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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Independent benchmarks comparing AI models and API providers on intelligence, speed, and cost across many leaderboards.
Autonomous AI software engineer by Cognition that plans and completes full coding tasks from a natural-language brief.
No public pricing
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
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦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)
- ✦Intelligence Index across many benchmarks
- ✦Model speed and cost comparisons
- ✦Coding, speech, image, and video leaderboards
- ✦Provider performance analysis
- ✦Personalized model recommender
- ✦Premium data and reports
- ✦Autonomous end-to-end task execution
- ✦Planning and multi-step reasoning
- ✦Code writing, running and debugging
- ✦Integrated shell, editor and browser
- ✦Application building and deployment
- →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
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Choosing an AI model or provider
- →Tracking frontier model progress
- →Comparing price and performance
- →Automating software engineering tasks
- →Building apps from a brief
- →Debugging and fixing code
- →Assisting development teams