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I open-sourced Teachable Figures, a skill for AI agents to create beautiful, interactive 2D and 3D lessons. Try it here: github.com/ywEdAi/teachable-… It started with something I wanted for my own learning. I was studying AI model architectures and AI infrastructure. While reading about systems like SGLang, I kept wanting to see what was happening. Where does the data go? How do the components work together? What changes when I adjust one part of the system? My first version was a skill to help me understand SGLang through visual, interactive explanations. Then I discovered Anatomy and iso-figure. Their work on mechanical structures, cutaways, exploded views, and functional isometric objects gave me ideas for how much further this could go. I combined their rendering approaches with my teaching workflow. That became Teachable Figures. One question kept coming up as I built it: What kind of representation would help someone understand this particular idea? - For a Transformer, I want to follow the data through the computation and see how a causal mask changes attention weights. - For anatomy, I want to rotate a structure, look inside it, and understand where things sit relative to one another. - For a mechanical assembly, I want to separate the components and inspect their connections. - For historical clothing, I want to understand the overlapping panels, construction, and folds. Each asks for a different way of showing things. I also ran into this during iteration. A rotating model still needs a recognizable shape. A garment needs folds that communicate its construction. Choosing the right renderer and source material became part of the teaching workflow. Learning science helped me think through those choices. - Dual coding encouraged me to connect verbal and visual explanations. - Spatial and temporal contiguity shaped where explanations appear and how they stay synchronized with changes. - Cognitive load shaped what to reveal first and what to leave for later. The interaction follows a simple learning sequence: Predict → try → observe → explain → apply it to a new situation. Does adding another gear always make the output slower? If a cone’s cross-sectional radius doubles, what happens to its area? Does a rocket lift off whenever its engine produces thrust? I want learners to make a prediction, change something, and have a result they can reason about. The current demos explore Transformer attention, chest anatomy, rocket structure and flight, cone cross-sections, a Ming-dynasty robe, and gear ratios. They support Chinese and English and can run offline. My hope is to make this a tool I can reach for whenever I encounter something I want to understand, and let an AI agent help me build a small, beautiful lesson around it. The video shows a few examples. Try the demos: ywedai.github.io/teachable-f… Code and installation: github.com/ywEdAi/teachable-… Thanks to the creators of Anatomy and iso-figure for the work this builds on. If you try it, I’d love to hear what you’re learning and where the explanation falls short. Stars and contributions are welcome!