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"type": "module",，这一点在新收录的资料中也有详细论述
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.，这一点在新收录的资料中也有详细论述
生态还在建设中（插件少、功能在快速迭代）。新收录的资料是该领域的重要参考