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Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
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
No public pricing
- ✦Codebase-aware developer chat
- ✦AI code completions and inline edits
- ✦Customizable and shareable prompts
- ✦Automatic bug identification and debugging help
- ✦Context filters to exclude sensitive repos
- ✦Integrates with major code hosts and IDEs
- ✦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)
- ✦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
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- ✦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
- →Engineers asking questions about an unfamiliar large codebase
- →Teams standardizing common coding tasks with shared prompts
- →Developers debugging errors faster with AI-assisted context
- →Enterprises running large-scale code migrations
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups
- →Collecting human preference data for RLHF or model evaluation
- →Running academic behavioral or market research studies
- →Sourcing domain-expert data for specialized AI benchmarks