It really depends on the context. On a pure computational code, it can work really well.

I recently optimized some code asking Claude to "make it faster" 2 different programs. For each, it wrote a benchmark, gathered initial data. Emitted some hypothesis and measured data around them with profilers, then did some changes (behind feature flags), checked the output was identical in either side and through profiling that the right code path was taken, and then benchmarked both.

And it did that for multiple successive changes in both codebases. Some changes were purely algorithmic (better complexity), some were related to tradeoffs between memory and computation (caching intermediate results better), some were about creating less intermediate objects to relieve the memory pressure, some where about a better memory layout to improve data locality.

The annoying part is that some of the code that was optimized was generated by Claude in the first place, so it's a bit frustrating that it didn't do those in the first place. But I guess, my iterative approach to making those tools didn't work on big enough data sets at first where it would have been an issue.