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. [zachdive]

Been waiting to share this for when there’s actually a good slate of games happening since the site is pretty bare otherwise.

Here is today's schedule:

Mets @ Reds - 9:40am PDT https://ribbie.tv/watch/game/824503

Royals @ Nationals - 10:05am PDT https://ribbie.tv/watch/game/822721

Marlins @ Phillies - 10:05am PDT https://ribbie.tv/watch/game/823450

Tigers @ Astros - 11:10am PDT https://ribbie.tv/watch/game/824178

Padres @ Cardinals - 11:15am PDT https://ribbie.tv/watch/game/823044

..and another 14 games throughout the later day.

I'm still early on in this project, but I've tried to add little details with actual stadiums, day and night modes, between inning graphics and interstitials, live scoreboards, etc.

Would love any feedback and ideas. Thanks for checking it out! [brownrout]

Now they can generate patterns at HD resolution in real-time, enabled by turning each CA cell into a Neural Field.

Try 3 demos: grow a pattern from a seed (and damage it, it heals), synthesize PBR textures that can regenerate, or create 3D textures like clouds. [esychology]

There are more details on the Github repo but it's made from an esp32-c3 as the microcontroller, with 3 capacitor slots. There are relays on each capacitor slot to put 15v reverse voltage on the capacitor, with 5.1 ohm resistors on each slot for current limiting in case the capacitor shorts out. I also chucked in an SSD1315 OLED to show the time and a menu to configure it, although there's a web UI as well. The esp32 also means you can fetch the time from NTP.

It also functions as a small heater since I used LDOs to step down 15v to 3.3v for the esp32, I was lazy and didn't use a buck converter circuit :) [arcaege]