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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
today's my birthday, and usually people should be giving me a gift, but instead I want to give everybody a gift 😁 another @VeezaAI launch video!!!
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Y Combinator retweeted
I have an announcement. Today, we make Bill Gates proud. @heyclicky is now on Windows.
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absolutely stacked lineup to learn SOTA inference. thank you @realDanFu, @jmorgan, @joefioti, and @lsyincs
This week’s Paper Club is all about inference. For every FLOP spent training a model, roughly six go to serving it each year — and getting that part right has produced over $220 billion in market cap in the last decade. But inference is much more than calling model.forward. Every token has to move through a complicated stack of routing, caching, scheduling, kernels, memory, and hardware, all while balancing speed, cost, and quality. We brought together researchers and builders from @togethercompute, @ollama, @luminal_ai, and @sgl_project & @radixark to take us through that stack from end to end. We’ll see how a token gets from an API request to a GPU and back, why the same open model can perform differently depending on who serves it, and how new approaches to compilers and caching are making inference faster and more efficient. 00:00 - Why inference matters more than training 08:24 - Three kinds of inference innovation 12:14 - Dan Fu (@realDanFu): the lifecycle of an inference request 42:18 - Jeff Morgan (@jmorgan): not all open model tokens are the same 1:04:31 - Joe Fioti (@joefioti): compiling models with search, not heuristics 1:30:30 - Liangsheng Yin (@lsyincs): SGLang and memory for the agent era
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This week’s Paper Club is all about inference. For every FLOP spent training a model, roughly six go to serving it each year — and getting that part right has produced over $220 billion in market cap in the last decade. But inference is much more than calling model.forward. Every token has to move through a complicated stack of routing, caching, scheduling, kernels, memory, and hardware, all while balancing speed, cost, and quality. We brought together researchers and builders from @togethercompute, @ollama, @luminal_ai, and @sgl_project & @radixark to take us through that stack from end to end. We’ll see how a token gets from an API request to a GPU and back, why the same open model can perform differently depending on who serves it, and how new approaches to compilers and caching are making inference faster and more efficient. 00:00 - Why inference matters more than training 08:24 - Three kinds of inference innovation 12:14 - Dan Fu (@realDanFu): the lifecycle of an inference request 42:18 - Jeff Morgan (@jmorgan): not all open model tokens are the same 1:04:31 - Joe Fioti (@joefioti): compiling models with search, not heuristics 1:30:30 - Liangsheng Yin (@lsyincs): SGLang and memory for the agent era
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Made a video with @ycombinator on how to launch. I hope this helps someone out there finally get their first 1000 views, first customers, whatever. This is a game of consistency, not one time viral hits. TLDR: Record a shitty demo, and just start showing your work.
There’s a misconception that a successful launch video needs to cost tens of thousands of dollars and feel like a high production commercial. But @FarzaTV has a different approach. Farza is the founder of @heyclicky and previously built Buildspace, one of the largest online schools for people working on their own ideas. He’s been making videos for over a decade, and today his posts regularly get millions of views. Rather than treating launches as rare, high-stakes moments, he’s made them part of the everyday process of building a company. Farza sat down with YC’s @aaron_epstein to share his playbook for turning demos into stories people actually want to watch. He breaks down how he scripts videos that still feel off the cuff, why founders should launch constantly, and and how to figure out which channels actually convert. 01:44 — Launch Constantly 03:38 — Start Shipping Demos 05:02 — Show the Process 06:07 — The Clicky Launch 07:43 — Write a Hook That Stops the Scroll 10:30 — Why Farza Scripts Every Video 12:04 — Designing the First 10 Seconds 15:15 — Demo-Driven Development 17:25 — You Have to Take a Thousand Shots 20:48 — Farza’s Formula for Videos That Convert 23:01 — Turning Demos Into Stories 24:53 — Show What You're Actually Building 29:11 — Finding Customers on Social Media 31:02 — Which Platforms Actually Convert? 39:01 — Why Farza’s First Hire Was a Filmmaker 40:49 — The Video Stack 45:41 — The 30-Day Launch Challenge
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Y Combinator retweeted
LLMs have a score doubling time of 53.9 days on a robotics benchmark between July and October 2026, while speed increased by 4.4x and cost fell by 6.2x. More results in our new paper: The Bitter Lesson Comes to Robotics 🧵
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🛰️ @OrbitalOps_ (W25) has been awarded up to $35M by the DoD to fly a full-scale, in-orbit demo of Astraeus, their cryogenic maneuvering spacecraft. A typical rocket stage lasts about a week in orbit before its propellant boils away. Astraeus carries onboard refrigeration that keeps its liquid hydrogen and oxygen cold for years, so it can wait in orbit and then maneuver within hours of a command. payloadspace.com/exclusive-d…
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Department of War awarded Orbital Operations $35M to demonstrate Astraeus in orbit. A full vehicle scale demo of high thrust propulsion and active refrigeration for long duration storage of cryogenic propellants. Exclusive from @Payloadspace: payloadspace.com/exclusive-d…
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Earlier this week, we announced our Series E. These are my thoughts on how we got here, and where we go next. legora.com/blog/scaling-fast…
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Introducing the people search API 1 billion people, one API > decision makers to sell to > founders to build with > engineers to hire Let your agents find them.
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persona-2.0-live just doubled its resolution. 2× the resolution, 4× the pixels. The same word-level control over expression. Try it now: keyframelabs.com In preview now for all customers, through the platform and the API.
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we've hired two new helpers for our dinner party: here's steve and miranda pouring pink lemonade for each guest fully autonomously, via a fine-tuned pi0.5 policy 🍋 @lucyjcai and i are testing all kinds of robot control in our quest to host the most robotic dinner party.
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Y Combinator retweeted
The Mintlify agent now runs your own skills. Upload your style guide, review checklist, or page templates once. The agent loads the right one whenever a task calls for it.
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We built our own email infrastructure from scratch. Announcing Resend Forge: ▪️ 129x faster at p99 ▪️ 99%+ delivery rate across inboxes ▪️ activate for your account today It's fast, reliable, and provides best-in-class deliverability. 👇
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Your AI is searching the wrong inbox. You ask ChatGPT if the signed term sheet came in. It checks your work email, finds nothing, and tells you it never arrived. It's sitting in your personal Gmail. Most of us juggle 3 to 5 inboxes. Every AI wants its own login, and each one only ever sees part of your mail. Slashy indexes every inbox once, Gmail and Outlook, and gives your AI one MCP to search all of it. ChatGPT, Claude, Cursor, whatever you use. Every inbox. One MCP. Try it for free at slashy.com :)
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iPhone Duo launches today. To test Empirical Health without hardware, we built a full 3D simulator with inverse camera projection, frosted-glass blur, hinge-anchored layouts, and path-traced lighting.
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Today we announce a $130M Series D at a $1.4B valuation led by @SapphireVC. 7 years ago we picked the market nobody wanted to touch. the machines behind steel, chemicals, energy, food, paper, data centers and many other sectors. The stuff the whole world runs on without thinking about it. We were stubborn about doing it the hard way. we built our own hardware stack, rugged enough to live on the line, and kept it running inside the biggest plants in the world. That stubbornness gave us something no shortcut can buy: years of real data from the lines that keep the economy moving. All of it came together in Janus, our multimodal industrial foundation model. It doesn't just watch a machine, it understands how an entire plant behaves, and it's pointing us toward one system running the whole operation We also built our proprietary AI Center to push it further, with our own supercomputing and real machines we break on purpose so the models learn failure from the very first symptom. 30 patents granted, 70+ pending, all built in house Nothing happened at the plant today. that's our goal back to work 🧢
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Announcing Dagger 1.0 beta. Replace Jenkins and GitHub Actions with CI that matches the speed and scale of coding agents without wasting compute or tokens. Featuring: test-native CI, dev shells, local and remote execution, official modules, and more. dagger.io/blog/dagger-10-bet…
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