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Connects Git repos to answer plain-English questions about your code with file references and dependency context.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Open-source, self-hostable AI coding agent with autocomplete, in-IDE chat and autonomous task execution for teams needing data control.
No public pricing
Free trial available
No public pricing
No public pricing
- ✦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
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- ✦150+ recommendations across 50+ AWS services
- ✦Zombie and unused resource cleanup
- ✦Over-provisioned rightsizing
- ✦Idle-resource scheduler
- ✦SpotBot for ECS Fargate spot/on-demand switching
- ✦AWS console extension with Slack/Teams alerts
- ✦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
- ✦Autonomous AI agent that plans and executes multi-step coding tasks
- ✦In-IDE chat for asking, editing, debugging and generating code
- ✦Real-time code autocompletion using retrieval-augmented generation
- ✦Repository search and analysis for context-aware execution
- ✦Integrations with GitHub, databases and CI/CD pipelines
- ✦Self-hosted/on-premise deployment option for data privacy
- ✦Support for choosing among different underlying LLMs
- →Onboarding new engineers faster
- →Answering questions about a codebase
- →Understanding how components connect
- →Finding and diagnosing bugs
- →Generating documentation from code
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Developer teams wanting an in-IDE autonomous coding agent
- →Organizations requiring on-premise/self-hosted AI coding tools for data control
- →Individuals doing 'vibe coding' with minimal manual coding
- →Teams fine-tuning an AI assistant to their own codebase