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Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Browser-based AI dev workspace by Google for full-stack apps; being sunset on 22 Mar 2027, no new workspaces.
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
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- ✦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
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦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
- ✦Cloud workspaces for full-stack development
- ✦App Prototyping agent from natural language
- ✦Gemini AI for coding, debugging and docs
- ✦Repo import from GitHub, GitLab and Bitbucket
- ✦Web previews and Android emulators
- ✦Deploy to Firebase App Hosting, Hosting or Cloud Run
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- →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
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Prototyping apps from a prompt or mockup
- →Building full-stack apps in the browser
- →Collaborating and sharing preview URLs
- →Deploying and monitoring apps quickly