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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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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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Debugg AI
✓ verifiedFreemium
Zero-config AI browser testing that auto-runs end-to-end tests on every GitHub PR and posts results as comments.
3.0K visits/mo6.3K saves
Pricing
No public pricing
No public pricing
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
Free trial available
Free: $0 (public repos, 100 tests/mo)
Pro: $20/mo (private repos, 1,000 tests/mo)
Grow: $40/mo (5,000 tests/mo)
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)
- ✦Zero-ETL data integration
- ✦Federated Query
- ✦Streaming Ingestion
- ✦Instant Replication with CDC
- ✦API to SQL conversion
- ✦NoSQL to SQL conversion
- ✦SQL to API conversion
- ✦Self-service Integration
- ✦Generate SQL with AI
- ✦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
- ✦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
- ✦No-config automated browser testing
- ✦GitHub-native PR testing with inline results
- ✦Fully managed cloning, build, and tunneling
- ✦AI app mapping and targeted test generation
- ✦Recorded, replayable test sessions
- ✦MCP server for Claude and Codex
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Query data directly from its source in real-time.
- →Process data wherever it is, blending data from different sources.
- →Ingest streaming data from Kafka, Segment, etc., into Peaka BI Table.
- →Replace nightly batch ingestion with real-time data access.
- →Treat every data source like a relational database by converting APIs to tables.
- →Use SQL to query NoSQL databases.
- →Query consolidated data and expose it with APIs.
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
- →Catch UI regressions before merge
- →Test user flows automatically on each PR
- →Validate flows against a local dev server
- →Replace hand-written Playwright/Selenium suites
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