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The company unites computational chemistry, structural biology, and medicinal chemistry to pioneer the next generation of AI-driven drug discovery platforms.", "one_liner": "Artificial intelligence for drug discovery.", "team_size": 67, "industry": "Healthcare", "subindustry": "Healthcare -> Drug Discovery and Delivery", "launched_at": 1416219336, "tags": [ "AI-powered Drug Discovery", "Deep Learning", "Biotech", "Drug discovery", "Oncology" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2015", "status": "Active", "industries": [ "Healthcare", "Drug Discovery and Delivery" ], "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/numerion-labs", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/numerion-labs.json", "year_founded": null, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/atomwise/", "x_url": "https://twitter.com/atomwiseinc", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/atomwise", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c3b218096497a702becc32895ca1b21c4cae78bd.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 7359, "full_name": "Abraham Heifets", "title": "Founder/CEO", "founder_bio": "Founder and CEO of A Company in Stealth Mode. Previously, cofounded Atomwise, the first company to deploy deep learning for structure-based drug design ($220+ million in funding; 100 hires; signed $5B in total partnership value with Sanofi, Bayer, Eli Lilly, etc; ran largest AI-driven drug discovery experiment w/ 600+ researchers in 30 countries). Even more previous: PhD @ U of Toronto; inventor on 15 patents + applications; IBM Research; AI for Cornell’s world champion robotic soccer team.", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/abrahamheifets/", "x_url": "https://twitter.com/AbeHeifets", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/571a64309f08a9ce37f2b403541f4d87c8410311.jpg" }, { "user_id": 10392, "full_name": "Alex Levy", "title": "Founder", "founder_bio": "Leveraging AI to revolutionize the physical sciences, Alex Levy is the CEO and co-founder of Atmo, the leading company integrating AI into global weather forecasting. Previously, he co-founded Atomwise (W15), a pioneering AI company in small molecule drug discovery, boasting over 800 active programs and $1 billion in pharmaceutical partnerships. An alumnus of the University of Toronto, he began his career by leading a computer vision research lab within the Computer Science Department.", "is_active": false, "linkedin_url": "ca.linkedin.com/in/levyalex", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9d6b2d29f328539e24d2cb0bac16217cc3f8838b.jpg" }, { "user_id": 10399, "full_name": "Izhar Wallach", "title": "Founder", "founder_bio": "-", "is_active": false, "linkedin_url": "https://www.linkedin.com/pub/izhar-wallach/11/38/a04", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/0dd953a0f7260cfc91ec2e0021a7c37e4238d45d.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 856, "name": "Mashgin", "slug": "mashgin", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd7ff2b3db9bbbc82a79afababdad873d16b6e54.png", "website": "http://mashgin.com", "all_locations": "San Francisco, CA, USA", "long_description": "Mashgin creates better retail experiences through visual automation. \r\n\r\nWe’ve built a self-checkout kiosk that uses computer vision to scan multiple items without barcodes, reducing checkout time by 10x. We’re completely recreating the checkout experience in an industry that’s had little innovation in decades.\r\n\r\nOur clients see dramatic reductions in lines and revenue increases of as much as 400% as a result.", "one_liner": "Self-Checkout using Computer Vision.", "team_size": 150, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1421189374, "tags": [ "Cashierless Checkout", "Deep Learning", "Hardware", "Computer Vision", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Winter 2015", "status": "Active", "industries": [ "B2B", "Retail" ], "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": [ { "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": "Back in the early 2010s, Mukul was hard at work helping Toyota determine how computers could use cameras to interpret and interact with the world. But Mukul had a problem - every day, he would go to get his lunch, and every day he’d spend most of that time (30-40 minutes) waiting in line in his corporate cafeteria. He decided to take a closer look at the issue and decided that computer vision could be the answer. After some initial testing, Mukul felt the idea was a big opportunity and flew to California to pitch his idea.There he reconnected with Abhinai, who was his classmate in the same dorm at IIT Delhi. He created an early demo that was so impressive that it convinced Abhinai to join him.Having worked in large companies and created some enormously successful projects, we were disheartened by the fact that when a project became successful, several people came in at the final stages to take credit. We decided to be founders and create a company that valued people’s contributions honestly and cut through the corporate bullshit.After successfully cold-calling head executives from some of the largest corporate cafeteria operators on the planet, we received many enthusiastic responses to our description of Mashgin’s Touchless Checkout System. He then set out to actually build it.After successfully building and demoing a Mashgin prototype to the YC team, Mashgin went from “project” to “company” over the course of the Winter 15 Batch." }, { "question": "How did you decide to apply to Y Combinator? What was your experience applying, going through the batch, and fundraising at demo day?", "answer": "We applied to YC very late, just a few days before the start of W15 batch. We were in the middle of raising a seed round and through our suboptimal interactions with investors, we realized that we needed YC guidance to help us raise a round on good terms. Our YC experience was amazing, if a bit overwhelming at times. YC partners forced us to think clearly about the next steps. At one point they had us go door to door to every business in Mountain View downtown to try and sell our product. We made our first sale that day and we’ll never forget that experience! Fundraising post YC was every bit what we imagined it to be. It made the entire process super smooth and easy." }, { "question": "What's the history of your company from getting started until the present day? What were the big inflection points?", "answer": "When we started the company in 2014, our initial goal was to improve the checkout experience in corporate dining facilities, cafeterias, and sports stadiums. We aimed to use computer vision and machine learning technology to create a much more efficient and convenient experience for customers. Entering YC and getting to demo day with a major letter of intent was a big moment for the company, giving us the experience and fundraising to make Mashgin market-ready over the following few years.However, with the outbreak of COVID-19 in 2020 and the subsequent lockdowns, we had to pivot our focus to the convenience store space as corporate offices closed and retail demand for self-checkout solutions skyrocketed.During the pandemic we were able to secure an enormous deal with Circle K, one of the largest convenience store chains in the world, for 10,000 kiosks across their global portfolio of stores. This elevated us from a burgeoning player in AI to the most market-tested solution in the world.In May 2022, we were able to announce a Series B funding round of $62.5 million with a valuation of $1.5 billion, earning us the title of “unicorn” company. The round was led by NEA, a global venture capital firm that’s invested in other successful startups like Cloudflare, Patreon Plaid, and Robinhood, with additional support from our existing investor, Matrix Partners.Today Mashgin continues to expand within the convenience, sports, and corporate dining industries while beginning to explore adjacent opportunities in spaces like ski resorts, quick service restaurants, and drug stores." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Online retail has gone through several iterations in the past few decades and the experience today is extremely frictionless. Physical retail on the other hand has not seen the same level of innovation and the experience hasn’t changed much since the early 90s. We want to use the latest advances in AI and Computer Vision to make physical retail as easy and frictionless as online retail.More specifically, Mashgin solves the problem of long wait times and checkout bottlenecks. Customers often experience frustration and dissatisfaction when they have to wait in long lines to check out, and retailers also face the challenge of managing and staffing checkouts. This is a big problem because lines are a waste of time for everyone involved. Over the course of a lifetime, the average person will spend years waiting in lines. And those lines aren’t just wasted time for consumers, they result in lost sales for retailers. Studies by Forrester and BlueDot have shown that retailers lose as much as 57% of their sales when customers perceive the line as long (which in a convenience store is 3 people).Besides being frustrated by our own lunch lines, we decided to work on this problem because we saw an opportunity to use computer vision and machine learning to create a more efficient and convenient shopping experience for customers across industries." }, { "question": "What is your long-term vision? If you truly succeed, what will be different about the world?", "answer": "At Mashgin, our long-term vision is to create a world where the shopping experience is completely seamless and frictionless. If we succeed, waiting in line to purchase goods will be a thing of the past. Consumers will have more time to do the things that they love with a faster, more convenient, and less stressful experience in retail and dining spaces.  For retailers and concessionaires the technology will free up time and resources, allowing them to focus staff on other key areas of the business like stocking, cooking, cleaning, and customer service." } ], "url": "https://www.ycombinator.com/companies/mashgin", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2015/mashgin.json", "year_founded": 2014, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/mashgin/", "x_url": "https://twitter.com/mashgin", "fb_url": "https://www.facebook.com/mashgin/", "cb_url": "https://www.crunchbase.com/organization/mashgin", "github_url": "https://github.com/mashgin", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd7ff2b3db9bbbc82a79afababdad873d16b6e54.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": 34574, "full_name": "Abhinai Srivastava", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/abhinai", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/14f887ad32b47d2b93bdee84edcd76e3e94995fe.jpg" }, { "user_id": 37288, "full_name": "Mukul Dhankhar", "title": "Founder", "founder_bio": null, "is_active": true, "linkedin_url": "https://linkedin.com/in/mdhankhar", "x_url": null, "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/39c2431da93e70eca62fac474dd89d47cc1d756c.jpg" } ], "jobs": [ { "id": 49134, "title": "Senior Software Engineer, Full-Stack", "url": "https://www.ycombinator.com/companies/mashgin/jobs/BaWLmBF-senior-software-engineer-full-stack", "location": "Palo Alto, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 49135, "title": "Software Engineer, Computer Vision and Deep Learning", "url": "https://www.ycombinator.com/companies/mashgin/jobs/Lwg6Tpa-software-engineer-computer-vision-and-deep-learning", "location": "Palo Alto, CA", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 64562, "title": "Senior Technical Product Manager", "url": "https://www.ycombinator.com/companies/mashgin/jobs/o1C6SdM-senior-technical-product-manager", "location": "Palo Alto, CA, US", "type": "Full-time", "role": "Product", "role_type": null, "salary_range": "$180K - $230K", "equity_range": "", "experience": "6+ years", "visa": "Will sponsor", "skills": [] }, { "id": 67554, "title": "Senior Software Engineer, Backend", "url": "https://www.ycombinator.com/companies/mashgin/jobs/kOoM7gQ-senior-software-engineer-backend", "location": "Palo Alto, CA", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$225K - $350K", "equity_range": "", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [ { "title": "Mashgin Hits $1.5 Billion Valuation With AI-Powered Self-Checkout System", "url": "https://www.forbes.com/sites/rashishrivastava/2022/05/09/mashgin-hits-15-billion-valuation-with-ai-powered-self-checkout-system/?sh=4a389369176a", "date": "May 09, 2022" } ], "launches": [] }, { "id": 999, "name": "Focal Systems", "slug": "focal-systems", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/e25ba04ae8686807d958c48f1372502741b4b2e3.png", "website": "http://www.focal.systems", "all_locations": "San Francisco, CA, USA; Toronto, ON, Canada; London, England, United Kingdom", "long_description": "Focal Systems is on a mission to lower the cost of living for all mankind by automating and optimizing Brick and Mortar Retail with the latest advancements in AI. Focal Systems is the industry leader in retail automation solutions. By digitizing store shelves hourly and unleashing FocalOS, retailers unlock huge operational efficiencies, optimized merchandising, and streamlined supply chains which deliver impactful financial results. Focal is transforming retail by empowering store management to make automated, data-driven decisions. We are the operating system of retail.", "one_liner": "Building the Operating System for B&M Retail using Deep Learning", "team_size": 170, "industry": "B2B", "subindustry": "B2B -> Retail", "launched_at": 1447280416, "tags": [ "Deep Learning", "Grocery", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2016", "status": "Active", "industries": [ "B2B", "Retail" ], "regions": [ "United States of America", "Canada", "United Kingdom", "America / Canada", "Europe", "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/focal-systems", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2016/focal-systems.json", "year_founded": 2016, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/focal-systems", "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/e25ba04ae8686807d958c48f1372502741b4b2e3.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": 111048, "full_name": "Michael Cantalino", "title": "Founder/COO", "founder_bio": "Founder at Focal Systems, the AI operating system of brick-and-mortar retail. Leading with a people-first mentality. Constantly aiming to leave the world better than I found it in all aspects of business and life. Alongside technology, heavily involved in the non-profit sector and all things outdoors.", "is_active": true, "linkedin_url": "https://www.linkedin.com/pub/michael-john-cantalino/12/b44/747", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e34fb29ffd13b8ff303a5d79248102c61444bc19.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 1033, "name": "Netomi", "slug": "netomi", "former_names": [ "msg.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/a39a790ec2d3d1805e80c70308782d153f1c2294.png", "website": "https://netomi.com", "all_locations": "San Francisco, CA, USA", "long_description": "Welcome to Netomi AI, where we are revolutionizing the world of customer experiences through cutting-edge artificial intelligence. Our mission is clear: to create AI that not only solves mission-critical problems for the world's largest brands but also fosters genuine customer love. Backed by industry titans like Y-Combinator, Index Ventures, Jeffrey Katzenberg, and Greg Brockman, Netomi AI is at the forefront of defining the future of AI-driven customer engagement.\r\n\r\nAt Netomi, we embody our core values in everything we do:\r\n\r\nPassion: We love what we do, genuinely. We persevere, creatively problem solve, and grow from adversity.\r\n\r\nCustomer Focus: We obsess over creating a positive impact and deliver experiences that meet or exceed the needs of our customers, and their customers.\r\n\r\nOne Team: We believe in working collaboratively as One Team to meet shared objectives and goals.\r\n\r\nJoining Netomi AI means being part of a dynamic, fast-growing team that values innovation, creativity, and hard work. As a key player in the Generative AI revolution, you'll have the opportunity to significantly impact our success while developing your skills and career in the ever-evolving field of AI.\r\n\r\nNetomi AI is not just a workplace; it's a community of visionaries shaping the future of customer engagement. If you're ready to be part of something extraordinary, where your passion aligns with our values and vision, we invite you to explore opportunities with us. 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Example applications include security camera monitoring, automating data entry, interpreting web scraped images, validated user photo inputs, augmented reality and mobile product scanning. \r\n\r\nIf you have visual or scanned data that you wish your software could interpret at scale, we can turn around a first proof of concept in under a week. Reach out and see how computer vision can change your business.", "one_liner": "Turn your data into a deployed computer vision model", "team_size": 6, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1512529341, "tags": [ "Deep Learning", "Indoor Mapping", "Machine Learning", "Computer Vision" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2018", "status": "Acquired", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Fully Remote" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/jido-maps", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2018/jido-maps.json", "year_founded": 2018, "city": "San Francisco", "country": "US", "linkedin_url": "", "x_url": "https://twitter.com/jidomaps", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/jido-maps", "github_url": "https://github.com/jidomaps", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/788dc05b2141606cea9f6805759306ec0ea99f5e.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": null, "founders": [ { "user_id": 221351, "full_name": "Mark Stauber", "title": "Founder/CEO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mark-stauber-01151638/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/e2c8b601d548a33299a1a5c1b9608bd1ddd84176.jpg" }, { "user_id": 221410, "full_name": "Jaeyong Sung", "title": "Founder/CTO", "founder_bio": "", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jaeyongsung/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/336c4cbc2668c71d178f5e77672d31cea01b3281.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 1910, "name": "Activeloop", "slug": "activeloop", "former_names": [ "Snark.ai", "Snark AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c516ed5054847ecb1afb63f795f712b8d5c7f23d.png", "website": "https://activeloop.ai/", "all_locations": "San Francisco, CA, USA; Mountain View, CA, USA", "long_description": "We provide a simple API for creating, storing, versioning, and collaborating on multi-modal AI datasets of any size. With Activeloop's open-core stack, you can rapidly transform and stream data while training models at scale. Deep Lake powers foundational model training by acting as a vector database with significant benefits, such as (1) the ability to use multi-modal datasets to fine-tune your own LLM models, (2) storing both the embeddings and the original data with automatic version control, so no embedding re-computation is needed (3) truly serverless service with no vendor lock-in. How cool is that?\r\n\r\nGitHub loves us - we're one of the fastest-growing libraries there, and we're used by little-known companies like Google, Waymo, and Intel. No big deal. \r\n\r\nOur founding team hails from places like Princeton, Stanford, Google, and Tesla, and we're backed by Y Combinator & other Silicon Valley heavyweights. \r\n\r\nActiveloop is hiring, and we want you! Check out our open roles on our YC page and join the fun.\r\n\r\n10-min demo: https://activeloop.wistia.com/medias/aibvo0dst2\r\nWhitepaper: https://www.deeplake.ai/whitepaper", "one_liner": "Database for AI", "team_size": 15, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1524690140, "tags": [ "Computational Storage", "Deep Learning", "Generative AI", "Computer Vision", "Open Source" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2018", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "Partly Remote" ], "stage": "Growth", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/activeloop", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2018/activeloop.json", "year_founded": 2018, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/activeloop", "x_url": "https://twitter.com/activeloopai", "fb_url": "https://www.facebook.com/activeloopai", "cb_url": "https://www.crunchbase.com/organization/snark-ai", "github_url": "https://github.com/activeloopai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c516ed5054847ecb1afb63f795f712b8d5c7f23d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 78090, "full_name": "Davit Buniatyan", "title": "Founder", "founder_bio": "Founding CEO Activeloop, PhD on leave from Princeton, AI/ML, Data and Infra, Y Combinator S18, UCL 16’ Working on Data 2.0", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/davidbuniatyan", "x_url": "https://twitter.com/DBuniatyan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/49efe4795d8a38cc955a0c873f7de567ef057a52.jpg" } ], "jobs": [ { "id": 79215, "title": "Member of Technical Staff", "url": "https://www.ycombinator.com/companies/activeloop/jobs/zn1plZe-member-of-technical-staff", "location": "Mountain View", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "$150K - $250K", "equity_range": "0.50% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Amazon Web Services (AWS)", "Google Cloud", "Kubernetes", "MongoDB", "PostgreSQL", "Python", "Redis", "SQL", "Docker", "Microservices" ] } ], "news": [ { "title": "Activeloop-L0: Agentic Reasoning on Your Multimodal Data", "url": "https://www.activeloop.ai/resources/activeloop-l-0-agentic-reasoning-on-your-multimodal-data/", "date": "May 04, 2025" }, { "title": "Introducing Deep Research for Your Multi-Modal Data", "url": "https://www.activeloop.ai/resources/introducing-deep-research-for-your-multi-modal-data/", "date": "Feb 10, 2025" }, { "title": "Releasing Deep Lake 4.0: The Fastest Multi-Modal AI Search on Data Lakes", "url": "https://www.activeloop.ai/resources/deep-lake-4-0-the-fastest-multi-modal-ai-search-on-data-lakes/", "date": "Oct 17, 2024" }, { "title": "Activeloop Named 2024 Gartner Cool Vendor in Data Management", "url": "https://www.activeloop.ai/resources/gartner-cool-vendor/", "date": "May 31, 2024" }, { "title": "Activeloop raises $11M to grow its specialized tensor database for AI training and inference - SiliconANGLE", "url": "https://siliconangle.com/2024/03/26/activeloop-raises-11m-grow-specialized-database-ai-training-inference/", "date": "Mar 26, 2024" } ], "launches": [ { "id": 102722, "title": "Hivemind: Continual Learning for Coding Agents", "tagline": "Capture every coding agent interaction across your team, train it into reusable skills, and propagate those skills to every agent in your org. On your cloud storage.", "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will reshape how engineering teams work with coding agents.\n\n**Hivemind is a continual learning layer for coding agents. It works across Claude Code, Codex, Cursor, OpenClaw, Hermes, and pi, and it stores all your data on your cloud.**\n\n\n\n### The Problem: Coding Agents Don't Learn\n\nEvery engineering team building with AI agents already knows the issues.\n\n* Your senior engineer's agent debugs a tricky bug Monday. Your junior engineer hits the same bug Tuesday and starts from zero.\n* Every session begins with re-explaining your codebase and your conventions.\n* Architectural decisions made in code review evaporate the moment the session ends.\n* Your team is solving the same problems over and over, paying for the same compute every time.\n\nCoding agents do not communicate with each other out of the box. Hivemind closes the gap. An agent figures something out, every agent inherits it. Your worst run starts from your best.\n\nThe tools tackling this treat it as a memory problem. They give each agent a personal notepad. Siloed. Invisible to the rest of the team. And most of them do it by shipping your code and context to their servers.\n\nBut memory without learning doesn't make agents better.\n\n**We decided to go beyond memory.**\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/51dc4451-7936-4e37-a0ad-e8139a646430)\n\n### Introducing Hivemind: Continual Learning for Coding Agents\n\nHivemind runs on a simple chain: **capture, codify, optimize, propagate.**\n\n**1. Trace capture.** Every agent interaction (prompts, tool calls, file reads, reasoning chains, outputs) is captured automatically as a structured trace.\n\n**2. Skill codification.** Repeated patterns across traces get codified into reusable skills. Mix of automatic detection and LLM-assisted extraction, with workspace-level scoping so skills don't leak between teams.\n\n**3. Skill optimization.** This is new today. Skills get trained and optimized. We've implemented SkillOpt (a text-space optimizer out of Microsoft, Shanghai Jiao Tong, and Fudan) directly into Hivemind. It improves each skill the way you'd tune a model, keeping only the edits that prove out on a held-out test.\n\nThat approach lifts agent accuracy by +19.1 points inside Claude Code and +24.8 inside Codex, and wins or ties on all 52 setups tested. It runs offline, so it adds zero cost at inference. Your skills get sharper over time instead of bloating.\n\n**4. Skill propagation.** Optimized skills flow into every agent's context at inference time, across every Hivemind-connected agent in your workspace.\n\nWorks across:\n\n* Claude Code\n* Codex\n* Cursor\n* OpenClaw\n* Hermes\n* pi\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/e1bddec6-5c2c-4409-aa2a-dd28fd8b2405)\n\n### On Your Cloud\n\nThis is the part our customers care about most. Your traces, your skills, your cloud storage. The other tools in this space treat your codebase as their training data. We don't. Hivemind store data on your cloud storage bucket, which is the only way a continual learning layer should ever touch your source.\n\n### **Codebase knowledge graph:**\n\n* Hivemind builds a graph of your codebase\n* Agents reason over how files, functions, patterns, and prior fixes connect\n* Structure beats keyword search: agents retrieve relevant context instead of grepping blindly\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/414a60d3-f2b1-4da0-8bec-236e44f16042)\n\n### Example\n\n> Your senior engineer's agent figures out a tricky migration pattern in your payment service on Monday.\n>\n> Tuesday, your junior engineer's agent hits the same migration shape in a related service. The skill has already crystallized, and SkillOpt has already sharpened it against the cases where the first version fell down. The agent executes it in one shot.\n\nRepeat work stops being repeat work.\n\n### The Results: 25% Lower Cost, 41% Fewer Tokens\n\nWe benchmarked Hivemind against memory tools using LoCoMo, the standard evaluation for long-horizon agent memory.\n\nHivemind matches Mem0 on accuracy.\n\n* **25% lower cost per 100 QA**\n* **41% fewer output tokens**\n* **31% fewer agent turns**\n\nSingle-agent benchmarks understate the real story. The compounding effect of org-wide skill propagation isn't captured in these numbers. In production, the gap widens every week as your trace library grows.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/3df657fa-e32b-4329-a667-d055d39d93be)\n\nFull methodology: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\n### Why This Matters\n\nMost developer tools deliver linear value. Hivemind compounds.\n\n* **Week 1:** your agents stop repeating mistakes.\n* **Month 1:** they're learning from each other.\n* **Quarter 1:** your whole engineering org is operating with capability that survives team changes, onboarding cycles, and turnover.\n\nYour team's hard-won knowledge accumulates.\n\n![uploaded image](/media/?type=post&id=102722&key=user_uploads/78090/6dfbd341-418b-4bd6-bd63-0d191625378c)\n\n### What's Next: From Skills to Custom Models\n\nBecause we store trajectories in Deeplake's tensor format, traces are ready as PyTorch datasets. A handful of advanced customers are already fine-tuning their own open-source models on the trajectories their Claude and Codex agents generated last week.\n\nSame data layer. Two paths to continual learning.\n\nFull tutorial and public examples shipping in the coming weeks.\n\n### Get Started\n\nTry Hivemind: [deeplake.ai/hivemind](http://deeplake.ai/hivemind)\n\nGitHub: [github.com/activeloopai/hivemind](http://github.com/activeloopai/hivemind)\n\nSkillOpt paper: [arxiv.org/pdf/2605.23904](http://arxiv.org/pdf/2605.23904)\n\nThree commands and your team's agents share one brain by end of day.", "url": "https://www.ycombinator.com/launches/Qio-hivemind-continual-learning-for-coding-agents", "votes": 9, "created_at": "2026-06-08T23:07:52.598Z" }, { "id": 95919, "title": "Activeloop Scientific Discover: The AI Analyst for Scientific Research", "tagline": "Search, reason, and discover across 450M+ pages and 175TB of scientific research papers. Not just text, but charts, molecules, formulas, and tables.", "body": "Hey 👋 Davit here, founder of Activeloop.\n\nToday we are introducing something we believe will fundamentally reshape how scientific discovery happens.\n\n**Activeloop’s Scientific Discover, an intelligence agent built on one of the largest datasets of indexed scientific research. Here are the details:**\n\nhttps://www.youtube.com/watch?v=x8Lv5-C9ntw\n\n## The Problem: Scientific Data Is Broken\n\nEvery scientist already knows the pain.\n\n* Critical insights are buried across PDFs, images, tables, and supplementary files.\n* Key figures live inside screenshots, not structured databases.\n* Twenty years of research is trapped in incompatible formats and outdated systems.\n* Every new project begins with weeks of manual data wrangling.\n\nThe White House recently launched the Genesis Mission, recognizing that fragmented scientific data is one of the biggest blockers to breakthroughs in drug discovery, materials science, climate modeling, and more.\n\nThe Genesis Mission aims to build an integrated AI platform that harnesses Federal scientific datasets. The goal is to train scientific foundation models and create AI agents that test hypotheses, automate research workflows, and accelerate scientific breakthroughs.\n\nThe country is trying to unify scientific data for AI.\n\n**We decided to show what that future looks like.**\n\n## Introducing L1: The Scientific Data Agent\n\nTo demonstrate what becomes possible when scientific data is indexed natively for AI, we built **Activeloop L1**, a multimodal scientific research agent running on top of:\n\n* 25M open access papers\n* 450M+ plus pages\n* 175TB of fully visual indexed scientific data\n\nUnlike traditional NLP or paper search, **L1 sees science**.\n\nIt understands:\n\n* charts\n* molecules\n* protein structures\n* equations\n* experimental tables\n* clinical graphs\n* and the text that surrounds them\n\nThis allows L1 to answer questions no text only model can touch.\n\n## The Results: A New State of the Art **48% SOTA on Humanity’s Last Exam with Tools**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/453774e7-7cc6-47de-a344-76a9f7ca2460)\n\nL1 (Gemini 3 Pro) outperforms top models including Grok Heavy with tools, and GPT 5 Pro with tools.\n\n### **Multimodal scientific reasoning**\n\nL1 synthesizes biochemical structures with clinical findings to answer end-to end research questions.\n\n### Example Query\n\n> Which compounds show novel synergy with metformin for treating type 2 diabetes?\n\nL1 scans molecular structures, clinical outcomes, and experimental tables at the same time and produces grounded, citation linked answers.\n\n## Why This Matters\n\nMultimodal scientific agents unlock capabilities that were not possible before.\n\nThis extends far beyond drug discovery. It applies to materials science, climate research, algorithmic innovation, and more.\n\nThe applications are enormous.\n\n**Try L1 Today with Our OpenAI Compatible API**\n\n![uploaded image](/media/?type=post&id=95919&key=user_uploads/78090/f45683d6-c74b-4994-88e7-d54bda796ba0)\n\n## Get Started\n\nTry the Science Agent: https://chat.activeloop.ai/science\n\nDocs and API: https://docs.activeloop.ai\n\nBuild with the OpenAI compatible API today.", "url": "https://www.ycombinator.com/launches/Ox5-activeloop-scientific-discover-the-ai-analyst-for-scientific-research", "votes": 29, "created_at": "2025-12-02T15:44:42.819Z" }, { "id": 94768, "title": "Activeloop - Unlock AI Data Analysis for GTM", "tagline": "Less Time in CRM, More Sales. Get instant insights across your entire stack from CRMs/ERPs to sales decks and product analytics. ", "body": "Hey 👋! Founder of Activeloop here. I am excited to introduce Activeloop AI Data Analysis for GTM operations. It lets you drill down from an executive dashboard to a single data point with just-in-time intelligence.\n\nWe didn’t built yet another dashboarding tool. It’s a solution to the single biggest bottleneck holding back your commercial strategy. The endless cycle of manual data preparation, integration, and reconciliation.\n\nhttps://www.youtube.com/watch?v=4Ua3Llox2HE\n\n### The Problem: You’re Drowning in Data Chaos\n\nThe story is always the same:\n\n* **Analysts as Data Janitors:** Your most valuable analysts are buried in manual work, stitching together data from multiple CRMs, reconciling Salesforce with SAP, and cleaning up messy, inconsistent fields.\n* **Reporting Backlogs:** A simple request for a new report kicks off a multi-week process. By the time it’s ready, the moment has passed and leadership doesn't trust the numbers anyway.\n* **Sales Reps Suffer:** Reps waste 4-6 hours every week just hunting for information and doing administrative work instead of selling.\n\nYou're tasked with driving strategy, but you're stuck managing data chaos.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/e282c1b1-d361-4f5a-a4ac-3a721b21d96d)\n\n### Our Solution: The AI Analyst that Vibes Intelligence\n\nActiveloop is an AI Data Analyst for your operations team. We automate the painful, time-consuming data work that currently consumes 70%+ of your team's time.\n\nInstead of fighting with brittle integrations, we connect your fragmented sources such as CRMs, ERPs, product analytics, databases, data warehouses, even unstructured contracts and call transcripts, into a unified, queryable resource.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/342debf4-c4fb-4a02-8480-ea5b128e26c9)\n\n### From Weeks to Minutes: Deliver “Just-in-Time” Intelligence\n\nActiveloop eliminates the wait, empowering your team with true self-service analytics.\n\n* **For Leadership:** In a forecast meeting, infinitely drill down into the data with simple questions like, \"Why did this region's deal cycle lengthen last quarter?\" Get immediate answers, without ever having to say, \"We'll get back to you.\"\n* **For Operations:** Free your analysts from being \"report builders\" and empower them to be strategists. They can finally focus on high-impact projects, confident that the underlying data is unified and trusted.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/f5a7e592-3655-494e-a620-f92e99dc74e9)\n\n### We Tackle Your Messy Data Head-On\n\nYou’re probably thinking: \"How can AI possibly navigate our messy data? We have 50 different fields for the same metric.\"\n\nYou're right. That's the biggest blocker for any data initiative. That’s why our platform includes an **AI-assisted data mapping and taxonomy management layer**. It helps you harmonize your complex environment to build a clean data foundation that generates insights everyone trusts.\n\n![uploaded image](/media/?type=post&id=94768&key=user_uploads/78090/22c38e19-0d94-49cb-9485-e5de80ce8e3d)\n\n### Let's Solve Data Chaos Together\n\nIf you’re tired of seeing your team buried in manual data work, we want to talk to you.\n\n**Book a Call:** https://activeloop.ai/sales/\n\n\\\n**Get Started Now:** https://chat.activeloop.ai", "url": "https://www.ycombinator.com/launches/OeW-activeloop-unlock-ai-data-analysis-for-gtm", "votes": 107, "created_at": "2025-10-20T15:41:53.206Z" }, { "id": 90294, "title": "Activeloop L0: State-of-the-Art RAG Accuracy on Your Data", "tagline": "Turn PDFs, images & tables into instant, cited answers. Agentic RAG that just works.", "body": "Hey! Davit here form Activeloop. While working on [Deep Lake](https://github.com/activeloopai/deeplake), I have seen many RAG systems collapse when exposed to production-scale corporate data. They often rely on predefined loops, custom logic and rigid agent scaffolds. Activeloop-L0 provides your agent with highly precise and answers grounded on your multimodal data.\n\nhttps://youtu.be/pnU9yd4KWy0\n\n**Why can’t we reliably analyze corporate documents?**\n\n* **Architectural hurdles**: messy data integrations, unexpected infra costs, and reliability/safety constraints.\n* **Commodity RAG** lacks depth for multimodal enterprise data (documents, images, audio).\n* **Infrastructure burden**: parsing, chunking, embeddings, indexing, vector DBs, and agent loops slow teams.\n\n**But wait, is RAG still relevant despite large context models?**\n\nLet’s consider four extensive NASA documents \\[[1](https://www.nasa.gov/wp-content/uploads/2022/03/sls-reference-guide-2022-v2-508-0.pdf), [2](https://www.nasa.gov/wp-content/uploads/2023/02/orion-reference-guide-111022.pdf), [3](https://www.lpi.usra.edu/lunar/artemis/Artemis-I-Reference-Guide_NP-2022-03-3045-HQ.pdf), [4](https://www.ulalaunch.com/docs/default-source/rockets/2023_vulcan_user_guide.pdf)\\], each between 80 to 100 pages, containing visual descriptions, and pose a highly complex question.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/c7a09db6-f7f7-46a4-a347-5b52154e6fc1)\n\nChatGPT with o3, despite having full PDFs in context, failed after 11 minutes of reasoning. Now, imagine you have thousands of corporate documents that can’t be contained in a context. In contrast, Activeloop-L0 provided the correct answer in 4 minutes and can scale to a million documents.\n\n**What is Activeloop-L0?**\n\n**Activeloop-L0** is a compound AI system that ingests your unstructured data and returns grounded answers. Behind the scenes, [Deep Lake](https://github.com/activeloopai/deeplake) indexes neural representations at scale, then fuses “thinking tokens” with high-precision retrieval for fast multi-hop reasoning.\n\nIt is available on [**chat.activeloop.ai**](http://chat.activeloop.ai) now.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/64161f83-f7ba-46eb-9445-5688f55506ab)\n\n**How is it different compared to a traditional RAG?**\n\n* **Multimodal:** Built-in support for images, PDFs, audio, and spreadsheets.\n* **Integrated Reasoning & Retrieval:** Eliminates the need for loops.\n* **Deep Indexing:** Cost-effective multi-layer indexing for richer context early on.\n* **Simple:** Focus on innovation, not maintaining infrastructure.\n* **Grounded and Accurate:** Clear citations for trustworthy insights.\n\n**How accurate is Activeloop-L0?**\n\nActiveloop-L0 achieves overall 85.6% state-of-the-art accuracy on 1,142 multimodal questions (292 PDFs, 5.5K pages). It outperforms text only RAG by +20%, visual RAG by +10%, and Alibaba’s ViDoRAG by +6% on their own ViDoSeek benchmark.\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/8275782e-ffde-4351-bdc8-0d1c40918169)\n\n**Is there an OpenAI-compliant API?**\n\nYes, Activeloop-L0 is available with an OpenAI-compliant API. You can easily plug into your agents for providing high relevant context. You can get started here. https://docs.activeloop.ai/setup/quickstart\n\n![uploaded image](/media/?type=post&id=90294&key=user_uploads/78090/996c0193-4dab-49eb-ab85-6e78828b19ec)\n\n**Ready to Deploy on Your Data?**\n\nActiveloop is trusted by F500 including the likes of [**Bayer**](https://www.activeloop.ai/usecase/bayer/), [**Flagship Pioneering**](https://www.activeloop.ai/usecase/flagshippioneering/), [Matterport](https://www.activeloop.ai/usecase/matterport/) (W12 acquired by CoStar).\n\n* **Your Cloud**: Deploy on your cloud, ensuring data never leaves your infrastructure.\n* **Your Models**: Integrate your LLMs.\n* **Your Security**: SOC2 compliance, fine-grained access control, and SSO.\n\n[**Book a call**](https://www.activeloop.ai/contact/) to discuss enterprise deployment.", "url": "https://www.ycombinator.com/launches/NUM-activeloop-l0-state-of-the-art-rag-accuracy-on-your-data", "votes": 34, "created_at": "2025-05-12T16:26:50.543Z" } ] }, { "id": 11895, "name": "Overview", "slug": "overview", "former_names": [ "Overview.ai" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/cd1326e4f02e779f1009ae931cfeb26eb1eb20b7.png", "website": "https://overview.ai", "all_locations": "San Francisco, CA, USA", "long_description": "Here’s a secret between you and me: even the world’s largest manufacturers, companies like Tesla and Toyota, waste billions of dollars every year making products with quality issues. 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With over 8 years of experience in manufacturing, he works with customers to ensure that customers can quickly adopt and use artificial intelligence-based computer vision systems.\n\nPreviously, Austin spent four years at Tesla in manufacturing and R&D.\n\nAustin holds BS Degrees in Manufacturing Engineering and Mechanical Engineering from Northwestern University.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/austin-appel-aa822b66/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/1e36e2fb2f960866341c37d8d0964d74e14d8233.jpg" }, { "user_id": 247841, "full_name": "Russell Nibbelink", "title": "Founder", "founder_bio": "I am the head of engineering and a co-founder at Overview. \n\nBefore joining Overview, I was a software engineer at Salesforce, where I worked on web-scale infrastructure projects, including the migration of Salesforce’s largest customers to upgraded hardware with no downtime.\n\nI studied EECS at UC Berkeley, where I was also the head teaching assistant in the field of High Performance Computing.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/russellnibbelink/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fa0586294cc7538ca638b3c8d3ef42d0a9b93813.jpg" }, { "user_id": 285339, "full_name": "Christopher Van Dyke", "title": "Founder", "founder_bio": "After 8 years at Tesla I founded Overview. 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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. AI developers resort to digging through a pile of hundreds to hundreds of millions of datapoints of unstructured text to understand how users interact with their product.\n\n### **The Solution:**\n\n[**Tidepool**](https://www.tidepool.so/) is a product analytics platform that solves these problems using **neural network embeddings**. 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": 22661, "name": "Quadrant Eye", "slug": "quadrant-eye", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/1f289b64a1313edc9a63d4a24460c74fe83cb97d.png", "website": "https://quadranteye.com", "all_locations": "San Francisco, CA, USA", "long_description": "Quadrant Eye is on a mission to improve eyecare access and fight preventable blindness. 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Previously engineer #6 at a16z backed UnitedMasters and a computational biologist with publications in data mining, computer science, & biology. ", "is_active": false, "linkedin_url": "https://www.linkedin.com/in/kristineyoshihara/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/eb24f125114938b4bf5c2be4ed82e3b7ce4a418a.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 22743, "name": "Segments.ai", "slug": "segments-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ac88c08aefaf0753bc9d29d8d7ced656148b852c.png", "website": "https://segments.ai", "all_locations": "Brussels, Brussels, Belgium; Remote", "long_description": "[Segments.ai](http://segments.ai/) is helping robotics and automotive companies label their multi-sensor data for AI training and validation. 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What were the big inflection points?", "answer": "In 2020, [Segments.ai](http://Segments.ai) launched as a data labeling solution designed explicitly for segmentation. Initially, we had many customers in the robotics and autonomous driving industries. This stimulated us to address the complex needs of autonomous navigation, leading us to introduce point cloud support.Through close collaboration with our customers, we quickly realized that manually labeling multiple modalities can be time-consuming, costly, and ineffective. Therefore, we shifted our focus towards providing multi-sensor support by integrating built-in projection steps and automation.HISTORY* Founded in early 2020 by Otto Debals (CEO) and Bert De Brabandere (CTO) after finishing their PhDs and gaining experience in the (automotive) industry* Funded in 2021 by YCombinator and other VC funds, including Merus Capital, Volta Ventures.* [Segments.ai](http://Segments.ai) is ISO27001 certified, GDPR compliant and EU based* We hired our first employee in 2021 and our first employee in the USA (where the majority of our customers are based) in 2023." }, { "question": "What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?", "answer": "Automotive and robotics companies increasingly use lidar, radar, and camera sensors during labeling and deployment. The more sensors they use, the more accurate and robust their ML models become.[Segments.ai](http://Segments.ai) is designed to address the challenges faced by ML engineers. According to a recent study, more than 70% of ML engineers spend over 50% of their time building training data. Using additional sensors can further increase the budget and time for development and training. With [Segments.ai](http://Segments.ai), you streamline this process, saving both time and resources.[Segments.ai](http://Segments.ai) is committed to providing the best solutions for computer vision engineers and data scientists working in autonomous robotics. 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We founded Anima out of a very personal problem. Again and again, we saw people dying because they got bad care plans, weeks or months late. \r\n\r\nWe build Care Enablement for care teams - combining online consultation with productivity tools in a realtime multiplayer dashboard. 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Previously Head of Innovation Finance & Entrepreneurship for UK Treasury. \n", "is_active": true, "linkedin_url": "https://uk.linkedin.com/in/rachel-mumford-83102272", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c6d830be1f3d6315c9c090b6eeafab31d0898da5.jpg" } ], "jobs": [ { "id": 45746, "title": "Staff Software Engineer", "url": "https://www.ycombinator.com/companies/anima/jobs/NQWCV4a-staff-software-engineer", "location": "Remote", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "£80 - £150 GBP", "equity_range": "", "experience": "3+ years", "visa": "Will sponsor", "skills": [ "JavaScript", "Node.js", "TypeScript" ] }, { "id": 49042, "title": "Design Engineer", "url": "https://www.ycombinator.com/companies/anima/jobs/yhwZten-design-engineer", "location": "Remote", "type": "Full-time", "role": "Design", "role_type": "UI / UX", "salary_range": "", "equity_range": "", "experience": "1+ years", "visa": "Will sponsor", "skills": [ "Figma" ] }, { "id": 61544, "title": "Account Executive / Manager (Fullstack Sales)", "url": "https://www.ycombinator.com/companies/anima/jobs/KiHHqcN-account-executive-manager-fullstack-sales", "location": "GB / Remote (GB)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "", "equity_range": "", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 61545, "title": "Implementation & Customer Success", "url": "https://www.ycombinator.com/companies/anima/jobs/7ZP8eZ4-implementation-customer-success", "location": "GB / Remote (GB)", "type": "Full-time", "role": "Support", "role_type": null, "salary_range": "", "equity_range": "", "experience": "1+ years", "visa": "US citizenship/visa not required", "skills": [] }, { "id": 61546, "title": "Clinical Engineer", "url": "https://www.ycombinator.com/companies/anima/jobs/dwSuRDL-clinical-engineer", "location": "Remote", "type": "Full-time", "role": "Engineering", "role_type": "Full stack", "salary_range": "", "equity_range": "", "experience": "1+ years", "visa": "Will sponsor", "skills": [ "JavaScript", "Node.js", "TypeScript" ] }, { "id": 68096, "title": "Staff QA Engineer", "url": "https://www.ycombinator.com/companies/anima/jobs/wQ0anQ1-staff-qa-engineer", "location": "Remote", "type": "Full-time", "role": "Engineering", "role_type": "QA engineer", "salary_range": "£100 - £250 GBP", "equity_range": "", "experience": "6+ years", "visa": "Will sponsor", "skills": [ "JavaScript" ] } ], "news": [ { "title": "Healthcare platform Anima brings Salesforce-like capabilities to clinics, raises $12M | TechCrunch", "url": "https://tcrn.ch/4a1QNyU", "date": "Mar 20, 2024" }, { "title": "Y Combinator alum Anima secures €11M for next-gen clinical operating system — TFN", "url": "https://techfundingnews.com/y-combinator-alum-anima-secures-e11m-for-next-gen-clinical-operating-system/", "date": "Mar 19, 2024" }, { "title": "Anima secures $12M in Series A led by Molten Ventures - Tech.eu", "url": "https://tech.eu/2024/03/19/anima-secures-12-million-in-series-a-funding-led-by-molten-ventures/", "date": "Mar 19, 2024" }, { "title": "Exclusive: Anima Targets Better Health Triage With $2.5M Seed – Crunchbase News", "url": "https://news.crunchbase.com/news/exclusive-anima-targets-better-health-triage-with-2-5m-seed/", "date": "Jan 28, 2022" } ], "launches": [] }, { "id": 23190, "name": "Biodock", "slug": "biodock", "former_names": [ "Biodock AI" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ef18ff7a47b6ccba783538bb34567deafd6e5a0f.png", "website": "https://www.biodock.ai", "all_locations": "Austin, TX, USA", "long_description": "Biodock's cloud platform accelerates microscopy analysis, automating months of microscopy analysis and infrastructure to minutes with our end-to-end AI architecture. 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My research focus was on stem cell biology and embryo development. I have experienced firsthand the problem we are solving with Biodock while doing my Ph.D. Previously, I also co-founded a med-tech company SmartLens but decided to focus on my Ph.D. I am also a history nerd and love visiting historical places. 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Our AI leverages standard CCTV cameras and learns how a manufacturing process looks like when things are going well and can automatically detect and track problems in real-time.\r\n\r\nFor example, one of our customers, a Pepsi supplier producing 500 bottles per minute now automatically detects and reacts to a fallen bottle before it starts blocking their production line. 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Karim grew up in Egypt where he was a professional athlete and captain of Egypt's Water Polo national team. He represented his country at three world championships while simultaneously receiving an electrical engineering degree from ETH Zurich. Prior to founding Cerrion Karim has built Unicorn Labs which has helped 30+ startups build their MVPs. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/karim-saleh99/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/cb42488af359b4f1c8fea2fff34c6605bde806f2.jpg" }, { "user_id": 140429, "full_name": "Nikolay Kobyshev", "title": "Founder", "founder_bio": "Nikolay is the co-founder and CPO of Cerrion. He is in charge of building a product that Cerrion's users will truly love, and customer success.\nHe is a serial entrepreneur in the AI and CV space. Prior to Cerrion, Nikolay has built Spectando, an AI engine which generates photorealistic 3D models of real estate objects, and Assaia, a B2B Computer Vision solution which detects irregularities in airports’ ground operations.\nNikolay holds PhD and MSc degrees in Computer Vision from ETH Zurich.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/nkobyshev/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/70bc945d7f0dadb179d9b7b52d4fa153e09efada.jpg" } ], "jobs": [], "news": [ { "title": "Cerrion raises $18M for factory intelligence platform", "url": "https://www.axios.com/pro/supply-chain-deals/2025/11/25/cerrion-raises-18m-for-factory-intelligence-platform", "date": "Nov 25, 2025" }, { "title": "Y Combinator-backed Cerrion raises $5 million in funding to boost AI Video tech in manufacturing - Tech.eu", "url": "https://tech.eu/2024/09/11/y-combinator-backed-cerrion-raises-5-million-in-funding-to-boost-ai-video-tech-in-manufacturing/", "date": "Sep 11, 2024" } ], "launches": [ { "id": 65196, "title": "Cerrion ⚡️ Computer Vision to automatically detect problems on production lines", "tagline": "Cerrion helps manufacturers automatically detect problems on their production lines using Computer Vision.", "body": "Hi everyone 👋 we are Karim, Nikolay and Michael, co-founders of [Cerrion](https://cerrion.com).\\\n\\\nWe help production teams understand when, where and why losses are happening on their production lines. Enabling them to act on problems faster, implement targeted improvement actions and understand the root cause of problems in a matter of minutes.\n\n**The problem: Production teams today struggle to track and eliminate deviations on their production lines**\n\nLack of transparency leads to around $1 trillion in annual preventable losses for the global manufacturing industry. Production teams today struggle to understand when losses on their production lines will happen and why they happen. To stay on top of your production a lot of trained and experienced eyes are needed. Given that today there is a 40% labor shortage in the US manufacturing industry alone, it is now more important than ever to help the existing workforce eliminate deviations on their production lines\n\n**The solution: Automatically monitor production lines for deviations**\n\nOur plug and play visual AI learns how a manufacturing process looks when things are going well and can automatically detect and track problems in real-time. This:\n\n\\- Eliminates the need for human monitoring;\\\n\\-Allows to detect subtle issues on the production line  before they become big;\\\n\\-Facilitates root cause analysis remotely and in a matter of minutes.\n\n![uploaded image](/media/?type=post&id=65196&key=user_uploads/1001364/919f2fc1-c895-4acd-ad72-16c22c3d17fc)\n\nOur AI in action ^\n\n**Can I deploy Cerrion in my production?**\\\nYes! Cerrion is already deployed in 4 countries and we are expanding. You can reach me at [karim@cerrion.com](mailto:karim@cerrion.com) to make it happen.", "url": "https://www.ycombinator.com/launches/GxY-cerrion-computer-vision-to-automatically-detect-problems-on-production-lines", "votes": 232, "created_at": "2022-08-08T12:43:31.235Z" } ] }, { "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": 28812, "name": "Cedana", "slug": "cedana", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/765d8a1a78a1c5939f354a26be87d3c1c1696290.png", "website": "https://cedana.ai", "all_locations": "New York City, NY, USA; Remote", "long_description": "Cedana (YC S23) brings hyperscaler and frontier-lab orchestration capabilities for AI workflows. Our core capability is live migration for CPUs and GPUs workloads. This increases cost savings up to 80%, accelerates time to first token 2-10x, and enables stateful reliability of training jobs even through catastrophic GPU failures. We've integrated our solution into K8s, and support Kueue and Slurm for training distributed jobs, and Kserve for serving inference.   \r\n\r\nOpenAI, Meta and Microsoft have flavors of these capabilities internally and we’re bringing them to everyone. \r\n\r\nOur vision is to transform cloud compute into a real-time, arbitraged commodity. \r\n\r\nhttps://www.cedana.ai", "one_liner": "Fast, reliable, reproducible AI with GPU live migration", "team_size": 5, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1689189501, "tags": [ "Deep Learning", "Developer Tools", "Cloud Computing", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United States of America", "America / Canada", "Remote", "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/cedana", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/cedana.json", "year_founded": 2023, "city": "New York City", "country": "US", "linkedin_url": "https://www.linkedin.com/company/cedanacorp/", "x_url": "https://twitter.com/cedana_ai", "fb_url": null, "cb_url": null, "github_url": "https://github.com/cedana", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/765d8a1a78a1c5939f354a26be87d3c1c1696290.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": 77597, "full_name": "Neel Master", "title": "Founder", "founder_bio": "CEO of Cedana. Previously CEO/co-founder of Engooden, AI-powered chronic disease management proven to improves outcomes and lower costs for patients (Series B). VP of Corp Development for Petra Systems (predictive smart grid/solar company) scaled from $0-$70M ARR. At TL Ventures ($1.6B VC fund) investing across semi, software and systems. Built a system for large-scale, automated ML and computer vision at MIT CSAIL. Patents and publications in AI, computer vision.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/neelmaster1", "x_url": "https://twitter.com/neelmaster", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/d563c8b75caf8e9bc463baf0be90836b8802c73f.jpg" }, { "user_id": 297703, "full_name": "Niranjan Ravichandra", "title": "Founder", "founder_bio": "Co-founder/CTO at Cedana. Previously Robotics at Shopify. Previously previously Mech/Aero, published in control systems, neuromorphic computing and satellite formation flight. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/niranjanravichandra", "x_url": "https://x.com/0xnravic", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/537093808141633a595f61b05e15face61dc9b9c.jpg" } ], "jobs": [ { "id": 95527, "title": "Forward Deployed Engineer: AI + HPC", "url": "https://www.ycombinator.com/companies/cedana/jobs/d1vYocG-forward-deployed-engineer-ai-hpc", "location": "US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$140K - $180K", "equity_range": "0.10% - 0.25%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] }, { "id": 95761, "title": "Systems Engineer: Linux Kernel Test and Validation", "url": "https://www.ycombinator.com/companies/cedana/jobs/BntWOIg-systems-engineer-linux-kernel-test-and-validation", "location": "US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Backend", "salary_range": "$140K - $180K", "equity_range": "0.10% - 0.30%", "experience": "6+ years", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 73681, "title": "Cedana - Real-time save, move and resume for compute", "tagline": "Solving critical GPU performance, reliability, capacity and cost problems", "body": "Hey, we’re Neel and Niranjan from [Cedana](https://cedana.ai/?utm_source=cedana&utm_medium=yc&utm_campaign=launch).\n\n![uploaded image](/media/?type=post&id=73681&key=user_uploads/77597/b1097aa9-66f2-4253-8243-63e63d5c23e9)\n\n### **The Problem**\n\nLosing work because of infra problems is painful. Imagine you have a long-running compute job and the instance fails. Your 20-hour job finished but because your pipeline was misconfigured you have to restart it from scratch.  \n\nBurning cash is stressful.  Poor utilization results in your inference jobs costing more.  If you’re managing a cluster of 1,000s of GPUs, poor utilization leaves money on the table even while demand is skyrocketing.    \n\nCold start times impact your customer satisfaction and their reliance on your solution.  \n\nLimited GPU access makes it difficult for you to innovate, and finding GPUs can be a full-time job of constantly identifying, evaluating, and adapting different vendors.    \n\n### **Our Solution**\n\nCedana is real-time migration for compute.  We automatically schedule and move workloads across instances and vendors without interrupting progress or breaking anything.\n\nThere are several critical benefits:\n\n* Utilization is maximized to save costs and eliminate idle resources.  \n* Job-level SLAs dynamically allocate compute between inference and training, prioritizing costs, latency, and performance based on preferences.  \n* Avoid re-running jobs from scratch due to infra, pipeline, or memory failures. Jobs continue their progress in the event of infra failure or spot revocation.\n* Access planet-scale compute through vendor aggregation\n* Solve the cold start problem by enabling fast auto-suspend-resume \n* Spot management that migrates and provisions new instances automatically upon revocation or failure.\n\n  \n\nOpenAI, Meta, Microsoft, and Databricks employ some of these methods internally and we’re bringing them to everyone.\n\n### **How it works**\n\n[Cedana](https://cedana.ai/?utm_source=cedana&utm_medium=yc&utm_campaign=launch) is available as an [open-source package](https://github.com/cedana/cedana-cli) and as a [managed service](https://www.ycombinator.com/launches/JAP-cedana-real-time-save-move-and-resume-for-compute). \n\n* Cedana needs no code modification and works with Linux processes or containers\n* Current use cases and customers range from AI Training and Inference, High-Performance Compute, DevTools, ML Ops platforms, and Computational Biology.\n* It automatically provisions and manages infra with your existing credentials. Our managed service can leverage our vendor relationships if preferred.\n\nHere’s a 1m30s [demo video](https://www.youtube.com/watch?v=KC4STzSQ_DU)\n\n### **Our Team**\n\nOur team has built real-world robotics and large-scale computer vision systems across a number of places including 6 River Systems/Shopify and MIT.  We’ve led the development, commercialization, and scale of NLP for clinical workflows used in the delivery of patient care.   Our team’s publications span computer vision, computer graphics, robotics optimization, and spacecraft/aerospace controls, with patents in AI use cases for grid energy management, optimal battery control, and healthcare. \n\n### **We kindly ask**\n\n* Give Cedana a try! [Check out our Github Repo](https://github.com/cedana/cedana-cli)\n* Please support us with a Github star", "url": "https://www.ycombinator.com/launches/JAP-cedana-real-time-save-move-and-resume-for-compute", "votes": 26, "created_at": "2023-08-01T22:03:04.520Z" } ] }, { "id": 28998, "name": "Automorphic", "slug": "automorphic", "former_names": [ "Sibyl" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/6632890863ba8f97b3c98f84ce71158a24887e9a.png", "website": "https://automorphic.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Automorphic has invented a way to infuse knowledge into LLMs via fine-tuning (surpassing context window limitations), enabling developers to rapidly iterate on and successively improve custom models cheaply and efficiently.", "one_liner": "Infuse knowledge into language models with just 10 samples", "team_size": 3, "industry": "B2B", "subindustry": "B2B", "launched_at": 1691588844, "tags": [ "AIOps", "Deep Learning", "Developer Tools", "Infrastructure", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2023", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/automorphic", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2023/automorphic.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/automorphic", "x_url": "https://twitter.com/AutomorphicAI", "fb_url": null, "cb_url": null, "github_url": "https://github.com/automorphic-ai", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/6632890863ba8f97b3c98f84ce71158a24887e9a.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Harj Taggar", "url": "https://www.ycombinator.com/people/harj-taggar" }, "founders": [ { "user_id": 1146368, "full_name": "Govind Gnanakumar", "title": "Founder", "founder_bio": "Often running away from robots (that I created). Building the next generation of artifical narrow intelligence.", "is_active": true, "linkedin_url": "https://linkedin.com/in/govind-gnanakumar", "x_url": "https://twitter.com/sandkoan", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/2f073adeb6c9ba6591e097d17f81f051d0ae71ff.jpg" }, { "user_id": 1381983, "full_name": "Maaher Gandhi", "title": "Founder", "founder_bio": "When not working on Automorphic, I'm building AI to beat me at chess", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/maaher-gandhi-31b40928b", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/38c64b0f499364e92826147408ed30c847ad9260.jpg" }, { "user_id": 1160097, "full_name": "Mahesh Natamai", "title": "Founder", "founder_bio": "Building AI agents that can remember and self-improve like we can at Automorphic.", "is_active": true, "linkedin_url": "https://linkedin.com/in/mahesh-natamai-b17683188", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c7ac55ddb274bbc274308cc40cf70b18aa906241.jpg" } ], "jobs": [], "news": [ { "title": "Computer Science Was Too Slow, so I Dropped Out to Focus on My AI Startup", "url": "https://www.businessinsider.com/computer-science-major-dropped-out-georgia-tech-build-ai-startup-2023-9", "date": "Sep 24, 2023" } ], "launches": [ { "id": 74582, "title": "Automorphic: Infuse knowledge into language models with just 10 samples", "tagline": "Rapidly build and improve custom LLMs", "body": "**TL;DR** - [Automorphic](https://automorphic.ai) enables developers to rapidly build and improve custom fine-tuned models. Using our LLM improvement platform, you can turn raw data into a secure, production-ready, self-improving LLM in minutes.\n\n—\n\nHey everyone! We’re Govind, Maaher, and Mahesh, from Automorphic. After experiencing the challenges of fine-tuning and refining language model systems, we’re on a mission to build an interactive LLM improvement platform that turns a tedious process into an addictive one.\n\n![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/14e3e547-96a5-4a74-91b1-3ad11af28680)\n\n### **❌ The Problem:**\n\nIt takes engineers at companies like Google and Meta 12 to 18 months to take a model from research to production – and this isn’t because they run one tuning job and call it a day.\n\nThey apply a series of successive improvements that may start by supervised fine-tuning, then aligning to human preferences, then distillation and pruning unnecessary weights, and then once it’s met a certain quality threshold, they don’t just say it’s good enough. They continuously improve it with RLHF, periodic re-tunings to control for data drift, and so on.\n\nUnfortunately, such an elaborate process is unrealistic for most companies—they can’t wait a year to get a custom LLM to production even if they have the engineering talent.\n\n### **⭐️ Our Solution**:\n\nAutomorphic makes it easy for developers to continuously improve their custom LLMs.\n\n1. Upload your raw text data\n2. Start an initial fine-tuning run with your data, and continue to fine-tune as needed\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/00398557-fca1-471a-9483-b5b75e506f0f)\n\n3. One line switch from the OpenAI API endpoint to ours\n4. Try inference on your model and improve it using RLHF\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/cc6a2fe8-2b30-4a72-ada2-eeb960f6afc2)\n\n5. Use additional data to train adapters and combine and commute them however you want\n6. Publish your custom models to the [hub](https://automorphic.ai/hub), and try out existing models there\n\n ![uploaded image](/media/?type=post&id=74582&key=user_uploads/1381983/fc5a26f7-f7aa-4984-bb8f-1e1fc8db427b)\n\n\nYou own the model weights, so you can train and run inference in your own cloud if you’d like.\n\n# **🙌** Our Ask:\n\nSign up for our [platform](https://automorphic.ai/signup) and train your first three models for free! We’d love feedback on how we can make your language model improvement experience better.\n\nIf there’s someone in your network like an ML engineer who works with LLMs, or folks looking to replace OpenAI / implement custom LLMs, we’d love to chat with them.\n\nHere’s a short blurb you can send them:\n\n> Automorphic, a YC-backed startup, enables developers to go from raw data to a continuously improving custom language model usable in production in minutes. They’d love to chat with ML engineers and companies looking to implement custom LLMs. Email them at [founders@automorphic.ai](mailto:founders@automorphic.ai), or book a meeting [here](http://calendly.com/automorphic)", "url": "https://www.ycombinator.com/launches/JOw-automorphic-infuse-knowledge-into-language-models-with-just-10-samples", "votes": 176, "created_at": "2023-09-01T21:50:34.474Z" } ] }, { "id": 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": 29412, "name": "Ligo Biosciences", "slug": "ligo-biosciences", "former_names": [ "Ligo", "Ligo Bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd381079ddb420940e9b57512009e6b5bc73e27a.png", "website": "https://www.ligo.bio", "all_locations": "San Francisco, CA, USA", "long_description": "We are building the next generation of deep-learning models for enzyme design to slash the cost of chemical manufacturing. The $6 trillion chemical industry is flawed: It produces 20% of industrial greenhouse gases, and is responsible for 15% of global energy usage. \r\n\r\nEnzymes offer a far more sustainable alternative to chemical synthesis and have already revolutionised how a select few chemicals are produced. The problem is each enzyme takes years of trial and error to develop. Our enzyme models learn the principles of catalysis, allowing us to design enzymes for each reaction, in days not years. \r\n", "one_liner": "Enzyme design models.", "team_size": 4, "industry": "Healthcare", "subindustry": "Healthcare -> Industrial Bio", "launched_at": 1723173017, "tags": [ "Deep Learning", "Synthetic Biology", "Biotech", "Climate", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2024", "status": "Active", "industries": [ "Healthcare", "Industrial Bio" ], "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/ligo-biosciences", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/ligo-biosciences.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ligo-biosciences/about/?viewAsMember=true", "x_url": "https://x.com/ArdaGoreci/status/1830744265007480934", "fb_url": null, "cb_url": null, "github_url": "https://github.com/Ligo-Biosciences/AlphaFold3", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/bd381079ddb420940e9b57512009e6b5bc73e27a.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": 1373049, "full_name": "Edward Harris", "title": "Founder", "founder_bio": "Deep learning for enzyme design!", "is_active": true, "linkedin_url": "https://linkedin.com/in/edward-harris-537837188", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/6d02ed151fb6828f9e341fd4a12eae59b64b4168.jpg" }, { "user_id": 1451202, "full_name": "Emily Egerton-Warburton", "title": "Founder", "founder_bio": "I'm a biochemist from Oxford University with experience in the intensive environment of biotech startups, working on everything from bacterial biofuel production to vaccine design. Now focused on designing enzymes to make the chemical industry more sustainable. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/emily-egerton-warburton-a70121211", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/4fd1ca272276b79309f71725f9ac77debe3b8fa4.jpg" }, { "user_id": 1447270, "full_name": "Arda Goreci", "title": "Founder/CTO", "founder_bio": "Founder at Ligo (S24). I studied Cell and Systems Biology at Oxford where I became a Google Cloud Research Innovator for my work in computational biology. Interested in deep learning, scaling laws, geometric DL, biomolecular design. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/arda-g-a26506118", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/f5e0fc43c349e97c738e48417637b35bb6172b26.jpg" } ], "jobs": [], "news": [ { "title": "Show HN: An open-source implementation of AlphaFold3 | Hacker News", "url": "https://news.ycombinator.com/item?id=41448439", "date": "Sep 20, 2024" } ], "launches": [ { "id": 83137, "title": "Ligo Biosciences - Generative enzyme design for the chemical industry", "tagline": "Next generation deep-learning enzyme models for cheaper and sustainable chemical manufacturing. ", "body": "**Tl;dr:**\n\n* [Ligo](https://www.ligo.bio/) is using deep learning to design **novel enzymes** to make **chemical manufacturing** cheaper and more sustainable.\n* The three founders met in a synbio lab at **Oxford University** where they decided to take on the **$6 trillion chemicals industry.**\n* Sign up for our [**waitlist**](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74) (Big news soon!)\n\n# **The Team:**\n\nHey - we are Ed, Emily, and Arda! We are building Ligo.\n\n![uploaded image](/media/?type=post&id=83137&key=user_uploads/1373049/1cde2313-c3fb-4cc4-a836-3dd991f69ae9)\n\n👨🏻‍⚕️CEO, [**Ed**](https://linkedin.com/in/edward-harris-537837188) — Ed studied **CS at Princeton** before transferring to **Oxford Medical School,** where he worked across three top synthetic biology labs. Ed bootstrapped his first startup at 19 in the food markets of Guadalajara, Mexico, and took it to **$1M in annual revenue.**\n\n👩‍🔬CSO, [**Emily**](https://www.linkedin.com/in/emily-egerton-warburton-a70121211/) — Emily is a top **biochemist** from **Oxford University** who honed her wet lab skills in the intensive environment of biotech startups. Working on everything from bacterial **biofuel** production to **vaccine design**, Emily is **happiest with a pipette in hand**!\n\n👨‍💻CTO, [**Arda**](https://linkedin.com/in/arda-g-a26506118) — Arda studied **cell and systems biology** at **Oxford,** where he became a **Google Cloud Research Innovator** for his work in computational biology. Arda’s obsession with deep learning for biomolecular design began when the original AlphaFold paper was released.\n\n---\n\n---\n\n---\n\n---\n\n---\n\n---\n\n## ❌ **The problem**:\n\n**(1) The $6 trillion chemicals industry is flawed:**\n\n* This industry produces **_20% of industrial greenhouse gases_** and is responsible for **_15% of global energy usage_.**\n* Traditional chemical manufacturing relies on hazardous materials and produces **_huge amounts of waste,_** resulting in **high costs.**\n* **You** unknowingly contribute to this problem by personally using **\\~160 of these chemicals daily.**\n\n**(2) Enzymes offer an amazing solution, but they are hard to develop**\n\n* Enzymes are biological catalysts. They accelerate reactions at mild temperatures and pressures, making chemical manufacturing **cheaper, faster, and more sustainable.**\n* **Major pharmaceutical companies** already use enzymes for manufacturing a limited number of drugs but developing them costs **tens of millions of dollars** and takes **multiple years.**\n* Current enzyme engineering approaches are **limited** because they must start from an enzyme that already exists.\n* No models currently understand the principles of catalysis, resulting in a narrow range of possible reactions that can be catalysed.\n\n## ✅ **Our Solution: Enzymes Designed From Scratch**\n\n* We are building **foundational enzyme design models** that learn from huge amounts of data to **understand the principles of catalysis.**\n* The model **generates structures capable of catalysing reactions** directly from transition state models, meaning we will **expand the number of reactions that can feasibly be accelerated** using enzymes.\n* These enzymes can catalyse reactions to **synthesise high-value chemicals** used in the pharmaceutical, agriculture, and consumer goods industries.\n\n[Our Diffusion Model](https://youtu.be/_V758AisYS8)\n\n# **Partnerships:**\n\nWe are happy to announce two key partnerships.\n\n* [**Basecamp Research**](https://www.basecamp-research.com) is on track to have **1000x more sequence diversity than public resources** - We are collaborating to use their data to improve our soon-to-be-released OpenSource model (sign up [here](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74)!).\n* [**Adaptyv Bio**](https://www.adaptyvbio.com) is building a next-generation protein foundry. Their engineers, Liza and Igor, are helping to build the **data pipeline for OpenSource model** using their state-of-the-art **bioinformatics tool, ProteinFlow.**\n\n# **We’d Love Your Help**❤️\n\n* Sign up for our [waitlist](https://foil-barometer-dc9.notion.site/Ligo-Biosciences-Technical-Waitlist-63a62e2b0f4a4b8dbaa31ce51b572d09?pvs=74) (Big news soon!)\n* We’d love intros to:\n * Small chemical companies in pharmaceuticals, fragrances, food&drink, detergents\n * Big Pharma and Agricultural chemical companies\n * **Any** **other biotech** companies! Email us [ed@ligo.bio](mailto:ed@ligo.bio)", "url": "https://www.ycombinator.com/launches/Lcv-ligo-biosciences-generative-enzyme-design-for-the-chemical-industry", "votes": 470, "created_at": "2024-08-16T21:11:49.384Z" } ] }, { "id": 29713, "name": "Anthrogen", "slug": "anthrogen", "former_names": [ "Arctic Capture" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fd834838e899b193bf63adbb6160e2a6faf66041.png", "website": "https://anthrogen.com", "all_locations": "San Francisco, CA, USA", "long_description": "Anthrogen is engineering post-modality biology.\r\n\r\nToday's modalities reflect historical contingencies in biological progress, not fundamental categories. We develop the AI systems that design modular biological machines and the experimental infrastructure to instantiate them.", "one_liner": "We work on generalizable tools for the next era of biotech. ", "team_size": 15, "industry": "Healthcare", "subindustry": "Healthcare", "launched_at": 1717790065, "tags": [ "Deep Learning", "Biotech", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": true, "nonprofit": false, "batch": "Summer 2024", "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/anthrogen", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2024/anthrogen.json", "year_founded": 2024, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/anthrogen/", "x_url": "https://x.com/AnthrogenBio", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/fd834838e899b193bf63adbb6160e2a6faf66041.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": 1462181, "full_name": "Ankit Singhal", "title": "Founder/CEO", "founder_bio": "Ankit is the CEO of Anthrogen. He was a Science Research Fellow and Named Scholar at Columbia (a designation reserved for the top ~10 STEM research students in each class). He has worked in both wet labs and computational labs (focusing on catalysis and structural biology/biophysics) for years. He published several first-author papers as early as high school, won and led national teams at international science/research competitions – he’s excited to lead Anthrogen’s damp lab approach.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ankit-singhal-columbia/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/29c9cbc7fa6612f3134bb083b2cb3461b03b8237.jpg" }, { "user_id": 1519501, "full_name": "Connor Lee", "title": "Founder/CTO", "founder_bio": "Connor is the CTO of Anthrogen. Before dropping out of Columbia as a sophomore, he was a researcher in Columbia's ROAM Lab and was the youngest ever Columbia Robotics president. He also placed 3rd Internationally for FRC and top 5 internationally for MATE ROV after over a decade of robotics experience.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/connor-lee-692a40263", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/53b3f0886ebc2aa7c3b1f2b0eb6170551df2ff6d.jpg" } ], "jobs": [ { "id": 83750, "title": "Member of Technical Staff (applied)", "url": "https://www.ycombinator.com/companies/anthrogen/jobs/xwfuwJL-member-of-technical-staff-applied", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$120K - $220K", "equity_range": "0.10% - 0.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 92760, "title": "Robotics Engineer", "url": "https://www.ycombinator.com/companies/anthrogen/jobs/M5BjQOQ-robotics-engineer", "location": "San Francisco, CA, US", "type": "Full-time", "role": "Engineering", "role_type": "Robotics", "salary_range": "$140K - $220K", "equity_range": "0.10% - 0.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [ "Arduino", "C", "Git", "Reinforcement learning (RL)" ] } ], "news": [], "launches": [] }, { "id": 30077, "name": "AutoComputer", "slug": "autocomputer", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/5a60f5ba14460a149ec6587f93611a37f06bcb11.png", "website": "https://www.autocomputer.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "We provide data research and post-training research services to help companies turn their traces into environments for evaluation and post-training.", "one_liner": "Forward Deployed Research Services", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1731450144, "tags": [ "Deep Learning", "B2B", "Enterprise", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Fall 2024", "status": "Active", "industries": [ "B2B" ], "regions": [ "United States of America", "America / Canada" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/autocomputer", "api": "https://devasheeshg.github.io/yc-api/batches/fall-2024/autocomputer.json", "year_founded": 2024, "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/5a60f5ba14460a149ec6587f93611a37f06bcb11.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/afa95922f3049294dfc4cc148547a093162d9bf2.png" ], "primary_partner": { "name": "Diana Hu", "url": "https://www.ycombinator.com/people/diana-hu" }, "founders": [ { "user_id": 331625, "full_name": "Madhav Singhal", "title": "Founder/CEO", "founder_bio": "Researcher and engineer.\n\nBefore this, I worked at Replit and was one of the first engineers on its AI team where I led research and training for all of Replit’s code models, and worked on data infrastructure, Replit’s core AI services, and Replit Agent. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ms337/", "x_url": "https://twitter.com/madhavsinghal_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9866cbe17c1919fe22e93f4ffed436e2f35b2eea.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 85524, "title": "AutoComputer - Zero setup AI automation for your computer", "tagline": "AI automation for your computer that predicts the next steps in your workflow, completing them with a single keystroke", "body": "AutoComputer helps you automate and accelerate workflows on your computer without any explicit configuration, while keeping you in-charge.\n\nWith just a prompt and your recent activity, our desktop AI system starts predicting the next sequence of actions – keystrokes, text input, mouse clicks – that you can keep accepting with a single keystroke. This saves you time and effort while providing you complete visibility and control over the completed tasks.\n\nWe’re starting with helping businesses and enterprises automate repetitive, but business-critical workflows such as financial data entry, record keeping, and transaction matching.\n\nWatch our demo video below to see AutoComputer in action!\n\n\n\n---\n\n\\\n \n\n## The Context\n\nAs engineers, we have tools like Github Copilot, Cursor and software engineering agents that help us accelerate and automate large swaths of our programming workflows with AI.\n\nHowever, outside programming, this is not the case. Many professionals in roles such as accounting, consulting and bizops have repetitive, critical workflows that they want to accelerate or automate with software, but they don’t yet have an ideal solution for this.\n\n---\n\n\\\n \n\n## The Problem\n\nBased on talking to these users, we learnt that the current generation of RPA and AI Agent-driven automation solutions for their workflows have 3 core issues:\n\n**Trust and Accountability**\n\nFor many tasks they need to ensure accuracy and take accountability. So RPA and Agents that just go off do the task for them are not a viable option. They **need full visibility and control** while the automation executes.\n\n**Configuration Complexity**\n\nReal-world workflows are complex and span multiple apps and files. Many solutions can’t handle them without the **heavy setup, onboarding, or migration required** with the current generation of RPA tools or AI agent-driven automation solutions.\n\n**Automation Flexibility**\n\nWorkflows change frequently across formats and tools and they want automations that can adapt on the fly without reconfiguration. Most **automations fail on the slightest variation** in data formats, schemas or sources introduced when they had new clients, cases and customers.\n\n---\n\n\\\n \n\n## The Solution\n\nWith just a prompt, our desktop AI system automates steps for any complex computer usage workflow that they can accept with a single keystroke. It makes repetitive but critical workflows such as financial data entry 2-5x faster, while providing complete visibility, control, and adaptability—all without requiring any setup, recording, or onboarding to a new platform.\n\nHere’s a how it works:\n\n1. The user opens the AutoComputer app on Windows/MacOS and provides a prompt for their task.\n2. AutoComputer understands the context of the task and comes up with a sequence of automated steps of mouse and keyboard input to complete the task.\n3. The user can simply evaluate and proceed through accepting the automated steps with a single key press, or correct or reject at the granular level without having to fix or re-run the whole workflow.\n\n![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXdA-_EassCAkh3NIgYNUy0QtBbuSm3b7rDw_Wdv-gvjYtwkxNH8_TJV7Cwi3084NeHRmOA2ouoeQCVdtvbBRH1JwGpxj0zSDiSWS0MfGTMbXQWV_o0pD_ZyNPEksny-Vysd8TglgW7oVXIukQ71ePbewo7R?key=-egX7nlYaXVPDP4d-BBxFX_D)\n\n---\n\n\\\n \n\n## The Team\n\nWe are [Madhav](https://x.com/madhavsinghal_) and [Rick](https://www.linkedin.com/in/rick-sugden-4a427b180/).\n\nWe met at university \\~7 years ago while doing research in Physics. We have been friends and roommates and have hacked on multiple research and side projects together.\n\nRick recently dropped out of his PhD where he worked on training transformers on brain-wave data for diagnosis, and built the first at-home brain-tumour monitoring system which he patented.\n\nMadhav worked at Replit as one of the first engineers on the AI team where he led research and training for all of Replit’s models, and worked on data infrastructure, Replit’s core AI services and, most recently, Replit Agent.\n\nBased on our experience in AI research and building products like the Replit Agent, we believe that SOTA models are just crossing the threshold of capability where they can start usefully automating and accelerating large parts of knowledge work beyond just software engineering. We are building AutoComputer to realize this for ourselves and for our users.\n\n---\n\n \\\n \n\n## Our Ask\n\nIf you are an enterprise or a business with teams doing repetitive back office workflows like data entry, who can benefit from AutoComputer, reach out to us at: [founders@autocomputer.ai](mailto:founders@autocomputer.ai)\n\nIf you are an individual or employee who wants to use AutoComputer to accelerate your workflows, you can sign up for our waitlist on .", "url": "https://www.ycombinator.com/launches/MFQ-autocomputer-zero-setup-ai-automation-for-your-computer", "votes": 27, "created_at": "2024-11-13T04:55:55.020Z" } ] }, { "id": 30672, "name": "Induction Labs", "slug": "induction-labs", "former_names": [ "Next Action" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c2f926be6434de6b8c75a3a873f2b79fe0bdf59.png", "website": "https://inductionlabs.com", "all_locations": "San Francisco, CA, USA", "long_description": "We are scaling foundation models that are curious about the world.\r\n\r\nWe’ve seen general intelligence appear exactly once, and its hallmark has been curiosity: a dissatisfaction with what we already know about the world, an ability to learn from world experience, and a disposition to share knowledge with other individuals. Across generations, these human traits have built modern science, technology, and culture.\r\n\r\nWe think superintelligence will come from models that work the same way: learning from their own observation, updating as they go, growing what they know. These models will start from what humanity knows and discover past us.\r\n\r\nWe believe that scaling curious intelligence is a defining problem of our time. If solved, we will live to see a new era of technological advances, scientific understanding, and human flourishing.", "one_liner": "Building intellectually curious AI", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1755632182, "tags": [ "Deep Learning", "Hard Tech", "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/induction-labs", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/induction-labs.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/induction-labs", "x_url": "https://x.com/induction_labs", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/0c2f926be6434de6b8c75a3a873f2b79fe0bdf59.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": 2757445, "full_name": "Jonathan Li", "title": "Founder/CEO", "founder_bio": "Building foundation models of the future @ Induction Labs", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jonatli/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/90c27a353548d1b9ce21de3e22843a28c5df654f.jpg" }, { "user_id": 3212248, "full_name": "David Li", "title": "Founder", "founder_bio": "Building foundation models of the future @ Induction Labs", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/davidy-li/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/fdfa320022a46b1bbdfc1ec63b6c4ca91cab0451.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 107033, "title": "Induction Labs - foundation models that learn from observation", "tagline": "Unlocking scalable video pretraining with imagination models", "body": "**TL;DR: We're Induction Labs, and today we're announcing research we've been working on: imagination models, a foundation model architecture that unlocks scalable learning from internet video. Our first model, Photon-1, beats a production LLM on internal computer use benchmarks with 30x less training compute, at 3x lower serving cost. \\[[Main Article](https://inductionlabs.com/news/scaling-video-pretraining)\\]**\n\n![uploaded image](/media/?type=post&id=107033&key=user_uploads/2757445/94f0365c-7de2-4792-ad4e-a8362f27c233)\n\nInternet video contains millions of hours of people using computers, doing skilled work, and interacting with the world.\n\nImagination models can scalably learn from this video to get better at completing tasks and understanding the world.\n\nWe tested the architecture with Photon-1, a 106B-A5B MoE transformer pretrained on 18 years of computer screen recordings.\n\n**Some Results:**\n\n* **Outperforms a production LLM (Gemini 3.1 Flash-Lite) on our internal computer use benchmarks**, with 30x less pretraining compute and 3x cheaper inference.\n\n![uploaded image](/media/?type=post&id=107033&key=user_uploads/2757445/7e217b7a-bbf0-40b0-a160-94855bbf7e90)\n\n* **Simulates full desktop sessions from a single screenshot**: it can imagine VS Code, Gmail, and ChatGPT frame by frame\n* **Generalizes beyond computers**: finetuned, it learns checkers and simulates billiard physics better than LLM baselines.\n* **Picks up human behavior from video**, like reprompting ChatGPT until it gets what it wants.\n* [**Demos here**](https://inductionlabs.com/news/scaling-video-pretraining)\n\nWe see imagination models as a path to intelligence that learns by observing the world directly, without a human first translating it into text. \n\n**Our Ask**\n\nWe’re working to scale this method to more kinds of video and looking for exceptional people to work with us! If you’re an exceptional thinker, engineer, or researcher (or know anyone who would be a great fit) - shoot us a message at [team@inductionlabs.com](mailto:team@inductionlabs.com)", "url": "https://www.ycombinator.com/launches/RqL-induction-labs-foundation-models-that-learn-from-observation", "votes": 15, "created_at": "2026-07-23T16:02:32.265Z" } ] }, { "id": 30771, "name": "Flywheel AI", "slug": "flywheel-ai", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/20c7285daed594e49818c41dd7e677796b8e4b61.png", "website": "https://useflywheel.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "Flywheel AI converts any existing excavators for contractors to enable remote ops to increase safety and productivity, and use robotics context dataset to train autonomous policies.", "one_liner": "Waymo for excavators", "team_size": 2, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1750156128, "tags": [ "Deep Learning", "Hardware", "Construction" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 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": null, "url": "https://www.ycombinator.com/companies/flywheel-ai", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2025/flywheel-ai.json", "year_founded": 2023, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/flywheel-company", "x_url": "https://x.com/flywheel_inc", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/20c7285daed594e49818c41dd7e677796b8e4b61.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": 367031, "full_name": "Jash Mota", "title": "Founder/CEO", "founder_bio": "building semi-autonomous heavy equipment", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/jashmota/", "x_url": "https://x.com/jash_mota", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3cf9f3041d7ae1817cb11e5af8709aed45ecff78.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 92796, "title": "Flywheel AI: Waymo for excavators", "tagline": "Enable remote teleoperation in excavators to increase safety and productivity", "body": "Hey everyone! We’re[ Jash](https://linkedin.com/in/jashmota) and[ Mahimana](https://www.linkedin.com/in/mahimanabhatt/) - co-founders of Flywheel AI - we enable remote control for excavators on construction sites.\n\nWe do this by enabling remote teleoperation. We then use the teleoperated excavators to collect the datasets needed to automate the machines.\n\nhttps://youtu.be/Kpbor3M6Mr8\n\n**🚧Problem**\n\nConstruction industry has some troubling trends:\n\n* **1 million worker shortage** in the US alone\n* **1000+ deaths annually** from safety incidents (and rising)\n* **Hundreds of thousands in penalties** + multi-week shutdowns per incident\n* **40% operational downtime** due to blind spots and inefficiencies\n\nIt’s **Dull, Dirty, Dangerous**, dialed to the max!\n\nThe problem is global and getting worse as the workforce ages and recruitment plummets.\n\n**👷The Flywheel Solution**\n\n**🚜Retrofit**\n\n![uploaded image](/media/?type=post&id=92796&key=user_uploads/367031/4e9829e3-a90f-4d93-b08f-d17cb961ea7f)\n\nWe engineered a new way to retrofit heavy equipment, which is machine-agnostic and compatible with pretty much any hydraulic equipment. These can be installed in few hours and don’t block manual operations.\n\n**🕹️Remote Teleop**\n\n![uploaded image](/media/?type=post&id=92796&key=user_uploads/367031/cb9452e8-c8eb-4b0f-9a2f-37cfb58a1314)\n\nWe built a low-latency remote teleop + dataset collection platform with the ability to remotely turn on/off the machines and let supervisors take over at any time. This enables controlling the fleet even from a minimal setup of laptop and joysticks.\n\n**⚡Workflow Autonomy**\n\n![uploaded image](/media/?type=post&id=92796&key=user_uploads/367031/a75da0c3-ee2b-4b86-9f6f-5108e7a5423b)\n\nWe use data from actual operations and robot learning to train models which does various tasks like dig and dump, trenching, grading and more. This will enable a single operator to manage more machines and sites at the same time.\n\n**We’re Flywheel AI**\n\n![uploaded image](/media/?type=post&id=92796&key=user_uploads/367031/e752210f-530c-4dd4-9b25-475ea1137089)\n\nJash previously built remote teleop tool with companies like Accenture as users. Mahi built the data and ML pipeline at Motional which runs over 50K simulations everyday. Together we have built various robots and spent many hours on the sites in hard hats and safety shoes!\n\n**Our Ask**\n\nIf you or a construction contractor/sub-contractor you know struggles with site safety and operator performance,[ book a meeting](https://calendly.com/jash-mota/flywheel-sales) to learn more about our solution and installation process!\n\nCall us at 415 714 7580 or email at [contact@useflywheel.ai](mailto:contact@useflywheel.ai)", "url": "https://www.ycombinator.com/launches/O8i-flywheel-ai-waymo-for-excavators", "votes": 161, "created_at": "2025-08-06T22:34:27.016Z" } ] }, { "id": 30904, "name": "Tensr", "slug": "tensr", "former_names": [ "Core", "Tensr", "Core" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f3743327b1fc85ded6caddbd532e0c600eb2cb0.png", "website": "https://tensr.com/", "all_locations": "Emeryville, CA, USA", "long_description": "Robotic factories that build robotics in the US. Currently operating a 12,000 sqft factory in the Bay Area and actively manufacturing customer orders for some of the largest robotics companies in the US and the International Space Station.\r\n\r\nWe’re a group of Berkeley graduate robotics researchers who previously won a full scale autonomous IndyCar competition at 160mph.", "one_liner": "Robotic factories that build robots", "team_size": 7, "industry": "Industrials", "subindustry": "Industrials -> Manufacturing and Robotics", "launched_at": 1763065446, "tags": [ "Deep Learning", "Hardware", "Robotics", "Automation", "Industrial" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 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/tensr", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/tensr.json", "year_founded": 2025, "city": "Emeryville", "country": "US", "linkedin_url": "https://www.linkedin.com/company/tensrinc/", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/7f3743327b1fc85ded6caddbd532e0c600eb2cb0.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": 2763027, "full_name": "Eric Berndt", "title": "Founder/CEO", "founder_bio": "Robotic factories that build robots in the US. Prev Robotics researcher @ UC Berkeley where I raced autonomous Indy500 cars at 160mph", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ekberndt/", "x_url": "https://x.com/ekberndt", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/8136fef854af650d0b22ac083d6585e09121a8ed.jpg" }, { "user_id": 916548, "full_name": "Adith Sundram", "title": "Founder/CTO", "founder_bio": "Founder @ Tensr. Robotics, ML and Controls @ UC Berkeley", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/adithsundram", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/dd20d8f9a84f4b33da6805740257605ff2175494.jpg" }, { "user_id": 2763062, "full_name": "C.K. Wolfe", "title": "Founder", "founder_bio": "Founder, Tensr. CS PhD (AI, Robotics), UC Berkeley. Former Technical Program Manager, UC Berkeley robotics lab under Berkeley AI Research (BAIR). Secured $17M+ in DARPA and industry sponsorship across Stanford and UC Berkeley via AI Racing Tech (2024). Team Manager during top-ranked U.S. league season. Editor-in-Chief of UC Berkeley BAIR’s official AI research publication (BAIR Blog).", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ckwolfe/", "x_url": "https://x.com/ckwolfeofficial", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9656e4b1ee0b0c58c5a7df8df5bcb18614685e68.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 112228, "title": "Tensr - Robotic Factories that Build Robots from Humanoids to Robots for the International Space Station", "tagline": "Robotic Factories that Build Robots", "body": "Robotic factories building robots. Today we have a 12,000 sqft factory in the Bay Area manufacturing everything from full humanoids to robots for the International Space Station.\n\nOur thesis: Revitalizing domestic manufacturing means driving costs down through automation. We are building an autonomous factory that is able to autonomously process a new order end to end from prototyping to large batch production. That means supply chain management, design for manufacturing, industrial machine retooling, robotic adaptation, quality inspection, packaging, and logistics all automated. Goal: Create a factory that can autonomously go from placed customer order to completed unit scale manufacturing run in 24 hours.\n\nWe are currently manufacturing production runs from top labs, full humanoids, and even have a partnership to manufacture robotics for the International Space Station. We've also built supply chain relationships in electronics, actuators, SMT, computing, pneumatics, and raw material suppliers.\n\nA snippet of some of our machines in the factory:\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/777065e0-e83d-4501-8799-8c490e883851)\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/30775e59-e171-4949-b959-3dc06ffa5322)\n\n## **Manufacturing at the Core**\n\nThis isn't just a robotic manipulation company. Its a manufacturing company.\n\nInternally we have pipelines autonomous building large databases of actuators, and raw tracking and trying to predict what items will need to order now to meet our next day targets.\n\n## **The Harness**\n\nThe entire factory is a data harness we can \"backprop\" learnings through from errors in the customer designs, failed supply chain prediction, slow retoolings, to inaccurate robotic manipulation. Our core belief is that robotic machine learning should not rely on human data to scale but instead learn to improve from its own mistakes in the real world in real time. This closes the manufacturing data gap that currently limits large-scale factory automation.\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/2baf7c74-80e3-49bb-a6bf-712ce0fb30fc)\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/4ed0acb3-e1bc-4210-b4d0-b3f3614bc8da)\n\n## **Any Robot Anywhere**\n\nOur thesis is simple. We should be able to make an robot for any environment whether that be a humanoid for the factory floor or a robot for Low Earth Orbit. We currently have a partnership with the ISS to manufacture a robots for the space station.\n\nNASA Houston, Johnson Space Center\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/e4558cfa-cf98-447a-acaf-7e83650e73a7)\n\n## **Team**\n\nAt heart we're a team of Roboticists. Before Tensr we were the leads for a Berkeley lab's autonomous racing team where we raced autonomous full-scale IndyCars at over 160mph as the ML, Sim, and Robotic Controls leads.\n\n![uploaded image](/media/?type=post&id=112228&key=user_uploads/2763027/c3772faa-f515-4ee0-b32e-2e586b3ae8e3)\n\nWe previously worked in industrial automation, aerospace manufacturing, medical device manufacturing, Berkeley AI Research (BAIR), physics simulation, and secured $17M+ in DARPA / industry sponsorship for UC Berkeley / Stanford research.\n\nOur founding engineers range from Berkeley Robotics PhD, ex-NASA, to 10+ years of manufacturing experience.\n\n## A Better World\n\nOn a personal note, not only do I believe we have the opportunity to build a great business but also to make a positive impact on the world. Automate away environmentally harmful materials, eliminate waste 0.1% at a time, and drive steady-state COGS toward only the fundamental inputs (materials and electricity), unlocking strong margins while collapsing the price of everyday goods / cost of living.\n\nLets build a better world :)\n\nA full view of our vision here:[ tensr.com/whitepaper](https://www.tensr.com/whitepaper)", "url": "https://www.ycombinator.com/launches/TC8-tensr-robotic-factories-that-build-robots-from-humanoids-to-robots-for-the-international-space-station", "votes": 6, "created_at": "2026-08-25T21:00:00.441Z" }, { "id": 95506, "title": "Tensr: Building Autonomous Robotic Factories", "tagline": "Building fully autonomous robotic factories that make scaling hardware as effortless as scaling on AWS", "body": "We're building fully autonomous robotics factories at Tensr to create the AWS of manufacturing. Our factories will receive on-demand orders from customers and spin up robots to operate industrial machinery, package, and ship the finished product all in 24 hours.\n\n![uploaded image](/media/?type=post&id=95506&key=user_uploads/2763027/704a7eb3-1ffc-4fc3-b806-32f3fd256418)\n\n[**https://youtu.be/HLijIfoDfpA**](https://youtu.be/HLijIfoDfpA)\n\n**Problem**\n\nMoving fast in manufacturing today doesn't exist. Iterating designs for manufacturing takes months of back and forth getting samples from China and pre-ordering manufacturing capacity several months before the holiday rush, only then to wait several weeks for shipping praying there isn't a strike at the docks. At the same time we see the complete collapse of America industrial capacity as American workers and experience leave the field and contracts are sent overseas.\n\n**Solution**\n\nWith the robotics revolution, for the first time it is possible to fully reimagine the factory floor - moving from rigid lines to modular stations, even in high mix settings. However, we believe that automating manufacturing is more than just deploying a foundation model or a humanoid into an existing factory.\n\nFactories need to be designed from the ground up _around_ robots, not the other way around.\n\nOver the past few weeks we turned our living room into an autonomous factory floor, with stations for CNC, moving stock, and a focus on assembly. After demo day, we'll expand to a full manufacturing facility, and start supporting batch orders of all sizes from other companies throughout the country. Currently we also are in talks to provide our own assembly models to other distributors.\n\n![uploaded image](/media/?type=post&id=95506&key=user_uploads/2763027/706f70f4-47a7-4466-8281-5cf6d081da43)\n\n![uploaded image](/media/?type=post&id=95506&key=user_uploads/2763027/e6420b31-3e74-49d3-b751-c901c5379665)\n\n**Team**\n\nWe're a battle-hardened team of UC Berkeley graduate robotics researchers (from Berkeley AI Research (BAIR)) who met in a lab, previously worked on and managed multiple DARPA contracts and led an autonomous IndyCar team where we raced full scale autonomous Indycars at 160mph (250kmh) as the Machine Learning, Simulation/Team Manager, and Robotics Control leads respectively.\n\n\\- [Eric Berndt](https://x.com/ekberndt) - MS/BS EE/CS, UC Berkeley, CoFounder + CEO\n\n\\- [Adith Sundram](https://www.linkedin.com/in/adithsundram/) - MS/BS ME, UC Berkeley, CoFounder + CTO\n\n\\- [C.K. Wolfe](https://x.com/ckwolfeofficial) - Current PhD AI/Robotics, UC Berkeley, CoFounder\n\nIn addition, our team brings direct operational experience from Rockwell Automation (industrial automation), aerospace manufacturing, and FDA-regulated medical-device production\n\n![uploaded image](/media/?type=post&id=95506&key=user_uploads/2763027/f907544d-cb70-45d2-b043-708a6739e40c)\n\n**Our Ask**\n\nWe're offering early manufacturing / prototyping pilots for hardware companies of all sizes as the new year starts and we open our first facility. If you or anyone you know fits in this bucket, reach out to [**founders@tensr.com**](mailto:founders@tensr.com)!\n\n![uploaded image](/media/?type=post&id=95506&key=user_uploads/2763062/4e53c620-4423-4e57-b0cf-a25b750d0539)\n\n", "url": "https://www.ycombinator.com/launches/OqQ-tensr-building-autonomous-robotic-factories", "votes": 365, "created_at": "2025-11-14T01:26:45.320Z" } ] }, { "id": 31019, "name": "Ndea", "slug": "ndea-com", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/51a1310dd1344aa031a62430dad084db50b7229d.png", "website": "https://ndea.com", "all_locations": "Remote", "long_description": "Ndea is building frontier AI systems that blend intuitive pattern recognition and formal reasoning into a unified architecture.\r\n\r\n# AI for Scientific Advancement\r\n\r\nUnlike all life before us, humanity's ascent is a story of ingenuity, not just biological evolution. Our progress has been driven by the curiosity to acquire knowledge, the ability to pass it on, and an intrinsic drive to innovate. We build technology which gives us leverage beyond our biology.\r\n\r\nWe stand at the top of a knowledge and technology colossus that we collectively created over the past ten thousand generations. Scientific progress has helped us overcome burdens that long defined human life — famines, plagues, and widespread illiteracy. Science will continue to redefine the boundaries of the human condition.\r\n\r\nToday, the acceleration of scientific progress hinges on one factor: AI capable of independent invention and discovery. This capacity is the gateway to advancements beyond our wildest imagination.\r\n\r\n# A New Research Lab\r\n\r\nWe're starting Ndea — an AI research and science lab. The name - like 'idea' with an 'n' - is inspired by the Greek concepts ennoia (intuitive understanding) and dianoia (logical reasoning), capturing our first goal to merge deep learning with program synthesis.\r\n\r\nNdea is entirely focused on developing and operationalizing AGI to realize unprecedented scientific progress in our lifetime for the benefit of all current and future generations.\r\n\r\nBuilding AGI alone is a monumental undertaking, but our mission is even bigger. We're creating a factory for rapid scientific advancement — a factory capable of inventing and commercializing N ideas.\r\n\r\nFrom our vantage point today, we see many 'known' frontiers like self-driving vehicles, drug discovery, sustainable energy, robotics, and space exploration. While AGI will benefit all of these, the most exciting adventure lies in the 'unknown'. AGI promises discoveries and progress we cannot imagine today.", "one_liner": "Building AGI that can innovate.", "team_size": 15, "industry": "Industrials", "subindustry": "Industrials", "launched_at": 1772669917, "tags": [ "Deep Learning", "Hard Tech", "Remote Work", "AI", "ML" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Winter 2026", "status": "Active", "industries": [ "Industrials" ], "regions": [ "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/ndea-com", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/ndea-com.json", "year_founded": 2024, "city": null, "country": null, "linkedin_url": "https://www.linkedin.com/company/ndea/", "x_url": "https://x.com/ndea", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/51a1310dd1344aa031a62430dad084db50b7229d.png", "app_video_url": null, "dday_video_url": null, "company_photos": [], "primary_partner": { "name": "Tyler Bosmeny", "url": "https://www.ycombinator.com/people/tyler-bosmeny" }, "founders": [ { "user_id": 2858111, "full_name": "Francois Chollet", "title": "Founder", "founder_bio": "https://fchollet.com/", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/fchollet/", "x_url": "https://x.com/fchollet", "avatar_thumb_url": "/avatars/thumb/missing.png" }, { "user_id": 898, "full_name": "Mike Knoop", "title": "Founder", "founder_bio": "https://mikeknoop.com", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/mikeknoop", "x_url": "https://twitter.com/mikeknoop", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/45810451d10680414c72b6b9db3769a697c4bc6d.jpg" } ], "jobs": [], "news": [], "launches": [] }, { "id": 31139, "name": "Ashr", "slug": "ashr", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/36b682fdfb87515f8ca423d8a006eaac7229e134.png", "website": "https://ashr.io", "all_locations": "San Francisco, CA, USA", "long_description": "Ashr Manifold is a self-contained model training, hosting, observability, and continual learning platform for purpose-built open-weight models. \r\n\r\nStartups can use Manifold to post-train and manage fleets of continuously improving open-weight models without having a dedicated research team or infrastructure. Ultimately, this reduces operating costs and allows a greater degree of control over proprietary company knowledge.\r\n\r\nEnterprises can use Manifold to encode institutional judgements and priorities into open-weight models, reduce token costs, and manage every level of the AI life cycle, from data curation, to model serving on weights and infrastructure they own.", "one_liner": "Enterprise post-training, monitoring, and continual learning platform", "team_size": 2, "industry": "B2B", "subindustry": "B2B", "launched_at": 1771535371, "tags": [ "Deep Learning", "Generative AI", "Reinforcement Learning", "Data Engineering", "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/ashr", "api": "https://devasheeshg.github.io/yc-api/batches/winter-2026/ashr.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/ashr-io", "x_url": "", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/36b682fdfb87515f8ca423d8a006eaac7229e134.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": 1467144, "full_name": "Rohan Kulkarni", "title": "Founder/CEO", "founder_bio": "Building custom models at Ashr Labs. Co-founder and CTO of Ask Geri (Acquired). Berkeley EECs. ", "is_active": true, "linkedin_url": "https://linkedin.com/in/rohankulkz", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/297857e249e4e8e3f4bce0f12fda39d67b33b506.jpg" }, { "user_id": 2584088, "full_name": "Shreyas Kaps", "title": "Founder", "founder_bio": "Building Ashr Manifold to help teams build and host purpose-built and trained models", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/shreyas-kaps-5b1a4420b/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/697406e5dc0beff2a338d68874683d7c8e1703c0.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 98286, "title": "Ashr: Mimic Your Production Environment and Users to Catch Agent Fails", "tagline": "Custom Tests and Evals Simulating Real User Behavior", "body": "\nHi YC, we’re Shreyas and Rohan — two ex-Berkeley students building Ashr.\n\n**TL;DR :** Ashr catches bugs and validates agent response quality by mimicking the patterns and quirks of real users. Developers today struggle to simulate these conditions in tests, resulting in real-time fails and unexpected behavior. We integrate as an SDK within your codebase.\n\n\n\n# The Problem\n\nAI automations are top of mind for every developer: large companies are integrating AI into older systems to stay technologically relevant, while AI-native companies are quickly emerging in every vertical. Unfortunately, most of these AI tools miss the mark: constant hallucinations, unexpected behavior, and dubious quality have eroded trust in agentic systems. In most cases, this is not because foundation models aren’t powerful enough, but because of subtle prompting, tooling, and scaffolding errors that only emerge with rigorous testing. The need for high volumes of realistic testing arises in many scenarios: we’ve spoken to companies who want to prove that their agents are capable of handling sensitive data to potential customers, developers who want to make their agents work better, and engineers who want to test edge-cases and new features but don’t have all the necessary tools.\n\nWe’ve seen teams confront this problem with a litany of half-measures: pushing to production and tanking user trust as a means of discovering bugs, half-baked test harnesses, and vibe-coded test suites that only scratch the surface. We built ashr to solve this.\n\n# Our Solution\n\nAshr is a modular testing and evaluation platform for your agentic system. We integrate as an SDK into your codebase, allowing you to request test suites and analyzing your agents’ output based off your tooling and ideal outputs. These tests mimic how real users utilize an agentic system: taking complex tool call paths, using obscure language with unclear instructions, and attempting to use restricted features. We develop environments, fine-tuned custom scorers, and stop at nothing to provide all the insights needed to bring your agent to a truly production-ready state.\n\nWe also work seamlessly with Claude Code and Cursor, allowing your coding agents to quickly prototype and test changes while receiving immediate feedback. Teams have run swarms of agents testing with ashr overnight, and have woken up to changes that reduced hallucinations and increased accuracy and user retention, all without writing a single line of code.\n\n# Our Ask\n\nIf you’re building agents, book a call today at [**https://ashr.io/**](https://ashr.io/). **All onboarding is done via call so we can ensure everyone is ready to go and start using as soon as possible.** In addition, we’d love feedback on our docs, opinions about the nature of test infra, and just chatting with a bunch of builders on how to make our product better.\n\nYou can reach us at [founders@ashr.io](mailto:founders@ashr.io)\n\n# The Team\n\n**We’re two young founders passionate about changing the world with agentic development**\n\n* **Rohan** built his first startup, an AI-native survey platform for old age homes, as a student at Berkeley, before exiting as part of an acquisition and joining YC.\n* **Shreyas** worked on AI-native workflows at Tessera Labs, mentored by a team of ex-Meta and Netflix research, and delivering value in the ERP transformations of Fortune 100 companies.", "url": "https://www.ycombinator.com/launches/PZG-ashr-mimic-your-production-environment-and-users-to-catch-agent-fails", "votes": 9, "created_at": "2026-02-27T22:43:17.702Z" } ] }, { "id": 31516, "name": "Expanse", "slug": "expanse", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/af6cfe1f95da6fb2bfc1789fb605ed70b77da3b4.png", "website": "https://expanse.sh", "all_locations": "London, England, United Kingdom", "long_description": "Expanse unlocks wasted GPU capacity. We recover idle compute through three capabilities: resource prediction (right-sizing job submissions before they reach the scheduler), optimisation suggestions (code and config changes researchers can apply themselves), and failure prediction (catching jobs that will fail before they consume hours of GPU time).\r\n\r\nWe’re four engineers. We ran HPC and GPU training workloads at the largest quant funds and national supercomputing centres. We faced this problem first hand and the only fix was to over-provision and burn millions. Ismaeel built the first multimodal HPC resource predictor as research at EPCC (Edinburgh’s Parallel Computing Centre), which beat every published baseline. This is the tool we wish we had.", "one_liner": "Unlock wasted GPU capacity.", "team_size": 4, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1777849866, "tags": [ "Deep Learning", "B2B", "Infrastructure", "ML" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Spring 2026", "status": "Active", "industries": [ "B2B", "Infrastructure" ], "regions": [ "United Kingdom", "Europe" ], "stage": "Early", "app_video_public": false, "demo_day_video_public": false, "app_answers": null, "question_answers": null, "url": "https://www.ycombinator.com/companies/expanse", "api": "https://devasheeshg.github.io/yc-api/batches/spring-2026/expanse.json", "year_founded": 2025, "city": "London", "country": "GB", "linkedin_url": "https://www.linkedin.com/company/expanse-compute", "x_url": "https://x.com/ExpanseCompute", "fb_url": null, "cb_url": "https://www.crunchbase.com/organization/expanse", "github_url": "https://github.com/expanse-labs", "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/af6cfe1f95da6fb2bfc1789fb605ed70b77da3b4.png", "app_video_url": null, "dday_video_url": null, "company_photos": [ "https://bookface-images.s3.us-west-2.amazonaws.com/attachments/1a2d766656b058e3ae5a7333ccf58b05bb9d8689.png" ], "primary_partner": { "name": "David Lieb", "url": "https://www.ycombinator.com/people/david-lieb" }, "founders": [ { "user_id": 3381342, "full_name": "Ismaeel Bashir", "title": "Founder/CEO", "founder_bio": "Ismaeel is co-founder and CEO of Expanse. Built the first multimodal HPC resource predictor at EPCC (Edinburgh’s Parallel Computing Centre), beating every published baseline. Previously: ran large scale ML models at one of the world’s largest quantitative funds (QRT). Studied Computer Science at the University of Edinburgh.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ismaeel-bashir/", "x_url": "https://x.com/ismaeel_bashir_", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3c4fa6230901fda37ce33a006271ece271e5e9e8.jpg" }, { "user_id": 3409042, "full_name": "Nikodem Bieniek", "title": "Founder", "founder_bio": "Nikodem is co-founder and CTO of Expanse. Trained and optimised speech recognition models on GPU clusters. Previously: managed the platforms researchers and engineers depended on at one of the world’s largest hedge funds (Millennium). Studied Computer Science at the University of Edinburgh.", "is_active": true, "linkedin_url": "https://linkedin.com/in/nikodemb/", "x_url": "https://x.com/nkdem_b", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/c2d55059ebf4b59335ad292b3cc38a1e9d667650.jpg" }, { "user_id": 3409418, "full_name": "Yafet Melake", "title": "Founder", "founder_bio": "Yafet is co-founder and COO of Expanse. Built the first GNN-based cluster graph network for predicting SLURM queue wait times at EPCC (Edinburgh’s Parallel Computing Centre). Previously: tooling and infrastructure for researchers at one of the world’s largest quantitative funds (G-Research). Studied Computer Science at the University of Edinburgh.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/yafet-melake", "x_url": "https://x.com/yafet_melake", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/db8ab1acbb09fe838ce4d380889c7d1c27bf0975.jpg" }, { "user_id": 2274795, "full_name": "Eren Mendi", "title": "Founder", "founder_bio": "Eren is co-founder and CPO of Expanse. Built state-of-the-art decentralised foundation model training systems and performance models. Previously: prototyped emerging technologies in quantitative finance (G-Research). Studied Computer Science at the University of Edinburgh and Imperial College London.", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/eren-mendi-929b55138", "x_url": "https://x.com/erenzmendi03", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/af898412d2d555432b879560ffcdfde4fbb3e53a.jpg" } ], "jobs": [], "news": [ { "title": "Why we started Expanse, and our $5.3M seed · Expanse Blog", "url": "https://expanse.sh/blog/expanse-raises-5-3m-seed", "date": "Sep 16, 2026" }, { "title": "What Y Combinator's Latest Batch Reveals About The Future", "url": "https://www.forbes.com/sites/dariashunina/2026/06/04/what-y-combinators-latest-batch-reveals-about-the-future/", "date": "Jun 08, 2026" } ], "launches": [ { "id": 100703, "title": "Expanse - Unlock wasted GPU capacity", "tagline": "Unlock wasted GPU capacity.", "body": "**TL;DR**\n\nExpanse unlocks wasted GPU capacity. We’re the intelligence layer for compute infrastructure. Submit jobs with the right resources. Optimise them to run faster. Debug failures in seconds, not days. Works on cloud and on-prem HPC.\n\nWant to know how much more you can get out of your hardware? Schedule a call: \n\n**Problem**\n\nRunning compute on a cluster means guessing memory, time, and GPUs before you submit. Get it wrong and your job fails mid-run. Over-guess and you pay for compute you never use. 30% of cloud spend is lost to over-allocation \\[1\\]. Microsoft Research found their own deep learning jobs hit 50% GPU utilisation or lower \\[2\\].\n\nSizing jobs takes decades of experience. Memory patterns, batching, GPU threading, CPU vectorisation. AI agents don’t have enough information to fix it either. SLURM and Kubernetes don’t expose enough structured data. Even strong platform teams are forced to choose: spend time debugging and optimising every job, or accept the waste. AI infrastructure spending will hit $2.52 trillion in 2026 \\[3\\]. The waste grows with every new cluster. This problem isn’t going away.\n\n**Solution**\n\nExpanse learns your cluster. We analyse your code and hardware telemetry, then track them over time. Our deep learning models build a picture of what your workloads actually need. Predictions get sharper with every job.\n\nOur models are deployed on your cluster. All analysis runs on your network. Your code and telemetry never leave your infrastructure. We can’t see your data, and you maintain full data sovereignty.\n\nOne command installs Expanse on your cluster. You get two tools.\n\n`expanse analyse` - for every job:\n\n* **Suggests the right resource configuration**\n* **Predicts failures from historical code and telemetry patterns**\n* **Surfaces code-level optimisations that increase efficiency**\n\n`expanse diagnose` - for every failure:\n\n* **Finds the root cause**\n* **Returns solution-oriented logs with the exact code and config changes to fix it**\n\nDays of debugging and optimising become seconds. Platform teams get what they need to do this in minutes instead of weeks.\n\n**Founders**\n\nWe're four engineers from the University of Edinburgh, all coming from top quant funds and national supercomputing centres. Ismaeel built the first multimodal HPC resource predictor at EPCC (Edinburgh's Parallel Computing Centre), beating every published baseline. Yafet built the first GNN cluster graph network for predicting SLURM queue wait times. Eren built decentralised foundation model training systems at Edinburgh. Niko trained large-scale speech ML models on GPU clusters and architected cloud and Kubernetes infrastructure at one of the largest hedge funds in the world. We left our jobs to fix this ourselves.\n\n**CTA / Ask**\n\nRunning compute and not getting enough out of what you’re paying for? Failing jobs, unclear errors, bills too high? We’d love to chat.\n\nKnow anyone in AI infrastructure (managing cloud and/or on-premise data centres), quant finance, drug discovery, or AI research labs? An intro would mean a lot :)\n\n**YC Deal**\n\nAll YC companies running serious ML training can contact us for a discounted pilot. We’ll find inefficiencies and wastage on your compute setup. Then we deploy Expanse, no changes to your workflows. You get more out of the hardware you’re paying for.\n\nReach out: [founders@expanse.sh](mailto:founders@expanse.sh) or \n\nReferences\n\n\\[1\\] \n\n\\[2\\] \n\n\\[3\\] ", "url": "https://www.ycombinator.com/launches/QCF-expanse-unlock-wasted-gpu-capacity", "votes": 53, "created_at": "2026-05-04T04:30:18.506Z" } ] }, { "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": 32261, "name": "Rasyn", "slug": "rasyn", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/ffda7ce34aee49cfec95fa8aef9ed1a70e4c3e28.png", "website": "https://www.rasyn.ai/", "all_locations": "San Francisco, CA, USA", "long_description": "We build foundational AI models for chemistry. Almost nothing you buy is a single chemical. Products like datacenter coolant, GPU thermal paste, paint, and glue are formulations, which are mixtures of around ten ingredients each, and demand for them has grown quickly because of AI and EVs.\r\n\r\nDesigning a new formulation is essentially a search problem, because there are more possible mixtures than anyone could ever test, and none of them can be predicted in advance. As a result, bringing a single formulation to market today takes three to five years of trial and error in a lab.\r\n\r\nOur models predict how a mixture will behave far faster than the standard methods used today, which lets us design new formulations in weeks rather than years, at a fraction of the cost. To prove this, we used them to invent three chemicals that never existed before, including a PFAS-free coolant for datacenters, and we have since synthesized and patented all three.\r\n\r\nWe are already working with some of the largest manufacturers to design formulations for them, and we are building our own lab to generate the data that will train our next models.", "one_liner": "Making chemicals 100,000x faster", "team_size": 3, "industry": "Healthcare", "subindustry": "Healthcare", "launched_at": 1785736030, "tags": [ "Deep Learning", "Biotech", "Manufacturing", "AI" ], "tags_highlighted": [], "top_company": false, "isHiring": false, "nonprofit": false, "batch": "Summer 2026", "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/rasyn", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/rasyn.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/rasyn-ycs26/", "x_url": "https://x.com/rasyn_lab", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/ffda7ce34aee49cfec95fa8aef9ed1a70e4c3e28.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": 3475945, "full_name": "Ansh Tiwari", "title": "Founder/CEO", "founder_bio": "Building AI for scientific discovery | Physics @ Caltech, ex-NASA, Author of 3x ICML papers | Masason Foundation Member", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ansschh/", "x_url": "https://x.com/4n5che", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3629530f2959475271a8d4c059a54c800cccaed5.jpg" }, { "user_id": 972683, "full_name": "Ayush Chauhan", "title": "Founder/CTO", "founder_bio": "AI for chemical discovery | ex-Speechify (50M+ users) & 2x YC engineer | NeurIPS-published ML research, used by OpenAI & Meta", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/ayushchauhan0203/", "x_url": "", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/3f433dd02a059cbf14b5ec7764ee6592562cddc2.jpg" }, { "user_id": 3481804, "full_name": "Daood Hashmi", "title": "Founder", "founder_bio": "Researching in the Arnold and Marcus Lab at Caltech", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/daood-hashmi/", "x_url": "https://x.com/hash_me_NOT", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/951a6c5546701cb614078a22a24b949c93cf48a8.jpg" } ], "jobs": [], "news": [], "launches": [ { "id": 108802, "title": "Rasyn: One place to run any Chemistry task", "tagline": "Towards a General Intelligence for Chemistry", "body": "**Hey!**\n\nWe're **Rasyn**, and we built a platform called **Marigold**. Give it any chemistry task in plain language and it pulls the literature, reads your files, picks the tools, sets up and runs the calculations on its own machine, and hands back the numbers along with its reasoning what it did to get them. \n\nIt's live at [app.rasyn.ai](http://app.rasyn.ai); here's a short demo: \n\n\\\n**Here's the team:** \\\nOur team has worked at NASA JPL, and researched with scientists from Nvidia and Deepmind. Our team has worked on softwares serving 50 million users, and their research was used by frontier AI labs including OpenAI and Meta. Our team studies Computational Chemistry at Caltech, has done Total Synthesis for 4+ years, and researched alongside Nobel Laureates, so they have faced these problems firsthand. They'd lose hours deciding which tool to call next, moving outputs between disconnected tools, and running a single program at a time.\n\n**Problem:** \\\nChemistry has countless software. There's one for predicting a structure, another for proposing a route, another for reading a spectrum. None of them talk to each other, so you're stuck feeding outputs between them; this clerical work is where the time goes, none of which is science.\n\n\\*\\*Solution:\\*\\*\\\nWe tried coding agents first. They're built to write software, so a chemistry task gets handled as a scripting problem and the chemistry goes unverified. Marigold checks between steps and stops when something doesn't hold.\n\nIt runs on its own machine, so it installs and executes what it needs. It works with the tools chemists already use, submits jobs to your cluster, and runs on whatever model endpoint you have, so your data can stay in the building. In the demo we hand it four thousand compounds and ask which are worth screening. It does the whole thing itself.\n\nLonger term we're building toward a general intelligence for chemistry. Marigold today reasons over existing tools, and that's where we start.\n\n**The Ask:**\n\nThis is only the first version; it will break. And we'd like it to break in front of chemists, so if you run any sort of chemistry, please try it and tell us where it falls over. \n\nWe'd also love intros to any lab doing chemistry, computational or wet, since we want to sit down and work through a real problem with people. \n\nHappy to answer any questions!", "url": "https://www.ycombinator.com/launches/SIs-rasyn-one-place-to-run-any-chemistry-task", "votes": 9, "created_at": "2026-08-03T19:40:21.411Z" } ] }, { "id": 33805, "name": "Edviro", "slug": "edviro", "former_names": [], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/c179a28044a3d4a8c3ceaf2a5b8cecbf90f66aa7.png", "website": "https://edviroenergy.com", "all_locations": "San Francisco, CA, USA", "long_description": "Edviro builds AI that operates energy infrastructure. We connect to meters, HVAC systems, batteries, solar, and other energy equipment and continuously handle the work required to keep them efficient: detecting and diagnosing problems to forecasting demand, simulating fixes, creating work orders, coordinating maintenance, and controlling equipment where authorized. Our energy world models learn how each site behaves, choose the highest-value actions, and verify the results against real operational, meter, and billing data.", "one_liner": "AI That Operates Energy Infrastructure", "team_size": 2, "industry": "B2B", "subindustry": "B2B -> Infrastructure", "launched_at": 1782807553, "tags": [ "Deep Learning", "B2B", "Energy" ], "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/edviro", "api": "https://devasheeshg.github.io/yc-api/batches/summer-2026/edviro.json", "year_founded": 2025, "city": "San Francisco", "country": "US", "linkedin_url": "https://www.linkedin.com/company/edviro", "x_url": "https://x.com/Edviro_energy", "fb_url": null, "cb_url": null, "github_url": null, "logo_url": "https://bookface-images.s3.amazonaws.com/small_logos/c179a28044a3d4a8c3ceaf2a5b8cecbf90f66aa7.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": 3164154, "full_name": "Hursh Shah", "title": "Founder/CEO", "founder_bio": "19, Founder & CEO at Edviro. Prev. NeurIPS 2024, ChatGPT Lab. Patent pending medtech research. Prev. founded Skyglass, smart glasses startup that got accepted to PearX. ", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/hursh-shah/", "x_url": "https://x.com/hursheybar2", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/9756e91b633ed2b9de6793c88f0e1daa8ff1d704.jpg" }, { "user_id": 1865110, "full_name": "Tanuj Siripurapu", "title": "Founder", "founder_bio": "19, Founder & CTO @ Edviro (YC S26). Prev. at RTX. Scaled a digital presence agency to 20k revenue in high school. Programming since age 9", "is_active": true, "linkedin_url": "https://www.linkedin.com/in/tanuj-siripurapu-747b58217/", "x_url": "https://x.com/kstanuj", "avatar_thumb_url": "https://bookface-images.s3.us-west-2.amazonaws.com/avatars/96aabcf62bd08eff9921363a085eb3a930988175.jpg" } ], "jobs": [ { "id": 113407, "title": "Member of Technical Staff", "url": "https://www.ycombinator.com/companies/edviro/jobs/PlLCBWC-member-of-technical-staff", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Engineering", "role_type": "Machine learning", "salary_range": "$160K - $260K", "equity_range": "0.50% - 2.00%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] }, { "id": 113409, "title": "Founding GTM", "url": "https://www.ycombinator.com/companies/edviro/jobs/xuWBSdL-founding-gtm", "location": "San Francisco, CA, US / Remote (US)", "type": "Full-time", "role": "Sales", "role_type": null, "salary_range": "$140K - $170K", "equity_range": "0.50% - 1.50%", "experience": "Any (new grads ok)", "visa": "US citizen/visa only", "skills": [] } ], "news": [], "launches": [ { "id": 112256, "title": "Edviro - AI that operates energy infrastructure", "tagline": "AI to operate energy infrastructure", "body": "# **Edviro: AI that operates energy infrastructure**\n\nHi all,\n\nWe’re Hursh and Tanuj, founders of Edviro.\n\n**TL;DR:** Edviro connects to a facility’s BMS, utility, sensor, equipment, and maintenance systems to build a world model of how the facility behaves. We use that model to detect problems, simulate interventions before anything is changed, and coordinate the best next action. We’re live in **34 facilities**, have identified more than **$400K in avoidable energy costs**, and are now opening our first partnerships with data center operators.\\\n\\\n\n\n## **The problem**\n\nData centers are expected to drive roughly half of U.S. electricity-demand growth through 2030.\n\nThis is usually framed as a problem of producing more electricity. That is only part of it.\n\nPower must be delivered to the right sites, on the timelines data centers are being built, with the reliability their workloads require. Once a facility has that power, its operators still need to extract as much useful compute as possible from every available megawatt without compromising uptime.\n\nInside a data center, power, cooling, controls, equipment, workloads, and maintenance are tightly coupled. A workload shift changes the thermal profile. A cooling-control change affects both energy use and equipment margins. A maintenance decision can alter redundancy and available capacity.\n\nYet the data and workflows used to manage these systems remain fragmented across BMS, DCIM, EPMS, meters, sensors, maintenance software, vendor portals, and spreadsheets.\n\nMost existing systems can display telemetry or raise an alarm. They cannot reliably model how the entire facility will respond before an operator changes a setpoint, shifts a workload, services equipment, or adds capacity.\n\nOperators are left making high-stakes decisions with incomplete system-level context.\n\n## **What Edviro does**\n\nEdviro turns a facility’s operational data into a continuously updated world model.\n\nBy “world model,” we mean a model that estimates the current state of the physical system, learns how its components interact, and predicts how it will respond to a proposed action. \n\nEdviro:\n\n* Integrates data from building controls, utilities, sensors, equipment, and maintenance systems\n* Detects anomalies, energy waste, equipment degradation, and capacity constraints\n* Simulates operational and maintenance interventions before they are deployed\n* Recommends the best action while accounting for cost, reliability, and physical constraints\n* Uses agents to coordinate approvals, work orders, investigations, and follow-through\n* Verifies whether the expected operational and energy impact actually occurred\n\nFor a data center, this could mean determining whether existing power and cooling infrastructure can support additional IT load, testing a control-sequence change before deploying it, identifying which equipment issue is constraining capacity, or prioritizing maintenance based on the capacity and reliability it would recover.\n\nWe initially keep operators in control of every consequential action. As the models are validated and earn trust, more of the operating loop can be automated.\n\n## **Why we started with schools**\n\nEdviro began when I was 17 and working with my high school’s facilities team. I was given a year of district utility bills to analyze, built software to parse them, and discovered roughly **$360,000 in gas overbilling**.\n\nWe found that this was not an isolated problem. Schools operate large portfolios of aging, mixed-vendor infrastructure with fragmented controls, incomplete data, lean facilities teams, and very little room for wasted budget.\n\nThey forced us to build for the messy reality of physical infrastructure rather than clean laboratory data.\n\nToday, Edviro is live in **34 facilities** and has identified more than **$400,000 in avoidable energy costs** for building operators.\n\nA school is obviously not a data center. Data centers have denser instrumentation, much tighter uptime requirements, and more complex coupling between power, cooling, and compute. \n\nWhat transfers is the underlying architecture: connecting heterogeneous operational systems, estimating the state of a physical facility, simulating interventions, coordinating action across teams, and verifying the result. We are now building the data-center-specific models, integrations, and safety constraints directly with operators. Our first relevant research benchmark can be found here: \n\n## **Where we’re going**\n\nThe current generation of facilities software tells operators what has already happened.\n\nWe want Edviro to understand what is happening now, predict what will happen next, and help execute the safest and highest-value response.\n\nAs electricity becomes one of the primary constraints on AI growth, building more infrastructure will not be enough. We also need to operate the infrastructure we already have far more intelligently.\n\nThe long-term goal is not another dashboard. It is infrastructure that can understand itself and increasingly operate itself.\n\n## **Asks**\n\n1. **We’re looking for data center operators to become early design partners.** We are especially interested in facilities dealing with power or cooling constraints, capacity-expansion planning, recurring manual investigations, fragmented controls, or reactive maintenance. We can begin read-only, with operators retaining approval over every action.\n2. **We’re hiring energy-modeling engineers** across thermal systems, power systems, controls, scientific machine learning, and physical simulation.\n\nIf you operate a data center—or know the team responsible for operating one—we’d love to talk.\n\n[**www.edviro.com**](http://www.edviro.com)", "url": "https://www.ycombinator.com/launches/TCa-edviro-ai-that-operates-energy-infrastructure", "votes": 5, "created_at": "2026-08-26T17:00:00.166Z" }, { "id": 104853, "title": "Edviro - Buildings are wasting money daily", "tagline": "Your building is wasting energy daily", "body": "**TL;DR:** Edviro helps facilities managers at large buildings save on their energy bills.\n\nFacilities managers are overwhelmed with dashboards, alarms, and analytics for hundreds of boilers, HVAC units, and heat pumps across their buildings. Edviro continuously monitors and optimizes buildings using energy world models to detect anomalies, simulate interventions, and suggest the best options to the facilities manager. In particular, in busy spaces like offices, we utilise Wi-Fi access points or our own occupancy sensors to reduce HVAC usage significantly. Our tools also allow institutions to maintain detailed service histories and equipment logs and to perform maintenance with AR. To date, we've saved clients six figures in multiple buildings. We’re also piloting with a construction company to conduct baselining and M&V for sites they work with.\n\n**The team:**\n\nWe’re young technical founders. I’ve published at NeurIPS ‘24, conducted patent pending VR Alzheimer’s work, and founded a previous Aviation smart glasses startup that was accepted into PearX. Tanuj has been coding since he was 9, scaled a previous digital agency to $20k in revenue and previously did dev work at RTX.\n\nThis all started when I was working with my own high school’s facilities team. I saw how much waste there was and how fragmented his energy data was despite loads of smart controls and BMS systems. Initially I built a dashboard for him, and the product quickly caught on to more schools in NorCal.\n\n**Our ask:**\n\nWe’re looking for intros to facilities teams at school districts, real estate or data centres.\n\nAlongside that, we’re looking to work with construction teams who need help baselining and doing M&V on their work post-project.\n\nIf you are connected to anyone in this space, would love to chat!\n\n![uploaded image](/media/?type=post&id=104853&key=user_uploads/3164154/9fe82a05-dd5a-4916-94f4-9cf0e8f54a05)\n\n", "url": "https://www.ycombinator.com/launches/RHB-edviro-buildings-are-wasting-money-daily", "votes": 5, "created_at": "2026-07-07T15:02:05.737Z" } ] }, { "id": 34794, "name": "Petrichor", "slug": "petrichor", "former_names": [ "Qokedas", "Petrichor Bio" ], "small_logo_thumb_url": "https://bookface-images.s3.amazonaws.com/small_logos/fdb7aea2b5558307f53190af023d8e6a2c7ac3be.png", "website": "https://petrichorbio.com/", "all_locations": "San Francisco, CA, USA", "long_description": "Real biology requires a wet lab, and building one takes years and a team most AI companies don't have. 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Frontier VLMs that can unlock this (like Gemini) are too expensive to run at the volume real industries have, too generic for any single task, and can't be deployed on-prem where sensitive video lives.\r\n\r\nWe are building an auto vision factory: take video plus a customer's objective, distill a frontier model, and post-train a small open model that is SOTA on that one task and serve it blazingly fast. Our first product, Marlin, is a 2B-parameter open-weights video VLM that matches Gemini-2.5-flash on dense captioning and temporal grounding at a fraction of the cost. 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