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This mirrors my own experiences with Claude with one caveat.

GenAI can get deeper into a solution that consists of well known requirements. Like basic web application construction, api development, data storage, and oauth integration. GenAI can get close to 100%.

If you’re trying to build something that’s never been done before or is very complex, GenAI will only get to 50% and any attempt to continue will put you in a frustrating cycle of failure.

I’m having some further success by asking Claude to build a detailed Linear task list and tackling each task separately. To get this to work, I’ve built a file combining script and attaching these files to a Claude project. So one file might be project-client-src-components.txt and it contains all the files in my react nextjs app under that folder in a single file with full file path headers for each file.

We’ll see how deep I get before it can’t handle the codebase.



In general it can get further the better the design/interfaces are. I find that if you can define your problem with a really clean set of interfaces, it can generally implement them perfectly. Most of the real thinking work is at the interfaces anyway, so this makes sense.

But for a really tricky logic problem, accurately explaining it in English to an LLM might be less natural than just writing the code.


>GenAI will only get to 50% and any attempt to continue

50% seems exceeding high for 'never done before'.


Even things that have never been done before are mostly made up of tasks that have been done many times.




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