Getting AI to produce a bunch of code and then you having to filter through it all is a massive waste of time. The focus should be on getting AI to produce better code in the first place (e.g., using detailed plans), rather than on the volume of code you can produce...

I have only had real advantages with AI for helping me plan changes, and for it helping me to review my code. Getting it to write code for me has been somewhat helpful, but only for simple tedious changes or first drafts. But it is definitely not something I want to leverage by getting AI to produce more and more code that I then have to filter through and review. No thank you. I feel like this is really the wrong focus for implementing AI into your workflows.

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fleischhauf 5 days ago | parent [-]

can I ask what language you are using AI for, there is also a difference in performance for AI in different languages

	▲	sothatsit 5 days ago \| parent [-]
		TypeScript with NextJS. I've also used AI tools with C and Zig, and AI is much better at writing TS. But even though TS works much better, it's still not that great. This is largely because the quality of the code that AI writes is not good enough, so then I have to spend a decent chunk of time fixing it. Everyone I know trying to use AI in large codebases has had similar experiences. AI is not good enough at following the rules of your codebase yet (i.e., following structure, code style, library usage, re-using code, refactoring, etc...). This makes it far less useful for writing code changes and additions. It can still be useful for small changes, or for writing first drafts of functions/classes/interfaces, but for more meaningful changes it often fails. That is why I believe that right now, if you want to maintain a large codebase, and maintain a high bar for quality, AI tools are just not good enough at writing most code for you yet. The solution to this is not to get AI to write even more code for you to review and throw out and iterate upon in a frustrating cycle. Instead, I believe it is to notice where AI is helpful and focus on those use-cases, and avoid it when it is not. That said, AI labs seem to be focusing a lot of effort on improving AI for coding right now, so I expect a lot of progress will be made on these issues in the next few years.