The thing that makes it work really well is to make sure it has all the tooling to verify its hypotheses. If you allow it to run the full lifecycle in loops you will be surprised how well it works.

People in the comments sound like they're literally asking claude: "hey claude, can you find areas for performance improvements." Nah, it doesn't work like that. You have to be more like: "can you profile [...] and identify hot spots." then looks at results that are suspicious. if you don't ground it to the real world reality of your project then don't be surprised when it makes up its own.

that's the first mistake i made as a new claude user. i only knew what prompts people were sharing on forums. but since they had been written by vibe coders it was all complete non-sense. also think about this: software engineering is filled with highly specialised tooling that can easily improve software quality. the thing is, most developers dont know how to use them, and if they do -- using them is so slow that its almost not worth it.

any tool you can name claude can use. obscure academic proof verifiers, memory corruption scanners, debuggers, profilers, coverage tools, backdoor supply chain pattern scanners... it can run these and have results in seconds. really is like living in the future.

I've had a completely different experience. I usually know the problematic part and then ask the AI to optimize that exact part with benchmarks, telling it to try various different approaches, and it very often gains massive performance increases on various tasks, with me of course steering it and giving it pointers every now and then.

I was able to optimize a physical water simulation that was almost unrunnable on browsers to a buttery smooth 60fps version.

Which model/agent/harness tool did you use? I've found what you describe was my exact experience some ~6-8 months ago, but since about a month or so the game has completely changed. Using 5.6 Sol with highest reasoning setting in Codex or Fable in Code, the models come up with a list of possible improvements from static analysis (ranked by complexity/benefit), write and run their own custom profilers and deliver significant performance improvements with barely any input needed from my side. So this is no longer a model issue, it's a user toolchain issue.

I did a lot of SQL optimisation with claude code over the last year, what made it work for me was making sure I verify everything with explain analyze. The whole loop was not fully “agentic” because of this but it was still faster and better than when done entirely manually. Claude also suggested plenty of small optimisations I would otherwise not do.

Ask it to create a small sample to reproduce the issue in isolation. If it can't reproduce the performance changes then it drops it.

That might work better. I haven't tried it with SQL but it worked with a few complex UI issues I had. It identified the actual issue after a few false starts.

The mistake there is to point it at code to figure out performance optimizations.

The place to find them would be performance profiles, query plans, telemetry. The guidance for perf still applies, measure before and after change.

The issue is that the code often does not contain the information to do a perf optimization. Eg. you can't tell your cache size, the volumes of data in your DB or the latency of your network through just the text. Should you provide this context, you can get better results.

I've noticed the same thing.

The trend I've noticed is that AI struggles to think outside the box when making optimisations, which exactly what's needed when you've made all of the practical DB and logic optimisations to the existing code.

Often you need to take a step back and question how the system is working and if there would be better ways to design it so the bottlenecks you're hitting wouldn't exist in the first place. Caching things, adding indexes, tweaking logic – these can help, but you'll quickly hit diminishing returns once you've done all of the obvious stuff.

I've seen people here say how AI is great at optimising code though, but I'm not sure if that's because they're giving it optimisation problems with a lot of low hanging fruit or if they're successfully getting AI to rework their systems to remove bottlenecks. This one area I find AI to still be particularly bad at.

Yep, as a performance specialist I've had the same outcomes - it'll find "smoking guns" galore which will sound plausible and be completely wrong.

But that's ok - I still get a lot of value of the tool elsewhere (e.g. writing data analysis scripts, exploring code, breaking down bug artifacts...), but it doesn't stop the tiring BS from other people going "why not just AI it?" (especially from managers).

I usually let them try to "just AI it" and most of them learn quickly that it's not that simple. (The others stay deranged and are making my job miserable.)

As others have said, these were uninformed guesses which LLMs seem still to be willing to hand out deliberately. If you provide more information (your schemas, the distribution of values in your database tables, the EXPLAIN ANALYZE outputs, DB configuration etc.) it will do a much better job.

Then you're not grounding it to reality properly. LLMs get bad pretty quickly when you just tell them to pull the answer out of thin air. Pin them to reality with actual performance tests to run to test theories and the results will be much better.

Performance optimization involves simulating real world loads and measurable results. Give them the opportunity to actually have a closed loop if you want more than trivial improvements.

> I've recently been using AI

That's uninteresting as long as you don't specify the model you used. For example, Mythos was far better at finding security bugs than previous models.