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Nat is a great sparring partner, coach and supporter. He has consistently pushed us to be even more ambitious while remaining practical. We are incredibly fortunate to have him as our major backer and now also as a board member at Magic.

Thanks Noam! :)

Daniel is one of the most ambition-provoking people on Earth. Always pushes pushes pushes. That's how it should be.

fixed a bug I've been hunting for days! Yay

Very excited to welcome Andrej Karpathy as Magic's latest investor!

Excited to partner with Google Cloud as we scale up on H100s and GB200s.

Excited to partner, thanks Sequoia Capital for helping fund the scale-up :)

This blog post by Magic does a great job highlighting the weaknesses of popular long-context evals and introduces HashHop as an alternative. Very impressive work from the Magic team and congrats on the new funding!

You know something is good when it aces existing tests and has to invent its own benchmark to flex. HashHop is a step forward, and I hope it becomes the norm, replacing the stupid "Needle In A Haystack" test for benchmarking long context windows.

Excited to partner with NVIDIA!

We're growing our Applied Team to work on post-training LTM2-medium (and once done pretraining, LTM2-large) into a useful assistant and capable agent.

How close are current AI agents to automating AI R&D? Our new ML research engineering benchmark (RE-Bench) addresses this question by directly comparing frontier models such as Claude 3.5 Sonnet and o1-preview with 50+ human experts on 7 challenging research engineering tasks.

We're hiring for a new team aiming to train AI SWEs to robustly complete long-horizon work on a no-restrictions computer via the GUI. Today's models excel at small, Olympiad-type coding tasks but struggle in complex codebases and aren't easy to integrate into existing enterprise