be7a
Joined 7 years ago
Karma
300
Submissions
13
Member since
Sep 14
https://github.com/0xbe7a
https://twitter.com/0xbe7a
View on Hacker NewsRecent submissions
Comment · 6 months ago
Users get two completions for their prompt and rank them. From this you can then use Bradley-Terry to get Elo scores per model.
Comment · 6 months ago
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.
Comment · 1 year ago
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 actually be true, will continue testing. Wow
Comment · 1 year ago
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 improved performance even outside of math competitions. It’ll be fascinating to see how much further this frontier can go.
The article also suggests that the system used isn’t too far ahead of their upcoming general "DeepThink" model / feature, which is they announced for this summer.
Comment · 1 year ago
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 rate limits (basically none, as long as they don't need to cut anyone off with provisioned throughput iiuc) and with a process to get guaranteed throughput.
Comment · 1 year ago
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, offers first-class support for multi-envs and lockfiles, and can be used to manage other system dependencies like CUDA. Their CLI also embraces much of the UX of UV and other modern dependency management tools in general.
Comment · 3 years ago
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 my Rust version with multi-threading, SIMD, WASM running on your device inside a WebApp: https://0xbe7a.github.io/mastermind/
Repo: https://github.com/0xbe7a/mastermind
It is quite fast (1.8 Billion position pairs evaluated in 1652ms on my device) and can also exploit some symmetries inside the solution space.