I actually have used other LLMs to review the code, in the past (not today, but in the past). It's fine, but it doesn't tend to catch things like "this technically works but it's loading a footgun." For example, the redux test I was mentioning in my original post, the tests were reusing a single global store variable. It technically worked, the tests ran, and since these were the first tests I introduced in the code base there weren't any issues even though this made the tests non deterministic... but, it was a pattern that was easily going to break down the line.

To me, the solution isn't "more AI", it's "how do I use AI in a way that doesn't screw me over a few weeks/months down the line", and for me that's by making sure I understand the code it generated and trim out the things that are bad/excessive. If it's generating things I don't understand, then I need to understand them, because I have to debug it at some point.

Also, in this case it was just some unit tests, so who cares, but if this was a service that was publicly exposed on the web? I would definitely want to make sure I had a human in the loop for anything security related, and I would ABSOLUTELY want to make sure I understood it if it were handling user data.

how long ago was this past? A review with latest models should absolutely catch the issue you describe, in my experience.