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GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
ImageKit is an image and video optimization and delivery API with a DAM and creative automation for developers and teams.
Data lab providing expert human data, RL environments, and contextual evaluations to train and assess AI models and agents.
No public pricing
No public pricing
Free trial available
No public pricing
Free trial available
No public pricing
- ✦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)
- ✦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
- ✦Real-time image/video optimization and URL-based transforms
- ✦AI and GenAI transforms (smart crop, background removal, generative fill, upscaling)
- ✦Global CDN delivery with sub-50ms response
- ✦AI-powered digital asset management
- ✦Creative automation for on-brand banners at scale
- ✦SDKs and integrations for major stacks and CMSs
- ✦Realm: RL environments and frontier evaluations
- ✦Cortex: contextual evaluation for production AI agents
- ✦Expert-demonstrated robotics training data
- ✦Benchmarks such as LongExtractionBench
- ✦Expert human data partnerships
- ✦Research lab on human data markets
- →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
- →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
- →Speeding up website and app media performance
- →Optimizing e-commerce and media images and video
- →Centralized digital asset management and collaboration
- →Adaptive video streaming
- →Generating thousands of on-brand banner variations
- →Train and evaluate frontier AI models
- →Improve agent performance in production
- →Source expert human data for AI labs
- →Gather demonstration data for robotics