All of the examples given in the article are contrived, textbook-style examples. Real world projects are far more messy. I want someone to talk about their flow

All of the examples given in the article are contrived, textbook-style examples. Real world projects are far more messy. I want someone to talk about their flow with Cursor on a mature codebase in production with lots of interlaced components and abstractions. I have a feeling that blindly building things with AI will actually lead to incomprehensible monstrous codebases that are impossible to maintain over the long run. Read “Programming as Theory Building” by Peter Naur. Programming is 80% theory-in-the-mind and only about 20% actual code. Here's an actual example of a task I have at work right now that AI is almost useless in helping me solve. "I'm working with 4 different bank APIs, and I need to simplify the current request and data model so that data stored in disparate sources are unified into one SQL table called 'transactions'". AI can't even begin to understand this request, let alone refactor the codebase to solve it. The end result should have fewer lines of code, not more, and it requires a careful understanding of multiple APIs and careful data modelling design and mapping where a single mistake could result in real financial damage.