一边是AI算力狂飙，另一边是碳排放硬约束收紧，AI的终极竞赛战场已然拓展到物理世界，在监管、能源与算力的三重绞索下，谁能找到成本的最优解，谁才能赢得未来。
如上，读史考诸古往今来升沉荣辱兴亡之变，不难显见。官员、富人，不能继续其禄爵财货，皆因不知其命中所负天职，只一味逞权享富贵，罔顾使命，至天职亏损尽，则爵禄止而财富罄。我的老师孙立教授说：所以过往富贵之家，都会养士，如此则可避免此类失职失格之事。现在的富贵人哪里懂得这个！只知一心聚敛无厌。也无此眼界，而且不知其所不足，被财富一叶障目，自以为是。，更多细节参见新收录的资料
"dynamicData": {，更多细节参见新收录的资料
'Shrinking' Season 3 review: My heart can't take it。业内人士推荐新收录的资料作为进阶阅读
An LLM prompted to “implement SQLite in Rust” will generate code that looks like an implementation of SQLite in Rust. It will have the right module structure and function names. But it can not magically generate the performance invariants that exist because someone profiled a real workload and found the bottleneck. The Mercury benchmark (NeurIPS 2024) confirmed this empirically: leading code LLMs achieve ~65% on correctness but under 50% when efficiency is also required.