Earlier quoted context omitted.
It will be a push and pull and I'm curious where we'll be in a couple of years. I can fly at 100mph if I let AI run loose and with a bit of steering I can get it to output what I'm looking for and generally pass verification and tests. If I care about the code though, and I want to keep it maintainable, the amount of time and tokens I need to spend correcting and iterating on the output quickly eats through much of t…
I also spend a lot of time at work trying to get the code into a shape I deem acceptable to open a MR for the team, without looking incompetent. At home I stopped reviewing the code for my mobile app, and I do not feel this has affected maintainability. With 150k LOC, the AI still performs changes as easily as it did three months ago, when I started the project. If anything, the new models made it easier. When I read…
It seems for any particular problem/comexity there is an estimate of how many LoC is needed (written by humans, good coding practices). And after reaching maturity (and not increasing scope) the LoC converges.
My experience with LLMs so far is it’s a constant battle to not explode the LoC. There’s always pressure away from “maintainability” and a “small elegant cohesive” codebase.