So, the French socialists, and Parliament members just voted a tax, that they don’t understand, which would cost VideoLAN more than 1 million euro per year. They are probably the most clueless politician on Earth? And they claim they want technological sovereignty… @LaurentLhardit

Claude and I developed a method that can quickly find the neuron you want! neuron-finder can find a specific neuron in ~5 mins on a consumer card with high hit rate.

arxiv.org/abs/2610.38331 Improving MTP by only keeping self-distillation of next token prediction in the loss term.

I've always believed that intelligence should come from simple, general building blocks that learn their own abstractions directly from raw data; in other words, the bitter lesson. I think we're finally seeing a real step towards AI that can learn from all data. Language modeling on raw bytes is a microcosm of this ideal, and to me it's a central problem in AI: if we can't build LLMs that learn end-to-end from bytes, how will we handle the rest of the world's data that we understand even less? Last year we introduced hierarchical networks (H-Nets) as a first step. Our new version finds up to 3-5x efficiency gains in controlled scaling law experiments, and at larger scale matches an open-source model trained on 11T tokens with ~10x less data and compute. The posts below walk through what we found and why we think it works.