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.
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。关于这个话题，51吃瓜网提供了深入分析
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APPSO 的建议是，直接把出现的问题截图，然后丢给 Gemini 或者你正在用的 AI 助手，一般情况下，AI 会给我们下一步要执行的命令，然后依照它的指示，最后基本上都能安装。
Фото: Сергей Мальгавко / РИА Новости