@xtzhou

General Partner at Triatomic

Palo Alto, CA
Joined April 2009
We’ve always been impressed at the technical depth and execution at @dMatrix_AI, and it’s great to see industry leaders such as @nvidia and @AsteraLabs recognizing this. Excited to see the 🔥 performance of Raptor + Vera Rubin!
Big news for d-Matrix and AI inference. We’re collaborating with NVIDIA to bring our next-gen Raptor inference XPUs into NVIDIA's MGX rack-scale infrastructure with NVIDIA NVLink scale-up interconnect. Ultra-low latency inference. Premium-level token services. Built to scale. Read the full announcement: d-matrix.ai/announcements/d-… #NVLinkFusion #AIInfrastructure #AgenticAI #dMatrix
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Peter Zhou retweeted
As it becomes viscerally apparent just how rapidly and thoroughly AI is conquering the realms of numbers and code, I keep thinking about what this means for the realm of atoms. While the concern about the risks is real and warranted, the potential for positive impact is immense and imminent. But the limits now shift to the complexity, time, and cost of interfacing with the physical world. What I love about biology, and what distinguishes it from most other realms of atoms, is the power of massive parallelism to overcome these limits. This is what drew me to the field over a decade ago. The moment has arrived for those of us who have been building massively parallel interfaces between the vast space of molecular designs and the complexity of biology. Our team at @ManifoldBio has been building an interface directly into whole organisms. We now have the potential to catalyze some of the most beautiful and beneficial applications of powerful AI.
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Peter Zhou retweeted
A note from our co-founder and CEO @zainasgar on Gimlet Labs' $300M Series B. This milestone belongs to the team building Gimlet Labs. We’re hiring across the company as we scale our multi-silicon inference cloud. If you want to tackle some of AI infrastructure’s hardest problems and help build for the agentic era, join us: gimletlabs.ai/join_us
1/ Today, we announced Gimlet Labs’ $300M Series B, led by @a16z, joined by @SapphireVC as a major investor bringing our valuation to $3B. We started Gimlet with a simple conviction: inference would become the dominant AI workload, and its infrastructure would need to be rebuilt from the ground up.
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This is really cool. Congrats to the @axiommathai team!
1/ ✨ World record today on bounded gaps between primes, one of number theory’s oldest open conjectures.  We show that infinitely many pairs of prime numbers are separated by 212 or less.
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Don’t use AI that forgets after every session. Bring your context to any model. Love what @ATCalder and @HeresTheChurch have built!
Today we're launching OM2. Your AI re-reads your entire company from scratch every time you ask it something. It’s why more than 50% of your token bill isn't in the answer, but in the search for it. OM2 gives your AI a permanent memory of your company. 🧠
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Maintain access to the global robotics hardware ecosystem with secure, compliant software from @dimensionalos 💪
Excited to announce that Dimensional is now THE compliant gateway for robots to access the US Big thanks to dimOS contributors cited in the National Security Determination and our supporters in government Working closely with our partners home & abroad to secure network attacks, eliminate data exfiltration, and protect American infrastructure from foreign backdoors
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Teleop is the quiet unlock for the whole robotics stack: training data, remote services, human-in-the-loop RaaS. dimTELE makes it open source and low-latency for ANY robot. Entire businesses will be built on this layer. 🤖
Announcing dimTELE. Remote control ANY robot, from ANYWHERE in the world, with ultra low latency, fully open source. Required for training data collection, remote professional services, human intervention for RaaS and more. Whole businesses can be built on teleop.
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Spatial intelligence and navigation start developing at infancy for human beings. @dimensionalos is democratizing this capability for robots 🤖
Announcing the first production robot navigation framework on $500 hardware Explore the world once → your robot agent will relocalize and build a persistant, spatial memory across sessions SLAM, relocalization, loop closure, map i/o, planning, control No ROS. Open source.
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Made it to Top 8 (for now) after Claude suggested taking a break from neural nets and submitting a strong rules-based agent. Submission derived from this Kaggle notebook, which is now all over the top of the ladder: kaggle.com/code/masamikobaya… There’s very little sign of generalizability for agents across deck archetypes so far. Deck choice and training data curation makes every agent, even the neural-net-based ones, a custom engineering effort.
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Top 4 now. But the top of the ladder only plays one another intermittently and it’s either mirror or Alakazam. Will be fascinating to see how this meta evolves in the next 6 weeks.
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237 out of 3075 teams one week in… Neutral net trained on real ladder games & targeted self-play data with no hard-coded rules or heuristics. Unfortunately it only plays Crustle 🦀 well 🤦🏻‍♂️ Fitting for an agentic, OpenClaw-like research loop 😂
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I'm not an engineer. But @claudeai and I now have a GPU server on @nebiusai grinding a nonstop self-play + real-data training loop for the Kaggle @PokemonTCG AI challenge. We retraced all of AI history to get here — heuristics → search → neural nets → RL → self-play. Turning a hobby into AI education 💪
本日6月16日(火)より、「ポケモンカードゲーム AI Battle Challenge」を開始! ポケモンカードゲームのプレイを競う、AIエージェントの開発コンテストだよ。 くわしくはこちら! ptcg-abc.pokemon.co.jp/ #ポケカ #ポケカABC #PTCGABC
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Proud to be backing @RadicalNumerics. Very rarely do you see the combination of "Big Idea" (models that can understand and generate the code of life) and a team that's purpose-built to tackle the challenge. They created the field of generative genomics, and I can't wait to see what they build next! fortune.com/2026/06/15/exclu…
Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: radicalnumerics.ai/blog/radi… At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms. We founded Radical Numerics to turn the tide. And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins. The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life. Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now). 1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer). 2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated. We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up. Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives. Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap , and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs. Fortune article: fortune.com/2026/06/15/exclu… Jobs: radicalnumerics.ai/join-us
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Peter Zhou retweeted
Together with my co-founders Michael @MichaelPoli6, Stefano @Massastrello and Armin @athmsx, I am excited to announce @RadicalNumerics is emerging from stealth with a $50M seed round to build general biological intelligence. We’re also sharing an early preview of our new model Omnii, the most powerful genome language model to date. Omnii preview link: radicalnumerics.ai/blog/radi… At Radical Numerics, our mission is to master the code of life, and to drive the frontier of biological AI for both design and defense. This is our dual mandate, which comes from something our own team helped make possible. Our founding team trained Evo and Evo 2, the largest biological AI models (40B params) trained on DNA sequences. Trillions of tokens across all of life, from microbes to mammals. It’s fully open source, and created the field now known as generative genomics. Last year, scientists used Evo to generate the world’s first complete genome from scratch using AI. Turns out it was a bacteriophage—a type of virus. It functioned in the real world, and in this case it was harmless. But for us, it was a clear turning point. It showed that AI is no longer just analyzing biology. It is on the cusp of generating functional lifeforms. Eventually, AI will have the power to design and control life itself. That should make all of us incredibly excited, and incredibly uneasy. (Anyone can design DNA with a new function, and have it synthesized and delivered, like something from Amazon Prime). The same technology that will help us cure cancer is the very technology that might create the next global pandemic, or worse, allow the creation of bioweapons that can wipe out populations. We believe these forces are inseparable. If you work on the frontier of biology, you have to build technology to safeguard it from its misuse. Existing biosecurity tools are sorely losing the arms race, relying on outdated “have I seen this exact thing before?” style algorithms. We founded Radical Numerics to turn the tide. And we can’t do that by training on textbooks and natural language. We must understand the language of biology from the raw physical data itself, to reason across every molecule and modality, from DNA to proteins. The next frontier for AI goes far beyond chatbots or video generators to models that can understand and engineer life. Today, we’re previewing Omnii, which is already far surpassing Evo 2, and will continue improving as we scale and add new modalities (training now). 1. For human health, Omnii can read and write whole genomes (more on writing later). It’s state of the art (SOTA) on detecting causal variants for disease, and can rank Alzheimer's mutations zero-shot. We’re partnering with a diagnostics company to use Omnii for early cancer detection (pancreatic and multi-cancer). 2. For defense, Omnii is SOTA at detecting AI-generated pathogens. We benchmarked existing detection tools, and they simply can’t detect the AI-generated ones (“deepfake viruses”). We’re partnering with a US national lab to pilot Omnii for detecting the next pandemic, both natural and AI-generated. We have a data center full of Blackwells in construction now to build the most powerful biological AI models ever. This mission takes a new kind of AI lab that can actually scale on physical, biological data: new alignment research (mid/post training), scaling long context, building out mech interp teams to dissect what these models learn, new architectures and systems designs, all from the ground up. Our team is made up of AI researchers and scientists from top labs and institutions (e.g. Stanford, MIT, Google DeepMind), but more importantly, we all share the belief that this is the most important challenge of our lifetime. If you feel similarly, we are hiring. We aim to bring the brightest minds in AI and science together to save lives. Thanks to our partners on this journey, led by Emergence Capital @emergencecap, with Obvious Ventures @obviousvc, Triatomic @TriatomicCap , and Patrick Collison @patrickc. Our advisors include Eric Horvitz @erichorvitz, CSO of Microsoft, Chris Re @HazyResearch of Stanford, George Church @geochurch of Harvard, and Andrew Weber @AndyWeberNCB, former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs. Fortune article: fortune.com/2026/06/15/exclu… Jobs: radicalnumerics.ai/join-us
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Peter Zhou retweeted
Last week, I was invited to present at @AnthropicAI'e Boston Tech Week event. Since the Claude Code moment at the end of last year, the way we do science at @ManifoldBio made a quantum leap overnight. But really, it just came full circle. Manifold was founded by “hybrid scientists” and most of our first few hires could and did both code and do wet lab experiments. This was critical to invent and build the foundational molecular barcoding tech that lets us do the million-scale experiments no one else in the world can do, including interrogating drug candidates directly in vivo. Soon, the form of most of the data generated at Manifold became next-gen DNA sequencing data. (At the Anthropic forum, I live vibe-analyzed a summary that showed we’ve now done 525 NGS runs generating over 70 tera-bases — 10,000 human genomes worth). Engineering biology through this NGS lens is both a feature and a challenge. A feature, because this is how Manifold unlocks massive parallelism (a.k.a. GPU-ification of biology). A challenge, because for many scientists this was the first time they couldn’t easily analyze their own data, and became bottlenecked by a dedicated computational counterpart to help turn around insights, which still took weeks. Over night, that bottleneck has evaporated, aided by a strong data integration layer and an agentic interface we’ve built on top. Once again, everyone at Manifold Bio has become a hybrid scientist, just in time as the in vivo engine has achieved both scale and richness that have not been possible before. Even before this, we had already started making immensely valuable discoveries, including shuttles exploiting novel portals to deliver medicines to the brain – a high value problem that led to our first landmark deal with Roche last fall. Now that the loop is closed for scientists (and agents) at Manifold Bio, the pace of these discoveries is accelerating and we’re about to sweep through many more of the grand challenges in medicine. Thanks again to the Anthropic team for the opportunity.
Last week, @Anthropic brought together a select group of founders for their Founders’ Lab during #BOSTechWeek. Super to have our Co-Founder & CEO, @glebkuz , be selected to share how @ManifoldBio is doing science in the new era of agents!
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Peter Zhou retweeted
I'm looking for a psychotic ex-founder to work alongside me for 16+ hours a day and live above the conference room as my Chief of Staff We're scaling team past 50 and we're the best capitalized robotics software / OS in the world DM me a video about why you need this role
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Just ported the original @DOOM source (released in 1997 only for Linux) to Windows with @AnthropicAI Claude Code. One main prompt, a couple of debug rounds --> working "doom.exe" in a couple of hours. Clone the repo, add your own WAD to the "windoom" folder and run: github.com/pztriatomic/doomp… For perspective: when @idSoftware released the source on Dec 23, 1997, it took the entire community ~8 weeks to ship the first working Windows port. This codebase is personal for me. As a huge fan of the game at 11 years old, I played around with the source for >2 years learning the intricacies of 2.5D graphics rendering, data structures for levels/textures, and game engine mechanics. Eventually at 13, I released a small source port called NetDoom that added game lobbies for multiplayer deathmatch. The end result wasn't much, but the learning process was invaluable. Watching an LLM untangle the original Linux mess into a usable binary in one sitting feels like time folding in on itself.
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Peter Zhou retweeted
1/ Since February, 8 papers across algebraic geometry, representation theory, number theory, combinatorics have been quietly appearing on arXiv. Proofs by AxiomProver. 5 papers are now accepted at solid peer-reviewed math journals. To our knowledge, a first for the literature.
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