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CodeReviewBot.AI
✓ verifiedFreemium
AI bot that reviews GitHub pull requests, flagging bugs, security and performance issues with detailed, consistent feedback.
👁 2.8K/mo♥ 790
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devActivity
✓ verifiedFreemium
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
👁 52K/mo
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GitFluence
✓ verifiedFree
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
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Aide Dev
✓ verifiedPaid
Aide helps developers code faster with parallel agents and automated workflows.
👁 7.6K/mo
Pricing
No public pricing
Opensource: $0/mo (100 reviews/mo, public repos)
Starter: $15/mo (40 PR reviews/mo, private)
Pro: $75/mo (500 reviews/mo)
Free trial available
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
Standard: $49 per month
Core features
- ✦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)
- ✦Automated AI reviews on GitHub PRs
- ✦Bug, security and performance detection
- ✦Detailed, consistent feedback
- ✦Interactive code-review tool for snippets
- ✦Multi-language explanations
- ✦Customizable review rules (Pro)
- ✦Self-host/custom LLM (Enterprise)
- ✦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
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦Parallel Agents for faster coding
- ✦GitHub native integration
- ✦Automated PR workflow
- ✦Smart PR suggestions
- ✦Automatic code reviews
- ✦Real-time progress tracking
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Automate pull-request reviews
- →Catch issues before merge
- →Get plain-English code explanations
- →Keep review quality consistent
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →Automating code reviews
- →Generating PRs automatically
- →Improving code quality through continuous improvements
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