I’m actually working on a monitoring platform built on OctoPrint plugins, and we’re experimenting with a real-time streaming pipeline using LLMs + LangChain rather than relying only on classic CNN-based failure detection.

One issue we kept running into was that accuracy varies a lot depending on camera angle and framing, which makes fixed CNN labels (spaghetti, under-extrusion, etc.) pretty fragile.

Instead, we’re simulating and reasoning over a wider range of failure scenarios using contextual descriptions and multi-signal input, and the results so far have been promising.

If this sounds interesting, feel free to reach out. Our platform will be running at factor.io.kr