[ACCount37]

It's tuned for the kinds of tasks where "just try" doesn't get good results.

A major complaint with AI code was that AIs struggle with complex codebases, don't respect existing conventions, reinvent functionality multiple times over, etc. So, newer high end AIs are tuned with the "explore/exploit" dial turned towards "explore".

You could probably get it to do things "quick and dirty" with prompting, but that, of course, requires prompting for it.

[adastra22]

I get what you're saying, but these instances are of a different type. It is along the lines of "if I pass None for this parameter, will it default to X or return an error?" and it looks, but finds that the actual logic is distributed across multiple files. And so it quickly falls down that rabbit hole.

All the while, it could have just done a two-line probe test and see what happens when it calls the API with "None" for that parameter. Or just assum it would act as expected and wire in debug logs in case it doesn't.

[giovannibonetti]

Perhaps what is missing is a better memory/caching layer to avoid doing the same for explorations over and over again.

[dpritchett]

That is the usual work of high end programmers, right? Growing codebases as consistent, dependable ontologies?

I feel like most mainstream programming languages do this sort of work for their standard libraries and their official docs. Go and Python come to mind, but plenty others do this reasonably well to the point where one mostly doesn’t need to read the implementation code to effectively use the standard library itself.

[hoppp]

I use the human-in-the-loop for managing the context.

Give it only what it needs and do things usually 1 file at a time.

Feels like I'm a sort of manual tape editor, if the context was a tape fed into machine, I assemble that and then watch the machine output the results I need.