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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.
。新收录的资料对此有专业解读
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Мир Российская Премьер-лига|20-й тур
对很多企业来说，这样的工作通常需要一整个团队完成，而BettaFish尝试用AI将这一过程自动化。