Uber and Lyft rides got nearly 10% more expensive in 2025, and a clear majority of riders say they are responding by pulling back on how often they use the apps.
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.，详情可参考新收录的资料
推动外贸稳规模优结构。加大信贷、信保支持，扩大人民币跨境使用。引导企业优化全球市场布局，推进贸易投资一体化、内外贸一体化发展。培育壮大贸易发展新动能，推动跨境电商加海外仓模式扩容升级、规范有序发展，加强国际寄递物流体系建设，拓展中间品贸易，发展数字贸易、绿色贸易，提升边境贸易。鼓励支持服务出口。积极扩大进口，推进贸易平衡发展。提高跨境贸易便利化水平。，更多细节参见新收录的资料
而要解开这一矛盾，我们需先拆解AI产业链的三层架构，看清各环节的价值逻辑与生存现状。