It's not perfect by any stretch, but it is surprisingly strong. It was able to create and debug some pretty complicated geometry by iterating with screenshots, adjusting view angle and zoom and rendering mode, updating parametric geometry generation, and working to fairly complex goals.
I have been using Claude to generate OpenSCAD for 3D printing. It works decently when the jobs are simple and can be easily described, but the description part really makes it clear how little vocabulary the ordinary person has to compose a good picture of any real item that isn't just a basic shape. It seems that the trick, like most things with getting LLMs to do something complicated and have it work well, is to be an expert in the field already.
I just posted this somewhere else -- but overall big fan of these text to cad rigs as projects.
Obligatory mention of https://zoo.dev/ who went to extreme lengths on this.
I will say I explored this reasonably deeply and came away with the conclusion that even though we have OpenSCAD and all these examples, LLMs are still very weak at spatial reasoning compared to diffusion models.
You can do all sorts of tricks like have a parts library to get around this and do physics checks but another inconvenient truth is whenever you design a complex assembly, every change to that part needs to be aware of the other parts in the design -- thus you need a global part-aware editing capability from diffusion.
That's getting solved already in china leading labs, and bottlenecked by the lack of good training data, which china is solving with mass labor.
This will be solved overseas first before we will in the US.
p.s. I am not affiliated with zoo or any of these other things FYI was just very curious about this whole area
> LLMs are still very weak at spatial reasoning compared to diffusion models
Don't know what diffusion model can do, but 100% agree with the "LLMS are very weak at spatial reasoning" comment.
I build a rather complex blueprint-image-to-3D-brep-model a couple of months back using codex ... ugh the damn thing has really no idea where things are in space, something a 3 year old figures out instinctively.
It did end up saving some time as compared to modeling the object myself in a CAD package, but there was so many completely obvious thing I had to explain ... very hard to believe when compared to what codex can pull of with code.
Not sure I understand ... no mention of an actual CAD engine backend ... did I miss it?
Or is this capable of generating STEP files directly from an LLM (which I doubt)?
[EDIT]: haha. the answer is hidden in:
.agents/skills/cad/requirements.txt
TL;DR:
build123d
ezdxf
numpy
trimesh
vtk
and the engine is build123d, which, from its home page:
Build123d is a Python-based, parametric (BREP) modeling framework for 2D and 3D CAD. Built on the Open Cascade geometric kernel, it provides a clean, fully Pythonic interface for creating precise models suitable for 3D printing, CNC machining, laser cutting, and other manufacturing processes. Models can be exported to popular CAD tools such as FreeCAD and SolidWorks.
prbly worth mentioning in the README, I can't be the only one wondering out there.
Also: these things seem to be sprouting all over the place these days (a good thing!) ... CAD modeling using LLMs is clearly an idea whose time has come.
I built https://github.com/brookstalley/cordyceps to do CAD work using claude code.
It's not perfect by any stretch, but it is surprisingly strong. It was able to create and debug some pretty complicated geometry by iterating with screenshots, adjusting view angle and zoom and rendering mode, updating parametric geometry generation, and working to fairly complex goals.
I have been using Claude to generate OpenSCAD for 3D printing. It works decently when the jobs are simple and can be easily described, but the description part really makes it clear how little vocabulary the ordinary person has to compose a good picture of any real item that isn't just a basic shape. It seems that the trick, like most things with getting LLMs to do something complicated and have it work well, is to be an expert in the field already.
You'll know how incapable it is when it doesn't seem to understand how servo motors work in the Demo Project.
I just posted this somewhere else -- but overall big fan of these text to cad rigs as projects.
Obligatory mention of https://zoo.dev/ who went to extreme lengths on this.
I will say I explored this reasonably deeply and came away with the conclusion that even though we have OpenSCAD and all these examples, LLMs are still very weak at spatial reasoning compared to diffusion models.
You can do all sorts of tricks like have a parts library to get around this and do physics checks but another inconvenient truth is whenever you design a complex assembly, every change to that part needs to be aware of the other parts in the design -- thus you need a global part-aware editing capability from diffusion.
That's getting solved already in china leading labs, and bottlenecked by the lack of good training data, which china is solving with mass labor.
This will be solved overseas first before we will in the US.
p.s. I am not affiliated with zoo or any of these other things FYI was just very curious about this whole area
> That's getting solved already in china leading labs, and bottlenecked by the lack of good training data, which china is solving with mass labor.
What work are you referring to here?
Zoo doesn't seem to be a great website, on my normally sized display there is a small horizontal scrollbar that moves like 5 pixels
overflow-x: hidden; and the pain goes away :-)
> LLMs are still very weak at spatial reasoning compared to diffusion models
Don't know what diffusion model can do, but 100% agree with the "LLMS are very weak at spatial reasoning" comment.
I build a rather complex blueprint-image-to-3D-brep-model a couple of months back using codex ... ugh the damn thing has really no idea where things are in space, something a 3 year old figures out instinctively.
It did end up saving some time as compared to modeling the object myself in a CAD package, but there was so many completely obvious thing I had to explain ... very hard to believe when compared to what codex can pull of with code.
This sounds like a cool project, I would love to hear more about it. I am trying to solve a similar problem myself.
Not sure I understand ... no mention of an actual CAD engine backend ... did I miss it?
Or is this capable of generating STEP files directly from an LLM (which I doubt)?
[EDIT]: haha. the answer is hidden in:
.agents/skills/cad/requirements.txt
TL;DR:
and the engine is build123d, which, from its home page:Build123d is a Python-based, parametric (BREP) modeling framework for 2D and 3D CAD. Built on the Open Cascade geometric kernel, it provides a clean, fully Pythonic interface for creating precise models suitable for 3D printing, CNC machining, laser cutting, and other manufacturing processes. Models can be exported to popular CAD tools such as FreeCAD and SolidWorks.
prbly worth mentioning in the README, I can't be the only one wondering out there.
Also: these things seem to be sprouting all over the place these days (a good thing!) ... CAD modeling using LLMs is clearly an idea whose time has come.
Based on requirements.txt it uses build123d so OpenCascade is the geometric kernel (CAD engine backend)
> it uses build123d so OpenCascade is the geometric kernel
yup, found it as you were typing this :D