<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> Learn FASTER is an AI-powered learning coach designed to accelerate technical skill mastery. It targets engineers and power users, offering personalized syllabi, spaced repetition, and active practice integrated via Claude Code. The tool helps users master technical skills faster using science-backed principles.

How It Works

The system uses AI (Claude Code) for tailored learning paths based on user goals and skill levels. It incorporates a spaced repetition system (SRS) for optimal review scheduling. A key differentiator is active learning via auto-generated exercises and a "Teach-Back" mode, where users explain concepts to solidify understanding. This is structured around the FASTER framework (Forget, Act, State, Teach, Enter, Review) for holistic learning.

Quick Start & Requirements

Installation requires the uv package manager (curl -LsSf https://astral.sh/uv/install.sh | sh). Persistent install: uv tool install learn-faster --from git+https://github.com/cheukyin175/learn-faster-kit.git. One-time use: uvx --from git+https://github.com/cheukyin175/learn-faster-kit.git learn-faster. Launch in any project directory with learn-faster. Prerequisites: Python 3.12+, Claude Code, uv. Setup is minimal; first run initializes project files (.claude/, .learning/). Repo: https://github.com/cheukyin175/learn-faster-kit.git.

Highlighted Details

• Personalized syllabi generation.
• Spaced repetition scheduling.
• Four learning modes: Balanced, Exam-Prep, Theory-Focused, Practical.
• Active practice with auto-generated exercises.
• "Teach-Back" for active recall.
• Progress tracking and statistics.

Maintenance & Community

The README provides no specific details on contributors, sponsorships, or community channels (Discord/Slack). The project is hosted on GitHub.

Licensing & Compatibility

Released under the MIT License, which is permissive for commercial use and integration within closed-source projects.

Limitations & Caveats

Requires integration with Claude Code, necessitating user access and familiarity. Effectiveness depends on active user participation in practice and "Teach-Back" features. The README does not specify alpha/beta status or list known bugs/platform limitations beyond Python version.