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中国信通院：截至2025年6月中国计算设备智能算力规模达到782 EFlops，同比增长96%
。关于这个话题，heLLoword翻译提供了深入分析
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.
[&:first-child]:overflow-hidden [&:first-child]:max-h-full"
陳秀蓮指出，這制度使移工在台無法自由轉換工作，尤其在受傷或遭遇勞資爭議後，往往缺乏制度保障與仲介支持，多數移工缺乏法律知識、難以舉證，即使移工成功獲准轉換雇主，「就業機會仍由仲介掌握」，要透過仲介支付「買工費」，換了新工作仍陷入債務循環。