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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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Ultralytics
✓ verifiedFreemium
End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.
1.1M visits/mo
Pricing
No public pricing
Free trial available
No public pricing
No public pricing
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
Free: $0/month (100GB storage, 100 models, 3 concurrent trainings)
Pro: $29/seat/month (500GB storage, 500 models, 10 concurrent trainings)
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
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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
- ✦Smart data annotation with SAM-powered one-click masks across six task types
- ✦Cloud training with 22+ GPU configurations from RTX 2000 Ada to B200
- ✦Support for YOLOv5 through YOLO26 model families
- ✦One-click deployment across 43 global regions with auto-scaling
- ✦Export to 18 formats including ONNX, TensorRT, and CoreML
- ✦Live training metrics and experiment comparison dashboard
Use cases
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
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- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Building and training custom object detection or segmentation models
- →Labeling large image/video datasets for computer vision projects
- →Deploying vision models to edge or mobile devices
- →Running quality control or defect detection in manufacturing
- →Powering retail, logistics, or agriculture vision applications
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