Visiting Partner @ycombinator. Backing high-output YC founders via @phosphorcap. Previously founded and sold @ZeusLiving (S11) & @auctomatic (W07).

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Joining @ycombinator as a Visiting Partner for the Spring 2026 batch. Feels a bit full circle.
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We built a Unix-like desktop in the browser for humans and agents. Composable tools, native DOCX/XLSX/PPTX editing and capability-level approval gates. The ambition: make entire projects delegable. Introducing the Vesence Agent-Native Desktop.
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New Vending-Bench results. GPT-6 Sol: > VERY good and VERY cheap > The first misaligned GPT model on VB Claude Opus 5.5: > Worse score than Opus 5 > Opus stopped colluding, still lies Grok 4.7: > The first misaligned Grok model on VB > Beats Opus 5.5
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this is what you're shown on day 1 of YC. don't be scared of launching early.
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Congratulations, @AnaisHowland18! Fixed Income needs a new set of AI enabled tools. Check out @OasiveAI!
Today, I'm excited to bring @OasiveAI out of stealth and announce that we're backed by @ycombinator! Oasive helps bond investors find better bonds to buy. Uncover opportunities you might otherwise miss, compare investments and test the risks before putting on a trade. Which Treasury maturities look cheap? Is there a curve trade worth putting on? Macro and Rates Research helps you explore both. In mortgage-backed securities (MBS), compare pools and see how changes in rates and prepayments affect your portfolio. Our AI analysts bring the research and proprietary models together, so you can go from idea to decision in minutes. Thank you to @kul and @garrytan for taking a chance on me and believing in @OasiveAI! Start your Macro and Rates Research free trial or request an MBS demo: oasive.ai/join Here's what you can do 👇
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Excited to work with @archgen_ to make chip design 1,000x faster!
Archgen Labs (@archgen_) is now backed by Y Combinator (@ycombinator) We are a research lab for silicon design, with one ambition: make chip design 1,000× faster. For decades, computing advanced by shrinking transistors. The next chapter for compute workloads will increasingly depend on building vertically - stacking active logic layers to create 3D chips. This makes designing those chips much harder but also opens up new possibilities for performance and efficiency. At @archgen_ , we’re bringing together foundational models, EDA tools, and physics-informed surrogate models to explore more designs, predict their behavior faster, and optimize how they’re built. Grateful to Kulveer Taggar (@kul), Garry Tan (@garrytan) and the @ycombinator team for backing us, and to the customers, mentors, and collaborators who have helped us get here. If you want to get to tapeout faster, we’d love to talk. DM us. #Semiconductors #OpenROAD #OpenLane #OpenSourceEDA #ChipDesign #VLSI #PhysicalDesign #AIForHardware #3DIC @naveen_venk
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The reason money motivated people make for bad venture backed founders is precisely because they have not actually internalized this truth about the nature of venture. Failure does not mean walking with nothing, it means taking the $30,$50 or $100MM personal liquid stake you have in hand and betting double or nothing on it over and over to have a slim chance at it becoming $10bn. It’s a bet no person who’s in it to be rich would take. It’s only those who are in it for status, glory and power.
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Today we’re announcing our pre-seed round and a new product: @starslingdev Review Runners - code review agents that use your model key, your team’s skills and scripts, and reviewers you define, all running in GitHub Actions. We’ve raised $3M from @BessemerVP and @ycombinator to make code verification agent-native. code → verification (ci + code review) → production
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We built a new harness using @typesafeai's Jev that cuts the cost of repetitive work by 90%. The harness learns the job as it runs, moving steps from LLM calls to code. Running 100,000 compliance alerts costs >$290K on Opus 5. With agentrun() we got it down to <$26K.
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since sharing we're joining yc f26, the question i've been asked the most: "why join yc now?" i.e. why join yc after raising money, after we have a product in the market, customers, revenue etc. etc. i've been fortunate enough to surround myself with yc founders from the last few batches, hear about their experiences and watch their companies accelerate at a pace that seemed crazy fast. so why we chose yc, coming up on ~2 years in, really came down to these 3 things: 1.⁠ ⁠community - just 2 days in and the ambition in the room is insane. yc makes the hard things easier - whatever problem you're stuck on, someone here has solved it or is three steps ahead of it. 2.⁠ ⁠pace - we're being pushed to ask how we grow 100x faster, not 50%. the first thing a former yc founder told me when they found out we were joining the batch: the best use of your time at yc is asking how you hit every goal 100x faster. 3.⁠ ⁠signal - yc has built a very special, high-trust community. some of our customers are yc companies themselves, and that was before we even joined the batch. tl;dr: we joined yc to change the trajectory of our company. ps: we have a camera roll full of these that didn't make the cut for the first announcement, so you're going to be seeing more of these
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I keep noticing the gap between the AI conversation on X and the conversations I have with customers in non-tech enterprise. Many of them are still figuring out how to use AI beyond copy-pasting prompts into Copilot. We need to close the gap.
Article

Bridges Before Rockets: The case for more boring AI

I was talking to a large enterprise customer recently and asked what AI tools their employees actually use. They said “Microsoft Copilot.” That’s basically it. And I've heard this over and over again

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Today I’m excited to show you our new harness called AgentRun, built with @pidotdev and @typesafeai's Jev. It’s built for an agent to learn how to do a job, code itself a general solution, and then get out of the way.
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Nicely done, Rishi!
Kastle is building AI employees for banks. Its agents automate operations across mortgage servicing, consumer lending, and other back-office functions. Today, Kastle works with 10 of the top 25 mortgage servicers and has processed more than $2 billion in transactions. The company recently raised a $24M Series A, just two years after going through YC. In this episode of Founder Firesides, founders @therishic and Nitish join @sdianahu to share why they started over just one month before Demo Day, how they found their way into one of the hardest industries for deploying AI, and what it takes to make agents reliable enough to handle real financial transactions. 00:05 — What Kastle Does 01:00 — Why Banks Need AI Employees 02:50 — Going Back to Zero Before Demo Day 05:00 — Finding the Idea for Kastle 06:43 — Landing Their First Customer 08:18 — Moving Into a Customer’s Office 08:59 — The Future of AI Employees in Banking
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GPT-6 Astra deciphered a 1918 German radio transmission that, to my knowledge, has never been deciphered before. The message below translates to: "EIN ENGLISCHER KREUZER EINLIEG X SEWASTOPOL X S4STEN X EIN GESCHWADER DER X ALLIIERTEN FOLGT 26STEN X" or, in English: "AN ENGLISH CRUISER ARRIVED AT SEVASTOPOL ON THE ?4TH AN ALLIED SQUADRON FOLLOWS ON THE 26TH" Astra even double-checked its work by determining that an English cruiser, HMS Canterbury, reported its arrival in Sevastopol on November 24, 1918 and the arrival of an allied squadron on November 26, 1918. This message is one of the ~20 WWI German radio messages that appear as one of the entries in the scienceblogs.de list of top 50 unsolved ciphers (scienceblogs.de/klausis-kryp…). A minor, but really cool result!
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Models are commodities. What your agents learn doing your work is the asset. Memorable lets you own it. Excited to be working with @advaiytsane and @nikhilk8754 on @memorablesh. Congrats on the launch!
Introducing Memorable (YC S27): PROCEDURAL MEMORY FOR AI AGENTS AI agents today are born, work, and die inside a single context window. They solve a hard problem once, then start from zero when it returns. Memorable turns successful runs into a graph of reusable procedures. So every task makes the next one: - Faster. - Cheaper. - More deterministic. We’re excited to share that Memorable is joining Y Combinator’s S27 batch. Try it: memorable.sh
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YC works with thousands of founders every year, which gives us an early look at how startups are changing. Right now, the shift is striking: startups are moving from bits to atoms, nearly one in five YC companies has a solo founder, and companies are reaching meaningful revenue faster than ever. In this episode of the Lightcone, @garrytan, @snowmaker, @sdianahu, and @harjtaggar dig into what’s driving these changes and what they mean for founders. They discuss how AI is making it possible for smaller teams to take on more ambitious problems, why experienced founders are having a resurgence, and why knowing what to build is becoming more important than simply knowing how to build it. 01:06 — Startups Are Moving From Bits to Atoms 03:29 — Why AI Is Making Hard Tech Easier 05:21 — Defense, Manufacturing, and the Return of Hard Tech 09:48 — AI Compute Is Becoming a Physical Infrastructure Problem 12:34 — Robotics Is Approaching Its ChatGPT Moment 15:40 — Software Isn’t Dead. It’s Becoming the Harness 17:59 — Why AI Startups Are Growing Faster 22:52 — The Hidden Boom in Data and RL Environments 27:08 — Why Robotics Will Need Specialized Models 29:38 — The Rise of the Solo Founder 32:54 — Why Experienced Founders Are Back 34:59 — What Founders Should Do Right Now
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we tell YC companies not to hire during the batch. founders often find that confusing - isn't hiring the ambitious thing to do to grow faster? basically there are two types of hiring. one is fine to do during the YC batch, the other might mess up your company. the type we don't recommend is traditional hiring. this is where you put up a job post, do some sourcing, ping people you know for referrals, and run people through an interview process. hopefully within three or four weeks you have an offer out. then it takes them another week or two to actually start the job, and at least a couple more weeks before they're onboarded. big problems with this approach: 1. it's slow. it's two months before you get any ROI, and that's optimistic. realistically more like three months. in YC, three months is the whole batch! 2. ...and all that assumes you actually hired somebody, that they're good, and that they became effective fast. you might spend a bunch of time and not make a hire at all! 3. it's a big time investment for you and will distract you from everything else you need to do at a critical point in time for your company. 4. and if it doesn't work out, you spend even more time and life-force managing them and firing them! here's the type of hire that's actually okay to make: somebody who you already know really well. why? you already know they're good and that you want to work with them. it derisks the entire process. you can talk to them the same day, give them a short technical interview and get them signed that week, and have them productive immediately. you're also way more likely to get a much stronger candidate this way (since your earliest hires will want to work for you not because of the company, but because of you!)
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Very excited by this, congrats @lukaspet!
Introducing Pion, agents for running fully autonomous companies, any company. We’ve used Pion to run vending machines, radios, stores, cafes & more. How much could Pion make running other companies? Find out yourself! Setup is trivial, the agents do the rest.
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I reread this every year. I cry like a baby every year. Todd Beamer died a father, husband, and hero. Todd: Hello… Operator… listen to me. I can’t speak very loud. This is an emergency. I’m a passenger on a United flight to San Francisco. Our plane has been hijacked. Lisa: I understand. Can they see you? Todd: No. There are three that we know of. They have knives — razor knives, like box cutters. Someone announced from the cockpit there was a bomb. It sounded fake. Lisa: Your name?Todd: Todd Beamer. United Flight 93.Todd: They killed one passenger in first class. They forced most of us back. Fourteen of us here. Five flight attendants. The guy with the bomb ordered us to sit on the floor. Lisa: Are you okay? Todd: We’re going down… wait. No. We’re leveling off. We changed directions. We’re flying east again. Todd: A guy named Jeremy called his wife. She told him two planes hit the World Trade Center. Lisa, is that true? Lisa: I have to tell you the truth. It’s very bad. Both towers are gone. A third plane hit the Pentagon. Our country is under attack. I’m afraid your plane may be part of their plan. Todd: Oh God. Lisa, will you do something for me? Call my wife and my kids. Promise me you’ll call. Lisa: I promise. Todd: Our home number is… You have the same name as my wife. Lisa. We’ve been married ten years. She’s pregnant with our third child. Tell her I love her. I’ll always love her. We have two boys — David, he’s 3, and Andrew, he’s 1. Tell them their daddy loves them and he is so proud of them. The baby is due January 12th. I saw an ultrasound. We still don’t know if it’s a girl or a boy. Lisa: I’ll tell them. I promise, Todd.(Lisa patches in the FBI.) Agent: Todd, your plane is on a course for Washington. Best guess is the White House or the Capitol. Todd: I understand. I’ll be back. Todd: Everyone knows this isn’t a normal hijacking. We have decided we will not be pawns in their plot. Lisa: What are you going to do? Todd: Four of us are going to rush the one with the bomb. Then the cockpit. A stewardess is getting boiling water. We’ll take them out. Todd: Would you pray with me?They pray the Lord’s Prayer. Then: Yea, though I walk through the valley of the shadow of death, I will fear no evil, for thou art with me. Todd: God help me. Jesus help me.Are you guys ready? Let’s Roll.
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Dominoes using @BrainbaseHQ to improve their agents dramatically.
For a QSR, local competitor analysis is a vital part of setting prices and planning promotions. Domino’s partnered with Brainbase to improve its competitor analysis agent from 41% to 97% accuracy while reducing cost per source by 52% across dozens of local markets. Read more about how we got there below:
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We’ve spent the past three years building agents for real production work. Our architecture changed a lot along the way. Some of those changes worked. Others produced expensive and occasionally painful lessons. Read this to learn from our mistakes.
Article

Ten Lessons Learned From Three Years of Building Agents

Lego Lamborghinis In November 2023, our team of five gathered at a WeWork near Mill Valley, north of San Francisco. A bookshelf in the common area concealed a secret door. Behind it were a speakeasy

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