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Users get two completions for their prompt and rank them. From this you can then use Bradley-Terry to get Elo scores per model.

242 Elo points clear of the next best model and 93% win rate against random models (96% against nano banana) while Gemini 3.1 (second best) sits at 67%. That’s quite the leap.

Related: Project Glasswing: Securing critical software for the AI era - https://news.ycombinator.com/item?id=47679121 Assessing Claude Mythos Preview's cybersecurity capabilities -…

The biggest takeaway is that they claim SOTA for multi-modal stuff even ahead of proprietary models and still released it as open-weights. My first tests suggest this might actuall…

Super interesting that they moved away from their specialized, Lean-based system from last year to a more general-purpose LLM + RL approach. I would suspect this likely leads to im…

The rate limits apply only to the Gemini API. There is also Vertex from GCP, which offers the same models (and even more, such as Claude) at the same pricing, but with much higher …

Have you checked out https://github.com/prefix-dev/pixi ? It's built by the folks who developed Mamba (a faster Conda implementation). It supports PyPI dependencies using UV, offer…

Mastermind intrigued me in the same way as the author some time ago, and I've used it as a standard problem when trying out new computational frameworks/methods ever since. Here is…