<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> 2anki/server provides a streamlined solution for converting study materials into Anki flashcards, targeting students and educators. It bridges popular platforms like Notion, Markdown, and HTML with Anki, automating card creation and preserving rich content, thereby enhancing learning efficiency.

How It Works

<2-4 sentences on core approach / design (key algorithms, models, data flow, or architectural choices) and why this approach is advantageous or novel.> The server acts as a conversion engine, ingesting diverse study formats including Notion pages (via API/export), Markdown, HTML, and Excel. It intelligently transforms content, such as toggle lists into cards and enabling cloze deletions, while preserving embeds, audio, images, code blocks, and LaTeX. This approach automates the tedious process of manual flashcard creation.

Quick Start & Requirements

• Primary install / run command (pip, Docker, binary, etc.).

• Non-default prerequisites and dependencies (GPU, CUDA >= 12, Python 3.12, large dataset, API keys, OS, hardware, etc.).

• Estimated setup time or resource footprint.

• If they are present, include links to official quick-start, docs, demo, or other relevant pages.

• Install: Clone the repository, navigate to the directory, run pnpm install, create a .env file, and start the server with pnpm dev. The server runs on http://localhost:2020 and the frontend on http://localhost:5173.

• Prerequisites: pnpm (Node.js package manager).

• Links: 2anki.net, GitHub Repository.

Highlighted Details

• Supports multiple input formats: Notion, Markdown, HTML, Excel (xlsx), zip bundles.
• Handles complex content: toggle lists, cloze deletions, embeds, audio, images, code blocks, LaTeX.
• Offers a self-hostable option for users who exceed free-tier quotas.
• Development process leverages AI agents (PM, Designer, Engineer) for rapid iteration and spec validation.

Maintenance & Community

• The project is led by Alexander Alemayhu and employs a novel development process utilizing AI agents (PM, Designer, Engineer) for parallel task execution, with human oversight. Contribution guidelines are available in CONTRIBUTING.md. No specific community channels (like Discord/Slack) are detailed in the provided README snippet.

Licensing & Compatibility

• License type and notable restrictions (GPL -> copyleft, SSPL, etc.).

• Compatibility notes for commercial use or closed-source linking.

• The code is licensed under the MIT License, copyright (c) 2020-2026. This license is permissive, generally allowing for commercial use and integration into closed-source projects.

Limitations & Caveats

<1-3 sentences on caveats: unsupported platforms, missing features, alpha status, known bugs, breaking changes, bus factor, deprecation, etc. Avoid vague non-statements and judgments.> The README mentions a "free-tier quota," indicating potential usage limitations for the hosted service. Specific details on these quotas or potential performance bottlenecks are not elaborated upon.