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Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Visual development platform with AI design-to-code, a visual editor and headless CMS so teams and agents ship UI in real code.
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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
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Cross-session project memory recalling prior decisions and state
- ✦Autonomous execution of long, multi-step notebook tasks
- ✦Reads cell outputs (plots, tables, metrics), not just code
- ✦In-notebook cell-level assistance and error fixing
- ✦Installs directly into existing JupyterLab via pip, no new editor
- ✦Concept explanations with runnable example cells
- ✦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 design-to-code (Figma to code)
- ✦Visual editor tied to your components
- ✦Headless/visual CMS
- ✦AI agents (Builder-Agent) that open PRs
- ✦Integrations: GitHub, GitLab, Bitbucket, Figma, VS Code
- ✦Roles, reviews and collaboration
- →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
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Data scientists running multi-week model iteration projects
- →Domain experts (e.g. risk/fintech) who know the problem but not deep Python
- →Researchers wanting an agent that remembers project context across days
- →Analysts needing help understanding unfamiliar algorithms or libraries
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Convert designs to production code
- →Let non-developers edit pages visually
- →Manage content with a headless CMS
- →Collaborate across design, PM and engineering