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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.
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Последние новости
OpenClaw 的官方文档写得很直白：养「龙虾」的花费不只来自核心模型回复，还来自网页读取、记忆检索、压缩总结、工具调用，以及系统提示里塞进去的 workspace 文件和 bootstrap 配置。
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Code dump for 2.16
Раскрыты подробности о договорных матчах в российском футболе18:01，更多细节参见新收录的资料