Yeah, the same thing. I basically think that machine review of code is very useful for code quality, and it can spot critical issues that I didn't notice myself. It can also be used to create human-readable documentation including mermaid diagrams, and write tests, and keep it all up to date with very little friction and pain.

I think at least on these support functions, use of LLM is pretty much a nobrainer, and these reasoning models are good enough to reason through the code flow and recognize the weak points in a program and then design tests to exercise them. To me, it's been insanely gratifying to find that LLMs can do these rather time-consuming chores, like read APIs and design meaningful tests and then can run these tests as part of the changes they make. They also automatically keep them up to date, e.g. add new functions that you added into the tests without even being especially prompted to do so (though this likely depends on the general system prompt or your agentic coding software, which may be instructing them to do so).

The fact that in time of LLMs, all my code is now well-documented, documentation is accurate, and tests can cover implementation at various ways, at virtually only trivial cost in terms of time, money, designing or maintaining is just super. LLMs seem to produce honest documentation that is clear about faults and gaps in implementation, and I appreciate the transparent and easy to read language that covers just the kind of stuff that is usually omitted in professional human-written developer documentation which usually implies that the code is perfect and has no faults, when such documentation exists at all.