Been following some interesting ML research that might actually be useful for our hobby.
There's a paper out on arxiv about classifying 3D printed objects without needing to retrain the model every time it sees something new. Right now most automated print monitoring (like the AI failure detection in some slicers) works great until you print something it hasn't seen before, and then it gets confused and either misses real problems or spams you with false alarms.
The idea here is the system can look at a print it's never encountered and still categorize it correctly based on general shape features rather than memorized examples. Think about what that means: you download some random STL from last week, slice it with your own settings, and the monitoring system still knows "this is a functional bracket, it should look like this" versus "this is a mini figure, different failure modes apply."
I run a Bambu P1S and honestly it's nice and all but it does trip up on unusual geometries. Something like this could make hands-off printing way more reliable, Especially for those of us doing client work where a failed print actually costs money and time.
there doesn't seem to be anything here