[mmillin]

>Yet even with a lot of manual steering, that type of code does not come out of LLMs naturally, and even if the code comes out naturally like that, they will still attempt to handle now impossible errors.

This is something I’ve struggled to fight against in many PR reviews. Especially once already written, convincing someone that their excessive null checking is harmful is an uphill battle. Short of better modeling (and languages that allow for sum types to enable it), I haven’t been able to come up with a universally convincing argument against this kind of “shotgun parsing.”

Maybe it really just isn’t that big of a deal? But when actually reading through and refactoring a codebase I’ve always found it frustrating to manage these unnecessary checks. Sometimes they’re nearly impossible to delete safely once present without first adding some kind of logging or broad investigation.

[handoflixue]

How impossible are we talking?

I tend to be a fairly defensive programmer - maybe nothing currently sends this function a negative value, but how hard is it for a future code change to alter that assumption? I always figured a clear error was best. It lets even someone unfamiliar with the code know what assumptions are being made about the valid range of inputs, so they don't have to consider impossible outliers.

[datadrivenangel]

And AI code reviews encourage overly delusional defensive paranoia. triple null checking deep inside a function is technically a real risk, but in practice should never be hit because you've checked for nulls in every function that calls or could call the function in question and is thus not necessarily worth guarding against.