Yeah, there are scenarios that it can ‘one shot’, either very straightforward variations of common themes or a use case that is comprehensively instrumented in a way to “know” if it is correct or not automatically, and having the solution iterate against that criteria evaluation until all criteria pass (though in the latter case, I have seen it change the criteria deciding the criteria was a mistake instead of fixing a problem).
If you spend enough tokens, let it rebuild and test on its own, and let it churn and churn, then I’m not too surprised it could make this launch and be superficially intact. The real pain starts when trying to close the gaps the project has in this sort of scenario.
I will say that GenAI makes golang a lot less tedious. Lot’s of obvious but required boilerplate to force the developer to abide by consistent structure. Though I still prefer reading python code since I don’t have to peruse a ton of boilerplate there.
Yeah, there are scenarios that it can ‘one shot’, either very straightforward variations of common themes or a use case that is comprehensively instrumented in a way to “know” if it is correct or not automatically, and having the solution iterate against that criteria evaluation until all criteria pass (though in the latter case, I have seen it change the criteria deciding the criteria was a mistake instead of fixing a problem).
If you spend enough tokens, let it rebuild and test on its own, and let it churn and churn, then I’m not too surprised it could make this launch and be superficially intact. The real pain starts when trying to close the gaps the project has in this sort of scenario.
I will say that GenAI makes golang a lot less tedious. Lot’s of obvious but required boilerplate to force the developer to abide by consistent structure. Though I still prefer reading python code since I don’t have to peruse a ton of boilerplate there.