[lolinder]

> just because it accelerates their hack project, it won't accelerate someone who's an expert.

I would say that this is the wrong distinction. I'm an expert who's still in the code every day, and AI still accelerates my hack projects that I do in my spare time, but only to a point. When I hit 10k lines of code then code generation with chat models becomes substantially less useful (though autocomplete/Cursor-style advanced autocomplete retains its value).

I think the distinction that matters is the type of project being worked on. Greenfield stuff—whether a hobby project or a business project—can see real benefits from AI. But eventually the process of working on the code becomes far more about understanding the complex interactions between the dozens to hundreds of components that are already written than it is about getting a fresh chunk of code onto the screen. And AI models—even embedded in fancy tools like Cursor—are still objectively terrible at understanding the kinds of complex interactions between systems and subsystems that professional developers deal with day in and day out.

[frankc]

My experience has gotten better by focusing on documenting the system (with ai to speed up writing markdown). I find reasoning models quite good at understanding systems if you clearly tell them how it works. I think this creates a virtuous circle where I incrementally write much more documentation than I ever had the stomach for before. Of course this is still easier of you started greenfield buts allowed me to keep claude 3.7 in the game even as the code base is now 20k+ lines.

[lolinder]

> even as the code base is now 20k+ lines.

That's better than my past experience with hobby projects, but also nowhere near as big as the kinds of software systems I'm talking about professionally. The smallest code base I have ever worked on was >1M lines, the one I'm maintaining now is >5M.

I don't doubt that you can scale the models beyond 10K with strategies like this, but I haven't had any luck so far at the professional scales I have to deal with.

[drodgers]

I've found claude-code good in a multi-million line project because it can navigate the filesystem like a human would.

You have to give it the right context and direction — like you would to a new junior dev — but then it can be very good. Eg.

> Implement a new API in `example/apis/new_api.rs` to do XYZ which interfaces with the system at `foo/bar/baz.proto` and use the similar APIs in `example/apis/*` as reference. Once you're done, build it by running `build new_api` and fix any type errors.

Without that context (eg. the example APIs) it would flail, but so would most human engineers.

[frankc]

Well I have also worked on systems of multiple millions of lines, well pre-llm, and I sure as he'll didn't actively understand every aspect of it. I understood deeply the area I work on and the contracts with my dependencies as well the contracts I provide. I also understand the overall architecture. We'll see how it goes if my project grows to that point, but I believe by clearing documenting those things, and overall focusing on low coupling, I can keep the workflow I have now, but with context loading for every session. Time will tell.

In general though, its been a lot of learning on how to make llms work for me, and I do wonder if people simply dismiss too quickly because they subconsciously don't want them to work. Also "llm" is too generic. Copilot with 4o sucks but claude in cursor and windsurf does not suck.