I think there is a lot of dev cope in this thread.

My workflow is very simple:

1. develop requirements for code change

2. take manual notes for implementation, maybe use LLM for some discovery/investigation

3. present notes to frontier LLM

4. develop implementation plan (bulk of work)

5. let LLM rip

6. review diff, manually fixing/refactoring code as necessary, sometimes prompting for revisions

7. get automated LLM review

8. get human review

this reliably produces the work of 2-3 pre-AI senior engineers with a lower bug rate, equivalent performance, robust edge-case consideration, etc.

Does the LLM produce over-engineered solutions? All the time. I stop it from doing that, or manually fix it myself.

Does the LLM always adhere to the best system design? No, not at all. I often have to guide its design into a better, north-star aligned one.

I don't just sit in front of my terminal and say, "Ok Claude, build the app." It is a very iterative process, and not without its potential pitfalls.

But it is very, very productive.

This has been my experience as well. The best model I have access to right now is Opus 4.8. It's really good at fixing bugs in an established architecture or adding a similar feature, but it's absolutely mid tier at putting together a simple effective architecture for even common software problems. I can tell that functional programming practices are not a large part of it's training material.