TreeMap
A free, open-source disk-space visualizer. Squarified treemap, duplicate hunting, disk-full forecasting, and cleanup that only ever moves files to the Trash.
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Convert any document into clean Markdown, then organize, connect, and synthesize it into one searchable knowledge hub your AI can actually use. Cut token costs and boost retrieval accuracy.
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生產力TypeScript · PLpgSQLGitHub @trenknerpeter上線中營運者未確認
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一分鐘完成驗證Document to Markdown converter for AI workflows — try it at https://mdspin.app
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取自儲存庫 README
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Convert PDFs, DOCX, PPTX, and more to clean, AI-ready Markdown.
MDSpin converts documents into clean Markdown optimised for LLMs, RAG pipelines, and AI workflows. Drop a file, get AI-ready text in seconds — no setup, no CLI, no Python.
• PDF, DOCX, PPTX, XLSX, HTML, TXT, RTF support
• Token reduction metrics after each conversion
• No signup required to try
• Conversion history for logged-in users
• Works with ChatGPT, Claude, Gemini, and any LLM檢視完整 README ↗Convert PDFs, DOCX, PPTX, and more to clean, AI-ready Markdown.
MDSpin converts documents into clean Markdown optimised for LLMs, RAG pipelines, and AI workflows. Drop a file, get AI-ready text in seconds — no setup, no CLI, no Python.
Next.js 16 · TypeScript · Tailwind CSS · Supabase · Vercel
git clone https://github.com/trenknerpeter/mdspin.git
cd mdspin
npm install
cp .env.example .env.local # fill in your values — see Environment Variables below
npm run dev
| Variable | Description |
|---|---|
BACKEND_URL | URL of the conversion API |
BACKEND_API_KEY | API key for the conversion backend |
NEXT_PUBLIC_SUPABASE_URL | Supabase project URL |
NEXT_PUBLIC_SUPABASE_ANON_KEY | Supabase anon/public key |
SUPABASE_SERVICE_ROLE_KEY | Supabase service role key (server-side only) |
The conversion backend is a separate private service. To use your own, implement an endpoint that accepts multipart/form-data with a file field and returns { markdown: string }.
PRs and issues welcome. This is a side project so response times may vary, but improvements to the UI, new format support, or documentation are all appreciated.
MIT © 2026 Peter Trenkner