are right. It is that they make visible, with unusual clarity, what they are
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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На российском рынке представлена эксклюзивная модификация Toyota Land Cruiser 2013 года14:54，详情可参考钉钉
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