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AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Connects Git repos to answer plain-English questions about your code with file references and dependency context.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
Interactive online platform for learning data science and AI skills.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦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
- ✦Natural-language search across a codebase
- ✦Architecture explanations and dependency graphs
- ✦Bug hunter that traces issues across files
- ✦AI code review before opening a PR
- ✦Automatic documentation generation
- ✦Multi-repo support via OAuth
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Interactive courses and coding challenges
- ✦Skill and career tracks
- ✦DataCamp Workspace for data analysis
- ✦Skill assessments
- ✦Certifications
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Learning Python, R, SQL, Power BI, and Tableau
- →Upskilling in data analysis, data engineering, and machine learning
- →Preparing for data science and analytics careers
- →Team training for data literacy and AI skills