576/03fe5788-d736-48c7-83f3-98bf11b31fca)\n\n", "url": "https://www.ycombinator.com/launches/OBl-deepaware-automation-for-next-gen-ai-data-centers", "votes": 15, "created_at": "2025-08-14T01:42:09.062Z" } ] }, { "id": 30791, "name": "Spotlight Realty", "slug": "spotlight-realty", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/677917fd980b1f33bfa85125a5b4023b60d9fa41.png", "website": "https://www.spotlight.realty", "all_locations": "San Francisco, CA, USA", "long_description": "We are a full-service sell-side residential brokerage that lists and markets your properties. We also screen and schedule tenants showings with our AI agent for a third of the normal commission. ", "one_liner": "AI powered brokerage reducing residential rental commissions in NYC…", "team_size": 4, "industry": "Real Estate and Construction", "subindustry": "Real Estate and Construction", "launched_at": 1755023391, "tags": [ "Machine Learning", "Real Estate" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2025", "status": "Active", "industries": [ "Real Estate and Construction" ], "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/spotlight-realty", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/spotlight-realty.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/spotlight-realty-ai/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/677917fd980b1f33bfa85125a5b4023b60d9fa41.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": 2080334, "full_name": "Raymond Allie", "title": "Founder/CEO", "founder_bio": "CEO of Spotlight Realty.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/raymond-allie/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a118647946ecc8fdc4b9c62982875cdac71e2a2b.jpg" }, { "user_id": 2658635, "full_name": "Jake Phelan", "title": "Founder", "founder_bio": "CTO of Spotlight Realty", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jacob-phelan-9359222a5", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e07d079b48359cea759dedb2dd7966eafd337fee.jpg" }, { "user_id": 2771398, "full_name": "Aryan Sharma", "title": "Founder/CFO", "founder_bio": "COO of Spotlight Realty", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/aryansharma12358", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e0f6c8b087fe1620a812aea40783410cfc1b17ec.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92952, "title": "Spotlight Realty - The AI Real Estate Rental Agent", "tagline": "A full service residential leasing experience", "body": "![uploaded image](/media/?type=post&id=92952&key=user_uploads/2771398/a21855d7-de1e-4bc1-9235-3fb7310a49d1)\n\nSpotlight Realty is an AI real estate agent for landlords and property managers to lease rentals without traditional brokerages, and charges only 4% in commission. \n\nBrokerage rental commissions have been north of 12% for over a century. That’s almost 2 month’s rent! This cost was typically on the tenant, but due to the FARES act in NYC, property owners bear it now. With rent prices rising 15% and supply dropping 30% since June 2025, a scalable solution is needed for the residential rentals market.\n\nMarketplaces like StreetEasy and Zillow make shopping for tenants easier than ever. Listing agents provide the heavy lifting, like screening applicants, coordinating schedules, and giving tours. Their value comes from managing transactions from start to end. Spotlight provides the full service hands-off experience at a third of the price.\n\nSpotlight Realty finds your future tenants with proprietary lead generation software that uses AI to scrape the internet and various other data sources. Spotlight also posts your listings to online marketplaces(e.g StreetEasy, Zillow, etc.) and shows it to prospective tenants with targeted social media ads, where renters’ eyes actually are. Inquiry handling, coordinating, screening, scheduling, and touring are all covered in-house. Handle all your offers through our platform. If you need help, we have a team of licensed agents and lawyers to guide you through the process.\n\n![uploaded image](/media/?type=post&id=92952&key=user_uploads/2771398/dc7db897-702f-44dc-94a0-fb454c7347dc)\n\n**Onboarding** 💡\n\nSpotlight makes listing easy. Fill out your property details and select a time to speak with one of our agents. In our meeting, we’ll discuss your specific goals on pricing and timing, along with what’s necessary to get your unit ready for market.\n\nNext, you’ll confirm our key pick-up and complimentary photography session. We’ll handle everything else.\n\n![uploaded image](/media/?type=post&id=92952&key=user_uploads/2771398/c80c14d7-00a5-41fc-bd68-5f08bec3e498)\n\n**Platform** 🧱\n\nIt’s built to handle as many units as you have. Our landlord dashboard holds all your current listings with analytics, emails, and call logs for every inquiry received. See how much reach your listings have with our marketing campaigns. We believe in transparency at the highest level.\n\n![uploaded image](/media/?type=post&id=92952&key=user_uploads/2771398/c7d9a72f-e099-48f0-8ffc-c8a84e189568)\n\n**Approve Applicants 🏆**\n\nEasily compare applications. Security deposits and first month’s rent get wired directly to you.\n\n![uploaded image](/media/?type=post&id=92952&key=user_uploads/2771398/4cdf0159-f2a0-4e9e-99cb-f47ca33986a7)\n\nServicing NYC’s rental market since June 2025.", "url": "https://www.ycombinator.com/launches/OBE-spotlight-realty-the-ai-real-estate-rental-agent", "votes": 16, "created_at": "2025-08-13T04:24:57.810Z" } ] }, { "id": 30835, "name": "Brickanta", "slug": "brickanta", "former_names": [ "Brickanta AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/2f5fc73a5deec1c3a7c627cea71c8345b56ca431.png", "website": "https://www.brickanta.com", "all_locations": "Stockholm, Stockholm County, Sweden", "long_description": "Brickanta – agentic AI for society builders. Hundreds of construction-specific AI agents for project analysis, insights, tenders, procurement, and more. Combine the latest AI technology with your organization's project data, templates, and workflows to identify risks and opportunities early and produce better decision support. Brickanta has raised $8 million from leading AI, construction, and real estate investors behind companies like OpenAI/ChatGPT, Airbnb, Klarna, Spotify, and Plangrid-Autodesk, as well as star investors such as Mario Götze, Anton Osika, and Northzone (see movie and press). The founding team has been building and implementing AI in construction and industry since 2018 at companies such as ABB, Fabege, Husqvarna, IKEA and Konecranes.\r\n", "one_liner": "Agentic AI for Society Builders", "team_size": 25, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1759093006, "tags": [ "Machine Learning", "Construction", "AI", "AI Assistant" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "Sweden", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/brickanta", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/brickanta.json", "year_founded": 2025, "city": "Stockholm", "country": "SE", "linkedin_url": "https://www.linkedin.com/company/brickanta/", "x_url": "https://x.com/brickanta", "fb_url": "https://www.facebook.com/people/Brickanta/61581151975393/", "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/2f5fc73a5deec1c3a7c627cea71c8345b56ca431.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 654502, "full_name": "Lucas Otterling", "title": "Founder/CEO", "founder_bio": "Building Brickanta - agentic AI for society builders (construction and real estate). GTM-focused co-founder, ex-professional footballer and ex-indie app developer. International digital nomad with experience from living in Sweden, the US, the UK, Germany, South Africa, Italy. Vipassana Meditator :-)\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lucas-otterling/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/73da19e06e37ceaf7619b5c12857ac4c440cff35.jpg" }, { "user_id": 2911315, "full_name": "Linus Bein Fahlander", "title": "Founder/CTO", "founder_bio": "Building Brickanta (F25) together with Lucas Otterling.\nWe are trying to give time back to one of the most critical roles in construction companies, the estimator.\nWe believe that builders shouldn't spend heaps of time searching through 1000s of documents - they should be building", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/linus-bf/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/141b6daae22ebd334ced9aa05a14199614305160.jpg" } ], "jobs": [ { "id": 88845, "title": "Business Intern - Summer 2027", "url": "https://www.ycombinator.com/companies/brickanta/jobs/LFdioyl-business-intern-summer-2027", "location": "Stockholm, Stockholm County, SE", "type": "Contract", "role": "Operations", "role_type": null, "salary_range": "$1K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 88846, "title": "Construction Engineer - Bygg / Entreprenadingenjör (Swedish) ", "url": "https://www.ycombinator.com/companies/brickanta/jobs/4o0G3W1-construction-engineer-bygg-entreprenadingenjor-swedish", "location": "Stockholm, Stockholm County, SE", "type": "Contract", "role": "Product", "role_type": null, "salary_range": "$1K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 88847, "title": "Software / AI Engineering", "url": "https://www.ycombinator.com/companies/brickanta/jobs/DKZym3b-software-ai-engineering", "location": "Stockholm, Stockholm County, SE", "type": "Contract", "role": "Engineering", "role_type": "Full stack", "salary_range": "$1K - $100K", "equity_range": "", "experience": "Any (new grads ok)", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 88848, "title": "Open Application - 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$100K", "equity_range": "", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [], "launches": [ { "id": 94176, "title": "Brickanta 🧱 the construction estimator's best friend 👷‍♂️🏗️", "tagline": "Cursor for construction estimation & project management", "body": "👋 We’re [Brickanta](https://brickanta.com/) (F25), building _cursor for construction estimation & project management._\n\n**TL;DR**: Our AI platform analyzes construction estimates and project details to automate admin work, reduce errors, and accelerate delivery. 👉 _We’d appreciate your social media hype support and any potential intros, as we look to make more connections in the construction and real estate industry._\n\n**Why this matters**\\\nConstruction projects run on mountains of paperwork, drawings, and codes. Miss one item, and budgets blow up — every year, it costs the small European country of Sweden over $11 billion (according to the government itself) and the global industry _hundreds of billions_ every year. Estimators and PMs are the heroes who keep projects on track, but today they’re buried in manual work.\n\n**How Brickanta helps**\\\nOur AI reads and organizes project docs, flags missing items & compliance risks, and supports cost analysis, tendering, and planning. It’s trained on standardized building protocols (starting with the Swedish versions of the Eurocodes, soon more countries) and integrates the systems that construction already runs on, like MS SharePoint, Outlook, and Teams — helping teams work faster, catch risks earlier, and protect margins.\n\n🎥 YC Launch Video:\n\nhttps://www.youtube.com/watch?v=za8Ya5GxMRE\n\n\\*\\*Excited to support?\\*\\*\\\n👷‍♂️🛠️👷‍♀️ We’re officially launching this week with exclusive media interviews, first public customer story, new brand + website, and more. Please check out and follow our newly created channels listed at the bottom to be part of the journey — and offer some hyping support :-)\\\n\\\n🏗️ We’d also love intros into construction and real estate companies - our MVP is currently focused on European markets (that share the fundamental [Eurocodes](https://eurocodes.jrc.ec.europa.eu/) framework)- where estimation and project management are critical.\\\n\\\n🧱🏗️  [**YouTube**](https://www.youtube.com/@Brickanta) │ [**LinkedIn**](https://www.linkedin.com/company/brickanta) │ [**X**](https://x.com/brickanta)  │ [**Facebook**](https://www.facebook.com/people/Brickanta/61581151975393/)  │ [**Instagram**](https://www.instagram.com/brickanta/?fbclid=IwY2xjawM7_n1leHRuA2FlbQIxMQBicmlkETBwMHVWZlByYk04ZW5iYnEyAR7vPpHY7lQF8Ow_5pNc9wsKP5V4us-fiDMJeepUZEwbeblZ39mJOYOA4lr1Eg_aem_5RLOiHcY_lmVUvys8OicmA) │ [**TikTok**](https://www.tiktok.com/@brickanta)\n\nYou can reach the founders on [founders@brickanta.com](mailto:founders@brickanta.com).\n\n🎥  Construction-audience-focused commercial launch video: https://www.youtube.com/watch?v=mwDEeXxtiQE", "url": "https://www.ycombinator.com/launches/OUy-brickanta-the-construction-estimator-s-best-friend", "votes": 17, "created_at": "2025-09-29T22:33:27.857Z" } ] }, { "id": 30854, "name": "Hyperspell", "slug": "hyperspell", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4149c6afc651c9770fc182e08dde776e24f88554.png", "website": "https://hyperspell.com", "all_locations": "San Francisco, CA, USA", "long_description": "Hyperspell is your Company Brain. AI agents are brilliant and clueless. They ace any test and still have no idea how your company works. Hyperspell connects your tools and synthesizes documents and conversations into a live, permissioned context graph. Any agent can read from it and write back to it like a filesystem, with every fact traced to source.", "one_liner": "Your Company Brain", "team_size": 8, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1760534513, "tags": [ "Machine Learning", "SaaS", "Data Engineering", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "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/hyperspell", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/hyperspell.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://linkedin.com/company/hyperspell", "x_url": "https://x.com/hyperspell", "fb_url": null, "cb_url": null, "github_url": "https://github.com/hyperspell", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4149c6afc651c9770fc182e08dde776e24f88554.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/af3a623c068400310c706ffc43c0b17d3b40a103.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/d96bd1f7c504fa8cd76323fbcd9b0bec0fda0da6.png" ], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 2003252, "full_name": "Conor Brennan-Burke", "title": "Founder/CEO", "founder_bio": "Founder of Hyperspell, Context & Memory for AI Agents", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/conorbrennanburke/", "x_url": "https://x.com/conor_ai", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8e06eab4105d354be854f0c45867d1cd63133fc9.jpg" }, { "user_id": 31259, "full_name": "Manu Ebert", "title": "Founder", "founder_bio": "4x Founder, 2 exits, 15 years in AI & ML", "is_active": true, "linkedin_url": "https://linkedin.com/in/maebert", "x_url": "https://x.com/maebert", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1fdf8a8308d72fd62ab8580049ab65377416d60f.jpg" } ], "jobs": [ { "id": 92642, "title": "Founding AI Engineer", "url": "https://www.ycombinator.com/companies/hyperspell/jobs/MvG48cH-founding-ai-engineer", "location": "San Francisco, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150 - $220", "equity_range": "0.50% - 1.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "Python", "Distributed Systems", "Machine Learning", "Natural Language Processing", "Prompt Engineering" ] }, { "id": 92644, "title": "Founding Platform Engineer", "url": "https://www.ycombinator.com/companies/hyperspell/jobs/rMoV1RO-founding-platform-engineer", "location": "San Francisco, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150 - $220", "equity_range": "0.50% - 1.50%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Kubernetes", "Python", "Distributed Systems", "ETL" ] }, { "id": 92647, "title": "Founding Forward Deployed Engineer", "url": "https://www.ycombinator.com/companies/hyperspell/jobs/iLXJT3r-founding-forward-deployed-engineer", "location": "San Francisco, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150 - $220", "equity_range": "0.50% - 1.50%", "experience": "3+ years", "visa": "US citizen/visa only", "skills": [ "PostgreSQL", "Python", "TypeScript" ] } ], "news": [], "launches": [ { "id": 94666, "title": "Hyperspell: Memory for AI Agents", "tagline": "Give your agents long-term memory and context from your entire workspace", "body": "[**Hyperspell**](https://hyperspell.com) **is the memory layer for AI agents.** We help developers connect data from tools like Slack, Gmail, Notion, and Google Drive so their agents can recall, reason, and learn over time.\n\n🎬 Watch our launch video: https://youtu.be/t6ji-n9qHYo?si=rALHJ2KaTiAieEvq\n\n---\n\n### Hi there!\n\n**Conor** led a $30m ARR API business at Checkr, building infrastructure used by tech companies like Doordash and Airbnb. **Manu** has 15+ years of Machine Learning experience. His previous ML/AI startup got acquired by Airbnb, where he went on to build Airbnb’s first Knowledge Graph. Manu has also owned .ai domains since 2014 when he bought one for his first startup (back then he had to send a fax to Antigua to get one).\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/5e5241c7-861b-4213-b864-c448b6091fba)\n\n> ---\n\n### **AI Agents are clueless geniuses**\n\nToday, AI agents show up like it’s their **first day on the job** — sometimes impressive, but with no awareness of your context, institutional knowledge, history, and preferences. Worst of all, without the ability to learn and improve, they will only ever be as good as they are on day one.\n\n---\n\n### **Hyperspell fixes that.**\n\nWith our SDK, developers can add a context and memory layer to any agent in minutes. Pre-built connectors let users connect their tools like Gmail, Notion, and Slack, so that **memory doesn’t start from zero**: Hyperspell will analyze all existing files and conversations, figure out the important people, projects, and facts, and make it all available to your agent through our Agentic Memory Network.\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/036418ad-5a83-4da0-95d0-c939ec3fafa2)\n\nBest of all, **it improves with every single interaction:** Our novel approach to memory reinforces what’s being recalled, and helps your agent improve over time.\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/282eb1cf-bd7f-49b5-81e3-a5fe74e53896)\n\n### **But wait, there’s more!**\n\nThere’s lots of different ways our customers use Hyperspell, and because of that there’s lots of different ways you can integrate Hyperspell into your own agent. To make this easier, we created the **first ever self-integrating SDK**. \n\nWatch how we go from zero to production-ready PR on an unknown codebase with our custom Claude Code skill:\n\nhttps://www.youtube.com/watch?v=gfYsXJJsX3k\n\n# **Our Asks:**\n\n* Book a demo right now: https://cal.com/conor-brennan-burke/\n* Spread the word on linkedin and twitter: \\\n [https://www.linkedin.com/company/hyperspell](https://www.linkedin.com/company/hyperspell/%5C)\\\n https://twitter.com/hyperspell\n* Share your feedback with [conor@hyperspell.com](mailto:conor@hyperspell.com)\n\n![uploaded image](/media/?type=post&id=94666&key=user_uploads/31259/f032811f-ff1e-4f87-af1c-edd2f051e25f)\n\n", "url": "https://www.ycombinator.com/launches/Ocs-hyperspell-memory-for-ai-agents", "votes": 426, "created_at": "2025-10-15T14:47:48.970Z" } ] }, { "id": 30876, "name": "Velum Labs", "slug": "velum-labs", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4835d7d3adbc5c3756ca811175142dfc73c7ddbe.png", "website": "https://www.velum-labs.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Velum is the operating system for data quality. Velum automatically monitors and enforces data quality across a company's data stack, so bad data never reaches dashboards. We turn data quality from a manual task into infrastructure that runs itself.\r\n\r\nData trust you can prove. From the pipeline to the boardroom.", "one_liner": "The OS for data quality across any stack", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1762847882, "tags": [ "Machine Learning", "Data Engineering" ], "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/velum-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/velum-labs.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/velum-labs-ai", "x_url": "https://x.com/velum_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4835d7d3adbc5c3756ca811175142dfc73c7ddbe.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": 1177505, "full_name": "Benjamin Muñoz-Cerro", "title": "Founder/CEO", "founder_bio": "CEO @Velum Labs | stanford quantum computing | harvard physics", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/benjamzc/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/840111d90db1dffc5c3e02d8728a42622fc82b0c.jpg" }, { "user_id": 2364271, "full_name": "Alen Rubilar-Muñoz", "title": "Founder/CTO", "founder_bio": "CTO @ Velum Labs | ml swe | aspiring mathematician | hackathon winner | world citizen", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/000alen/", "x_url": "https://x.com/000alen", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9019d7f13c58bfa58a7c0e71f53084bca1d3b444.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 30895, "name": "Allus AI", "slug": "allus-ai", "former_names": [ "Allus AI Inc." ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e556d72ba8162e6d77731f52ccea802c98b54fa3.png", "website": "https://allus.ai", "all_locations": "Atlanta, GA, USA", "long_description": "Allus builds next-gen vision foundation models that bring real intelligence to manufacturing. Enabling factories to see, understand, and improve production in real time. ", "one_liner": "Transforming manufacturing with our next-gen Vision Foundation Model", "team_size": 6, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1761108820, "tags": [ "Machine Learning", "SaaS", "Computer Vision", "Manufacturing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/allus-ai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/allus-ai.json", "year_founded": 2025, "city": "Atlanta", "country": "US", "linkedin_url": "https://www.linkedin.com/company/allus-ai/", "x_url": "https://x.com/allus_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e556d72ba8162e6d77731f52ccea802c98b54fa3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/19ed8bfd28856edefbda0d3ee4d4d3017054ed8a.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/02b1652b4ead3ef7bd979f7db3e7461b111e93bf.png" ], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 1667481, "full_name": "Kai Cui", "title": "Founder/CEO", "founder_bio": "Founder & CEO @ Allus AI. BS + MS in CS from Georgia Tech. Building vision foundation model to transform manufacturing.", "is_active": true, "linkedin_url": "https://linkedin.com/in/kaixiang-cui", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d012cfddf81fb9af30743cc5270cb84579450398.jpg" }, { "user_id": 2667730, "full_name": "Zhisen An", "title": "Founder/COO", "founder_bio": "Co-Founder @ Allus AI Inc. | MS CS @ GaTech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zhisenan/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d36b723641b04f1a88fef304237446298bdc8919.jpg" }, { "user_id": 2673772, "full_name": "Shijie Wang", "title": "Founder/CTO", "founder_bio": "Co-Founder & CTO @ Allus AI | CS @ Georgia Tech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shijiew/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/26e530b95b0469b4c70162d6c284bcf3d4df99b7.jpg" } ], "jobs": [ { "id": 92048, "title": "Founding AI Agent Engineer", "url": "https://www.ycombinator.com/companies/allus-ai/jobs/AcYMVC9-founding-ai-agent-engineer", "location": "Cupertino, CA, US / Atlanta, GA, US / Remote (CA, US; GA, US)", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$100K - $200K", "equity_range": "0.30% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "Amazon Web Services (AWS)", "Microsoft Azure", "Node.js", "Python", "React", "Redis", "Rust", "TypeScript", "SQL", "Docker", "Software Architecture" ] } ], "news": [], "launches": [ { "id": 95347, "title": "Allus AI — Vision Foundation Model for Manufacturing", "tagline": "Bringing real intelligence to factories", "body": "Hi YC,\n\nThis is [Kai](https://bookface.ycombinator.com/user/1667481), [Shijie](https://bookface.ycombinator.com/user/2673772), and [Jason](https://bookface.ycombinator.com/user/2667730), cofounders of Allus AI.\n\nWe’re building a vision foundation model for manufacturing.\n\n**TL;DR**: Traditional manufacturing vision systems are slow, complex, and expensive. Allus delivers a million-dollar vision solution in minutes.\n\nhttps://www.youtube.com/watch?v=pNV7Ekyzfm0\n\n**A Vision Foundation Model for Manufacturing**\n\nAllus is a 1B parameter vision foundation model trained on over 1.5 billion industrial, robotics, and manufacturing data pairs collected over five years. It achieves state-of-the-art accuracy in defect detection (> 99.95%) and process monitoring (> 99.2%).\n\nAllus understands manufacturing the same way ChatGPT understands language. The dataset we used to train Allus is more than 100x larger than comparable industrial datasets, giving Allus a strong baseline familiarity with the entire factory ecosystem, including machines, people, and processes.\n\nToday 95% of manufacturers still rely on manual inspection to detect defects and monitor process compliance on the factory floor. This work can be automated with computer vision, but these solutions are domain-specific, take months to implement and cost millions of dollars. \n\nWith our implementation agent, Allus can be trained to handle specific use cases in only a few minutes using a small number of reference examples. \n\nIn the last three months, we’ve deployed it on production lines run by five global fortune 500 manufacturers, where we’ve been able to improve defect detection accuracy in some cases by more than 100x.\n\n**Our Team**\n\n![uploaded image](/media/?type=post&id=95347&key=user_uploads/1667481/c1a959c6-45ec-441b-b904-0ef212f17f53)\n\nWe combine cutting-edge AI with challenges that have troubled manufacturing for over a century. Kai, Shijie, and Jason began exploring how AI could empower industry while studying at Georgia Tech. Kai comes from a family deeply rooted in manufacturing and grew up in factories, managing production for IPhone back panel at the age of 18. Shijie previously worked as an LLM scientist at NVIDIA Lab, where he gained firsthand insight into the pain points of manufacturing. Jason has always operated on the front lines of industrial enterprises and developed universal solutions for robotics companies.\n\n**Ask**\n\nWe’re working with manufacturers across the globe, from electronics and automotive to food & beverage, FMCG, and high-performance materials.\n\nIf you want to bring intelligence to your factory, talk to us!\n\n👉 [Allus.ai](http://Allus.ai)\n\n📧 [kai@allus.ai](mailto:kai@allus.ai)\n\n![uploaded image](/media/?type=post&id=95347&key=user_uploads/1667481/802e4649-e04c-4d8f-beec-a357eef0f593)\n\n", "url": "https://www.ycombinator.com/launches/Onr-allus-ai-vision-foundation-model-for-manufacturing", "votes": 282, "created_at": "2025-11-10T07:02:52.620Z" } ] }, { "id": 30928, "name": "Darwin", "slug": "darwin", "former_names": [ "Thesis Labs", "Thesis", "Darwin", "Thesis" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/834b16b30c5de79bf9dbcd0a4819289bfa937936.png", "website": "https://darwinrobotics.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Darwin Robotics creates general-purpose robots to build and maintain the machines civilization runs on.\r\n\r\nWe're starting with AI infrastructure, where skilled technicians perform a long tail of physical work across assembly, installation, commissioning, and maintenance. Our robots learn new tasks from demonstrations rather than being explicitly programmed for each workflow.\r\n\r\nFirst deployments are planned for early 2027.", "one_liner": "Robots to build civilization", "team_size": 4, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1762476616, "tags": [ "Hard Tech", "Machine Learning", "Robotics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "We are brothers and have been building things together our whole lives. We grew up in the Caribbean with our mom, obsessed with math and computers. Sergio taught himself calculus from MIT OpenCourseWare at 12, published mathematics as a teenager, and later worked on AI and robotics at Stanford, Nvidia, and Google X. Luigi studied math and CS at Columbia, then co-founded a payments company where he scaled engineering from zero to billions in transaction volume.\r\n\r\nAs kids, we built worlds in Minecraft. Now, we’re building robots." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "We sit at the dawn of a new era in civilization. AI is beginning to solve problems across science and mathematics that were previously impenetrable. Yet this intelligence is still largely confined to the screen.\r\n\r\nWe believe the next frontier is physical. Our goal is embodied artificial general intelligence.\r\n\r\nWhen we succeed, billions of intelligent robots will work alongside humans, performing useful work and expanding what humanity is capable of." } ], "url": "https://www.ycombinator.com/companies/darwin", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/darwin.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/darwin-robotics-inc", "x_url": "https://x.com/darwin_robotics", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Darwin-Robotics-Inc", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/834b16b30c5de79bf9dbcd0a4819289bfa937936.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/415cd09f20efd72acec243c4e7b73ccb3b01cd35.png" ], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 455353, "full_name": "Sergio Charles", "title": "Founder/CEO", "founder_bio": "Robots to build civilization. Prev: AI R&D at Google X, NVIDIA, and Stanford AI Lab, working with Chelsea Finn on autonomous RL for robotics and Andrew Ng on computer vision. Stanford B.S. Math & CS, M.S. Stats.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sergio-charles-5a8052134/", "x_url": "https://x.com/eigentopology", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ba455346463b0d6acae18a8bcd3d2bba294bfdb5.jpg" }, { "user_id": 1021038, "full_name": "Luigi Charles", "title": "Founder", "founder_bio": "Building Westworld. Previously founded a fintech payments startup, scaling volume from zero to billions. Columbia CS & Math.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/luigicharles", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/ca6e645d2ffabef7f901b4d6e1aec82d02f81657.jpg" } ], "jobs": [ { "id": 96229, "title": "Member of Technical Staff, AI & Robotics", "url": "https://www.ycombinator.com/companies/darwin/jobs/R8xvEoc-member-of-technical-staff-ai-robotics", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$120K - $170K", "equity_range": "0.75% - 2.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "C++", "Git", "Python", "Torch/PyTorch", "Machine Learning", "Deep Learning", "Reinforcement learning (RL)", "CUDA", "Microcontrollers", "Diffusion Models" ] } ], "news": [], "launches": [ { "id": 95309, "title": "δ Thesis: Building the frontier of AI discovery", "tagline": "Making it possible for researchers anywhere to discover the next Transformer or invent the next AlphaFold.", "body": "Thesis is an applied AI lab accelerating the frontier of AI discovery.\n\nWe're making it possible for researchers anywhere to discover the next Transformer or invent the next AlphaFold.\n\n**Mission**\n\nOur mission is to make state-of-the-art AI discovery 10x faster and 10x cheaper. The big labs are racing to get there, but their need to productize and turn a profit has already weighed them down. Thesis embraces rapid experimentation and novel fundamental research. Our singular focus, outcome-driven mindset, and early results give us confidence that we can get there first.\n\n**Results**\n\nThesis is state-of-the-art on OpenAI’s Machine Learning Engineering benchmark ([MLE-Bench](https://github.com/openai/mle-bench?tab=readme-ov-file#mle-bench)).\n\nMLE-Bench tests how well AI systems can train ML models autonomously, a first step toward self-improving systems. We achieved this result in the record time of 1 month and with only $10k in compute. Agents will get you far, but not far enough. Our methods mix old school with new, and have already outperformed teams of researchers at Microsoft, Google, Meta, and Baidu.\n\n![uploaded image](/media/?type=post&id=95309&key=user_uploads/455353/310c8083-1512-40c3-8e31-c1cc941f3634)\n\n**Team**\n\nLuigi and Sergio are brothers. Back in 2021, Sergio [wrote about a future where AI could create its own algorithm](https://cs229.stanford.edu/proj2021spr/report2/82285254.pdf)s. With today's foundation models, that future is suddenly within reach.\n\nSergio worked on AI R&D at Google X, Nvidia, and Stanford’s AI Lab (SAIL). He has published work at NeurIPS and ICML, and has worked with Chelsea Finn and Andrew Ng.\n\nLuigi built Sphere, a fintech company that processes billions of dollars every year in global cross-border payments. Sphere was recently valued at $250 M+ in its Series A round.\n\n**10 Year Vision**\n\nThe holy grail of AI is building self-improving systems that, guided by human creativity, enable breakthroughs and new paradigms achieving state-of-the-art results under resource constraints.\n\nIn the limit, our engine will expand beyond ML to every domain of science, from computational biology, to theoretical mathematics and physics. Thesis will autonomously discover new knowledge and become the driving force behind humanity’s scientific progress.\n\n**Our Ask**\n\nWe collaborate with leading institutions, startups, and independent researchers around the world. If you’re interested in what we’re building, please get in touch: \\[[ask@thesislabs.ai](mailto:ask@thesislabs.ai)\\](mailto:[ask@thesislabs.ai](mailto:ask@thesislabs.ai))", "url": "https://www.ycombinator.com/launches/OnF-thesis-building-the-frontier-of-ai-discovery", "votes": 232, "created_at": "2025-11-11T16:40:14.609Z" } ] }, { "id": 31010, "name": "Amika", "slug": "amika", "former_names": [ "Fixpoint" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c42840d914de4ea4ec78f647bf0db2e93434ca5d.png", "website": "https://www.amika.dev/", "all_locations": "New York City, NY, USA", "long_description": "Amika is infra to build your software factory.\r\n\r\nWe give your team sandboxed AI coding agents in the cloud: the same infrastructure Ramp, Coinbase, and Stripe built in-house. Kick off work from a web UI, CLI, Slack, or API. Use your preferred coding agent (Claude Code, Codex, etc.). Each agent understands your codebase, runs autonomously, and ships real PRs with live previews of the apps it changed. Use it as your daily driver or plug it into your own code-gen pipeline via API.", "one_liner": "Infra to build your own software factory", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1759722442, "tags": [ "Machine Learning", "B2B", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2025", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/amika", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2025/amika.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/amikadev/", "x_url": "https://x.com/amikadev", "fb_url": null, "cb_url": null, "github_url": "https://github.com/gofixpoint/amika", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c42840d914de4ea4ec78f647bf0db2e93434ca5d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tom Blomfield", "url": "https://www.ycombinator.com/people/tom-blomfield" }, "founders": [ { "user_id": 101903, "full_name": "Dylan Mikus", "title": "Founder/CEO", "founder_bio": "Making infra for software factories. Helping people spin up sandboxed coding agents, and then control the interactively or programmatically so they can automate their entire SDLC.\n\nCMU grad in comp sci and ML\n2x YC founder", "is_active": true, "linkedin_url": "https://linkedin.com/in/dbmikus", "x_url": "https://twitter.com/dbmikus", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/10786d1817ed7d689f6f3313f3b2c73f38c9f9a5.jpg" }, { "user_id": 1013839, "full_name": "Jakub Cichon", "title": "Founder/CTO", "founder_bio": "co-founder / CTO at Amika. Building infra for software factories. \n\n2x YC founder.", "is_active": true, "linkedin_url": "https://linkedin.com/in/jakub-cichon-14016a3b", "x_url": "https://x.com/jdcamika", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/095af1aef16261010001d5599b3059bc222330e3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 103929, "title": "Amika - the operating system for AI agents to write and verify code", "tagline": "Run fleets of human-in-the-loop or background coding agents inside cloud computers. Let them automate coding and verify it's correct.", "body": "Software isn't a craft anymore, it's a manufacturing problem.\n\nAt Amika, we're building **the open source operating system for coding agents to automate their work**, like how the factory automated manufacturing.\n\n**Amika gives each agent a cloud computer, and lets you build automated or human-in-the-loop workflows for agents to write code and prove that the code is correct.**\n\nA couple of examples:\n\n\\- Have Amika monitor production and auto-fix errors, testing the fix against your real app\n\n\\- Add Amika to shared customer Slack channels. Auto-implement customer requests and share a live preview the updated app with them\n\nCheck out some more examples in our video 👇\n\n\n\n# The problem\n\nAI coding agents made code generation cheap. Producing correct code at scale, with real quality controls, is still unsolved. You're left with a tradeoff: pore over every change and go slow, or vibe-code and ship slop.\n\nCoding agents are useful, but the surrounding system is missing.\n\n\\- They lack the right **dev environment to verify their work**\n\n\\- They lack the right **context to understand the full problem**\n\n\\- They lack **deterministic guardrails controlling what they can change**\n\n\\- They produce 10x more code, but teams lack **a system for 10x more review**\n\n# How Amika solves this\n\nAmika composes 5 primitives into its agent operating system:\n\n1\\. **Sandboxed environments**: isolated cloud computers with the repos, tech stack, MCP servers, skills, and context an agent needs.\n\n2\\. **Workflows**: programs as code or markdown files that define when an agent runs and what job it does.\n\n3\\. **Deterministic guardrails**: rules enforced on every code change, like \"do not cross this module boundary\" or \"every database query must include a customer ID filter.\"\n\n4\\. **Automated verification**: test, review, fix, and merge loops that check the work against your real tech stack before it ships.\n\n5\\. **Audit trails**: the agent chat session, code changes, which checks passed, and what the run cost.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/a1246cf3-1ed1-435d-94fb-2cb7437b4995)\n\nAmika works with your existing coding agent subscriptions (Claude Code, Codex, etc.) so **you're not locked into one vendor**. Start agents from the web, Slack, GitHub, the CLI, or the API. At any point, a human can take over the sandbox and inspect the running apps, or drive from their own code editor.\n\nBecause each sandbox runs your full stack, you review the agent's work in a live preview of your real app, plus the verification artifacts it produced, like screen captures or API test requests, instead of poring over the diff line by line.\n\nWith Amika's workflows and environments, you can spend time building higher level loops for your agent to write and verify code, speeding up your product development.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/60e16093-3dcf-4285-b73b-998c74507145)\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/3b84372e-0a00-46a3-afd1-d711547c256b)\n\n# Who we are\n\nWe're [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/), two time YC cofounders.\n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/497da8a5-bfa8-407e-98f6-b613d9389b50)\n\nWe pivoted our YC company to bet on this, and we already use Amika for the majority of our PRs. We're building it in the open: each primitive ships as open source, and our cloud platform packages the whole operating system into a software factory for teams.\n\n# Our ask\n\n\\- Sign up for a **free two-week trial at [amika.dev](https://www.amika.dev/)**\n\n\\- Dig into the **open source repo** on GitHub: \n\n\\- If you want **help setting up your software factory**, book at demo at \n\n![uploaded image](/media/?type=post&id=103929&key=user_uploads/101903/3e200f7d-2b3f-4e3e-99f3-d0249987c72c)\n\n", "url": "https://www.ycombinator.com/launches/R2H-amika-the-operating-system-for-ai-agents-to-write-and-verify-code", "votes": 18, "created_at": "2026-06-24T17:06:48.500Z" }, { "id": 94868, "title": "Fixpoint - Uber for AI trainers and human-in-the-loop supervisors", "tagline": "Fixpoint helps AI data companies automate sourcing, vetting, and all HR for your expert annotator teams", "body": "Hello, world! We’re [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/), the co-founders of [Fixpoint](https://fixpoint.co/).\n\n**Fixpoint helps AI training data companies hire the expert humans that create AI training data**.\n\nAI needs human experts to teach it new skills and benchmark its performance: PhDs for science, lawyers for contract review, etc. But finding and vetting these experts is time-consuming, and without properly vetting experts, you’re paying for bad data!\n\nFixpoint automates this process, connecting companies with verified experts at the push of a button, so you can focus on creating quality AI training data instead of recruiting.\n\n[Launch Video](https://youtu.be/9TCgpe0tT3w)\n\n**We have two products:**\n\n1. **Worker Vetting API** - Plug Fixpoint’s API into your onboarding workflow and vet the credentials and backgrounds of workers. **Catch 10x the worker fraud!**\n2. **White-Glove Expert Staffing** - When you need annotators fast, Fixpoint can source and hire a team of qualified experts. **Staff a team of 100s in just a couple weeks.**\n\n**Our track record:**\n\n* We’ve caught 10x the fraudulent applicants for top AI data companies\n* We’re partnered with data companies to staff teams for the top AI labs\n* We are GDPR ready and SOC 2 certification is underway\n\nUltimately, we're building the platform for \"human data ops\". Just as chip designers use TSMC for chip manufacturing, AI companies can use Fixpoint for the human expertise and tools needed to create specialized training data and evals.\n\n# Our ask\n\nSend us an email at [**founders@fixpoint.co**](mailto:founders@fixpoint.co) or [**grab a demo**](https://cal.com/fixpoint/demo) if:\n\n* You’re an AI data company or RL company and need a team of expert annotators\n* You’re an enterprise that needs to eval or benchmark your AI agents in a specialized domain (healthcare, legal, etc.)\n* You’re on the “human data” team at a model lab and have trouble managing experts or their data\n* You don’t fit in the above, but you or someone you know could use our services!\n\n# About us\n\nWe’re [Jakub](https://www.linkedin.com/in/jakub-c-14016a3b/) and [Dylan](https://www.linkedin.com/in/dbmikus/). We’re second time YC founders who’ve worked together since we met at Oscar Health in 2018. We’ve built everything from search systems, to chatbots, to data infrastructure, to AI web research agents.\n\n![uploaded image](/media/?type=post&id=94868&key=user_uploads/101903/ad8316ad-d855-46de-9ffc-67bb1fd62847)\n\n", "url": "https://www.ycombinator.com/launches/Og8-fixpoint-uber-for-ai-trainers-and-human-in-the-loop-supervisors", "votes": 25, "created_at": "2025-10-23T21:41:36.522Z" } ] }, { "id": 31168, "name": "Instinct", "slug": "instinct-xyz", "former_names": [ "Assonant" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/95bc87269a98980418f0859561c98e4d5a516586.png", "website": "https://instinct.xyz", "all_locations": "San Francisco, CA, USA", "long_description": "Capital allocation is becoming a more important part of individual economic life.\r\nFinance is moving toward a global, onchain, 24/7 future with thousands of markets trading at once.\r\n\r\nThe asset management industry is built around the scarcity of financial expertise and the cost of organizing people around capital. \r\n\r\nInstitutions cover global markets by employing analysts, researchers, and quants around the clock. \r\n\r\nAI turns this organizational capacity into software.\r\n\r\nInstinct is building the harness for this transition.\r\n\r\nInstinct is a proactive team of agents that searches thousands of markets, investigates opportunities, and operates 24/7. The individual becomes the portfolio manager, Instinct becomes the investment firm operating around them.\r\n\r\nAs institutional advantages become widely accessible, the edge concentrates in each trader’s worldview, judgment, and bets. \r\n\r\nThe first one-person investment firm managing $1 billion will emerge within two years.\r\n\r\nIt will run on Instinct.", "one_liner": "Trade with superhuman instinct", "team_size": 5, "industry": "Fintech", "subindustry": "Fintech", "launched_at": 1779953002, "tags": [ "Machine Learning", "Trading", "AI", "Cryptocurrency" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Fintech" ], "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/instinct-xyz", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/instinct-xyz.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/instinctxyz", "x_url": "https://x.com/instinct_xyz", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/95bc87269a98980418f0859561c98e4d5a516586.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": 3171357, "full_name": "Joon Won Song", "title": "Founder/CEO", "founder_bio": "Quantitative Developer @ Squarepoint Capital\nEECS @ Imperial College London", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mericsong", "x_url": "https://x.com/mericsong", "avatar_thumb_url": "/avatars/thumb/missing.png" }, { "user_id": 3198682, "full_name": "Eunju Moon", "title": "Founder/CTO", "founder_bio": "PhD dropout @ USC (Quantum Computing)\nTheoretical Physics @ Imperial", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/eunju-moon-a65823202/", "x_url": "", "avatar_thumb_url": "/avatars/thumb/missing.png" } ], "jobs": [], "news": [], "launches": [] }, { "id": 31191, "name": "Traverse", "slug": "traverse", "former_names": [ "Clice AI", "Clice" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/35a08dba94f5c7c4efe9e2c566eb21cc8f85b0e1.png", "website": "https://www.traverse.so", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "Custom models and inference, built and run for enterprises", "team_size": 1, "industry": "B2B", "subindustry": "B2B", "launched_at": 1767260558, "tags": [ "Machine Learning", "Reinforcement Learning", "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/traverse", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/traverse.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/traverse-so/", "x_url": "https://x.com/traverseso", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/35a08dba94f5c7c4efe9e2c566eb21cc8f85b0e1.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": 2775848, "full_name": "Lance Yan", "title": "Founder/CEO", "founder_bio": "Seeking truth.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/lance-yan/", "x_url": "https://x.com/lanceyyan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/19d6b3aa8f7c40b89a2db639a278aa326cc3a1f5.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98535, "title": "Traverse - Data Research for the Non-Verifiable", "tagline": "We give AI labs the training data that enables their frontier models to acquire taste", "body": "**TLDR:** Traverse is a data research lab focused on the hardest unsolved problem in frontier model training: **ambiguous, taste-dependent work**. \n\nWe capture real human behavior in highly controlled settings and turn judgment, taste, and long-horizon reasoning into high-fidelity environments for frontier models.\n\n\n\n**The Problem**\n\nReinforcement learning has already helped frontier models achieve superhuman ability in math and coding since these domains have very clean and deterministic feedback loops. \n\nHowever, the next frontier domains (law, healthcare, sales, writing, etc) aren’t as black and white. Current approaches fail to accurately represent subjective scenarios, despite these domains being where most of the economic value is.\n\n**The Solution**\n\nWe are building data factories to create high fidelity real life work environments at scale.\n\nTraverse captures what people actually think while doing their work; we are pioneering a new category of training data that is yet to exist.\\\n\\\n**If you’d like to understand the details, reach us at**\\\n[lance@traverse.so](mailto:lance@traverse.so)\\\n\n\n![uploaded image](/media/?type=post&id=98535&key=user_uploads/2775848/4586ce6d-bd41-40af-9be6-586ab6226d02)\n\n", "url": "https://www.ycombinator.com/launches/PdH-traverse-data-research-for-the-non-verifiable", "votes": 41, "created_at": "2026-03-06T16:12:51.971Z" } ] }, { "id": 31223, "name": "Datoric", "slug": "datoric", "former_names": [ "Flow AI", "Arzule" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fa894803120df3a3a9250c66e4232e33e2a223d0.png", "website": "https://www.datoric.com", "all_locations": "San Francisco, CA, USA", "long_description": "Datoric develops custom datasets for voice models, robotics, and world models, treating research, collection, verification, and production as one continuous process. We work closely with frontier model teams to turn emerging limitations and model failures into testable data hypotheses, while running our own experiments ahead of customer demand. This allows us to operationalize validated methods into repeatable collection systems at scale.\r\n\r\nData is collected through private invite-only applications separated by modality, customer, and trust level. Every submission remains linked to the contributor, device, task, session, consent, rights, and processing history that produced it, giving our internal QA and fraud models the context to detect problems that may appear legitimate in the finished file. Each collection reveals new failure cases and quality signals that improve the systems behind the next dataset.\r\n\r\nOnce a collection method is validated, we scale it up as a solution to the model failure and it also becomes a reusable data recipe for future custom projects or independently collected, rights-cleared data products. ", "one_liner": "The secure training data R&D engine for physical AI", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1767154035, "tags": [ "Machine Learning", "Robotics", "Data Labeling", "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/datoric", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/datoric.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/arzulehq/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/fa894803120df3a3a9250c66e4232e33e2a223d0.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harshita Arora", "url": "https://www.ycombinator.com/people/harshita-arora" }, "founders": [ { "user_id": 1486120, "full_name": "Nikhil Reddy", "title": "Founder/CEO", "founder_bio": "CEO @ Datoric. Prev Quant & SWE Intern. Math/Econ/CS @ UChicago; Bypassed Google OAuth 2.1 and QA systems on data annotation platforms to automate training tasks in 2023, then disclosed the loopholes to the affected platforms.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nikhilrreddy", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/85cf42094155ddf41c9ca0e99b42a85e2ed763d1.jpg" }, { "user_id": 2310490, "full_name": "Jeffrey Lin", "title": "Founder/CTO", "founder_bio": "CTO @ Datoric. Prev AI/ML & SWE Intern. Math & CS & Robotics at NYU. Created a bounding box algorithm for more efficient labeling as Head of Data for the NYU Robotics Team. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jeff-j-lin", "x_url": "https://x.com/Jeffjinlin", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4f3068998c884af4a2668f4da4bbe3efa8e796d3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108251, "title": "Datoric | Trustworthy data for the next generation of models", "tagline": "Collecting the human experience AI needs, securely.", "body": "Datoric provides custom, security-first training data for voice models, robotics, and world models. Our private, project-specific contributor systems have led to a dramatic improvement in quality for customers and helped us generate nearly seven figures in revenue over the last 30 days.\n\n## **Our background**\n\nIn 2023, we built large-scale automations that completed paid LLM training tasks across major crowd-work platforms by bypassing QA testers. We earned six figures in three months, and even helped fix some of the vulnerabilities that we found. From this experience, we learned just how easily training data can be compromised, and how it can lead to unsafe model behavior.\n\n\n\n## **The problem**\n\nLabs are searching for the high-quality, authentic human data their models need, but quality and provenance become harder to control at scale, especially when relying on outside data providers. Poor-quality or compromised data can distort model behavior, while unqualified contributors, automated submissions, unclear instructions, and weak consent records can still slip through QA. At the same time, data providers hold concentrated stores of customer data, contributor identities, and payments that attract fraud and security threats.\n\n## **What we built**\n\nDatoric manages the full collection process for multilingual speech, egocentric video, and action-conditioned data. We collect this data through more than 300,000 active contributors who record conversations, everyday tasks, and other project-specific activities through our private apps. Because Datoric has no public marketplace or open upload system, every collection can run inside its own isolated environment, keeping customer projects separate and limiting unauthorized access. Each recording also remains linked to the verified contributor who created it and the consent they provided. This blocks automated or manipulated submissions and protects quality throughout the collection process rather than leaving it to final QA.\n\n## **Traction**\n\nWe’ve seen a major uptick in quality compared to other providers, and in the last 30 days, Datoric has generated nearly seven figures in revenue.\n\n## **Our ask**\n\nWe would love introductions to teams building voice models, robots, or world models that need difficult, custom human-generated data.\n\nSend us your hardest collection specification at [**founders@datoric.com**](mailto:founders@datoric.com), or visit [**datoric.com**](http://datoric.com).", "url": "https://www.ycombinator.com/launches/S9z-datoric-trustworthy-data-for-the-next-generation-of-models", "votes": 4, "created_at": "2026-07-29T16:22:25.416Z" } ] }, { "id": 31258, "name": "Dream", "slug": "dream", "former_names": [ "Dream Browser" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/479d7e4f9e6b74b1df9f87c67fe614babba0a9b5.png", "website": "https://www.pingdream.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Dream makes AI cameras that catch damage teams miss. Car dealerships, equipment rental companies, fleets, and more move expensive assets in and out all day, and undocumented damage is a costly leak.\r\n\r\nDream cameras are simple to set up and work automatically. Each vehicle or machine that passes is photographed from every angle, identified, and reconstructed into a complete condition record. On return or intake, Dream instantly flags new dents, scrapes, and changes your team might miss, giving one source of truth for every asset's condition over time.\r\n\r\nRyland worked at X/xAI and built aerial damage-detection vision at Vexcel Imaging. Alexander worked at AWS and trained edge-camera models deployed in the wild across Africa and India to detect poachers.", "one_liner": "Using VLMs to Catch Damage", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786646316, "tags": [ "Hardware", "Machine Learning", "Computer Vision", "B2B", "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/dream", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/dream.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/pingdream", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/479d7e4f9e6b74b1df9f87c67fe614babba0a9b5.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": 1393934, "full_name": "Ryland Birchmeier", "title": "Founder", "founder_bio": "Ryland Birchmeier is Co-founder of Dream (YC S26), building pocket-sized, battery-powered AI cameras that automatically capture assets and catch damage teams miss at car dealerships, equipment rental companies, and more.\n\nPreviously, Ryland worked at X/xAI and built aerial computer vision at Vexcel Imaging. He studied computer science at the University of Virginia.", "is_active": true, "linkedin_url": "https://linkedin.com/in/rylandbirchmeier", "x_url": "https://x.com/rylandbirch", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/37f594b7f6b8754e9966db95609cba5942fd3185.jpg" }, { "user_id": 1456894, "full_name": "Alexander Halpern", "title": "Founder", "founder_bio": "Alexander Halpern is Co-founder of Dream (YC S26), building pocket-sized, battery-powered AI cameras that automatically capture assets and catch damage teams miss at car dealerships, equipment rental companies, and more.\n\nPreviously, Alexander built agents for AWS Marketplace and trained models for edge computing cameras deployed across Africa and India to detect poachers hunting endangered wild cats at Panthera. He studied computer science at the University of Virginia.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexanderhalpern", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4702d251513a653e5c3f7e4d1307df73e45916e3.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 110473, "title": "Dream - Pocket-sized AI cameras that catch asset damage", "tagline": "Dream makes pocket-sized, battery-powered AI cameras that catch damage teams miss.", "body": "Hey everyone 👋, we are Ryland and Alexander, co-founders of Dream.\n\n**TL;DR: Dream automatically photographs every vehicle or machine that passes through your gate, builds a 3D condition record, and uses AI to catch new damage the moment an asset passes through.**\n\n**Launch Video: [https://youtu.be/uMP12lCb12E](https://youtu.be/uMP12lCb12E)**\n\n**The problem**\n\nDamage is an expensive leak for anyone who moves large, expensive assets in and out all day. It shows up two different ways:\n\n**Car dealership service departments**\n\n* As a car pulls into the service entrance, damage that could be identified and fixed goes unnoticed, which is lost revenue.\n* Without a record of the car's condition on arrival, disputes come down to the customer's word against the dealership's, and the dealership gets blamed for pre-existing damage.\n\n**High-value asset rentals (heavy equipment, trucks, boats, planes, trailers)**\n\n* Teams are inconsistent about photographing assets as they go out and come back, so new damage gets missed before the customer is billed.\n* When damage is missed, there's no evidence to charge the customer, so the company eats the repair cost out of pocket.\n\n**What Dream does**\n\nDream cameras sit at your gate, lane, or service drive and turn every pass into evidence, automatically:\n\n* **Continuous capture:** every vehicle or machine that moves through is photographed from every angle. No one has to remember to do it.\n* **Asset identification:** Dream knows exactly what you need to look at.\n* **3D reconstruction:** every pass becomes a complete 3D condition record for that asset.\n* **AI damage detection:** on return or intake, Dream compares against the last record and flags new dents, scrapes, and visible changes right away.\n\nThe result is one clear source of truth for every asset's condition over time: rental companies catch damage before they bill so they stop eating repair costs, and dealership service departments find more fixable damage and resolve customer disputes with evidence.\n\nWorks across cars, vans, trucks, heavy equipment, and more.\n\n**Why us**\n\nWe have spent years training models for aerial imaging and edge computer vision devices. Ryland has worked on the exact problem of detecting damage to assets from aerial imaging, such as degraded paint. Alexander has trained models that run on edge cameras deployed in the wild in Africa and India to detect poachers.\n\n**Our ask**\n\nWe're onboarding partners now. **We'd love intros to:**\n\n* **Car dealerships**\n* **High-value asset rental companies** (heavy equipment, trucks, boats, planes, trailers)\n\nIf you run one of these, or know someone who does, please message us on LinkedIn!", "url": "https://www.ycombinator.com/launches/Sjp-dream-pocket-sized-ai-cameras-that-catch-asset-damage", "votes": 7, "created_at": "2026-08-14T17:00:00.348Z" } ] }, { "id": 31333, "name": "PerfectBit, Inc.", "slug": "perfectbit-inc", "former_names": [ "Gikl, Inc", "PerfectBit" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/900c210e3f57ee42221dbb5ea21f81418b553e70.png", "website": "https://perfectbit.ai", "all_locations": "San Francisco, CA, USA", "long_description": "PerfectBit builds learned policies for robots that do real work. We train manipulation and control policies that hold up outside the lab, in the unstructured environments where real tasks actually happen. Founded by two alumni of Meta's Superintelligence org: Péter Vajda led Media GenAI foundation-model R&D (Emu, Movie Gen, image and video editing), and Seiji Yamamoto led teams in the Core Llama group across LLM pre-training and post-training, inference optimization, and computer vision. We're early, deliberately quiet about the details, and hiring the first researchers who will shape what this becomes. San Francisco, on-site.", "one_liner": "Robotics", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1773359306, "tags": [ "Machine Learning", "Robotics", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Spring 2026", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/perfectbit-inc", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/perfectbit-inc.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/perfectbit", "x_url": "https://x.com/PerfectbitAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/900c210e3f57ee42221dbb5ea21f81418b553e70.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Garry Tan", "url": "https://www.ycombinator.com/people/garry-tan" }, "founders": [ { "user_id": 3007971, "full_name": "Seiji Yamamoto", "title": "Founder", "founder_bio": "Led teams in the Core Llama group at Meta Superintelligence Labs. Senior Staff Research Scientist across 9 years at Meta spanning LLM pre-training and post-training, inference optimization, full-duplex speech models, and computer vision vision models. Before tech: PhD in Physics, published in Proceedings of the National Academy of Sciences and Physical Review Letters, co-authored with Fields Medalist. Educated at Stanford, Rice, Columbia, post-doc at a National Lab. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/seiji-yamamoto-a630897/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b3404aa262c227d308c4e89f01657ebd082beaef.jpg" }, { "user_id": 45070, "full_name": "Peter Vajda", "title": "Founder", "founder_bio": "\nI worked as Director of Media Generation at Meta before 2026 for 11 years.\nI was managing the Media GenAI foundation model research and development, including efficient media generation, text to image generation (Emu), image editing, Movie gen, text to video, video editing and character consistent image and video generation. Previously, led efficient deep learning for computer vision teams supporting on-device models for AR/VR. \nI was Assistant Professor at Stanford University", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/p%C3%A9ter-vajda-9a03aaa/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0315fd092411900f3bed846cb6807bfb6d6290d6.jpg" } ], "jobs": [ { "id": 96987, "title": "Hiring Research Scientists and Research Engineers at PerfectBit", "url": "https://www.ycombinator.com/companies/perfectbit-inc/jobs/snGw8px-hiring-research-scientists-and-research-engineers-at-perfectbit", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$100K - $300K", "equity_range": "0.10% - 3.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "C++", "Python", "Deep Learning", "Reinforcement learning (RL)", "Computer Vision", "CAD Design", "Blender", "LLMs", "Transformers", "AI Agents", "Multimodal AI", "Diffusion Models" ] } ], "news": [], "launches": [ { "id": 101365, "title": "PerfectBit: AI Training data, correct by construction", "tagline": "Specialized data for the world's most powerful models.", "body": "# **TL;DR**\n\n_PerfectBit creates high-quality training data that's correct by construction. We verify against physics simulators, scientific databases, formal proof systems. It's what models need to cure hallucination, close common-sense gaps, and reach superintelligence. Reinforcement Learning from Verified Rewards (RLVR) was an early step in this direction. We go further. LLMs, robotics, AI for Science, and more._\n\n\n\n# **The problem: the failures standing between today's models and superintelligence**\n\nFrontier labs are bottlenecked on four data problems:\n\n* **Hallucination.** Models trained on noisy web scrapes produce confident, fluent statements that are wrong.\n* **Common-sense failure.** Models that solve graduate-level math still trip on trivial problems for a five-year-old.\n* **Information scarcity.** Scientific facts, formal derivations, peer-reviewed findings — they're a thin minority of tokens in any open-web scrape, drowned out by orders of magnitude of recipes, marketing copy, forum chatter, and AI-generated slop. Scaling the scrape doesn't change the ratio.\n* **Human annotation doesn't scale.** It's slow, expensive, and capped at human ability — superintelligence won't come from labeling at human throughput.\n\n# **What we do**\n\n* PerfectBit creates high-quality training data that's correct by construction. Every sample is generated against an oracle — physics simulator, scientific database, formal proof system — and verified before it ships, in text, code, image, audio, or video. For LLMs, we project observations about the world into natural language; for verticals like robotics and AI-for-science, we ship purpose-built datasets.\n\n# **Why us**\n\nWe've trained the models we're now building data for.\n\n* **Peter Vajda** — Former **Director of Media Generation at Meta**. Eleven years at Meta. Most recently led Media GenAI foundation-model R&D: **Emu** (text-to-image), image editing, **Movie Gen**, text-to-video, video editing, character-consistent generation. Earlier led efficient deep learning for computer vision powering on-device Augmented Reality and Virtual Reality (AR/VR) models. **Assistant Visiting Professor at Stanford** before Meta. PhD in Computer Science.\n* **Seiji Yamamoto** — Led teams in the **Core Llama group at Meta Superintelligence Labs**. Nine years at Meta as a **Senior Staff Research Scientist** across LLM pre- and post-training, inference optimization, full-duplex speech, and computer vision. **PhD in Physics**, publications in **Proceedings of the National Academy of Sciences** and **Physical Review Letters** (co-authored with a Fields Medalist). Stanford, Rice, Columbia; postdoc at a National Lab.\n\nBetween us: foundation-model training across text, image, video, and speech, shipped to billions of users — plus scientific chops to design the verifier stacks.\n\n# **We’d love to talk more**\n\nWe're talking to a small number of frontier AI labs about pilot engagements.\n\n* **Heads of data / data partnerships** at frontier labs\n* **Research leads** running pre-training, mid-training, post-training, or reasoning programs and shopping for a corpus that doesn't exist yet\n* **Model-training teams** preparing a next-gen training run who want a correct-by-construction supplement in the mix\n* **Researchers in formal methods, scientific computing, or domain-specific simulators** who want to contribute a verifier stack — we'd love to talk\n\nWe're also **hiring research scientists and engineers in San Francisco** (in-person). If you've shipped foundation-model training, built formal/simulation tooling or spent time developing AI training data at production scale, please reach out.\n\n# **Contact**\n\n* **Web:** [**https://perfectbit.ai**](https://perfectbit.ai)\n* **Email:** [**founders@perfectbit.ai**](mailto:founders@perfectbit.ai)\n* **Founders:** Peter Vajda, Seiji Yamamoto", "url": "https://www.ycombinator.com/launches/QMv-perfectbit-ai-training-data-correct-by-construction", "votes": 8, "created_at": "2026-05-14T19:03:28.026Z" } ] }, { "id": 31495, "name": "Mentlio", "slug": "mentlio", "former_names": [ "Adjoint" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f7a733c432ee3fbe8a11ed5dec1520431b7f8d09.png", "website": "https://mentlio.com", "all_locations": "San Francisco, CA, USA", "long_description": "Mentlio an applied research lab focused on optimizing AI ROI for the modern enterprise. We help engineering teams draw a line between their AI spend and delivery, per engineer. We then cut ~30% of AI spend via Mentlio's custom ML-based intelligent routing, context optimization, and prompt compression.", "one_liner": "Engineering Intelligence and Token Optimization for the AI-Coding Era", "team_size": 0, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786589390, "tags": [ "Developer Tools", "Machine Learning", "Productivity" ], "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/mentlio", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/mentlio.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mentlio/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f7a733c432ee3fbe8a11ed5dec1520431b7f8d09.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/9be0890b6c5594bbc3af263ac4e025a316283383.png" ], "primary_partner": { "name": "Pete Koomen", "url": "https://www.ycombinator.com/people/pete-koomen" }, "founders": [ { "user_id": 2693539, "full_name": "Ashank Shah", "title": "Founder/CEO", "founder_bio": "Building Mentlio | Prev. CS @ Caltech | Researched generative and graph-based DL for biomedical problems @ Dartmouth Health.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ashank-shah/", "x_url": "https://x.com/ashankshah_x", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/758354f37ee3a77f1731eb04c7889b1d7bb48571.jpg" }, { "user_id": 2587306, "full_name": "Ahmet Demirbas", "title": "Founder/CTO", "founder_bio": "Tokenmaxxing → ROImaxxing | Prev CS @ Caltech | 5x Published ML, NLP, Agents Research @ IEEE, ACM, BMC", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ahmet-demirbas7/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/078fa095bf49a870046553858e43d9b8fc0cb577.jpg" } ], "jobs": [], "news": [ { "title": "Factory Acquires Mentlio: Building Intelligent Token Allocation for the Software Factory", "url": "https://factory.com/news/mentlio", "date": "Oct 09, 2026" } ], "launches": [ { "id": 111055, "title": "Mentlio: Engineering Intelligence & Token Optimization for the AI-coding Era", "tagline": "The spend and ROI command center for the modern engineering org ", "body": "**TL;DR:** Mentlio is the engineering intelligence platform for the AI-coding era. We help engineering organizations cut AI costs by up to 30% and finally see what ROI that spend is actually producing. All without ever collecting your prompts or source code.\n\n**Launch Video**\n\n\n\n**Problem**\n\nEvery engineering org is now an AI org. Token usage is growing exponentially, and so is the bill. The following questions have been largely unanswered:\n\n* How can we cut our usage-based AI spend without bottlenecking usage or efficiency?\n* What ROI are we getting for every dollar of AI spend?\n* To what extent is AI-generated code creating quality debt?\n\nBrian Armstrong recently laid out how Coinbase is reducing AI spend while token usage grows exponentially on X: better defaults, smarter routing, cache awareness, lean context, and full visibility into usage and impact. \n\n![uploaded image](/media/?type=post&id=111055&key=user_uploads/2693539/7bf48740-dd37-4015-9da9-ec0f9bd900ed)\n\nCoinbase built a custom harness, LLM gateway, routing, and usage visibility internally. We believe they have the right thesis, but most engineering orgs don’t want to run on a custom harness. **Mentlio gives every eng team a better version of those savings & visibility without changing any workflows.**\n\n**Solution**\n\nMentlio gives you:\n\n1. **ROI.** We translate every PR into normalized units of work: Delivery Points. We consider associated spend, code reusability, and historical patches to turn \"we spend $X on AI\"  -> \"we spend $X per impact point.” compared to industry data. \n2. **Cost intelligence + optimization.** See exactly where AI spend goes across teams, tools, and models. Optionally catch and cut waste across cache misses, oversized models on trivial tasks, bloated context and cut it without capping/bottlenecking accuracy. \n3. **Quality signal.** AI-assisted code that ships fast but breaks later isn't a win. We connect AI usage to delivery and quality outcomes so you can see the full picture\n\nWe’re the only platform with the above capabilities that never collects prompts or source code.\n\n**Our Asks**\n\nIf you are or know a Head of Eng, VP Eng, CTO, or platform lead at a 15+ engineer org with real AI spend, reach out! You can book a demo at[ mentlio.com/intro](http://mentlio.com/intro) or reach us at [founders@mentlio.com](mailto:founders@mentlio.com).\n\n**The Team**\n\nAshank and Ahmet met while studying Computer Science at Caltech while they were working part-time Founding Engineer roles at Series A and Seed stage startups. Ashank previously researched generative image AI & graph deep learning for pathology, genomics, and drug design at Dartmouth & Purdue and won internationally at the International Science & Engineering Fair & Junior Science & Humanities Symposium. Ahmet previously published his research in transformers and AI in IEEE, BMC AI, and ACM and also won internationally at the National AI Olympiad, International Science & Engineering Fair, and was named a Top 300 Science Scholar by Regeneron.   \n\n![uploaded image](/media/?type=post&id=111055&key=user_uploads/2693539/d04ec655-df84-4d43-836d-160313b4b133)\n\n", "url": "https://www.ycombinator.com/launches/StD-mentlio-engineering-intelligence-token-optimization-for-the-ai-coding-era", "votes": 88, "created_at": "2026-08-18T22:00:00.168Z" } ] }, { "id": 31555, "name": "rekursiv.ai", "slug": "rekursivai", "former_names": [ "rekursiv" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8606b08cc0428757361006dc3171d7b460ae47e.png", "website": "https://rekursiv.ai", "all_locations": "San Francisco, CA, USA", "long_description": "We're scaling self-improving AI scientist teams to ideate/experiment/discover, generating new knowledge autonomously. Their discoveries reduced ARC-1/2 costs by 10,000× while maintaining state-of-the-art accuracy and yielded material advances on combinatorial ML problems.\r\n\r\nWe believe that AI is bounded not by compute, but ideas. Scaling to millions of sessions of discovery enables leveraging its own discoveries and presents a novel training source capable of ushering a new era of self-reliant foundational models.", "one_liner": "Scale AI scientists whose own breakthroughs accelerate the next.", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Productivity", "launched_at": 1784428991, "tags": [ "Deep Learning", "Machine Learning", "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", "Remote", "Partly Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "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": "AI research is bottlenecked by humans. Every advance in ML still runs through a small number of researchers who can only devise and test ideas so fast. Experiments take days, most fail, and the field's progress is gated by how quickly a limited pool of experts can iterate.\r\n\r\nWe think this is the single biggest lever on the future of AI. Our thesis is that AI progress isn't bounded by compute or data, but it's bounded by the rate at which good ideas get devised and tested. Compute keeps scaling, but the human idea-generation loop doesn't. If you could automate the loop itself: hypothesize, experiment, learn, and repeat, then you could compound discoveries far faster than any human team.\r\n\r\nWe decided to work on it because we've lived the bottleneck. Between us we spent \\~30 years doing ML research at Google, DeepMind, and Luma AI. We kept hitting the same wall: the hard part was never running the code, it was the slow, serial process of engineering the training loops to inform what to try next.\r\n\r\nWhen we built a system that could run that loop autonomously, we found we could shorten the gap between having an idea and knowing whether it was a good one. This has allowed us to explore many exciting ideas that we would have otherwise not tried because it was too time-consuming for us. The results convinced us a self-improvement loop now possible, and that we're the ones to build it." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "Long-term, we're building autonomous AI scientists and scaling them from one to millions: a fleet running the scientific method in parallel, each discovery accelerating the next.\r\n\r\nAnd this process will start to compound. Each new method our scientists invent makes the next round of research faster and cheaper, a self-improving loop where discovery accelerates discovery. Concretely, that means a world where breakthroughs that would have taken a field a decade happen in months, where the cost of pushing the frontier drops by orders of magnitude, and where a small team can direct the research output of an entire institution. We start with ML research because it's the loop closest to us and the one that speeds up all the others: an AI scientist that improves AI compounds fastest of all." } ], "url": "https://www.ycombinator.com/companies/rekursivai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/rekursivai.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/rekursiv-ai/", "x_url": "https://x.com/hyperparticle", "fb_url": null, "cb_url": null, "github_url": "https://github.com/rekursiv-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f8606b08cc0428757361006dc3171d7b460ae47e.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": 3527624, "full_name": "Joshua Dillon", "title": "Founder", "founder_bio": "Ex-Google, Ex-Deepmind, Ex-LumaAI staff research scientist for a combined total of 14 years. Led foundational model pre-training at Luma AI. Creator of TensorFlow Probability (1M+ downloads / month), built Veo's first prototype, contributor to Gemini and VideoPoet (ICML 2024 Best Paper). 11,600+ citations.\n\nNow co-founder of rekursiv.ai, automating the science of ML research.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jvdillon/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a33102fd458800ada457193e9d61bd8e1fadc988.jpg" }, { "user_id": 3527564, "full_name": "Dan Kondratyuk", "title": "Founder", "founder_bio": "Ex-Google, Ex-LumaAI scientist for a combined total of 7 years. First author of VideoPoet (ICML 2024 Best Paper), led the World Models team at Luma, and built large-scale multimodal LLMs and diffusion models for image, video, audio, and text.\n\nNow co-founder of rekursiv.ai, automating the science of ML research.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/dankondratyuk/", "x_url": "https://x.com/hyperparticle", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d45c58f9c92bb89b4f492a735fdc90392813af11.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 106821, "title": "rekursiv.ai: AI scientists that invent new knowledge", "tagline": "Scale AI scientists whose own breakthroughs accelerate the next.", "body": "Hey YC! We're [Josh](https://rekursiv.ai/josh) and [Dan](https://rekursiv.ai/dan), co-founders of [rekursiv.ai](http://rekursiv.ai) 🧪.\n\n[rekursiv.ai](http://rekursiv.ai) builds fleets of **AI scientists** that do ML research on their own: they come up with ideas, run thousands of experiments in parallel, learn from the results, and accelerate their own discoveries. In a few days of self-directed research, they invented ideas and algorithms that **matched state-of-the-art accuracy on ARC-AGI of frontier LLMs at up to 10,000× lower cost**, and became the first system to hit **100% on Sudoku** with a neural network trained only on input-output pairs.\n\n![uploaded image](/media/?type=post&id=106821&key=user_uploads/3527564/0444a495-03fa-4f41-9762-ba134521b2f4)\n\n**Why this matters.**\n\nAI research is bottlenecked by humans. Every advance still runs through a small number of researchers who can only devise and test ideas so fast. Compute keeps scaling; the human idea-generation loop doesn't. We believe **AI progress isn't bounded by compute, it's bounded by the rate at which good ideas get tried and tested**. So instead of scaling one model, we built a system that generates and tests ideas on its own.\n\n**It already works.**\n\nIn a few days of self-directed research, our AI scientists set a groundbreaking records on hard benchmarks.\n\n* On **ARC-AGI** (easy for humans, hard for AI), our AI scientists matched top frontier LLM models at up to **10,000× lower cost**.\n* On **Sudoku**, they achieved 100% accuracy by running hundreds of experiments over a few days, wrote 100k+ lines of their own code, and invented a new inference method (_Hypothesis-Pinning Search_).\n* Every gain came from ideas and algorithms **the system invented itself**. It even debugged its own training bugs and abandoned its own ideas that led to dead ends.\n\nFull write-ups: [ARC-AGI](https://rekursiv.ai/blog/pushing-limits-arc-agi/) · [Sudoku](https://rekursiv.ai/blog/100-percent-accuracy-on-sudoku/)\n\n![uploaded image](/media/?type=post&id=106821&key=user_uploads/3527564/676b13b6-cc1b-4545-8d14-cc7ae1e9b5a5)\n\n**Who we are.**\n\nWe're two ML researchers with 21 years of combined experience at **Google DeepMind and Luma AI**. We spent our careers building frontier generative models. Now we're pointing that experience at a harder problem: AI that discovers on its own.\n\n* **Josh Dillon** — Staff Research Scientist at **Google Research & DeepMind for 13 years**, then led foundational-model pre-training at **Luma AI**. Created **TensorFlow Probability** (\\~1M downloads/month).\n* **Dan Kondratyuk** — First author of **VideoPoet (ICML 2024 Best Paper)**. Co-developed the models powering **Luma's Dream Machine** and led Luma's realtime World Models team. Previously 5 years at **Google Research**.\n\n**What's next.**\n\nWe're pointing the system at harder, more varied challenges and building a platform to share results in real time. The bottleneck now is experiment scale: the more experiments we run, the more discoveries the our AI teams can make. We plan to scale from a few AI scientists to millions, each discovery accelerating the next, until the rate of scientific progress is no longer limited by the number of human researchers.\n\n**Asks.**\n\n* **Got a hard problem or benchmark?** If you have a benchmark or research problem you'd love an autonomous team of scientists to attack, we want to hear it. Email [contact@rekursiv.ai](mailto:contact@rekursiv.ai).\n* **Researchers & engineers:** We're hiring **Founding Scientists** and **Engineers** (Bay Area or remote). If you love running thousands of experiments and figuring out what the results mean, reach out at [hiring@rekursiv.ai](mailto:hiring@rekursiv.ai)\n* **Just want to nerd out about autonomous science?** Drop us a line, always happy to chat.\n\n— Josh & Dan, [rekursiv.ai](http://rekursiv.ai)", "url": "https://www.ycombinator.com/launches/Rmv-rekursiv-ai-ai-scientists-that-invent-new-knowledge", "votes": 7, "created_at": "2026-07-22T14:06:11.738Z" } ] }, { "id": 31591, "name": "RightNow", "slug": "rightnow", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/59b2e02a314037738d9f32192f83e8bd442be246.png", "website": "https://runinfra.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "RightNow AI is a research lab for GPU kernel optimization. Our platform RunInfra serves top open-source models optimized for AI agents at a fraction of the cost of closed-source APIs\r\n\r\nRightNow AI lab: https://rightnowai.co\r\nRunInfra platform: https://runinfra.ai.", "one_liner": "Co-designing the fastest intelligence", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1779474756, "tags": [ "Developer Tools", "Generative AI", "Machine Learning", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/rightnow", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/rightnow.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/rightnowai/", "x_url": "https://x.com/rightnowai_co", "fb_url": null, "cb_url": null, "github_url": "https://github.com/RightNow-AI", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/59b2e02a314037738d9f32192f83e8bd442be246.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Nicolas Dessaigne", "url": "https://www.ycombinator.com/people/nicolas-dessaigne" }, "founders": [ { "user_id": 1615104, "full_name": "Jaber Jaber", "title": "Founder", "founder_bio": "Technical Co-founder at RightNow (YC F26)", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jaber-jaber-b65246234", "x_url": "https://x.com/Akashi203", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d84f84e8e758be1fe000c134de47a36196f14705.jpg" }, { "user_id": 3147259, "full_name": "Osama Jaber", "title": "Founder/CMO", "founder_bio": "Commercial Co-founder at RightNow (YC F26)\n", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/osama-jaber-osama2001/", "x_url": "https://x.com/OsamaKakashi", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f6bf68d49afc56cca1afb65f6882a4b4cf721300.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 32133, "name": "Belvedir", "slug": "belvedir", "former_names": [ "Fractal", "Fractal Research", "Fractal Machine Research", "Belvedir", "Fractal Machine Research" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f68cfb5228c7e522deffed4be2be94da5eab6d3.png", "website": "https://belvedir.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "", "one_liner": "The autonomous AI model factory", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1784319754, "tags": [ "Machine Learning" ], "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/belvedir", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/belvedir.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/belvedir/", "x_url": "https://x.com/belvedir_ai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f68cfb5228c7e522deffed4be2be94da5eab6d3.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harshita Arora", "url": "https://www.ycombinator.com/people/harshita-arora" }, "founders": [ { "user_id": 2576778, "full_name": "Zachary Yu", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/zacharyyu/", "x_url": "https://x.com/thezacharyyu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cdfdf5e0aa8f760e0a5a920eaa5ad0f3b215ab37.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 109999, "title": "Belvedir: Making private AI models cheap and easy so the world can have trillions of them", "tagline": "Make a custom AI model that’s 10x cheaper and 2x better with less than 5 mins of set up", "body": "Hey, I’m[ ](https://bookface.ycombinator.com/user/2576778)Zachary Yu, the founder of [Belvedir](https://belvedir.ai/). It’s nice to meet you!\n\n**TD;LR:** \n\nPrivate AI models are cheaper, better, faster, and protect your data. But right now they’re only used by big companies since they’re really difficult and expensive to make. Belvedir makes private AI models autonomously with less than 5 minutes of set up. By making it cheap and easy to make models, we hope people use Belvedir to create trillions of them.\n\nRight now, we’re live with 10 companies who are seeing up to 10x lower inference costs and 2x improved performance on benchmarks. \n\n \n\n**How it works:**\n\n1. Get an API key from [Belvedir ](https://belvedir.ai/)and install our SDK\n2. Belvedir turns your traces and logs into organized training sets and RL environments\n3. We autonomously train models from your data and optimize harnesses on top of these models\n4. We make and run benchmarks on these new models and harnesses\n5. We deploy these models and give you an API key to access them through \n6. We give you a router that gradually send more traffic to your new model as it out performs your old one\n7. As you use the new model we collect more traces and logs\n8. The loop repeats\n\nYour data is only used for you. Your models are only accessible by you. We use LoRA and can charge by the token by hosting multiple adapters on one cluster (so no worries if you have spikey traffic). \n\n**About Me:**\n\nI’m Zachary Yu, the founder of [Belvedir](https://belvedir.ai/). Before this, I founded [Traverse](https://www.traverse.so/): a YC startup that sells RL environments to frontier labs, studied CS at UWaterloo, and made RL environments at Mercor.\n\n**Long Term Ambitions:**\n\nWe want to make private AI the default for every use case. Starting with startups / SMBs and then consumers. \n\nWe are bullish on a world where there are trillions of private AI models. We want to build the ecosystem where these models are made, hosted, and interact with each other.\n\n**Asks:**\n\n1. If you have a high inference bill, want better performance from your agent, or want to protect your data from frontier labs, **we can help you out**.\n2. We are looking to hire engineers who have worked on RL / optimization, model inference, training infra, or just have that dog in them\n\nContact us at [founders@fractalresearch.ai](mailto:founders@fractalresearch.ai) ", "url": "https://www.ycombinator.com/launches/ScB-belvedir-making-private-ai-models-cheap-and-easy-so-the-world-can-have-trillions-of-them", "votes": 64, "created_at": "2026-08-11T22:00:00.240Z" } ] }, { "id": 32134, "name": "Parasma", "slug": "parasma", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/65a63b540239ee3a5538326d626bcceee19f52d9.png", "website": "https://parasma.com", "all_locations": "San Francisco, CA, USA", "long_description": "We train human brain cells for compute", "one_liner": "Replacing GPUs with human brain cells", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1782285260, "tags": [ "Machine Learning", "Neurotechnology", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "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/parasma", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/parasma.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/parasma", "x_url": "https://x.com/ParasmaAI", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/65a63b540239ee3a5538326d626bcceee19f52d9.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/adc99c68d73202b3a5001d44bbd5177d7a8335d0.png" ], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 3388159, "full_name": "Sean Cole", "title": "Founder/CEO", "founder_bio": "Founder at Parasma (YC S26)\n\nSkipped BS, MS in Artificial Intelligence w/ Distinction", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sean-cole-8985a4207/", "x_url": "https://x.com/vytalow", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6f2d0bdfcf98a7f2843ea62be9a5893871caf196.jpg" } ], "jobs": [ { "id": 98189, "title": "Founding Scientist", "url": "https://www.ycombinator.com/companies/parasma/jobs/Vg7GVVl-founding-scientist", "location": "San Francisco, CA, US / Remote", "type": "Full-time", "role": "Science", "role_type": "Research", "salary_range": "$1M", "equity_range": "1.00% - 3.00%", "experience": "11+ years", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "id": 109992, "title": "Parasma: Training human brain cells for compute", "tagline": "In March, we trained them to play Doom; now we got them to do next-token prediction.", "body": "We train human brain cells for compute. In March, we trained them to play Doom; now we got them to do next-token prediction.\n\n![uploaded image](/media/?type=post&id=109992&key=user_uploads/3388159/cbefdb79-ad5b-4e03-ae35-d2a482a05351)\n\n![uploaded image](/media/?type=post&id=109992&key=user_uploads/3388159/ce53a90f-7180-4eab-b23e-d770a5e375a7)\n\n**The Problem**\n\nModern models require enormous amounts of energy and training data, while still struggling with continual learning and adapting efficiently to new tasks. Biological brains solve these problems very differently. They learn continuously, adapt from limited experience, and operate on roughly the power of a light bulb.\\\n\\\n**Solution**\n\nParasma is developing the algorithms and infrastructure needed to turn human brain cells into useful compute. The human brain is the most capable and energy-efficient learning system we know. Our goal is to reliably train biological substrates and use them for general computation, robotics, and eventually direct biological training. In March, we trained human brain cells to play Doom, which got hundreds of millions of views across social media. Since then, we’ve been working toward more general tasks. Most recently, we got the cells to perform token prediction: given a sequence of tokens converted to electrical stimuli, the neurons learn to predict what comes next with no GPU.\n\nLong-term, biological compute will offer orders-of-magnitude improvements in energy and sample efficiency, while enabling native continual learning.\n\n**Team**\n\nI previously worked on neuromorphic computing and reinforcement learning during my master’s.\n\nBefore that, I built a successful digital-item marketplace with > $1.7M of listed goods and played games professionally.\\\n\\\nCheck out our press release here:\\\n", "url": "https://www.ycombinator.com/launches/Sc4-parasma-training-human-brain-cells-for-compute", "votes": 10, "created_at": "2026-08-13T20:30:00.164Z" } ] }, { "id": 32191, "name": "Lyon", "slug": "lyon", "former_names": [ "Lyon (Branding TBD)" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/27ce3fff3af03da64bc8cfefeb3efef7b6d080b7.png", "website": "https://lyon.so", "all_locations": "San Francisco, CA, USA", "long_description": "Lyon builds private foundation models for banks, insurance companies, and fintechs. Each model learns from the company’s proprietary transactions, payments, clicks, and customer interactions to predict credit, fraud, collections, income, churn, and lifetime value; and runs inside the company’s cloud, so its data and intelligence remain under its control. Lyon is already working with a major insurer and trained a model on 28B transactions for a fintech serving tens of millions of active users, identifying premium-card converters 4x more precisely than its existing rules; the model is now being deployed for credit.", "one_liner": "Foundation models on enterprise transaction data.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1786694987, "tags": [ "Machine Learning", "Finance", "Data Science", "Big Data" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "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/lyon", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/lyon.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/lyon-ai/", "x_url": "https://x.com/gabrielnoya7", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/27ce3fff3af03da64bc8cfefeb3efef7b6d080b7.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": 1690305, "full_name": "Gabriel Noya", "title": "Founder/CEO", "founder_bio": "Stanford EE and CS 26'", "is_active": true, "linkedin_url": "https://linkedin.com/in/gabriel-noya", "x_url": "https://x.com/gabrielnoya7", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4af339e08470b73536f535219d74828f249a98b7.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 32206, "name": "DeepReach Inc.", "slug": "deepreach-inc", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/165caaebecbd3f0b9470bb5817a0a1817520c935.png", "website": "http://www.deepreach.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Physical AI — robots and world models — is bottlenecked on one thing: diverse,\r\nreal-world data at massive scale showing how humans actually do work in the\r\nphysical world. It was never written down, and you can't buy it, because no one\r\nhas ever captured it. Only real local experts, the people who do this work every\r\nday, can produce data this authentic and valuable.\r\n\r\nDeepReach builds the network that reaches them. We design and manufacture\r\nwearable stereo capture devices, then place them with local data partners who\r\nrun collection as their own business — in warehouses, workshops, farms, kitchens\r\nand repair shops. They own the local relationships; we supply the hardware, the\r\nsoftware platform, the quality pipeline and the payments. Anyone can run a data\r\nbusiness.\r\n\r\nThat structure is what produces diversity. Instead of thousands of hours inside\r\none environment, we deliver hours spread across thousands of environments —\r\nwhich is what actually makes robot policies generalize.\r\n\r\n475 devices in the field and 100+ data partners are live across 7 countries, adding new\r\npartners every week. They've collected nearly 150,000 clips in the past three\r\nmonths, with volume roughly doubling month over month. Our data is in production\r\nwith multiple frontier model and robotics companies, and we're scaling to\r\n10,000+ devices across 50 countries.", "one_liner": "Network for Physical AI Data Business", "team_size": 6, "industry": "B2B", "subindustry": "B2B", "launched_at": 1786563914, "tags": [ "Hardware", "Machine Learning", "Robotics", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "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": [ { "question": "How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)", "answer": "Chris and I are from the same hometown and have known each other for ten years, long before either of us thought about robots.\r\n\r\nIn that time I built two companies around the same problem: how do you organize people you'll never meet, in places you'll never visit, to do consistent work? The first was HireIO, a cross-border staffing platform out of Silicon Valley. The second was Talex, an expert network for LLM data annotation — around 70,000 people across ten countries. Chris went the other direction: a CS PhD at USC in computer vision, ML infrastructure at Meta, then founding ML engineer at Aven.\r\n\r\nRunning an annotation network for language models, I kept hitting the same wall from the supply side. Text was running out, and the next generation of models wasn't going to learn from the internet. It was going to learn from the physical world — and nobody had captured it. What a warehouse picker, a line cook or a phone repair technician knows is real expertise, and none of it was ever written down. You can't buy that data, because it doesn't exist yet. Chris was hitting the same wall from the model side, watching what was missing from training sets.\r\n\r\nThe default answer at the time was to put a lab in one city and record thousands of hours in the same room. That produces volume, not diversity, and robot policies don't generalize from volume. What the models needed was the opposite shape: a few hours each from thousands of different real workplaces, all over the world. Which meant the bottleneck wasn't an AI problem. It was a distributed human network problem — reaching thousands of ordinary businesses across dozens of countries, and giving the people already working in them a reason to capture what they do. That was the problem I'd spent two companies learning to solve.\r\n\r\nWe started DeepReach in 2025. Chris owns perception and the data engine; I own the network, hardware operations and the commercial side. Ten years of knowing each other means we skipped most of the negotiation that takes co-founders their first year." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Core problem: robot models can't generalize, because the data they learn from all looks the same.\r\n\r\nPhysical AI needs to see how work actually happens — a mechanic torquing a bolt, a baker shaping dough, a technician opening a phone. None of it was ever written down. The internet captured what people say about the world; it never captured how people act in it. So unlike language models, this generation can't be trained on scraped text. Every hour has to be collected on purpose. \r\n\r\nToday the field has roughly tens of thousands of hours of this data against a need estimated in the hundreds of millions. But the gap isn't really a volume gap. It's a diversity gap. The standard approach is to build a lab, hire operators, and record for months in one controlled space. That yields thousands of hours that all look alike, and a policy trained on it fails the moment the lighting, the counter height, or the tool changes. Our customers' acceptance specs now reflect this directly: they cap how much of a delivery can come from any single environment type, and reject batches that are too concentrated. Diversity is the binding constraint, and it's now contractual.\r\n\r\nDiversity is hard for an economic reason, not a technical one. Collecting one hour each from a thousand real workplaces means negotiating access to a thousand real businesses — each with an owner who has no reason to let a stranger film their operation, in a country where you have no presence. That cost is why everyone else builds the lab instead. It's a distribution problem wearing an AI problem's clothes. \\\r\n\\\r\nThat's why we built the company as a network rather than an operation. The people who already work in those places, and already have the trust and the access, run collection as their own local business. We give them the hardware, the software and the payments. Access stops being something we buy and becomes something the network already has. \\\r\n\\\r\nI decided to work on this from the supply side. I'd spent two companies building cross-border human networks — a staffing platform, then a 70,000-person annotation network across ten countries — and kept watching demand arrive for data that no existing structure could produce. The bottleneck was never the model. It was that nobody had built the network that reaches the physical world." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "The long-term vision is a network that can reach any workplace on earth, and a record of how human work is actually done.\r\n\r\nConcretely: 10,000+ devices, 50 countries, one million commercial scenes, ten million task types. Every deployment adds to what we call the Human Skill Library — a permanent record of how people do physical work, built from the inside by the people who do it. Much of this knowledge exists nowhere else. It lives in the hands of a mechanic who has been doing the job for thirty years, and when he retires it's gone. Text and video on the internet captured what people say about work. Nobody has captured the work. \r\n\r\n\\\r\nIf we succeed, two things are different：\\\r\nFirst, robots stop being confined to the places that can afford to build around them. Today automation only pays at Amazon scale — a facility large enough to redesign itself for the machine. That's a rounding error of the world's actual workplaces. A policy trained on a million real environments doesn't need the environment to change; it works in a repair shop with bad lighting and a counter at the wrong height. That's the difference between robotics as a capability the largest companies buy and robotics as infrastructure a small business can use.\\\r\nSecond, the people whose work teaches these models are inside the economy that results. The last generation of AI was trained on data scraped from people who were never asked and never paid. This one has to be collected on purpose, from real workers in real workplaces, which means it can be built the other way — as something people opt into and earn from. We already have data partners in ten countries running this as a business they own. At 10,000 operators that isn't a side effect of how we source data. It's the point." } ], "url": "https://www.ycombinator.com/companies/deepreach-inc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/deepreach-inc.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/in/timothyli-deepreach/", "x_url": "https://x.com/DeepReach_AI?s=20", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/deepreach-ai", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/165caaebecbd3f0b9470bb5817a0a1817520c935.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/482534d82410b1adf1774b961acd50c6704093e0.png" ], "primary_partner": { "name": "Brad Flora", "url": "https://www.ycombinator.com/people/brad-flora" }, "founders": [ { "user_id": 3114257, "full_name": "Chris Liu", "title": "Founder/CTO", "founder_bio": "Co-founder at DeepReach Inc. Founding MLE & tech lead of Aven scaling to #1 in DM volume in the US. Research Scientist at Meta working on core ML systems. PhD in computer vision from USC. B.S. from Peking University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/drxcliu", "x_url": "https://x.com/drxcliu", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1f9e9212493e67c1bb9a3a913e2ab4c6fa9b897e.jpg" } ], "jobs": [ { "id": 112083, "title": "VP / Head of Global Data Partner Network & Operations", "url": "https://www.ycombinator.com/companies/deepreach-inc/jobs/r0itCEs-vp-head-of-global-data-partner-network-operations", "location": "San Jose, CA, US", "type": "Full-time", "role": "Operations", "role_type": null, "salary_range": "$150K - $200K", "equity_range": "0.50% - 1.00%", "experience": "3+ years", "visa": "Will sponsor", "skills": [] } ], "news": [], "launches": [ { "id": 110389, "title": "DeepReach: Launch a real-world data business today", "tagline": "Multi-million-dollar contracts secured with frontier AI labs — and growing", "body": "**TL;DR:** DeepReach is a global data network that equips local entrepreneurs with wearable devices to capture the real-world operation data for Physical AI training. By decentralizing collections across thousands of entrepreneurs who have access to local businesses, they provide high-diversity training data that cannot be accessed elsewhere. DeepReach already signed multiple-million-dollar contracts with frontier AI labs.\\\n\\\n**DeepReach is building a global network for real-world human data to train Physical AI.**\n\nWe enable local entrepreneurs to operate data-collection businesses using our wearable capture devices, while we provide the hardware, software, QA, and customers.\n\nThe result is a distributed network that produces the diverse human demonstrations Physical AI labs cannot obtain anywhere else. Every new data partner expands the diversity and value of the network.\n\nIn just three months, DeepReach has helped **150+ entrepreneurs** build data businesses employing **1,000+ local experts** across **7 countries**. Together, they’ve collected **500,000+ real-world operation clips**, captured **150,000+ professional skill demonstrations**, and secured **several million dollars in customer contracts** with frontier AI labs and robotics companies.\n\n▶ **Watch how it works:** \n\n---\n\n# **The Problem**\n\nFrontier AI is already learning from the physical world.\n\nThe challenge is no longer collecting data, it’s collecting **diverse** real-world data at scale.\n\nMost companies solve this by building collection labs or hiring workers directly. Both approaches scale headcount, but neither scales access. The hardest part is reaching millions of real workplaces across different industries, countries, and working conditions.\n\nWithout that diversity, AI struggles to generalize beyond a small number of controlled environments.\n\n---\n\n# **What We Built**\n\nInstead of building another data collection company, **we built a platform where entrepreneurs launch independent real-world data businesses.**\n\nDeepReach provides the wearable devices, software platform, quality infrastructure, customer demand, operational support, and payments. **Entrepreneurs build and grow the businesses.**\n\nThey recruit local experts, develop trusted relationships with local businesses, and continuously expand into new industries, communities, and real-world environments.\n\n**Every new business expands the platform’s reach into the physical world, creating access to places, people, and skills that centralized data collection simply can’t reach.**\n\nThe result isn’t a larger workforce, it’s **a growing ecosystem of independent data businesses** that continuously expands the world’s supply of diverse real-world data for Physical AI.\n\n---\n\n# **Where We Are**\n\nIn just three months:\n\n* **150+ entrepreneurs** have launched data businesses on DeepReach\n* Those businesses now employ **1,000+ local experts** across **7 countries**\n* **500,000+ real-world operation clips** collected\n* **150,000+ professional skill demonstrations** captured\n* **Several million dollars in signed customer contracts** with frontier AI labs and robotics companies\n* Scaling toward **10,000+ devices** across **20 countries**\n\n# **Where This Goes**\n\nOur model is simple: labs list the tasks and environments they need, our entrepreneurs pick up the orders and go record them, and every delivery makes the network more capable of fulfilling the next one. It also compounds into something bigger, what we call the **Human Skill Library**: a growing record of how people interact with the physical world.\n\nMost of this knowledge has never been documented. It lives in the hands of experienced workers, passed down through practice rather than words, and it often disappears when they retire.\n\nOur vision is to preserve and scale humanity's physical knowledge, so every AI system can learn from the collective experience of human work.\n\n# **About Us**\n\n\\\nTim built HireIO into a leading global workforce company with 15 millions in annual revenue, then founded [Talex.ai](http://Talex.ai), an AI-powered expert network, before starting DeepReach. Chris comes from a computer vision background. We discovered the problem firsthand while labeling data for robotics startups: the real bottleneck wasn’t annotation, it was capturing diverse real-world human skills at scale.\n\n![uploaded image](/media/?type=post&id=110389&key=user_uploads/1294315/9bb8c293-ab57-42aa-b756-58562e546b78)\n\n---\n\n# **How You Can Help**\n\nIf you’re building frontier AI systems and need more diverse real-world data, we’d love to talk.\n\nIf you’re an entrepreneur interested in building a real-world data business, applications are open here: [www.deepreach.ai/join](https://www.deepreach.ai/join)\n\nTim Li\\\nFounder & CEO, DeepReach AI\n\n[**tim.li@deepreach.ai**](mailto:tim.li@deepreach.ai)", "url": "https://www.ycombinator.com/launches/SiT-deepreach-launch-a-real-world-data-business-today", "votes": 470, "created_at": "2026-08-13T18:33:15.556Z" } ] }, { "id": 32650, "name": "Risklytics", "slug": "risklytics", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/879622d4fa103941586542ac659eaf9b60d897f0.png", "website": "https://www.risklytics.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Risklytics is the insurance company for frontier tech such as physical AI, robotics, and data centers. Companies answer 6 questions and we bind a tailored policy that other insurance companies won’t.", "one_liner": "AI-Native Insurance for Frontier Tech", "team_size": 2, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1783123197, "tags": [ "Hardware", "Machine Learning", "B2B", "Insurance", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 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/risklytics", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/risklytics.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/risklytics", "x_url": "https://x.com/Risklytics", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/879622d4fa103941586542ac659eaf9b60d897f0.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 2973325, "full_name": "Samuel Gold", "title": "Founder/CEO", "founder_bio": "Founder at Risklytics (YC S26). Building a brokerage for frontier tech. On leave from Harvard.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/samuelggold", "x_url": "https://x.com/SamGGld", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/b99591b78044a8c663b7b4eb65a1fe000a6bb179.jpg" }, { "user_id": 2666435, "full_name": "Alexander Risio", "title": "Founder/CTO", "founder_bio": "Founder at Risklytics (YC S26). Building a new type of insurance brokerage.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/alexanderrisio/", "x_url": "https://x.com/alexrisio", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dc585a4120c666a66e51d372101b7f5ed304f437.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 110197, "title": "Risklytics: Insurance for the Frontier", "tagline": "Insurance for companies pushing the frontier of what is possible with AI, from robot fleets to agents in production.", "body": "**TLDR:** Risklytics is a commercial insurance brokerage for companies building with AI in the physical space, from robot fleets on customer sites to agents running in production. This year many insurance companies stopped covering risks that involve AI. We find the insurers that will still cover it, translate what you're building into terms they can price, and read every clause before anything is signed.\n\n\n\n**The problem**\n\nStarting this year, many insurance companies began excluding coverage for injury and property damage arising out of AI.\n\nFor a hardtech company that language reaches everything you do. If AI touched the work anywhere, including the tools your engineers designed it with or the product itself, the insurer can deny the claim.\n\nThe problem with these exclusions is that they get added account by account, so the same hardware can be covered at one insurer and excluded at the next, and nothing in the process makes that obvious. It shows up as a form number on the endorsement list attached to your renewal, which is why most founders don't learn they have one until they file a claim.\n\n**Why now**\n\nPhysical AI is scaling faster than the insurance market underneath it. Insurance moves slower, and its first instinct with an unfamiliar risk is to exclude it and wait for loss data. Cyber went the same way: exclusions first, then specialists who understood it, then a real market. AI is at the exclusion stage now, and these companies pushing the frontier can't wait for the cycle to finish on its own. \n\n**What we do**\n\nWe handle the whole coverage process end to end.\n\nYou send us an application. We take what you're building and put it in terms an insurance company can understand and price.\n\nThen we go to market. We have access to hundreds of insurance companies, the ones that actually write this kind of risk, and we take your file to them. Most will pass. The few that want it compete on price and terms, and we keep shopping until every requirement you have is met, including the limits your customer contracts demand.\n\nWhen offers come back we read the policies line by line. If an AI exclusion is attached, we push to get it removed, or we find a company that writes the AI coverage in on purpose. Then we tell you in plain terms what you've got.\n\nWe cover the lines your company actually needs: general liability, umbrella, property, equipment in transit and on customer sites, workers' comp, professional liability, management liability, and cyber.\n\nYou never pay us. The insurance company pays us a commission out of the premium when a policy is placed, so applying costs you nothing and there's no additional fee from us at any point.\n\n**Where this goes**\n\nPhysical AI is expanding faster than any industry, and every company in it buys insurance every year for as long as it exists. The programs grow as the fleets grow.\n\nWe want to be the brokerage that grows with them: the people who know this risk best, hold the relationships with the markets that write it, and build the loss data of the future.\n\n**Why us**\n\nWe're Sam and Alex, on leave from Harvard. We were already in insurance building models when we noticed that batch mates building some of the most innovative technology in the world couldn't get covered at all. Since then we’ve landed partnerships to offer these frontier companies easier access to better policies.\n\n![uploaded image](/media/?type=post&id=110197&key=user_uploads/2666435/74c5c462-a2ff-44b8-ac6c-f8ebc7807dbd)\n\n\n**The ask**\n\nIf you're building anything on the frontier, apply at[ risklytics.ai](http://risklytics.ai). It takes a few minutes, it costs nothing, and we'll come back with what the market says.\n\nIt doesn't matter what you have in place today. If you're building, we want to get you covered.\n\nAnd if you're an insurer, MGA or wholesaler with appetite here, we want to talk:[ risklytics.ai/markets](https://www.risklytics.ai/markets/)", "url": "https://www.ycombinator.com/launches/SfN-risklytics-insurance-for-the-frontier", "votes": 53, "created_at": "2026-08-13T16:30:00.597Z" } ] }, { "id": 33019, "name": "Agnost AI", "slug": "agnost-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e1595f92ea75b1f367addedb5703852b1c7efded.png", "website": "https://agnost.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Agnost AI reads every conversation a company's AI Agent has with its users to find where it's silently failing and then turns that same data into custom models that run the agent better, faster & cheaper than frontier models.", "one_liner": "We find where AI agents fail, then train better models to run them.", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Analytics", "launched_at": 1783481375, "tags": [ "Developer Tools", "Machine Learning", "Analytics", "Monitoring", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "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/agnost-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/agnost-ai.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://linkedin.com/company/agnostai", "x_url": "https://x.com/agnostai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e1595f92ea75b1f367addedb5703852b1c7efded.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/b09996d90ed38a4b5d62581651fffd7e0885e6b2.png" ], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 1987056, "full_name": "Shubham Palriwala", "title": "Founder/CEO", "founder_bio": "Co-founder and CEO of Agnost AI.\nYoungest Engineer on Cisco's Analytics team, First Hire at Formbricks (Open-Source Qualtrics), wrote code in Bitcoin & other open-source projects across OWASP and the Linux foundation.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shubhampalriwala/", "x_url": "https://x.com/ShubhamInTech", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/16f7d9652057acfedcb3ad0c94ecab3e15758aba.jpg" }, { "user_id": 2055517, "full_name": "Parth Ajmera", "title": "Founder/CTO", "founder_bio": "Co-Founder/CTO at Agnost AI. Graduated in Computer Science from IIT Madras, ranking 159 among over 1 million candidates. Previously led graphics engineering at Infurnia and built terabyte scaled data pipelines at Microsoft", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/parthajmera/", "x_url": "https://x.com/prrthh132", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/93dc08b7da172c3288f1f798f2b9a0b27db75f16.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111310, "title": "Agnost AI: Turn Your Agent Traces Into a Faster, Cheaper, & more Accurate Custom Model", "tagline": "We use your agent’s production traces to train a specialized model that performs better, responds faster, and costs less.", "body": "Your agent logs aren’t just for debugging. They’re your training data.\\\nHey YC! We’re Shubham and Parth, childhood friends and founders of Agnost AI.\n\n**TL;DR**: Agnost AI uses your agent’s production traces to train a model specialized for your exact workload. Our first custom model for our customer delivered them a:\n\n* 22.9% higher task success,\n* 90.2% lower median latency, and\n* 94.5% lower cost than Opus 4.8.\n\nDemo: \n\n### What we found\n\nA few weeks ago, we launched Agnost AI as product analytics for AI agents. We read conversations between users and agents to uncover failures, behavior drift, hallucinated links, frustration, and churn signals.\n\nWe expected to find bugs. Surprisingly, we also found a lot of infrastructure waste. Many customer-facing agents use frontier models for narrow, repetitive jobs:\n\n* Route a request to the correct tool\n* Have a narrow system prompt with the given inputs\n* Retrieve the relevant data from internal tool calls\n* Parse & format the final response\n\nThe underlying frontier model can write code, solve advanced mathematics, understand biology, and reason across hundreds of domains. **Your agent uses a small, repeatable slice of that capability while paying frontier-model prices for every request.**\n\n### We trained a specialist\n\nWe used historical production traces to train our first workload-specific model for an early customer: **agnost-<redacted>-0.1**\n\nWe evaluated it against Opus 4.8 on 780 held-out customer traces for an Identifier Extraction Agent. The results:\n\n* Task success: 71.5% → 87.9%, 22.9% relative improvement\n* Median latency: 4.80s → 0.47s, 90.2% lower\n* Tail latency: 11.20s → 1.35s, 87.9% lower\n* Cost per 1,000 completed tasks: $42.00 → $2.30, 94.5% lower\n\nThis is one model for one workload, not a claim that specialist models should replace frontier models everywhere.\n\nFrontier models remain the right choice for open-ended work. But once an agent’s production job becomes predictable, **paying for general intelligence on every request stops making sense**.\n\n### How it works\n\n1. Send your existing traces to Agnost through OpenTelemetry or our SDK.\n2. We identify the recurring workflows your agent performs.\n3. We create an eval set from held-out production traces.\n4. We train a specialist model on the historical workload.\n5. We benchmark it against your current model on success, latency, and cost.\n\nYou only consider switching when the specialist wins on your own workload. Your logs go from debugging data to evaluation data and finally to training data recursively.\n\n### Who we are\n\nWe’re childhood friends, eight years and counting.\n\n* Shubham: Youngest engineer on Cisco’s analytics team, first hire at Formbricks, and contributor to Bitcoin, OWASP, and the Linux Foundation.\n* Parth: IIT Madras CS, ranked 159 out of 1.5 million. Built Spark pipelines at Microsoft and led GPU technology at Infurnia.\n\n### Our ask\n\nIf you have meaningful daily traffic, send us an anonymized trace export. We’ll analyze the workload, build a held-out evaluation set, and tell you whether a specialist model is worth training. If it is, we’ll benchmark it against your current frontier model.\n\nEmail me at [shubham@agnost.ai](mailto:shubham@agnost.ai) or book a call at [call.agnost.ai](http://call.agnost.ai)\n\nMore about us at: [agnost.ai](https://agnost.ai)\n\n![uploaded image](/media/?type=post&id=111310&key=user_uploads/1987056/6599ed29-ddf9-4d02-9232-425af1535be7)\n\n", "url": "https://www.ycombinator.com/launches/SxK-agnost-ai-turn-your-agent-traces-into-a-faster-cheaper-more-accurate-custom-model", "votes": 97, "created_at": "2026-08-20T16:00:00.252Z" }, { "id": 107260, "title": "Agnost AI: Product Analytics for AI Agents", "tagline": "We read every conversation your users have with your agents & chatbots and surface hidden insights & product feedback. We are working with teams at Google, Exa and Corgi", "body": "**Your users are telling your agent exactly why they churn! You’re not listening.**\n\nHey YC! We’re Shubham and Parth, childhood friends, founders of **Agnost AI**.\n\n**TL;DR: Agnost AI reads every conversation your chat or voice agent has with your users. We show you what your users want, where they get frustrated, and why they leave.**\n\n**The feedback hiding in your logs becomes discoveries, evals, and fixes ready to ship. We’re already processing over 1 million events every day and growing.**\n\nTry us out at[ agnost.ai](http://agnost.ai)\n\n### \n\n### **The problem**\n\nTraditional product analytics were built for clicks.\\\nObservability was built for token costs, latencies, and deterministic errors.\n\nBut every product is rapidly moving from UI to conversation, and agent failures don’t look like traditional failures. The LLM returns 200 OK and says “sorry, I can’t do that.” The real failures are hiding in your conversations:\n\n* “can it remember this for next time?” → feature request\n* “you said it’s done, but nothing happened” → bug\n* “i’ve told you this three times already” → frustration\n* “nvm i’ll just do it myself” → churn\n\nToday teams throw more evals at this. But evals only tell you whether you fixed something, not what to fix. You can’t write a test for a problem you haven’t discovered yet.\n\n### **How it works**\n\n1. **Install our skill:** 3 lines of code, or point your OpenTelemetry exporter at us. Any LLM, any framework. Add any custom metadata like user tier, model versions, etc. We’re at:[ docs.agnost.ai](http://docs.agnost.ai)\n2. **Every conversation becomes a signal:** feature requests, bugs, frustration, churn risk, failed upgrades. Detected for your product, with the exact users & conversations behind each.\n3. **The insight becomes action:** when hundreds of users rageprompt at your agent due to a specific bug, Agnost AI hands you the insight with the exact conversations behind it: \n * create an eval from it\n * debug it,\n * or let Agnost fix it itself: our GitHub app opens a PR, tested against your past conversations, so you know it works before it ships.\n\n Your agent gets better every week.\n\n### **Who we are**\n\nChildhood friends, 8 years and counting.\n\n![uploaded image](/media/?type=post&id=107260&key=user_uploads/2055517/e3c2eaad-8b6e-4ec0-abfa-b16074688963)\n\n**Shubham**: Youngest engineer on Cisco’s analytics team, First Hire at Formbricks, Wrote code in Bitcoin, OWASP, and the Linux Foundation.\n\n**Parth**: IIT Madras CS (ranked 159 of 1.5M), built Spark pipelines at Microsoft, Led GPU tech at Infurnia (\\~$1M ARR).\n\n### **Our Asks**\n\n1. **Building a chat or voice agent?** Sign up & I’ll personally find 3 insane things going on in your agent traces that you’d have no idea about.\n2. **Know a founder whose product is an agent?** Intro us, 40’ monitors (to monitor your agents) on us: [shubham@agnost.ai](mailto:shubham@agnost.ai)\n3. **Try it:** Free tier:[ agnost.ai](http://agnost.ai)", "url": "https://www.ycombinator.com/launches/Ru0-agnost-ai-product-analytics-for-ai-agents", "votes": 191, "created_at": "2026-07-24T15:53:30.811Z" } ] }, { "id": 33051, "name": "Tokenless", "slug": "tokenless", "former_names": [ "Tokenless", "Touchy" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f250ec6cc81104148d8bd5bafdd6b96bc77d023.png", "website": "https://usetokenless.com", "all_locations": "San Francisco, CA, USA", "long_description": "Tokenless is an applied AI research lab. Our goal is to end reward hacking. We do research and build tools to detect issues with environments and evals, understand model behavior in those environments, and prevent those issues from happening. Correcting them makes training more effective, makes model performance easier to interpret, and is crucial to solving the AI alignment problem.\r\n\r\nIf you’re training models with RL, building environments, or running agent evals, and want more confidence in your environments, we're here to help.", "one_liner": "We find flaws in RL environments and evals and help you fix them.", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1786732497, "tags": [ "Developer Tools", "Machine Learning", "Reinforcement Learning", "B2B", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "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/tokenless", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/tokenless.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/usetokenless/", "x_url": "https://x.com/usetokenless", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f250ec6cc81104148d8bd5bafdd6b96bc77d023.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/579511aa3dc1cfb92c5f713907f31dd6fe36c640.png" ], "primary_partner": { "name": "Vivian Midha Shen", "url": "https://www.ycombinator.com/people/vivian-midha%20shen" }, "founders": [ { "user_id": 3697324, "full_name": "Rohit Agarwal", "title": "Founder", "founder_bio": "Founder at Tokenless (YC S26). CS PhD student at Princeton PLI, Hazan Lab, Google Deepmind. Previously at Duolingo and theory research at Berkeley, was EECS #1 BS grad in 2024.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/rohit-agarwal-b647951ba/", "x_url": "https://x.com/Rohit_Writes", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/86b74b1209c81d1d158af5587de149a3e8c2292c.jpg" }, { "user_id": 3706728, "full_name": "Andrew Liu", "title": "Founder", "founder_bio": "Founder at Tokenless (YC S26). Formerly a researcher at UC Berkeley working on language documentation tools. BA in Computer Science, Mathematics, and Linguistics from UC Berkeley. Previously TA'd CS61C.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/andrew-liu-b50873261/", "x_url": "https://x.com/landrew7363", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/04017660afe1efc138b242cbcc7c4efffc5f5bc4.jpg" }, { "user_id": 3732665, "full_name": "Kevin Wang", "title": "Founder", "founder_bio": "Founder at Tokenless. Prev MTS at SkyPilot building multi-cloud GPU infra for top neolabs, Founding Engineer at Eventual from seed to Series A building the data engine for physical AI, EECS at UC Berkeley.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/kevinzhiwang/", "x_url": "https://x.com/kevzwang", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1a21a2cf6802cfdcf6769408ee9d25a254858622.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 111407, "title": "Touchy 🙌: Voice-first AI Assistant for iPhone that understands the world around you", "tagline": "Your personal AI assistant that sees what you see, remembers what matters to you, and gets things done across all your tools. Just ask out loud.", "body": "**TL;DR:** We’re building Touchy 🙌, a voice and image-first AI assistant for the iPhone that personalizes itself to you. You can activate Touchy from your lock screen to instantly get things done across your apps or get answers about the world around you. Touchy is free to try, get it here: \n\n\n\n## **The Problem**\n\nYou probably know the feeling:\n\nYou ask the assistant on your phone, across the room, to send a colleague a message.\n\nIt cheerfully replies that it found 10 search results for “text Bob on Slack”.\n\nSo you trudge to your phone, find the app, open the DM, and begrudgingly type out your message instead.\n\nWe wanted a better personal assistant, one that had the intelligence and integrations we’ve come to expect from our AI agents, with the convenience of one that was native to your devices. We also believe that for AI to truly be personal, it needs to be deeply integrated into your life and the world around you. Right now, AI systems are constrained largely to using digital tools, yet the physical world is rich with audio and visual information that your phone is capable of receiving.\n\n## What We Built\n\nSo we built Touchy. One button press and you're instantly talking to an assistant that can see what you see, remember what you've said, and take action across all the tools you use. \n\nWhat sets Touchy apart:\n\n* **Reimagined UX.** Touchy is a fast, fully iOS-native app with a unique interface that is delightful to use and brings it closer to the world around you.\n* **Sees and hears the world around you**. Speak to Touchy and it can speak back. Easily snap a photo with the app’s camera to give Touchy access to what you’re seeing.\n* **Gets things done across your apps**. Touchy comes with built-in integrations with 1,000+ popular apps, as well as iOS-native capabilities such as you contacts and location.\n* **Make it yours over time**. You chat with Touchy in a single, continuous conversation. It automatically collects memories and preferences, and tailors itself to be more useful to you as you use it.\n\nIt's built to give you your attention back. Ask it something, let it act, and get back to your day — no app fighting for your attention.\n\n![uploaded image](/media/?type=post&id=111407&key=user_uploads/3697324/23ce7916-fce5-44c4-9ebf-f04049fee639)\n\n## The team\n\nWe’re Rohit, Andrew, and Kev. Before this, Rohit was doing a PhD on AI alignment at Google DeepMind Princeton and graduated #1 in Berkeley EECS. Andrew built educational software that scaled to half a million students worldwide. Kev was a startup founding engineer, developing data and GPU infrastructure that power many of the leading AI neolabs today. \n\nWe met during undergrad at UC Berkeley, and we’re coming back together to research, productionize, and scale the future of personal technology.\n\n## Our ask\n\nIf you have an iPhone, download Touchy and try it out: \n\nWe would love to learn what people find themselves using Touchy for, and what they wish they could use it for. You can also report bugs by shaking your phone or just telling Touchy. You can also reach us at [founders@touchyapp.com](mailto:founders@touchyapp.com)", "url": "https://www.ycombinator.com/launches/Syt-touchy-voice-first-ai-assistant-for-iphone-that-understands-the-world-around-you", "votes": 24, "created_at": "2026-08-20T21:00:00.530Z" } ] }, { "id": 33278, "name": "PRINCEPS", "slug": "princeps", "former_names": [ "Princeps Dev" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/8face6fd127a729a79aa67ef1d9e4669f91060ab.png", "website": "https://princeps.dev", "all_locations": "London, England, United Kingdom", "long_description": "PRINCEPS is the first purpose-built insurance company for the trillion-dollar compute buildout. We underwrite SLA, GPU residual value, and outage risk, and price the coverage that unlocks cheaper financing for data centers and neoclouds.", "one_liner": "AI-native insurance company for the compute economy", "team_size": 2, "industry": "Fintech", "subindustry": "Fintech -> Insurance", "launched_at": 1786600478, "tags": [ "Fintech", "Machine Learning", "Insurance", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2026", "status": "Active", "industries": [ "Fintech", "Insurance" ], "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/princeps", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/princeps.json", "year_founded": 2026, "city": "London", "country": "GB", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/8face6fd127a729a79aa67ef1d9e4669f91060ab.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Vivian Midha Shen", "url": "https://www.ycombinator.com/people/vivian-midha%20shen" }, "founders": [ { "user_id": 3685149, "full_name": "Conrad Frøyland Moe", "title": "Founder/CEO", "founder_bio": "Co-Founder of PRINCEPS. Previously Treasurer of the Oxford Union (£3M+ institution). Experience from national Norwegian politics. Co-Founded an events company at whilst at Oxford University: scaled to 650+ tickets per event at £15K+ revenue. \nB.A Oxford (History & Politics)", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/conrad-frøyland-moe-7950b1229/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f9d3c85109c1e611b1a72db32bde5c3f770bd8f.jpg" }, { "user_id": 3671622, "full_name": "Anya Trofimova", "title": "Founder/COO", "founder_bio": "Co-Founder of PRINCEPS. Previously worked on data centre project finance, including a $2bn Gulf data centre deal and the first ever space data centre project, and built software for UK Parliament on energy and industrial policy. Youth Fellow at Chatham House researching defence and infrastructure security. Oxford Casberd academic Scholar and former VP of the Oxford Union ($3M+ institution). Designed and led a UN digital campaign at 16.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/anyatrofimova", "x_url": "https://x.com/anya_trofimova7", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/46f402a445d84bb9a5f2b87c6c18d4cc67c14ea4.jpg" } ], "jobs": [ { "id": 114920, "title": "Founding Engineer", "url": "https://www.ycombinator.com/companies/princeps/jobs/nLXB8CE-founding-engineer", "location": "London, England, GB", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$80K - $150K", "equity_range": "0.50% - 1.50%", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] } ], "news": [], "launches": [] }, { "id": 33316, "name": "Tracer", "slug": "tracer", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7ad5cca73c945be7e986c41180c71d7ef7a7b19c.png", "website": "https://tracerml.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Tracer is an AI research lab building more capable and efficient AI through model coordination. We research adaptive inference, ensembling, and systems in which multiple open-source models work together to produce capabilities beyond any single model.\n\nOur work spans reasoning, code, research, and agentic systems, with the goal of making advanced intelligence more accessible and economical.", "one_liner": "Combining open-source AI models for better answers at lower cost", "team_size": 1, "industry": "B2B", "subindustry": "B2B", "launched_at": 1785291116, "tags": [ "Machine Learning", "Open Source", "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/tracer", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/tracer.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/116774192/", "x_url": "https://x.com/tracerml", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7ad5cca73c945be7e986c41180c71d7ef7a7b19c.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": 1217984, "full_name": "Adam Rida", "title": "Founder/CEO", "founder_bio": "I’m the founder of Tracer (S26), an AI research lab building more capable and efficient intelligence by coordinating open-source models.\n\nBefore Tracer, I built DeepRecall solo to €100k ARR in three months, scanning 100k+ marketplace products per day for product-safety risks.\n\nMy background is in applied machine learning and explainable AI. I was an AI PhD candidate at Sorbonne University, CNRS, and AXA, and previously worked at Société Générale, Rebellion Research, Qantev, and MFG Labs.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/adam-rida-581296142/", "x_url": "https://x.com/_adamr_1", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2fea71058220568dee5bc25f3d49796d0da28cad.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108361, "title": "Tracer: Fable-level AI at 1/3 the cost using open-weight models", "tagline": "Adaptive AI systems that combine open-weight models for better answers at lower cost.", "body": "Hi! I’m Adam, founder of Tracer.\n\nWe built **Echo**, an adaptive AI system built entirely from open-weight models. Instead of making you choose between models, Echo decides how much compute each request needs, which models should work on it, and how their answers should be combined.\n\n**Across our current eval mix, Echo reaches Fable-level performance at roughly 1/3 the inference cost.**\n\nToday, Echo is strongest on math, science, and the coding benchmarks we currently cover. Multilingual and broader knowledge performance still has room to improve, while longer-horizon coding and agentic evaluations are underway.\n\nEcho is continuously updated as we expand the model pool and improve ensembling and cross-model coordination.\n\n\n\n## **Why we built Echo**\n\nThere are now dozens of strong models, each with different strengths, weaknesses, and prices.\n\nWe started with a simple experiment: what if you had a perfect oracle that always knew which model would perform best for each request?\n\nThe oracle was substantially stronger than any individual model in the pool.\n\nEcho is our attempt to recover that advantage without knowing the answer beforehand.\n\nInstead of picking one model for everything, Echo goes beyond routing and dynamically allocates compute across a pool of open-weight models behind a single interface.\n\n## **The result**\n\nWe evaluate Echo against frontier models across reasoning, math, coding, multilingual and other benchmarks.\n\nWe measure both **quality and the actual model inference cost used to produce the answers.**\n\n![uploaded image](/media/?type=post&id=108361&key=user_uploads/1217984/867cc6aa-16b8-4330-b497-c33d78e62b24)\n\nOn MATH-500, Echo reached **98.6% at $4.58**, compared with Claude Fable at **99.8% at $12.64**.\n\nOn LiveCodeBench, Echo reached **92.8% at $5.35**, compared with Claude Fable at **92.0% at $8.71**.\n\nThese results show where Echo is already strongest. LiveCodeBench is encouraging, but it does not capture the full difficulty of repository-level and agentic coding. We are currently running longer-horizon evaluations where Echo must plan, use tools, modify code, and recover from errors across multiple steps.\n\nThose results are visible in the Eval Observatory and are active areas of improvement.\n\nWe publish the underlying evaluations, including individual answers, costs, wins, and losses:\n\n**→[ Explore the Echo Eval Observatory](https://echo.tracerml.ai/eval?utm_source=chatgpt.com)**\n\n## **From open source to Echo**\n\nWe first explored this idea through Tracer's open-source work: learning from real AI workloads where expensive model calls are actually necessary and where cheaper paths are enough.\n\nThe project has now crossed **1,000 GitHub stars**, with developers using and experimenting with the approach in their own AI pipelines.\n\nEcho is the next step. Instead of optimizing individual calls with a routing layer, we're building one adaptive system that decides how intelligence should be allocated end to end.\n\n**→ [GitHub](http://github.com/adrida/tracer)**\n\n## **Every company’s AI traffic has a shape**\n\nOur public evaluations test Echo across a broad mix of general tasks. Production workloads are different.\n\nEvery company has its own traffic distribution and quality requirements. Some requests genuinely need expensive frontier-level intelligence. Many don’t.\n\nFor teams that work with us, Echo can be configured to learn the structure of their workload and identify where expensive intelligence is useful and where cheaper paths are enough.\n\n**Public Echo does not train on users’ private traces. Workload-specific learning is enabled only for teams that explicitly work with Tracer to configure it.**\n\nWe set up this optimization with each team around its workload, quality bar, and data requirements.\n\nThe goal is simple: **an AI system that continuously learns how to spend the minimum amount of inference required to maintain the quality you need.**\n\nThat means **\\~3x cheaper is not necessarily the ceiling for a real production workload.**\n\n## **Try Echo now**\n\nEcho is publicly available through a chat UI and an **OpenAI-compatible API**.\n\nYou can try it directly, connect an existing application by changing the base URL, or plug Echo into OpenCode and other agent harnesses.\n\nEcho is continuously updated as we improve model selection and ensembling.\n\n**→[ Try Echo now](https://echo.tracerml.ai)**\n\n## **We’re looking for teams with large inference workloads**\n\nIf your company is running meaningful LLM inference in production, we’d like to test Echo against your workload.\n\nWe’ll map the quality-cost frontier on your traffic and determine how much of your inference actually requires expensive intelligence.\n\nTeams can then work with us to activate private, workload-specific optimization.\n\n→ [**Optimize Echo for your workload**](https://cal.com/adam-rida-tracer/tracer-first-call)\n\n## **About Tracer**\n\nI’m **Adam Rida**, founder of Tracer. I was previously a PhD candidate at **Sorbonne University/CNRS**, where I conducted and published research in ML interpretability. Before founding Tracer, I built applied ML systems across finance, insurance, and supply chain, then grew [DeepRecall](http://deeprecall.io/) to \\~**€100k ARR as a solo founder in less than 3 months**.\n\nI’ve spent most of my career studying how models make decisions and turning that research into production systems.", "url": "https://www.ycombinator.com/launches/SBl-tracer-fable-level-ai-at-1-3-the-cost-using-open-weight-models", "votes": 18, "created_at": "2026-07-31T21:01:56.160Z" } ] }, { "id": 33348, "name": "Autoresearch, Inc.", "slug": "autoresearch-inc", "former_names": [ "hiloop" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5b91efea37962386a163af58e2f2920b1e9d0aa7.png", "website": "https://autoresearch.inc", "all_locations": "San Francisco, CA, USA", "long_description": "Autoresearch helps teams train agents for tasks where general models are not good enough. Give us a task, your current agent or model, and an evaluation. Autoresearch runs an autoresearch campaign across data, SFT and other post-training methods, continual learning, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs hosted or in your cloud.\r\n\r\nWe provide the research system around models: persistent memory, full experiment lineage, compute orchestration, and statistical verification. We’re starting with agent and model training, continual learning, and optimization.\r\n\r\nReach out to us for early access at founders@autoresearch.inc.", "one_liner": "Autoresearch as a service: we solve your hardest measurable problems", "team_size": 3, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1785348094, "tags": [ "Developer Tools", "Machine Learning", "Reinforcement Learning", "Infrastructure", "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/autoresearch-inc", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/autoresearch-inc.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/autoresearchinc/", "x_url": "https://x.com/hiloopai", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/5b91efea37962386a163af58e2f2920b1e9d0aa7.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Ankit Gupta", "url": "https://www.ycombinator.com/people/ankit-gupta" }, "founders": [ { "user_id": 1714115, "full_name": "Karan Brar", "title": "Founder/CEO", "founder_bio": "Founder at Autoresearch, Inc. Previously ML at Reducto, Head of ML Infra at DynamoAI.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/brarkaran", "x_url": "https://x.com/deepmatmul", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e596ed985a539262821de87d80ea418725ab7a90.jpg" }, { "user_id": 1597736, "full_name": "Thomas Boser", "title": "Founder/CTO", "founder_bio": "Founder at Autoresearch, Inc. Previously engineer at Reducto, founding engineer at Crosswise, senior ML engineer at Discord, ML at Sentropy & SoFi.", "is_active": true, "linkedin_url": "https://linkedin.com/in/tboser", "x_url": "https://x.com/thomasboser", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/7c1d2d3062a34b6d55d2d4e7aef55e887853400b.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108337, "title": "hiloop: we run thousands of experiments to improve your models", "tagline": "Send us a hard task, your model or agent, and an eval. We run the research campaign and return a verified improvement.", "body": "We're Karan and Thomas, and we're building hiloop: **infrastructure for automated research**.\n\n**TL;DR: we gave two stock coding agents 50 B200s and our infra. They ran 4,188 experiments in two days and beat the published state of the art on [Karpathy's autoresearch benchmark](https://github.com/karpathy/autoresearch). The same machinery found a [NanoGPT speedrun](https://app.primeintellect.ai/speedrun/nanogpt) candidate that beats the best known result. [See](https://hiloop.ai/blog/search-is-enough/) full writeup.**\n\nNow we want to point it at your hardest problems.\n\n![uploaded image](/media/?type=post&id=108337&key=user_uploads/1714115/63396f7f-3e21-41ea-83ea-54cbaaa9f63f)\n\n**The results**\n\nOn Karpathy’s autoresearch benchmark, our final recipe reached **0.9016 val_bpb**. The previous best published result was [Recursive’s 0.9109](https://www.linkedin.com/pulse/first-steps-toward-automated-ai-research-recursive-si-kybtc) on B200 hardware. For NanoGPT, our implementation had **12.54% lower runtime** than upstream.\n\nThere was no bespoke research agent or elaborate scaffold. We used Claude and GPT-5.5 out of the box. What changed was the system around them.\n\n**What hiloop does**\n\nGive us a hard task, your current agent or model, and an evaluation criteria. Hiloop runs an autoresearch campaign across training, data, prompts, tools, harnesses, and systems, then returns the best verified improvement. It runs in your cloud or hosted by us.\n\nWe’re starting with agent and model training: SFT, post-training, continual learning, and optimization.\n\n[https://drive.google.com/file/d/1Rv8HttSHZ-yvSJk6hsWQsqCXWTHJQogK](https://drive.google.com/file/d/1Rv8HttSHZ-yvSJk6hsWQsqCXWTHJQogK/view?usp=sharing)\n\n**Who we are**\n\nWe met at Reducto, where we built its ML, post-training, and platform systems. Karan previously led ML infrastructure at DynamoAI . Thomas was a founding engineer at Crosswise and an MLE at Discord and SoFi.\n\n**Two asks**\n\n1. **Want your models or agents better, faster, or cheaper?** If you have a hard objective in post-training, continual learning, inference optimization, or classic ML, send us the task, your baseline, and the eval. We’ll personally run a campaign with you through verified improvement.\n2. **Building autoresearch or RSI loops?** Reach out to give our system a try! We’d love to compare notes.\n\nEmail [**founders@hiloop.ai**](mailto:founders@hiloop.ai) to get in touch with us!", "url": "https://www.ycombinator.com/launches/SBN-hiloop-we-run-thousands-of-experiments-to-improve-your-models", "votes": 8, "created_at": "2026-07-29T19:50:09.250Z" } ] }, { "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": 34696, "name": "ChemPilot.ai", "slug": "chempilotai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/4c3f8543148c3a53ebe369d21ece622228018917.png", "website": "https://chempilot.ai/", "all_locations": "Haifa, Haifa District, Israel", "long_description": "We use AI and quantum-based simulations to discover and develop new materials for defense and dual-use applications, from energetics and propellants to carbon fibers, polymers, coatings and composites. Developing a new material today takes 10+ years of trial-and-error lab work. We compress that to months, using physics-based simulation, machine learning and AI agents that only send labs candidates that can actually be made.\r\n\r\nSam, Ofir and Maor have spent the past two years researching the discovery of these exact materials together at the Technion lab of our CSO, Prof. Alon Grinberg Dana (former MIT postdoc), working directly with defense primes and government R&D agencies. Together we bring deep expertise in chemical engineering, computational chemistry and AI, and the production systems to put them to work. ChemPilot spun out of that work.\r\n\r\nThe defense and dual-use industrial base runs on materials with fragile, sole-source and often adversarial supply chains, from energetics and propellants to carbon fibers, high-temperature composites and specialty polymers. Demand for new capabilities is surging, and materials take decades to develop. The West needs new materials it can make securely, fast and cheap - ChemPilot will help find and develop them.", "one_liner": "Discover novel defense related materials/molecules.", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Defense", "launched_at": 1791753586, "tags": [ "Machine Learning", "Advanced Materials", "Defense" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Industrials", "Defense" ], "regions": [ "Israel", "Middle East and North Africa" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/chempilotai", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/chempilotai.json", "year_founded": 2026, "city": "Haifa", "country": "IL", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/4c3f8543148c3a53ebe369d21ece622228018917.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": 4000868, "full_name": "Sam Cogan", "title": "Founder/CEO", "founder_bio": "Founder at ChemPilot.ai. MSc in Biochemical Engineering from Technion. Love competing, both in business and in any sport.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/sam-cogan-16435916b/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/52cee213d8d6c3ed123701998ac0813c86155160.jpg" }, { "user_id": 4006886, "full_name": "Maor Goldman", "title": "Founder", "founder_bio": "Founder at ChemPilot.ai | Ethical Hacker | BS Computer Engineering from Technion | Served in 8200 ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/maor-goldman-03b43a119/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cfb6b71947255e63c3da4ad31df3bcdb630bad8a.jpg" }, { "user_id": 4003220, "full_name": "Ofir Simhi", "title": "Founder/CTO", "founder_bio": "Founder and CTO at ChemPilot.ai. Previously a full-stack developer, one of the first developers in an acquired startup (Skiff, acquired by Notion), and a main developer in another acquired startup (StoreMaven, acquired by Zynga). \nBSc. in Biochemical Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ofir-simhi-b8098a196/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/a3d87d13345612a8a48e34155eb8d933ed91977a.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 34862, "name": "Saccade", "slug": "saccade", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/751432518cccc3177c9fa321a442fcfaf7e00fdd.png", "website": "https://saccade.health", "all_locations": "San Francisco, CA, USA", "long_description": "Alzheimer's testing today is bottlenecked around the onset of symptoms, when the damage is already done. Current treatments, including the FDA-approved drugs released in 2024, don't reverse the disease; they only slow it down. Earlier detection means earlier intervention.\r\n\r\nEye movement changes linked to Alzheimer's surface sooner than any other biomarker, up to 15 years before symptoms appear.\r\n\r\nWe deliver a 15-minute VR assessment delivered to patients in longevity clinics:\r\n• Tasks: patients perform visual tasks designed by neurologists to trigger a distinct oculomotor response in people with Alzheimer's or on its path\r\n• Hardware: our patent-pending attachment connects to any VR headset on the market and captures these eye movement patterns.\r\n• Analysis: the patterns are compared against our ML model to produce a risk analysis.\r\n\r\nOnce classified, patients can access new interventions, such as the FDA-approved drugs. They're also referred to longevity practices, neurology practices or both, depending on their stage of decline.\r\n\r\nTesting for the earliest biomarkers allows the earliest possible intervention. In some cases, that slows the disease enough that patients outlive the onset of symptoms and never face memory loss in their lifetime.", "one_liner": "World's Earliest Test for Alzheimer's Biomarkers", "team_size": 2, "industry": "Healthcare", "subindustry": "Healthcare -> Consumer Health and Wellness", "launched_at": 1791296673, "tags": [ "Hard Tech", "Hardware", "Machine Learning", "B2B", "Healthcare" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Healthcare", "Consumer Health and Wellness" ], "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/saccade", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/saccade.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/saccadehealth", "x_url": "https://x.com/saccadehealth", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Saccade-YC-F26", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/751432518cccc3177c9fa321a442fcfaf7e00fdd.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": 2316918, "full_name": "Shreyas Bhardwaj", "title": "Founder/CEO", "founder_bio": "Founder at Saccade. Prev built medical devices for hospitals; soft robotics researcher", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bhardwajshreyas/", "x_url": "https://x.com/shreyasbhardwj", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/60555f50c4d0f03a61fcc2d5ac9eb26a042d362a.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 121747, "title": "Saccade: World's Earliest Test for Alzheimer's Biomarkers", "tagline": "World's Earliest Test for Alzheimer's Biomarkers", "body": "**TL;DR:** Saccade is a 15-minute VR test that catches Alzheimer’s biomarkers earlier than ever before. That lets people start lifestyle changes and new FDA-approved drugs years sooner, when they work best. Sometimes that pushes symptoms past life expectancy, so they never show up. \\\nWe’re live in San Francisco clinics, testing people in their 30s and older for early risk indicators. \\\nYou can book a test at [saccade.health](http://saccade.health) today.\\\n\\\n\n\n**Problem**\\\nCurrent Alzheimer's testing only screens patients after clear symptoms appear, at which point an individual has already suffered significant cognitive decline. New FDA-approved drugs have been proven to slow this decline, but not reverse it. By the time most people get their diagnosis, they've missed their best window to act.\n\n**Insight**\\\nEye movement changes linked to Alzheimer's can be detected up to 15 years before symptoms appear - earlier than any other markers we can screen for. Our product is designed to service those exact changes, giving patients the insight they need to act now.\n\n![uploaded image](/media/?type=post&id=121747&key=user_uploads/2316918/a60db064-10c6-4ca0-bd9c-29389ea2ef4f)\n\n**Product**\n\n* **Tasks:** users complete visual tasks designed by neurologists that trigger distinct oculomotor responses in people with early-stage, pre-symptomatic Alzheimer's\n* **Hardware:** our patent-pending attachment connects to any VR headset on the market and captures high-precision recordings of eye movement patterns.\n* **Analysis:** the patterns are analyzed by our proprietary ML model to produce a risk score.\n\n**Next Steps for Patients**\\\nOnce classified into one of three risk categories, patients can start interventions ranging from preventative longevity practices, further neurological evaluations, and new FDA-approved drugs. Starting treatment early can significantly slow the progression of the disease, allowing some patients to outlive the onset of symptoms and never suffer from memory loss in their lifetime.\n\n**Get tested today: Sign up at [saccade.health](http://saccade.health) and schedule a screening today.**\n\n**Asks**\\\nIntros to US longevity practices and clinics that want to offer early Alzheimer’s testing to their patients. The test takes 10 minutes. I’m happy to meet in person: [shreyas@saccade.health](mailto:shreyas@saccade.health)\n\n![uploaded image](/media/?type=post&id=121747&key=user_uploads/2316918/51c6bcc9-d56f-4332-be61-510f5525d259)\n\n", "url": "https://www.ycombinator.com/launches/Vff-saccade-world-s-earliest-test-for-alzheimer-s-biomarkers", "votes": 6, "created_at": "2026-10-13T18:00:00.187Z" } ] }, { "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": 36279, "name": "Native", "slug": "native", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/150e28c9ae3ba8f6c0bac4696c53752a638f59ff.png", "website": "https://native.no/", "all_locations": "San Francisco, CA, USA", "long_description": "Native is building an autonomous marketing platform where business can do all of their marketing within one platform using our artificial intuition model called Norn, that predicts what people will respond to, researched and built in house.", "one_liner": "Artificial Intuition for marketing", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Marketing", "launched_at": 1791575550, "tags": [ "Machine Learning", "Social Media", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "B2B", "Marketing" ], "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/native", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/native.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/trynative/", "x_url": "https://x.com/trynativeapp", "fb_url": "https://www.facebook.com/native.no", "cb_url": null, "github_url": "https://github.com/wepost-no", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/150e28c9ae3ba8f6c0bac4696c53752a638f59ff.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/b0877b1c3a7ec9ee92d9b60a7be6118d6b8f77a4.png", "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/3eea1a8bb874e54e6a55502c860a90103ed95d66.png" ], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 1927795, "full_name": "Benjamin Bekken", "title": "Founder/CEO", "founder_bio": "Founder at Native (YC F26). Previous Minecraft YouTuber and video creator with 15 years of experience in marketing, and millions of views. Got a double master's degree in media, communication & IT.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/bekken/", "x_url": "https://x.com/BenjaminBekken", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d6923430733c9716418ec8ac86102f5a4dfdf586.jpg" }, { "user_id": 2244185, "full_name": "Håkon Græsberg", "title": "Founder/CTO", "founder_bio": "Founder at Native (YC F26). Previously co-founder & CTO at Wepost, a B2B marketing platform. MS in Computer Science with AI specialization from EURECOM and NTNU, plus MSc in Entrepreneurship. Early experience in AI/ML at DNV and Norconsult, working on computer vision and radar technology applications.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hakon-grasberg/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8d9e84db93a4997f1891723f024b7138970d206e.jpg" }, { "user_id": 1927747, "full_name": "Trond Hauga", "title": "Founder/COO", "founder_bio": "Cofounder of Native (YC F26). First co-founded and sold out music festival at 17, then founded marketing agency while doing master's degree in Tech Venture from NTNU (Norwegian MIT). Last year at college I cofounded Wepost which we built for 2.5 years, merging that to start Native in January 2026.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/trondha/", "x_url": "https://x.com/Tronhauga", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cb29c298c9dc5f35f7963e6eddab95195dc305c6.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 121621, "title": "Native: Artificial intuition for marketing", "tagline": "Native predicts what you should post, and then runs it across your socials and ads.", "body": "Hey everyone! 👋\n\nBen, Håkon and Trond here, founders of Native (YC F26). \n\nToday we're introducing Norn, a model that predicts which marketing ideas are outliers, before the content is made. We call it artificial intuition, and have our first model live in Native helping over 2000 companies run their marketing. By dogfooding we've reached $2M ARR within our first 6 months.\n\n\n\n**The problem**\n\nAI has made it much easier to produce text, images and video. But you still have to decide what’s worth saying, which angle fits your brand and what your audience might care about.\n\nProducing more content doesn’t answer those questions. It just produces more noise.\n\nOur bet: knowing what to say becomes more valuable as creating it gets cheaper.\n\n**How Norn works**\n\nJust type in your website. Native gathers your business and brand context, and Norn ranks ideas using four signals:\n\n* Brand personality: your voice, positioning and style\n* Social performance: what happened after content was published\n* Trends: what people are paying attention to\n* Taste: which ideas you approve, rewrite or reject\n\nNorn’s output is an idea: the point to make, the angle and the format. Native uses frontier models to turn it into images, video, ad creatives and social posts. You review the results, and Native handles scheduling and publishing.\n\nThe original idea, your feedback and the published result become part of the learning loop.\n\n**Already live**\n\nMore than 2,000 businesses use Native, and Norn v1 is already working inside it.\n\nThat gives us something useful to build on: feedback from the whole creative process, from the first suggestion to what happens after publishing.\n\n**Why us**\n\nBen comes from YouTube and agency work, with 15 years in content and marketing. Trond built a marketing agency and founded a music festival. Håkon built machine learning models for visual imagery and brings the AI and engineering background.\n\nBetween us, we’ve spent years creating content and building the software behind it. Native brings those experiences together.\n\n**Next: the Norn API**\n\nWe’re developing an API so other products can ask for ranked ideas before creating content: a marketing tool, an agency workflow or an AI agent deciding what to make next. Like Jev for marketing.\n\nIn the video, we explain the model and where we want to take it.\n\n**Our ask**\n\nIf you’re building a product that creates content, join the Norn API waitlist and tell us what you’d build: ", "url": "https://www.ycombinator.com/launches/Vdd-native-artificial-intuition-for-marketing", "votes": 7, "created_at": "2026-10-13T16:00:00.176Z" } ] }, { "id": 37732, "name": "Hundred", "slug": "hundred", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9f26995d92445a7e77c0839448bb04ca0c89f08.png", "website": "http://hundred.systems/", "all_locations": "San Francisco, CA, USA", "long_description": "Hundred is an automated inspection system for the components that make up consumer devices like phones, smartwatches, and laptops. Hundred helps factories cut waste and prevent line-down issues by catching sub-millimeter cosmetic defects before parts reach the assembly line. \r\n\r\nIn the consumer electronics industry, designs change so fast that traditional machine vision can't keep up, forcing factories to rely on manual inspection, literally people checking parts under microscopes to sort out bad parts. Human inspectors miss roughly 15% of defects, and a single bad part can turn a finished device into scrap. Hundred is replacing manual inspection with an imaging rig that scans an entire shipping tray of parts at once. \r\n\r\nThe Hundred rig blocks ambient light and uses varied controlled lighting configurations to detect defects smaller than 100µm on a range of materials. The controlled environment means Hundred only needs to scan a single good part before it can find defects, categorize them, and assess their severity accurately.\r\n\r\nDefects cost U.S. discrete manufacturers $45 billion a year, a problem Hundred's CEO, Erica, saw firsthand as a mechanical engineer designing devices at Meta, Google, and Magic Leap. She met Imani, Hundred's CPO, at Columbia University's admitted-students day in 2017 and Imani went on to spend five years at Twitch building software that handles billions of requests a day. Erica later met Iris, Hundred's CTO, in the Bay Area. Iris worked as a quant at Citadel before pivoting to study at Imperial College London where she researched machine learning on 3D meshes and other structured data. Together, they're building Hundred because manual inspection shouldn't still exist in 2026.", "one_liner": "100% automated inspection for consumer electronics parts", "team_size": 3, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1790785673, "tags": [ "Hard Tech", "Hardware", "Machine Learning", "Computer Vision", "Industrial" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2026", "status": "Active", "industries": [ "Industrials", "Manufacturing and Robotics" ], "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/hundred", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2026/hundred.json", "year_founded": 2026, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/hundred-systems-inc/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/f9f26995d92445a7e77c0839448bb04ca0c89f08.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": 3535016, "full_name": "Erica Diaz", "title": "Founder/CEO", "founder_bio": "Erica is co-founder and CEO of Hundred (YC F26).\nPreviously spent 5.5+ years at Meta, Google, and Magic Leap designing hardware for consumer electronics, and resolving issues on the ground with vendors in China and Mexico.\n\nColumbia University, BS Mechanical Engineering.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/erica-diaz1/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/5ec31907973b972abef2c8b6371b1ae58f453c85.jpg" }, { "user_id": 317644, "full_name": "Imani Gosserand", "title": "Founder/CPO", "founder_bio": "Co-founder and CPO of Hundred (YC F26).\nPreviously Software Engineer at Twitch working at the intersection of notifications, short-form content, and ML.\n\nBS Computer Science from Columbia University", "is_active": true, "linkedin_url": "https://linkedin.com/in/imani-gosserand-804a10175", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/21384b93ca0a4b32ae2f6ed591a1d96b794799e0.jpg" }, { "user_id": 4149004, "full_name": "Iris Liao", "title": "Founder/CTO", "founder_bio": "Co-founder and CTO of Hundred (YC F26).\nPreviously Quantitative Researcher at Citadel.\nSpent the last year researching machine learning methods on 3D meshes and other structured data. \n\nMSc Applied Mathematics, Imperial College London\nBA Mathematics, Columbia University", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/iris-liao-03b041152", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e8c56193fe88be22d7e68840f5171512f9f41919.jpg" } ], "jobs": [], "news": [], "launches": [] } ]