十余年后，当曾被预言的新时代已经降临，这个新鲜的行业需要与时代探索出共存的新方式。
This simple example is already more nuance than would be ideal to juggle when writing code. ↩ ↩2
，更多细节参见新收录的资料
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.
As Covid spread, the family were told that all visitors would be banned.
mcp2cli --mcp https://mcp.example.com/sse search --query "test"