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GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
No-code AI platform using multi-agent 'employees' to research, build, deploy and market full-stack apps from a prompt.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
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- ✦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)
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦Multi-agent AI team (PM, engineer, analyst, etc.)
- ✦Chat-to-build full-stack apps
- ✦Built-in backend: auth, database, Stripe
- ✦SEO and ads agents
- ✦Race Mode across multiple models
- ✦Code export and GitHub sync
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
- →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
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Build SaaS and e-commerce apps
- →Launch MVPs in minutes
- →Add payments and user login
- →Drive SEO and ad growth
- →Export code and self-host
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents