From what I read about the freelancers’ perspective, a lot of the newer gigs they get are to clean up work already done by AI instead of the opportunity to start new like they did in the past.
Pretty challenging once the codebase gets large, but also a lot of fun to take a big mess and bring it under control.
I'm asking because I have read these comments here and there about fixing the mess AI made. I know what mess it usually creates and it doesn't get better with the new models. But I also know that whatever is created by AI kind of works more often than not, and once something breaks, it's just one prompt away to fixing it.
So what I do is mostly use the AI to understand things like architecture, encapsulation, deployment pipelines, types, migration structure, etc.
It's a lot of "Audit the callers of X and give me a report" as well as reading code. I'm looking for stuff that doesn't pass my smell test, especially relating type types/interfaces. I look at the AI recommendations, but they're only useful once I've scoped them down to more granular questions, such as assessing the interfaces between specific layers of a pipeline.
That process is about understanding the shape of the solution that exists and then trying to formulate what shape it should have.
Then it's a lot of identifying a very boring, vanilla architectural target and moving towards it in phases, using the strangler fig pattern pretty aggressively. I review every PR, but mostly at the level of architecture rather than every specific line.
There's also a well documented phenomena [1] where LLMs struggle to recall information in the middle of the context.
[1] https://charlesanthonybrowne.medium.com/how-llms-end-up-igno...
Yeah over the past year I've become convinced that humans' limited working memory is actually a superpower, because it allows us to avoid this type of problem.
Previously, I'd be contracted to clean up messes from cut-rate dev shops. Now, there's plenty of opportunity for me to clean up messes from AI-driven development.