this post was submitted on 02 Jun 2026
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I bet they end up using a fucking llm
Yes that's probably how you would do this. Get a bunch of data of gcode of 3d printed gun parts and not-gun parts, for different slicers and printers. Then train some transformer as a classifier. Based on how good object recognition is, i would say its possible that you would get reasonably good accuracy and precision. And because you are scanning for code the architecture will likely be similar to an llm.
Then five minutes later, someone figures out how to make a 3d printable gun that bypasses the gun detector on the 3d printer. It's not like you're printing a whole gun; you're printing parts, most of which look nothing like a gun. How hard would it be to design an algorithm that takes a gun part cad file and then adds a bunch of extraneous pieces to it that can be easily removed? Just keep adding extra crap until the system no longer detects it as a gun part.