I've been puzzled by something: AI generation can produce an elaborate figurine, a cartoon character, even a convincing Super Mario — yet it can't reliably make a simple wedge ramp so my robot vacuum can climb a step.

  For context: I bought a Bambu P2S but can't model. I tried the "describe
  it and get a model" AIs — the output is unusable, you can't adjust it,
  it's never quite what I meant. I tried having an agent write Python to
  build geometry directly — it tops out at simple primitives.
 
  What finally worked: geometric decomposition. I break a complex part into
  ordered, grouped steps, describe each as a small spec, and let an agent
  execute them in Blender (via blender-mcp). That process turned out to
  abstract into a small engine — the key insight being it converts the 3D
  spatial reasoning LLMs are bad at, into the structured code they're good
  at. I wrote it up here: https://github.com/zhuchaokn/spec-3d-model
 
  My questions:
  - Why is "functional part" generation so much weaker than
  "figurine/aesthetic" generation? Is it data (no parametrized-CAD training
  sets), representation (mesh vs B-rep), or evaluation (nobody benchmarks
  "does it print / is it watertight")?
  - Is "turn 3D modeling into code for an LLM" the right framing, or am I
  missing something better?