The scale of this is staggering. But... Why? Why is OpenAI trying to solve hundreds of math problems to be released all at once? I think we got the point when they found a counterexample to Navier-Stokes -- internal model = great. I am not sure at this point what this (hostile?) takeover of the mathematical landscape is trying to achieve other than, again, a massive PR stunt.

THE BLOCK: The Ethereum Foundation launched zkAPI on mainnet, based on a design co-authored by Vitalik Buterin. It lets users pay for AI and other APIs from prepaid $ETH or $USDC using zero-knowledge proofs, so providers can't link requests to who paid.

“YOU’RE A PUSSY” - Nick Sirianni when Jalen Hurts got sent to the medical tent after a late hit

The AI containment debate may already be over. @friedberg argument is that powerful AI is no longer controlled by a handful of American companies running enormous data centers. Open weight models are becoming capable and efficient enough to download, modify, and run on consumer hardware, making the technology nearly impossible to contain once released. The recent launch cycle shows how quickly this is happening. DeepSeek released a faster and cheaper model, Alibaba published downloadable weights for Qwen Image 2.1, and PrismML compressed a 27 billion parameter Qwen model into just 5.9 gigabytes. PrismML says its compressed model retained 98.2% of the original model’s benchmark performance while becoming small enough to run on a laptop or consumer GPU. At the same time, closed model companies continued releasing more capable and affordable systems. Anthropic’s Opus 5.5 reportedly matched its more expensive flagship while costing approximately 40% less to run than its predecessor. Stanford found that the cost of running a model with GPT 3.5 level performance fell more than 280 fold in roughly 18 months. It also found that the performance gap between leading open-weight and closed models narrowed from 8% to 1.7% on some benchmarks in one year. That is the real meaning of Friedberg’s argument. Governments can regulate large data centers, chip exports, and commercial AI providers but they cannot easily remove models that have already spread across the internet and onto personal computers. The debate must therefore shift from whether powerful AI should exist to how it should be used and who becomes responsible when it causes harm.