Paste a URL. Pick your level. Get a course.
Distill takes expert-level content — deep blog posts, arXiv papers, GitHub repos — and generates progressive courses with lesson documents, scaffolded exercises, and observable milestones. Each course builds toward reproducing the author's results.
Lesson documents, not summaries — 3,000–10,000 word teaching documents with running examples, inline code, comprehension checks, and formula walkthroughs.
Exercises you can actually run — scaffolded
.pyfiles with TODO blocks, docstrings, and a__main__test harness. Solutions included.
git clone https://github.com/TAOGenna/Distill.git
cd Distill
uv sync
uv run python -m distill
# → opens http://localhost:8420Phase 1 — Blueprint. Reads the full source material and produces a curriculum: module dependencies, scaffold contracts per exercise (what's provided vs what the student writes), key excerpts, and validation criteria.
Phase 2 — Generate. Each module gets a multi-turn conversation with the full source. The model writes the lesson first (deep processing), then exercises one at a time. Solutions are executed between turns — real output from exercise 1 feeds into exercise 2's prompt. Modules generate in parallel.
Phase 3 — Review. Pre-flight checks (syntax, TODOs, output patterns) catch structural issues. LLM review checks pedagogical quality and contract compliance. Failed modules are re-generated.
Inspired by karpathify, Stanford's CS231n assignments, MIT 6.102 course readings, Simon Boehm and Alexa Godric's blog style and beautiful excalidraws.