GoKawiilWhat this is
Math-To-Manim started on the morning of Donald Trump's second inauguration. Around 4:30 a.m. my time on January 20, 2025, DeepSeek, a Chinese AI lab, released R1 on Hugging Face. I read the timing as deliberate: a Sputnik-style signal that open reasoning models were now a geopolitical fact. My first move was to clone R1 and point it at math reasoning.
The interesting part was not just that a reasoning model could solve math; it was that the path to a good explanation could be made visible. A topic could become prerequisites, then a teaching order, then equations, then screen beats, then Manim code, then a movie.
M2M2 is the pipeline that grew out of that experiment. A teacher, tutor, parent, student, researcher, or agent can bring a short question, a lesson idea, or a dense technical note and get back an inspectable explanation: the concept, the missing prerequisites, the order of ideas, the screen beats, the generated Manim code, and optionally the rendered video.
The render can be a strong first pass at a four-to-five-minute explainer, with real mathematical and physical notation in LaTeX instead of decorative pseudo-math. The product is not only the MP4. Each run bundle preserves the reasoning artifacts and math that led to the scene, which makes the output useful for debugging, agent handoff, and reinforcement-learning work such as the Prime Intellect repair environment.
The next direction is recursive editing. Inspired by Recursive Language Models, the goal is to treat a finished movie and its run bundle as an environment an agent can inspect and revise. A request like, "rerender this, but tilt the camera so the equations are easier to read," should route back through the scene plan and Manim code, recompute the right change, validate it, render again, and leave a new trace that can train the next policy.
This repo is the build log for that loop: agents learning to reason through complex topics, preserve their work, and turn the reasoning into visual explanations.
Christian
Today, that means a durable agent pipeline with:
audience-aware request artifacts, from grade-school intuition to advanced notation;
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