Deterministic feedback is precisely how frontier models are trained. It’s called RLVR. You let the agent run on a problem and then calculate a deterministic score of how well it did. Repeat 1000x times and you can “brute force” a good solution. (Which includes all thinking traces and you add it to your training data.) And then a Chinese model can copy your advance for 1000x less compute. Which is why US labs call this not learning, but a distillation “attack”. It’s an attack on the business model.

This looks pretty cool but I'd want to be able to setup a bunch of my own project specific code smells, so it's not just a few generic rules. Is that the approach you've taken?

Then for example you could write your own hook and convert existing code smell documentation which agents ignore into a format that works with the hook.

The readme reads like AI slop. Why would one believe the tools would prevent AI slop?

This sounds interesting but (from the homepage too) I don't understand how it's different than having it run any other linter?

I suspect strategies like this might be even more powerful with less intelligent agents. Could a 40B model outperform a 400B model with good feedback and instructions?