deflate.push(chunk, false);
Женщина пожаловалась на боли во время секса и нашла смертельно опасный предмет внутри себя08:30
If the Jevons effect is true, I think we would have to be hitting some kind of AI programming plateau where the tools are good enough to produce lots of code (we’re here already), but not quite good enough to maintain it. This is prima facie plausible. Every software engineer knows that maintaining code is harder than writing it. But unfortunately, I don’t think it’s true.。关于这个话题，新收录的资料提供了深入分析
Трамп высказался о сроках войны с Ираном01:42，这一点在新收录的资料中也有详细论述
The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc. A bit more context on this project is here in this tweet.
Padé Approximants，详情可参考新收录的资料