[leptons]

>I can tell if it makes a mistake because... the code doesn't do what I want it to do

Sometimes it does what you want it to do, but still creates a bug.

Asked the AI to write some code to get a list of all objects in an S3 bucket. It wrote some code that worked, but it did not address the fact that S3 delivers objects in pages of max 1000 items, so if the bucket contained less than 1000 objects (typical when first starting a project), things worked, but if the bucket contained more than 1000 objects (easy to do on S3 in a short amount of time), then that would be a subtle but important bug.

Someone not already intimately familiar with the inner workings of S3 APIs would not have caught this. It's anyone's guess if it would be caught in a code review, if a code review is even done.

I don't ask the AI to do anything complicated at all, the most I trust it with is writing console.log statements, which it is pretty good at predicting, but still not perfect.

[rrradical]

So the AI wrote a bug; but if humans wouldn’t catch it in code review, then obviously they could have written the same bug. Which shouldn’t be surprising because LLMs didn’t invent the concept of bugs.

I use LLMs maybe a few times a month but I don’t really follow this argument against them.

[leptons]

Code reviewing is not the same thing as writing code. When you're writing code you're supposed to look at the documentation and do some exploration before the final code is pushed.

It would be pretty easy for most code reviewers to miss this type of bug in a code review, because they aren't always looking for that kind of bug, they aren't always looking at the AWS documentation while reviewing the code.

Yes, people could also make the same error, but at least they have a chance at understanding the documentation and limits where the LLM has no such ability to reason and understand consequences.