<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> TLDW addresses the challenge of consuming lengthy YouTube videos by transforming them into a structured learning workspace. It targets users who need to quickly extract key information from long-form content, offering a significant benefit by enabling users to absorb hours of video material in minutes through AI-generated summaries and highlights.

How It Works

TLDW leverages a modern stack including Next.js 15, React 19, and Turbopack for a fast development experience. It integrates Google Gemini 2.5 for AI-powered video analysis and Supabase for robust backend services like authentication, data persistence, and rate limiting. Transcripts are sourced via Supadata. The architecture prioritizes efficient iteration with Tailwind CSS v4 and shadcn/ui, enabling features like AI highlight reels, structured summaries, and transcript-synced chat grounded in video content.

Quick Start & Requirements

Installation requires Node.js 18+ and npm. Users must set up a Supabase project and obtain API keys for Google Gemini and Supadata. The process involves cloning the repository, installing dependencies (npm install), configuring a .env.local file with credentials and salts, applying database migrations and functions within Supabase, and running the development server (npm run dev). Detailed setup instructions and architecture notes are available in CLAUDE.md.

Highlighted Details

• AI-generated highlight reels with "Smart" (quality) and "Fast" (speed) modes.
• Gemini-powered quick preview, structured summary, suggested questions, and memorable quotes.
• AI chat feature grounded in the transcript with timestamped citations and fallbacks.
• Transcript viewer synchronized with the YouTube player, allowing click-to-jump.
• Personal notes workspace with cross-video aggregation and a dashboard (/all-notes).
• Aggressive caching, background refresh tasks, and tiered rate limiting for anonymous and signed-in users.
• Comprehensive security middleware including CSP, CSRF protection, and body-size caps.

Maintenance & Community

The project welcomes issues and PRs, utilizing Anthropic Claude Code Actions for automated PR reviews guided by CLAUDE.md. This document also serves as an extended architecture and contributor handbook. No specific community channels (e.g., Discord, Slack) or roadmap links were detailed in the provided README.

Licensing & Compatibility

The project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). This strong copyleft license mandates that derivative works or modifications distributed over a network must also be made available under the AGPL-3.0, potentially posing compatibility challenges for integration into closed-source commercial products.

Limitations & Caveats

Setup complexity is a notable barrier, requiring a Supabase project, multiple API keys, and database configuration. The AGPL-3.0 license imposes significant obligations on distributed derivative works. The use of Next.js 15 and React 19 suggests a potentially evolving codebase. Performance may depend on external API availability and rate limits.