[esperent]

> spend hundreds of thousands of tokens just rewriting entire features until there aren't any errors left

If it works, what's wrong with doing this? Obviously, don't turn your brain off. Be critical and work with the AI. But it's not like there's a shortage of tokens. They're only getting cheaper as time goes by. If, by spending enough tokens, you end up with a working feature, then this is a valid method of doing the work.

[mpalmer]

> If, by spending enough tokens, you end up with a working feature, then this is a valid method of doing the work.

It's just several really big ifs:

- Does "no errors" == working feature? Only if the tests are good, and even then...

- Are the tests good? Only if the person overseeing the LLM is checking the thoroughness and quality of the tests; being critical, as you say

- Is the developer willing to be/capable of being critical, or are they using the tooling as a way to avoid such things as much as possible?

[botanical76]

Well, rewriting entire features without tact can be a little toxic in collaborative environments. There may be good reasons for the design that does already exist, and those reasons may not be backed by the test suite, and you may only find out about them once you've burned through a month of dev time and further changes on top of the rewrite.

You should consult the code owner or primary set of authors before proposing a large rewrite. But if you do this, you should understand very well the pros/cons of throwing away all of this old code, documentation, and unit tests that have an implicit dependency on the existing structure.

I worry that if you are just vibe coding and letting the AI rewrite everything at will, you could not be further from understanding the details involved.

[ipaddr]

You lose track of the model the bigger it scales. Each rewrite brings you further away from understanding. Using it in spots rather then huge rewrites makes things much easier.