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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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Opera One Browser
✓ verifiedFree
Free Chromium-based desktop browser with tab grouping, built-in AI, free VPN, ad blocker and sidebar apps.
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Kaggle
✓ verifiedFree
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
Pricing
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
No public pricing
Core features
- ✦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)
- ✦Tab Islands for organizing tabs into groups
- ✦Built-in Opera AI assistant with page context
- ✦Free VPN and ad blocker
- ✦Modular sidebar with apps and messengers
- ✦Split-screen browsing and immersive themes
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
Use cases
- →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
- →Browsing with organized tab groups
- →Using AI assistance while browsing
- →Protecting privacy with a free VPN
- →Multitasking with sidebar apps and split screen
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks
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