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.
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DigitalPrintPrint + Digital。关于这个话题，新收录的资料提供了深入分析
However, it is still much higher than it was before the US-Israel war with Iran began on 28 February when Brent was priced at around $73 a barrel.，详情可参考新收录的资料
in a series of papers, including the。业内人士推荐新收录的资料作为进阶阅读
FoodChain ID commonly observes fraud that involves swapping out one species for another.