I open-sourced Teachable Figures, a skill for AI agents to create beautiful, interactive 2D and 3D lessons.
Try it here: http://github.com/ywEdAi/teachable-figures
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: http://ywedai.github.io/teachable-figures
Code and installation: http://github.com/ywEdAi/teachable-figures
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!