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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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OpenTrain AI
✓ verifiedFreemium
Talent marketplace linking AI labs with 257,000+ vetted data labelers and trainers for RLHF, red-teaming and evaluation.
574K visits/mo
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Appen
✓ verifiedPaid
Long-standing provider of human-labeled, expert-validated training data and model evaluation services for building frontier AI.
1.2M visits/mo
Pricing
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
Self-Service: 10% marketplace fee + $9.95 contract initiation fee
Managed Service: 20% management fee
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
- ✦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)
- ✦Network of 257,000+ pre-vetted AI data experts
- ✦AI-matched shortlists with skills tests and interviews
- ✦Bring talent into any annotation platform, no lock-in
- ✦Self-service or fully managed engagements
- ✦Job feed aggregating 20+ platforms for freelancers
- ✦Frontier alignment data (RLHF, SFT, red teaming)
- ✦Speech and audio data
- ✦Multimodal / VLM annotation
- ✦Physical AI data (LiDAR, robotics, sensor fusion)
- ✦Model integrity, bias and hallucination audits
- ✦1M+ vetted contributors, 500+ locales
- ✦SOC2 and ISO 27001 certified
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
- →Sourcing experts for RLHF and model evaluation
- →Staffing red-teaming and data-labeling projects
- →Scaling annotation teams into existing tools
- →Finding AI training gigs as a freelancer
- →Source training data for AI models
- →Evaluate and benchmark models
- →Annotate multimodal and sensor data
- →Run safety and bias audits
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