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Wild move for Snap to try claiming state actor status. Definitely sets a strange precedent for corporate liability and user data.

Maybe Snap's lawyer's AI did not see the second order effects of the idea.

My own attempts at 17th-century handwriting were a nightmare. If an LLM can parse that mess, sign me up for digital humanities.

Have you tried Gemini on them?

Pushing past log2(3) for real. This could drastically shrink LLMs for embedded systems, making them truly portable.


The two-year publication rule is the interesting part. OpenAI doesn't need the million, and the community will judge the result regardless of whether Clay ever accepts it.


Clay will award the prize (or choose to not award it) when there is an expert consensus that the problem has been solved and the solution is correct. The rules merely state what that would mean in some typical cases. There is always an option that Clay changes the rules to match the reality better.


The result was never the point.

Clearly the real-world cannot "blow-up" - real-world water vortices do not reach infinite velocity, etc.

The point of having Navier-Stokes as a Millennium prize was to hopefully generate new mathematics and techniques along the way, and auto-generating a sprawling AI-slop proof or millions of lines of Lean does not accomplish that result.

Clearly OpenAI has no interest in the math itself - to them this was just a trophy animal to shoot and stuff. I would be very surprised if they now helped analyze the proof and try to extract the mathematical value out of it, and this would obviously require outside help who are probably not inclined to help OpenAI math-wash their behavior.


Right, the real world doesn’t blow up. So if N-S does then it means in some situations it doesn’t model the real world well. That’s important because if we can understand those situations we can avoid erroneously relying on it.


Did anyone ever think that N-S was a perfect model?


Curious if this finally makes Sketch or Affinity Photo usable on my Linux box. Apple Silicon VM performance is usually the bottleneck.


CDLMs sound promising for smoother, more coherent text generation. Excited to see how they tackle the token-level discontinuities.


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