> Are you sure claude didn't do exactly the same thing but the harness, claude code, just hid it from you?

What do you mean? Are you under the impression I'm not even reading the code? The code is actually the most important part because I already have working software but what I want is working software that I can understand and work with better (and so far, the results have been good).

Reading the code and actually understanding the code is not that the same thing.

"This looks good", vs "Oh that is what this complex algorithm was" is a big difference.

Effectively, to review that the code is not just being rewritten into the same code but with C++ syntax and conventions means you need to understand the original C code, meaning the hard part was not the code generation (via LLM or fingers) but the understanding and I'm unsure the AI can do the high level understanding since I have never gotten it to produce said understanding without explicitly telling it.

Effectively, "x.c, y.c, z.c implements a DSL but is convoluted and not well structured, generate the same DSL in C++" works great. "Rewrite x.c, y.c, z.c into C++ buildings abstractions to make it more ergonomic" generally won't recognise the DSL and formalise it in a way that is very easy to do in C++, it will just make it "C++" but the same convoluted structure exists.

> Reading the code and actually understanding the code is not that the same thing.

Ok. Let me be more specific then. I'm "understanding" the code since that's the point.

> I'm unsure the AI can do the high level understanding since I have never gotten it to produce said understanding without explicitly telling it.

My experience has been the opposite: it often starts by producing a usable high-level description of what the code is doing (sometimes imperfectly) and then proposes refactors that match common patterns -- especially if you give it enough context and let it iterate.

> "Rewrite x.c, y.c, z.c into C++ buildings abstractions to make it more ergonomic" generally won't recognise the DSL and formalise it in a way that is very easy to do in C++, it will just make it "C++" but the same convoluted structure exists.

That can happen if you ask for a mechanical translation or if the prompt doesn't encourage redesign. My point was literally make it well-designed idiomatic C++ and it did that. Inside of the LLM training is a whole bunch of C++ code and it seems to be leaning on that.

I did direct some goals (e.g., separating device-specific code and configuration into separate classes so adding a device means adding a class instead of sprinkling if statements everywhere). But it also made independent structural improvements: it split out data generation vs file generation into pipeline/stream-like components and did strict separation of dependencies. It's actually well designed for unit testing and mocking even though I didn't tell it I wanted that.

I'm not claiming it has human-level understanding or that it never makes mistakes -- but "it can't do high-level understanding" doesn't match what I'm seeing in practice. At minimum, it can infer the shape of the application well enough to propose and implement a much more ergonomic architecture, especially with iterative guidance.

I had to have it introduce some "bugs" for byte-for-byte matching because it had generalized some of the file generation and the original C code generated slightly different file structures for different devices. There's no reason for this difference; it's just different code trying to do the same thing. I'll probably remove these differences when the whole thing is done.

That clarifies a lot.

So effectively it was at least partly guided refactoring. Not blind vibe coding.