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The New GitBook
✓ verifiedFreemium
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
653K visits/mo2.9K saves
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RepoClip
✓ verifiedFreemium
AI tool that turns a GitHub repo URL into a narrated promotional demo video in minutes.
5.3K visits/mo2.1K saves
Pricing
No public pricing
Free trial available
No public pricing
Historical Data Pack: $49.9
Base Plan: $14.9/month
Advanced Plan: $24.9/month
Enterprise Plan: $34.9/month
No public pricing
Credit Pack: $5 one-time (40 credits)
Kling Pack: $9.99 (1 premium video + 40 bonus credits)
Starter: $24/month (50 credits/month)
Pro: $66/month (200 credits/month)
Agency: $166/month (800 credits/month)
Core features
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Commits and Pull Requests Dashboard
- ✦Advanced Developer Skills Analysis
- ✦Strategic Investment Balance Monitoring
- ✦Collaborative Developers Map
- ✦Benchmarking Comparison with Other Teams
- ✦Smart Notifications
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- ✦AI code analysis to generate a video script (via Gemini)
- ✦AI-generated still images and cinematic video clips (Nano Banana 2, Kling 3.0 Pro)
- ✦AI narration using preset OpenAI text-to-speech voices (no voice cloning)
- ✦Support for private repositories via GitHub OAuth
- ✦Public API and GitHub Action for CI/CD-triggered videos
- ✦Credit-based free and paid tiers with resolution/watermark differences
Use cases
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Visualize historical graphs of code evolution
- →Assess development team performance using RSI and EMA
- →Understand developer skills and identify areas for improvement
- →Categorize commits by type (fixes, refactoring, etc.) to analyze investment balance
- →Identify individual and collective contributors within the team
- →Compare team performance with industry benchmarks
- →Receive weekly and monthly reports with AI-extracted insights
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- →Developers announcing new features with a shareable demo video
- →Founders creating investor-pitch product demos from their codebase
- →Open-source maintainers promoting a project to attract contributors
- →Teams automating a demo video on every release via CI/CD
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