Joseph Shetaye

(1/n) Evolutionary frameworks like AlphaEvolve and GEPA use diversity and fitness to select which subset of past experiments to condition the next generation on. Why not let an agent choose instead? To this end, we introduce Coding Agents as Text Optimizers (CATO). We beat AlphaEvolve on 2 out of the 3 problems we try. Work done with @shaurnav. Blogpost and details in thread.

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(1/7) We're releasing ThunderKittens 2.0! Faster kernels, cleaner code, industry contributions, and new state-of-the-art BF16 / MXFP8 / NVFP4 GEMMs that match or surpass cuBLAS! Alongside this release, we’re equally excited to share some insights we learned while squeezing every last TFLOP out of Blackwell: (with @hazyresearch & generously supported by @cursor_ai)

So @Stanford makes all of its students carry very good insurance OR get bullied into its 8k a year EPO plan. Wait, did I say EPO? Nope! They recently announced that to seek care even in network (including primary) you HAVE to see campus health first for referral. 8k a year HMO.

Introducing 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗯𝘂𝗯𝗯𝗹𝗲𝘀: a *fully unsupervised* LM for input-adaptive parallel latent reasoning ✅ Learn yourself a reasoning model with normal pretraining ✅ Better perplexity compared to fixed thinking tokens No fancy loss, no chain of thought labels 🚀

Happy Throughput Thursday! We’re excited to release Tokasaurus: an LLM inference engine designed from the ground up for high-throughput workloads with large and small models. (Joint work with @achakravarthy01, @ryansehrlich, @EyubogluSabri, @brad19brown, @jshetaye, @HazyResearch, and @Azaliamirh)