@ycombinator

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San Francisco, CA
Joined February 2010
Applications for the YC Winter 2027 batch are now open! Apply by November 2: ycombinator.com/apply
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Y Combinator retweeted
Our agent harness allows Astra to learn from its previous runs. We found that, over time, it improves significantly at this task through learning several key things: - "A flat fork required nearly vertical fingers" (to grasp it) - "A thin utensil may require grasping close to tabletop height" - "A tilted wrist may be unable to descend at long reach; approaching above the container at a lower height before tilting can make placement reachable"
Part 2 of our robot arm Podrick's dinner party training. Today, controlled by Astra, he places multiple utensils upright into a utensil holder!🍴
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PhD teaches you time is cheap and money is expensive. Startup teaches you the inverse.
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Introducing the new Wally model catalog. 7 new open models, live now: - Kimi K3 - DeepSeek V4 Pro (Fast) - GLM-5.3 (Fast) - MiniMax M3 - DeepSeek V4.1 Flash (Fast) - DeepSeek V4.1 Flash 1017 - DeepSeek V4 Flash 0731 10 models. One API key.
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Breaking news: Nothing Happened at the plant floor today.... That's the point!
Congrats to @igmarinelli, Gabriel, and @Tractian (W21) on their $130M Series D at a $1.4B valuation! Tractian builds sensors and AI that predict when industrial machines will fail, before they do. Most industrial equipment has no instrumentation at all, so factories find out about problems when something breaks. Tractian retrofits machines with a dozen kinds of sensors measuring vibration, temperature, and pressure, and its AI recommends maintenance ahead of the breakdown. They’ve now passed $100 million in ARR, growing 90% year over year. theinformation.com/briefings…
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A million hours of what? If we’re scaling human demonstrations for robot learning, what should each hour contain? We built S1, our skill capture glove, alongside our robot hand to record joint movements that map to robot targets, with touch and video on the same timeline.
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I'd like to share a little about what I've been working on at my new company @ultrametricai with my cofounder @tylerlastovich. We have been exploring how we could automate the startup itself, leaving founders with more time to focus on their exact mission. Enter our first release of the Open Startup Repo: github.com/ultrametricai/ult… A common foundation for transparent automation of the startup. After close to 20 years in silicon valley, backing hundreds of amazing founders & startups, being in the trenches of founding Heyzap (YC08), then solo founding @golden, going through 2 acquisitions and working behind the scenes with some of the iconic companies today, I'm witnessing the largest change to how we work I could have imagined - a truly monumental era of creative abundance. I believe the next generation of companies will largely run themselves: from operations, code, product upgrades, growth, vendor control & selection, process execution, special situations, strategy etc etc. That leaves founder time for 'the art' at the very niche core: the (currently) unautomatable, the special leaps humans tend to make, and north star setting. We want founders to go super deep into the main problem they are trying to solve and not get held up doing anything else, from searching for domain availability with trademark checks to setting their company up, to running their processes on autopilot. So to help that broad foundational layer for starting and running a company, we have spent around a year exploring how to help founders focus on their mission across different verticals, geos, stages and initial starting conditions. Our first few attempts at making something useful actually failed as the market and AI was moving so fast; it just got absorbed into the models or other product releases. We saw polsia happen, openclaw, then grokbot, muse, dots, etc era as the astras, fables, opuses, kimis and jevs rolled out. What seemed to have been missing along the way was a common repo to identify, unify, open and normalize all the lego bricks that startups have to interact with (vendors, processes, business logic, law, best practices, surface areas, art of the possible). As the number of software vendors blew up, the interfaces to drive them multiplied, the international startup founder blew up as silicon valley moved from a specific location to a state of mind and global movement. Unlimited pre seed funding in unlimited vertical sub markets and permutations has brought us to the age of infinity but we have yet to achieve being able to type into an agent a very simple sentence with very complex implications 'just setup my company and make $1m/m'. We have ended up with a hybrid mismash of computer use, API calls, plugins, shifting surface areas (API/MCP/CLI/plugins/chats/musish futures), and changing model absorption to vendor balance making it an exciting and unstable time. As our first step we started to rank all the vendors that startups interface to when going about their journey in terms of how agentically controllable they are via their APIs, MCPs, CLIs etc and computer use compatibility as we are largely switching from a UI driven world to the agentic control surfaces and onto full automation. We have 100 ranking areas you can check out with an overall open ranking here: ultrametric.ai/overall Then we looked at the processes that startups do and how the vendors rank for the sub steps or process as a whole and how this changes across geos. Open processes: ultrametric.ai/processes Then we started to hack on an open repo for startups that would contain all the components we are going to need for a common foundation for founders across the globe even if our gov processes vary so much from Bengaluru to SF or Lisbon to Shenzhen. The founder spirit is geo-invariant. We have looked at special founder/startup situations, the business logic that will make open modules for the startup that we can hack on together, ranked law firms, government services and interfaces, stacks that go well together. We have mapped the law and lore that maps to startups and the schemas that make up the data objects and documents. But also the open documents and the missing gaps. MUCH TO DO. All the BS processes and fluff work that founders waste their time on needs to goto zero. I want to see more supersonic planes, cancer drugs, seemingly impossible missions done and increase the bang for buck founders have with their funding. This is a work in progress and we are keen for the community to come hack on the repo with us here: github.com/ultrametricai/ult… Lets get into the structure of the general repo and the visualizations we made on ultrametric.ai site with example links: - Open rankings: ultrametric.ai/overall - Open rankings example (online payments): ultrametric.ai/arena/payment… - Open ranking example 2 (cap table): ultrametric.ai/arena/equity-… - Open processes: ultrametric.ai/processes - Open process example (Incorporate C-Corp) ultrametric.ai/processes/inc… - Open situations that founders face: ultrametric.ai/situations - Open startup simulator (you can test our different initial starting conditions): ultrametric.ai/startup-sim - Open modules (e.g. cap table, payroll logic etc - we'd love help improving these) ultrametric.ai/open-modules - Open law (we are interested in how law changes the processes, situations and normalizing this over time) ultrametric.ai/open-law - Open lore (we are interested in the historical lore of startups, the rules of thumb and heuristics that might guide your agent): ultrametric.ai/open-lore - Open fields (we are interested in how the schema for the data connects to the open modules, business logic, open documents and artifacts of each process eg how is a EIN defined?) ultrametric.ai/fields - Open documentation (eg SAFE notes and missing! docs) ultrametric.ai/open-document… We have looked at YC companies as well in terms of which ones do well agentically: ultrametric.ai/yc We are interested in stacks and how people are changing their work: ultrametric.ai/stacks We are interested in what is automatable and where the agentic ceiling is currently and how to push that up in all areas: ultrametric.ai/rankings/proc… There is more for you to explore and a lot more coming in terms of how you will be able to navigate business in an optimal way in your chosen surface area; whether you are hanging in grokbot, dots, muse, claude, terminal, chat, or whatever is coming up in this exciting time. We have a lot of data to collect, clean up and help agents navigate the vast landscape emerging functionality the entire startup ecosystem has to offer. Come join us getting your vendor in, ranked or that access pattern you have wanted or getting the data more accurate in terms of which vendor is best for a process or steps. Or open a module of something that currently cost money that could be free. Also come play in our discord for founders and join the chaos (we can solve each others issues including fund raising tips from me personally): discord.com/invite/3aHky836q… It's time to lift all the boats in the harbor and I'm extremely excited as to what the community of hackers and painters will hack into the repo, what asks they will have and whether we can shift normalization of processes globally, gov agentic interfaces and have common repo for founders that grows over time. I expect a lot of experimentation along the way, bugs and upside... I appreciate our backers having faith in us as well from @ycombinator with @garrytan to @immad doing a sizable check (congrats on $1bn rev homie) to @darbyw (congrats on Stripe buying @clerkyinc), @drewhouston form @Dropbox and also @pioneer_fund @tomerlondon cto @GustoHQ and hopefully this is 'boring' and complex enough for Charlie Songhurst :> @delingpole @Liquid2V @ivolo from @segment @ryanpowe @sonicaghi of @kong @DellyTamer+ other supporters (@michaelwfrank, Fundamental ventures and if I missed you sorry moving fast!). [And don't worry, all vendor rankings are open scoring methods so no favors are being done if someone backed us and is a vendor they have to compete with how agentic and automatable they are just like anyone else.] PS: we have bugs and many quality upgrades to make but thought better to go out early and fix as a community. PPS: we have other stuff to launch we just thought this was a great place to start and have 200 PRs coming out pretty soon but I still want to know what is broken or doesnt make sense or inaccurate. Special thanks to current and past team members for getting us here and all the experiments we did you know who you are.
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Y Combinator retweeted
Today we're launching Ploy Effects. Ask for a liquid-metal logo, a halftone photo, or an animated background, and Ploy builds it into your site, tuned to your brand. A site that grows should also feel great to visit. Built on @paper's shaders.
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Three major announcements today: 1/ our first supersonic turbine core is complete and shipping to test 2/ our 2GW Superpower Superfactory in south Denver is already making parts 3/ we are building our own advanced all-digital foundry to make single crystal turbine blades
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Your AI works fast. You're still working late. We just shipped Supernova, the new Awesomic. Agents, your team and vetted experts work on the same project, on a real cloud machine. Here's what's under the hood 🧵
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Established companies will at best adapt to AI. Newly founded startups will be AI-native from the start. This is an interesting example of a company in the middle, which (by great effort) remade itself as an AI-native company.
We built a $150M ARR business twice. The first time took 8 years. The second took 2.5. Today, Podium crossed $150M in AI annual recurring revenue. Three years ago, we already had a great software business. Well over $100M in revenue, tens of thousands of customers, and a product we'd spent nearly a decade building. And we decided to start over. The business was performing well. But we believed AI was going to fundamentally change the software industry and make much of traditional software obsolete. As a founder, you pour your life into what you're building. An insane amount of time, sacrifice, and effort. The hard part is not getting too attached to it. When we started building our first AI agents, we had to confront the possibility that much of what we'd built might not be that valuable in the future. That's a tough pill to swallow. But we also realized those same 10 years had put us in a unique position to take advantage of what AI could do for our customers. Instead of protecting what we'd spent a decade building, we decided to bet the future of the company on AI employees that actually do the work for our customers. We massively underestimated what we'd signed up for. We ended up building entirely new operating systems for the industries we serve, from the AI agents all the way down to the systems of record. Now customers run their entire businesses on Podium. Reinventing our product wasn't even the hardest part. We had to reinvent how the entire company operated. When you've been running a company for over a decade, the operational inertia is incredibly powerful. We had to rethink how we build products, how we sell, how we serve customers, and how we run the business. We effectively started a second startup inside an already successful company. And we're still at the very beginning. Today, we have 20,000 AI agents working for our customers. They've helped generate more than $13 billion in revenue for those businesses. And we're barely scratching the surface of what AI will be able to do for them. And we've done all of this while becoming profitable and generating free cash flow. We're building Podium to be around for decades. Huge credit to our team for having the guts to make this bet and the ability to actually pull it off. We still have a long way to go and tons of problems to solve, but it's so damn fun to be winning when the opportunity ahead is this big. In the age of AI, you can either disrupt your own company or wait for somebody else to do it. I'd rather do it ourselves.
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Super smart idea, this is going to be the default way to buy and sell data
We're announcing the public beta of datavendor.ai We spent the last two years building the best solution for people buying and selling data, whether you're a researcher in a lab or a company trying to sell data. Data is the fundamental bottleneck for AI progress, and DataVendor will be the infra that solves it.
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introducing land search and enrich api + mcp billions of real world data points, structured for agents to make decisions. now your agents can: > find matching land across 160M+ parcels. > filter and rerank using 550 available data fields. > compare assets using structured data with sources. fetch, our location data api, got 4M+ calls. and we just made it completely free. built for agent speed and scale. dirt cheap. sign up and get $10 for free: mireye.com
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Today Boom unveils our first completed supersonic turbine core, a 2GW Superfactory, and—one more thing—the world's most advanced all-digital foundry to cast single crystal turbine blades and vanes
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The next YC Paper Club is AI for Bio! It’ll be a great time — join!
Innovations like AlphaFold, Evo2, ESM, Hippocratic, and OpenEvidence have transformed the way drugs are developed and disease is diagnosed. AI is now used in some step to aid in the development of almost every drug coming to market. 81% of US physicians now integrate AI into clinical workflows. We are even seeing this scale to individual therapeutics, curing cancers that were never before possible, such as when GitLab founder Sid Sijbrandij recently generated 25 terabytes of single-cell sequence data to achieve measurable remission for recurrent osteosarcoma (a rare, aggressive type of malignant bone cancer). For the next YC Paper Club, we invite the authors of these works to discuss the exciting future of AI for Biology. Please join us in Mountain View on 11/4! RSVP below: events.ycombinator.com/yc-pa…
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We’re thrilled to share that Lamin is now generally available! With just a few clicks, anyone can create a database on lamin.ai In 2022, we set out to build an open-source data management tool that’d make it easier to scale AI workflows in the life sciences. Over the past few years, we’ve served research labs and enterprises of all sizes with what used to take a dozen tools: - Data lineage - Format-agnostic lakehouse - Notes, ELN, LIMS, ontologies - Versioning, branching - Data lake, catalog - Agents, workflows, notebooks - Feature store, MLOps With Lamin, it’s just Postgres and storage in a Python package, plus an optional GitHub-like web platform. Give it a try: pip install lamindb Thanks to @_DimensionCap and @ycombinator and many others who made this possible
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“Customers are our religion, we’re talking to them every single day” – @alexdanilowicz No surprise @magicpatterns has been growing so fast, and the new MagicOS is how design will happen in the future.
Magic Patterns (W23) is one of the fastest-growing design tools in AI, helping product teams turn ideas into interactive, code-backed prototypes in minutes. @alexdanilowicz and his co-founder, both front-end engineers, built @magicpatterns to solve a problem they had themselves: finding a better way to turn ideas into something visual. They were among the first teams building prompt-to-design tools back in 2023. In this episode of Design Review, Alex joins YC’s @aaron_epstein to demo Magic Patterns and its new MagicOS canvas, where teams can bring together prompts, images, PRDs, and other context to explore ideas visually. Then they review a handful of startup websites to break down what makes a site feel “vibe coded,” where founders go wrong, and how to make AI-generated design feel more intentional. 02:11 — From YC Pivot to Product-Market Fit 03:44 — Staying Ahead as AI Design Tools Improve 04:37 — Demo: Designing With Magic Patterns 09:52 — MagicOS 11:23 — Designing With Context on a Canvas 14:50 — How to Get Better Results From AI Design Tools 15:35 — How Far Can AI-Generated Design Go? 18:17 — Website Review 1: Ego 23:59 — Website Review 2: Sentient OS 32:01 — Website Review 3: Cerenovus 36:59 — Website Review 4: Slashy 43:33 — How to Develop Better Design Taste 44:20 — Why Everyone at Magic Patterns Talks to Users 45:07 — Balancing Speed and Design Quality 46:07 — The Future of Design and Magic Patterns
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Introducing Context Awareness. Your browser is now your second brain. ⋅ Remember everything on your screen ⋅ No need to explain your context every time ⋅ Saved privately on your device ⋅ Connect Aside as memory for your personal assistant aside.com
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today, we're excited to announce change reports. @raindrop_ai watches every PR and sends you slack dm's on how your agent changes actually performed in production
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Introducing AI Skills: a framework to score how well an engineer manages coding agents. In the last year, the biggest challenge in engineering became how do you manage agents. Today, we've solved that. We built a model to judge every prompt across six dimensions:
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Today we're sharing a major milestone for BioStack: We’ve raised $9.5 million from @peakxvpartners, @EmCollective, @DCVC, @AforeVC, @VerdictCap and @ycombinator among several others to build the data foundation for the next generation of healthcare AI. 🧵
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