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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Pay-per-use cloud API to run, fine-tune, and deploy thousands of open-source and proprietary AI models with one line of code.
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
No public pricing
Free trial available
Free trial available
No public pricing
No public pricing
No public pricing
- ✦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
- ✦One-line API calls to run community and proprietary AI models
- ✦Support for image, video, speech, and LLM generation models
- ✦Fine-tuning and custom model deployment via Cog
- ✦Per-second usage billing on shared or dedicated hardware
- ✦Automatic scaling for high-traffic private models
- ✦Thousands of community-published models with production APIs
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
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- →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
- →Developers embedding image/video/speech generation into an app via API
- →Teams deploying and scaling their own fine-tuned models
- →Builders comparing outputs from multiple AI models in one playground
- →Companies avoiding GPU infrastructure management for ML inference
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
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