Rainy Friday vibes got me diving into AI trust mechanisms. Been looking at how proof-of-inference is quietly revolutionizing zk-ml space.

Think about it - RLHF loops where human preferences shape model behavior, but here's the kicker: zero-knowledge snarks audit the entire alignment process without exposing sensitive feedback. No data leaks, just cryptographic proof that the training stayed true to intent.

That's the kind of privacy-preserving ML infrastructure that actually makes sense for decentralized AI.