[conrs]

Fair, my mistake.

I've built a lot with LLM's, my experience sort of but not really tracks that. I've had to course correct a few bad abstractions but the larger the code base becomes the better it seems to be at reusing things. Maybe this is because of types, or spec-first development (with OpenAPI), or black box integration testing - but also maybe not. But generally I have to think about the abstractions and let the LLM fill in the details with rare exception.

[conartist6]

I built a web-OS, a graphical IDE, and a version control system to replace git, all in about 40,000 lines of highly abstracted Javascript. If you're thinking about how important it is to be able to maintain million-line codebases, I suspect you might have substituted a metric for the actual end goal.

[psychoslave]

That looks like a nice feat, can you share a repo?

That said, reality at scale always come with details that will break the model, and the main roads when it happens are to ignore/reject any change proposal in the model, go in the mystic quest to reach a model that will fit it all including these new cases with an elegant simple solution, or accommodate special cases on the side until it grows too big or just percolate too fast in the main part to let it be sustainable.

[conartist6]

It's a GitHub org: https://github.com/bablr-lang

Basically we've taken the "mystic quest" route, but we now have a pretty damn good data model