Adam (YC W25) – Open-Source AI CAD

I'm Zach from Adam (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:youtube.com/watch Try it: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.