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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
Open-source SDK and viewer for logging, querying, and visualizing multimodal robotics data, with a paid managed Hub for scale.
AI tool for engineering teams that automates code review, status updates, and answers questions about what's changing in code.
No public pricing
Free trial available
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
- ✦Library of sample database designs
- ✦Visual database designer / diagram tool
- ✦AI database generator
- ✦Modify and optimize existing schemas
- ✦SQL script export
- ✦Dialect converters (MySQL/PostgreSQL/MSSQL)
- ✦Open-source Python, Rust, and C++ logging SDK
- ✦Interactive desktop and web viewer for reviewing recordings
- ✦SQL and dataframe queries across logged data
- ✦Column-chunk .rrd storage format for multimodal data
- ✦PyTorch dataloader for training directly on recordings
- ✦Commercial Hub with managed catalog, SSO, and byte-range indexing
- ✦Used in robotics projects like LeRobot, Brush, and PyCuVSLAM
- ✦AI code review
- ✦Automatic engineering status updates
- ✦Agent that answers questions and takes action
- ✦Metrics on coding time and project focus
- ✦Pushed vs landed tracking
- ✦Commit and contributor insights
- →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
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →Robotics teams debugging calibration and training runs
- →Visualizing and querying large multimodal sensor datasets
- →Streaming training data mixes directly to GPUs at scale
- →Sharing annotated recordings across a robotics engineering team
- →Automating code reviews
- →Keeping stakeholders updated on engineering progress
- →Understanding what's changing in a codebase
- →Tracking team productivity metrics