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Qoder
✓ verifiedFreemium
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
2.7M visits/mo32K saves
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devActivity
✓ verifiedFreemium
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
52K visits/mo
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Cursor
✓ verifiedFreemium
AI coding agent and editor that understands your codebase, runs autonomous agents and edits across your stack.
4.6M visits/mo102K saves
Pricing
No public pricing
No public pricing
Free trial available
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
Hobby: Free
Pro (Individual): $20/mo
Teams: $40/user/mo
Core features
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- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦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)
- ✦Codebase-aware AI completions and edits
- ✦Autonomous and cloud agents
- ✦Access to frontier models (GPT, Claude, Gemini, Grok)
- ✦CLI, Slack and GitHub integration
- ✦Scheduled automations and triggers
- ✦Team marketplace and enterprise controls
Use cases
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- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →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
- →AI pair programming
- →Multi-file refactors
- →Autonomous feature building
- →Automated PR reviews and CI fixes
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