Comment by Roxxik
11 hours ago
Not only you understanding the how, but you not understanding the goal.
I often use AI successfully, but in a few cases I had, it was bad. That was when I didn't even know the end goal and regularly switched the fundamental assumptions that the LLM tried to build up.
One case was a simulation where I wanted to see some specific property in the convergence behavior, but I had no idea how it would get there in the dynamics of the simulation or how it should behave when perturbed.
So the LLM tried many fundamentally different approaches and when I had something that specifically did not work it immediately switched approaches.
Next time I get to work on this (toy) problem I will let it implement some of them, fully parametrize them and let me have a go with it. There is a concrete goal and I can play around myself to see if my specific convergence criterium is even possible.
LLMs massively reduce the cost of "let's just try this". I think trying to migrate your entire repo is usually a fool's errand. Figure out a way to break the load-bearing part of the problem out into a sub-project, solve it there, iterate as much as you like. Claude can give you a test gui in one or two minutes, as often as you like. When you have it reliably working there, make Claude write up a detailed spec and bring that back to the main project.
Yup, same sort of experience. If I'm fishing for something based on vibes that I can't really visualize or explain, it's going to be a slog. That said, telling the LLM the nature of my dilemma up front, warning it that I'll be waffling, seems to help a little.