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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Independent benchmarks comparing AI models and API providers on intelligence, speed, and cost across many leaderboards.
Data-intelligence and storage platform powering large-scale AI and HPC, aimed at maximizing GPU utilization.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦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
- ✦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
- ✦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
- ✦AI-native data intelligence platform
- ✦High-performance storage appliances (AI400X series)
- ✦EXAScaler and Infinia software
- ✦Multi-tenant secure data isolation
- ✦Real-time encryption for data sovereignty
- ✦Integrations with NVIDIA AI infrastructure
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →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
- →Choosing an AI model or provider
- →Tracking frontier model progress
- →Comparing price and performance
- →Feeding data to large GPU training clusters
- →Building AI factories and sovereign-AI platforms
- →Powering HPC and supercomputing storage
- →Accelerating research in finance, life sciences and automotive