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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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Gitmore
✓ verifiedFreemium
Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.
👁 7.6K/mo
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SQLAI.ai
✓ verifiedPaid
AI SQL toolkit for analysts and developers to generate, optimize, validate, format and explain queries across 30+ database engines.
👁 26K/mo♥ 2.7K
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
Free trial available
Hobby: $4/mo (50 queries/month)
Starter: $6/mo (200 queries/month)
Explorer: $10/mo (1,000 queries/month)
Pro: $20/mo (3,000 queries/month)
Free trial available
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
- ✦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)
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- ✦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
- ✦Natural-language to SQL/NoSQL query generation
- ✦AI-driven query optimization with rewrite suggestions
- ✦Syntax validation with automated error fixes
- ✦Query formatting and cross-engine conversion
- ✦Schema-aware data source connections with autosuggest
- ✦Rule-based guardrails per connected data source
- ✦Support for large schemas with 900+ tables
Use cases
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
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- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
- →Analysts writing SQL without deep query-syntax knowledge
- →Developers debugging and optimizing slow queries
- →Teams standardizing SQL formatting across a codebase
- →Migrating queries between database engines
- →Learners wanting plain-language explanations of SQL statements
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