[hadlock]

I've had pretty good success with using LLMs to generate basic shapes using python and cadquery (which generates real parametric step files you can edit in fusion, not glorified triangulated STLs). Yesterday I had GPT5.5 build a python script to generate fender style Mandolin with separate neck and body, with correct bolt holes for the neck, gibson style bridge, and stop-tail, even fretboard with the little dots and cutouts for the fret wire (I didn't ask for these, but it added them anyways). Everything looks correct and to scale. These should generate (after pip install cadquery) a .step and .stl which you can open in something like PrusaSlicer or Fusion:

Neck: https://pastebin.com/Sg3LmmUq Body: https://pastebin.com/FE9nikYB

edit: screenshot, too https://i.imgur.com/FZGyyVO.png

[vmbm]

I am interested. Every few months, I loop back to using LLM's for this type of task but have always had fairly mixed results. Not sure if it is my prompt, model choice, or the part itself being too complex. And I haven't had the time to really dig into why things aren't working out. But would be nice to find a workflow that gets good results as I regularly 3D print stuff for hobby projects but find 3D modeling to be the most tedious and time intensive task.

[ActorNightly]

My general goto for tasks that are n level complex is to have the agent store summaries after every generation. I do this for interacting with websites - Ill sit there and type text for the agent to correctly inject js to do something on a website, and every iteration is asyncronously writes a history in a background thread of what it has done and what the result was. On every invocation, it injects that context.