[bunderbunder]

From what I've seen they're great at identifying trees and bad at mapping the forest.

In other words, they can help you identify what fairly isolated pieces of code are doing. That's helpful, but it's also the single easiest part of understanding legacy code. The real challenges are things like identifying and mapping out any instances of temporal coupling, understanding implicit business rules, and inferring undocumented contracts and invariants. And LLM coding assistants are still pretty shit at those tasks.

[manishsharan]

Not always.

You could paste your entire repo into Gemini and it could map your forest and also identify the "trees".

Assuming your codebase is smaller than Gemini context window. Sometimes it makes sense to upload a package,s code into Gemini and have it summarize and identify key ideas and function. Then repeat this for every package in the repository.then combine the results . It sounds tedious but it is a rather small python program that does this for me.

[bunderbunder]

I've tried doing things like that. Results reminded me of that old chestnut, "Answers: $1. Correct answers: $50."

Concrete example, last week a colleague of mine used a tool like this to help with a code & architectural review of a feature whose implementation spanned four repositories with components written in four different programming languages. As I was working my way through the review, I found multiple instances where the information provided by the LLM missed important details, and that really undermined the value of the code review. I went ahead and did it the old fashioned way, and yes it took me a few hours but also I found four defects and failure modes we previously didn't know about.