Jihui Yang (@alsiens) / X

Very cool!

We’ve spent the past year building high-throughput labs and infrastructure for large-scale physics experiments in our search for better superconductors and magnets. We ran our first experiments by hand. Now, experiments run continuously in the labs. We’re building AI to make

Liam Fedus

@LiamFedus

Sep 15

We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained

Miles ahead

Today we're launching Miles v0.1, an open-source RL framework for LLMs and multimodal models. RL training is easy to start and hard to debug. Miles helps you ensure your run is correct, use hardware efficiently, and keep RL running at scale. Over the past 9 months, 72

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🔥

Sequel for my H100 blog is out now. We implement Blackwell matmul for NVFP4 from scratch, and outperform cuBLAS by 4.7% at N=8192. This time we have something better than Hilbert curves - that exact-fits for the Nvidia hardware. Full blog post with all details:

赢，不需要被解释

CRACKED ALERT: The startup powering @sgl_project, @radixark, has some of the most hardcore engineers in inference. They power production at @xai and at many Chinese labs.

Try muse code

muse code in beta is live. first coding agent from msl, built on muse spark 1.2. install: curl -fsS dev.meta.ai/install.sh | bash here's what you should know: