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GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Crowdsourcing platform using 8M+ global workers to deliver AI training data, labeling, surveys, testing and store checks.
Vibe-coding builder creating full-stack apps by chatting with AI.
Research participant marketplace that gives AI teams and academics fast access to verified, screened human data and feedback.
No public pricing
No public pricing
No public pricing
- ✦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
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦8M+ verified global crowd
- ✦AI training data (image/video/audio/text) and annotation
- ✦Survey tools and respondents
- ✦Store checks and mystery shopping
- ✦Crowdtesting (web/app/games)
- ✦Tagging and categorization
- ✦Managed or self-service plus API
- ✦CodeFlying enables full-stack app creation via chat in minutes
- ✦300,000+ verified, screened participants
- ✦300+ audience targeting filters
- ✦Representative and quota-based sampling
- ✦API and no-code survey tool integrations
- ✦AI-powered participant quality monitoring (Protocol)
- ✦Managed services with dedicated project teams
- ✦Access to vetted domain experts
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Sourcing AI training and labeled datasets
- →Running surveys and market research
- →Point-of-sale store checks
- →Crowdtesting apps and websites
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- →Collecting human preference data for RLHF or model evaluation
- →Running academic behavioral or market research studies
- →Sourcing domain-expert data for specialized AI benchmarks