Thread · HN Continuity

The “tells you the same thing three times” finding really rings true. That’s the structure every content-marketing course taught long before LLMs, and the models seem to have learned the most average version of it.

What I like about structural features is that they make for a good eval, not just a detector. If you fine-tune a model to write for your brand, “did the new version get less sloppy than the one we’re running?” becomes a measurable question.

Disclosure: I work on Ookami (github.com/KatyarAILabs/Ookami), an open-source layer for serving and fine-tuning models. It only puts a fine-tuned model live if it beats the current one on evaluators you plug in.

Since Slopshape is open source, it could be one of those evaluators: fine-tune on your best human-written posts, and the gate rejects any version whose outputs score as more AI-shaped than the live model.

I haven’t tried it yet, but that’s the kind of use case I find interesting.

One question: were the 19 misclassifications writing to SEO templates (listicles, “ultimate guides,” etc.)? If so, the classifier might be detecting “written to a template” more than “written by a model,” which is arguably the more useful signal.