I wanted to know whether a small local LLM, given nothing but a topic name, can autonomously build a working, pedagogically useful browser simulation — no human writing a line of the HTML/JS. So I set up an agentic pipeline and pointed four different open models at the same list of Math, Physics, and Neural Network topics.
👉 Open the full gallery — 88 applets, 8 models, all playable.
The pipeline
Each applet is produced by three roles, all played by the same local model:
- Planner — turns a topic (“Pythagorean theorem”, “buoyancy”, “self-attention”) into a short spec: the concept to teach, the interactions a learner should have, the visual layout.
- Coder — writes a single self-contained
applet.html(Canvas/SVG + vanilla JS, no build step) implementing the spec. - Reflector — loads the result, tests it, and either approves it or sends it back to the Coder with specific fixes. This loop runs for up to 5 turns before publishing whatever it has.
Averaged across all 88 applets: 33s planning, 2m 17s initial coding, 10m 32s of reflection/revision — 13m 22s total to go from a topic string to a published, working applet. The full breakdown (and a diagram of the loop) is on the gallery home page.
The models
| Model | Subjects | Hardware |
|---|---|---|
| gemma4-31b (5 reflection turns) | Math, Physics | A100 40GB |
| ornith-35b | Math, Physics, Neural Networks | A100 40GB |
| gpt-oss-20b | Math, Physics | A100 40GB |
| qwen3.6-35b | Math | L4 |
Results so far
gemma4-31b is the clear overall winner on both Math and Physics — most reliably correct, interactive, and bug-free on the first few reflection turns. ornith-35b consistently produces the best-looking applets (🎨 best aesthetics badge on the gallery) even when the underlying logic needs more revision turns to get right. Neural Networks is currently ornith-35b only (11 applets) — the other models haven’t been run on this subject yet.
| Subject | Models | Applets |
|---|---|---|
| Math | 4 | 48 |
| Physics | 3 | 29 |
| Neural Networks | 1 | 11 |
A few favorites
Math — gemma4-31b, Unit Circle Trigonometry Explorer
Physics — ornith-35b, Ray Optics: Lenses and Mirrors
Neural Networks — ornith-35b, Self-Attention Explorer
Browse everything, including the per-model breakdowns, generation time, and token counts for every single applet, in the full gallery. The static-site generator that builds the gallery from the raw run folders is on GitHub; the planner/coder/reflector pipeline itself runs against local Ollama models and isn’t part of this repo.
This is a living comparison — new applets get added as more models and topics are run, and the numbers above will grow.