The idea is interesting but in looking at methods and github I feel the tooling leaves me wanting. I mean you are trusting LLMs here to establish, vet, and detect your various thresholds that were then used to train the classifier. I'd rather see this sort of thing done deterministically with actual code vs lossy human english prompts and a dependency on token spend to a single third party (who will probably pull the underlying model used in what a few short years probably) to replicate the results or try and use different training data.

The idea is that AI slop is non-creative boring crap, to really determine if you are able to identify AI slop then it should be determined if you misidentify human slop as AI slop.

Idea 1: Identify the worst most boring human marketing, organizational, bureaucratic texts from the a time before AI was writing it, anonymize this text to make sure there is no reference to current events that can be used to determine that it is not AI. And then see if the AI will say hey, that is not AI slop.

Idea 2: Have people parody AI slop. Can it determine the parody is still not AI slop?

Is the goal of this to help AI pick corn kernels out of it's own shit for the purposes of slightly raising the bar on how sloppy the slop is? Or are you trying to trick AIs into eating a higher amount of their own shit than they already are? This feels like a factory built specifically to manufacture pollution.

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