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Crowdsourcing platform using 8M+ global workers to deliver AI training data, labeling, surveys, testing and store checks.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Marketplace that matches vetted human experts with AI labs to produce training data and model evaluations.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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)
- ✦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
- ✦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
- ✦Curated roles across many expert domains
- ✦Expert vetting and matching to AI projects
- ✦Hourly-paid contract work for specialists
- ✦APEX benchmarks and off-the-shelf datasets
- ✦Enterprise data partnerships and evaluations
- ✦Daily payouts to contributors
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Sourcing AI training and labeled datasets
- →Running surveys and market research
- →Point-of-sale store checks
- →Crowdtesting apps and websites
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Source expert human data for AI training
- →Run domain-expert model evaluations
- →Find paid remote work as a domain specialist
- →Build custom datasets for AI labs
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents