Follow AI Builders — Not Influencers
Adaptive self improvement https://ii.inc/blog/post/zenith
We pointed Meta-Zenith at one task: make Qwen3.8-27B-NVFP4 faster on a single RTX 5090. 111 self-directed trials later, no human in the loop: +63.5% throughput (geomean, 18 settings) 4.1× at 65K context tool-call quality unchanged https://github.com/Intelligent-Internet/Qwen3.8-Inference-MetaZenith
RT Intelligent Internet We pointed Meta-Zenith at one task: make Qwen3.8-27B-NVFP4 faster on a single RTX 5090. 111 self-directed trials later, no human in the loop: +63.5% throughput (geomean, 18 settings) 4.1× at 65K context tool-call quality unchanged https://github.com/Intelligent-Internet/Qwen3.8-Inference-MetaZenith
away for 3 days to check how's the chalet building is going 🏔️ since I got my pilot licence 2 months ago, I thought about doing it by plane wife: "are you f***ing crazy!!????" ok let's dismiss that 😅 and tbh, it's quite comfy on the Tesla the hardest is not seeing the kids for a few days, miss them already 😢
friends are regularly surprised when i say i don’t think math is (yet) “solved,” so in the wake of the ns result i figured i’d explain this take a bit more widely. first, this is massively impressive and clearly an example of AI being, in some respects, far more powerful than the human mind. the team deserves huge praise for attempting and succeeding at this. i’d love to read a technical report on the project (one can always hope :) but second, here again we ended up with a counterexample rather than a full proof: option C won the NS problem by proving the conjecture false (which was one valid way to solve the problem for sure) notice a pattern in many of the recent frontier results in ai for math? a striking number involve counterexamples or finding a needle in a haystack. now don’t get me wrong: this is extraordinarily hard and commendable. but it is only one aspect of mathematicians’ work. Math is also about: - finding deep, general mathematical understanding and explanations within proofs - revisiting proven results to find more « elegant » proofs - proposing new conjectures and hypotheses that might open « fruitful » directions - taking the leap of faith of proposing entirely new, « exciting » research programs that may take decades to bear fruit you may say these are simply further increments on the same intelligence scale, and perhaps point 2 above is already within the reach of current models. possibly but you could also see this string of results as an extraordinarily powerful, massively parallel extension of search: explore the haystack, find the needle i.e. the counterexample that breaks the conjecture. that would already be remarkable. but it would still leave open whether models understand the words I emphasized above: « elegant », « fruitful », « exciting ». as tristan put it in his brief report: “the first llm generated proof levent sent me was the most horrendous i have ever read.” ...
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem
