Hey HN! I'm Zach from Adam (https://adam.new/). We're building AI agents for mechanical CAD software. We’ve built the company on two fundamental beliefs:

- AI will be the primary medium for creating mechanical designs just like it is in software today.

- The best paradigm for CAD generation is to generate CAD as code (text -> code -> CAD).

We’re building CADAM, an open source Text to CAD platform. It's a React app (TanStack Start) with a Supabase backend for auth, database, and file storage. Think of it like AI TinkerCAD.

Demo: https://www.youtube.com/watch?v=iESOr7EGWqk Try it: https://adam.new/cadam/

What it does:

- Generates parametric 3D models from natural language, with support for both text prompts and image references.

- Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking

- Exports as .STL or .SCAD (plus OBJ, GLB/GLTF, FBX, and DXF)

Under the hood:

- One agentic endpoint with two modes that swap system prompts and tools: a parametric mode that writes/edits OpenSCAD via a build_parametric_model tool, and a mesh mode that generates 3D textured meshes.

- Simple parameter tweaks bypass the model entirely; adjusting a slider does a deterministic regex update on the SCAD source, requiring no LLM call.

- Model-agnostic via the Vercel AI SDK: Anthropic (Claude), Google (Gemini), and OpenAI/others through OpenRouter, with adaptive thinking auto-enabled on newer models. Surprisingly, in our evals Gemini 3.1 Pro is the top model.

- Runs fully in-browser by compiling OpenSCAD to WebAssembly (in a Web Worker, so the UI never blocks) and rendering with Three.js via React Three Fiber

- Supports BOSL, BOSL2, and MCAD libraries, plus custom font support (Geist) for text in models

Future improvements:

- Support both build123d and CadQuery. This will allow us to move beyond CSG primitives to constraint-driven modeling and provide direct comparisons to other code-as-CAD primitives.

- Better spatial context: UI for face/edge selection and viewport image integration to give LLMs spatial understanding

You can clone the repo and run it locally! Contributions are very welcome.