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Gitmore
✓ verifiedFreemium
Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.
7.6K visits/mo
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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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Coddy - Code Makes Perfect
✓ verifiedFreemium
Gamified platform to learn 20+ programming languages via interactive lessons, a browser playground and an AI tutor.
2.5M visits/mo2.0K saves
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
No public pricing
No public pricing
Core features
- ✦AI-summarized commit and PR reports
- ✦Daily and weekly scheduled digests
- ✦Slack and email delivery
- ✦One-click OAuth or webhook setup
- ✦GitHub, GitLab and Bitbucket support
- ✦Templates for standups and reports
- ✦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
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- ✦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)
- ✦Interactive lessons in 20+ languages
- ✦Browser playground to run code with no setup
- ✦AI tutor that explains and debugs code
- ✦Shareable completion certifications
- ✦Cheat sheets, docs and developer tools
- ✦Team/business training option
Use cases
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
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- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Learning to code from scratch
- →Practicing a new programming language
- →Getting instant AI help while coding
- →Earning certificates to share
- →Onboarding and training engineering teams
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