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Lerni

A local-first, privacy-preserving learning system that combines the Feynman Technique with spaced repetition (SM-2) to help you learn deeply and remember permanently.

Philosophy

  • Learn by teaching: The best way to understand something is to explain it simply
  • Forgetting is natural: Spaced repetition fights the forgetting curve systematically
  • Privacy first: Your learning data stays on your machine
  • AI as coach, not crutch: Optional AI agents challenge your understanding — they never generate content for you

How It Works

  1. Capture — Write what you know about a topic (raw notes)
  2. Simplify — Explain it like you're teaching someone else
  3. Identify gaps — What's still unclear? What questions remain?
  4. Refine — Improve your explanation with analogies and examples
  5. Review — SM-2 algorithm schedules reviews at optimal intervals

Features

  • CLI-based workflow (study new, study review, study today)
  • 4-step Feynman technique with version tracking
  • Concept-based knowledge graph with typed relationships (parent, prerequisite, related)
  • SM-2 spaced repetition scheduling
  • macOS notifications for daily review reminders
  • Optional AI coaching — planned for Phase 2

Status

Phase 1 (MVP) is complete. Core system is functional: data layer, Feynman workflow, SM-2 review scheduling, concept graph, and CLI.

See docs/ for detailed specifications:

Tech Stack

  • Python 3.11+
  • SQLite (local database)
  • typer (CLI)
  • Anthropic Claude / OpenAI (optional, user-provided API keys)

License

MIT

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Local-first learning system combining Feynman Technique with SM-2 spaced repetition

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