dreams do come true 🥹. excited to see our work (w/@JonSaadFalcon, @HazyResearch, john hennessy and @Azaliamirh) feat. in a major way in @FT. the world is becoming increasingly less dependent on centralized cloud ai. we are just getting started 🚀🌖

So well deserved! The AI era is compute-constrained, and chip design is one of the deepest bottlenecks. @annadgoldie & @Azaliamirh are true forces of nature: they saw this early (before this has now become obvious!) and built @RicursiveAI to solve for it Now they’re proving AI for chip design in production: real industrial chip designs, against commercial tools, with step-function results: - Dramatically faster runs - Cleaner layouts - The ability to take on problems existing workflows struggle to handle Their early results are groundbreaking, and everyone from chip incumbents, frontier labs, to hedge funds are now asking for it

We’ve seen an explosion of new ML chips with unique architectural features, but software support remains the critical bottleneck Achieving peak performance increasingly relies on hardware-specific optimizations in the kernels, but we observe that coding agents are particularly weak at this Introducing Hawkeye, a framework that brings hardware-awareness to coding agents by grounding them in a minimal and comprehensive taxonomy of optimization strategies For new GPU or ML accelerator architectures, you only need to write 10 unit tests and solution kernels (one per optimization strategy), and we show that coding agents can effectively scale test-time compute with this minimal supervision to write hardware-aware kernels Hawkeye can port kernels across architectures (Ampere, Hopper, Blackwell), vendors (NVIDIA, AMD), and precisions (FP8, NVFP4, MXFP4) while consistently leveraging hardware features and approaching expert kernel performance Work co-led with @keramakr and done in collaboration with Alexander Ingare @simonguozirui @18jeffreyma @ZishenW @simran_s_arora @Azaliamirh @profvjreddi

Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper 💰 As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost. For example, we find that sampling just 5 solutions with DeepSeek V4 Flash and ranking them using the same model with LLM-as-a-Verifier can lead to a significant boost in accuracy (79% → 88%), outperforming closed frontier models on Terminal-Bench. Try it out today: More on verification scaling in my previous post.

@Azaliamirh @Sanjay_Ghemawat @quocleix @OriolVinyalsML Thanks, @Azaliamirh, and thanks to both you and @annadgoldie for being so kind in offering advice to us as we start this journey and having us over for Ricursive's happy hour a little while ago! We're inspired by what you all are building there!

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at:

@RicursiveAI on @nvidia 's slide at the #DAC2026# opening keynote. Thank you for the shoutout, Timothy Costa. Next up: our co-founder @Azaliamirh on the "New Minds, New Models" panel, 10:30am, Room 104A. Then find us at Booth #1049#.