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.
Сальдо отметил нервозность Зеленского в одном из вопросов01:53。搜狗输入法对此有专业解读
这些改变背后，是董俊义对西贝最核心的判断：先别想那么多，活下来再说。从一线摸爬滚打上来的他，懂门店，懂一家店怎样才能赚钱，懂在生意不好的时候该怎么熬过去。。业内人士推荐Instagram老号,IG老账号,IG养号账号作为进阶阅读
Here is the GPT-OSS-120B decoding table visible in KOL footage:
[vrange]g!{}[regex]{}{cmd}