Sharing a demo that might interest this sub: a small active-inference vision system doing shape detection and next-frame prediction, running locally on an ordinary desktop with no large language model and no network connection. The point is less the raw performance and more the architecture - it perceives by predicting the next frame and correcting on the error, rather than classifying static inputs the way a standard feed-forward vision net does. It is a concrete, watchable instance of the perception-as-prediction idea rather than a diagram. Demo: https://youtu.be/OSHaoXROlIs Disclosure: this is my own project. I am interested in how people here read the trade-offs of a predictive / active-inference approach to perception versus conventional feed-forward recognition - especially where you would expect it to break. submitted by /u/AI_Conductor [link] [comments]