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Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
AI co-pilot for technical diagrams and design docs, with diagram-as-code and integrations for engineering teams.
AI agent-based end-to-end testing platform for SaaS teams that runs exploratory and PR-triggered tests without maintaining test scripts.
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- ✦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
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦AI-generated diagrams from prompts
- ✦Diagram-as-code editing
- ✦Markdown design docs
- ✦Eraserbot auto-updating codebase diagrams
- ✦Integrations: GitHub, Notion, Confluence, VS Code
- ✦Export to PNG/SVG/PDF/MD and MCP server
- ✦AI agents that visually explore and test UI like a real user
- ✦Automatic PR-triggered test runs via GitHub/Vercel preview integration
- ✦Self-healing tests that adapt to UI and workflow changes
- ✦Mobile web, iOS, and Android app testing support
- ✦Detailed debugging with screenshots, logs, and failure reasoning
- ✦Cloud-native execution with no source-code access required
- →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
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Create architecture and cloud diagrams fast
- →Write and maintain design docs
- →Keep codebase diagrams up to date
- →Embed live diagrams in Notion/Confluence
- →Engineering teams wanting regression testing without maintaining scripts
- →SaaS companies needing continuous QA feedback on every pull request
- →Teams replacing manual QA hours with automated agent-driven testing