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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Google's cloud-based, AI-assisted development environment, now rebranded and merged into Firebase Studio.
AI coding assistant for editors and IDEs that explains, refactors, documents, and generates code across 56 languages.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Open-source GenBI platform that turns plain-English questions into governed SQL, charts and dashboards for data teams.
No public pricing
No public pricing
Free trial available
No public pricing
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Cloud-based IDE accessible from the browser
- ✦AI-assisted coding
- ✦Cross-platform app development
- ✦Preconfigured workspaces and templates
- ✦Now part of Firebase Studio
- ✦Bug detection and fix suggestions
- ✦Code and CSS framework conversion
- ✦Unit test and documentation generation
- ✦Regex, SQL query, and CI/CD pipeline generation
- ✦Code explanation and style checking
- ✦Editor extensions for VS Code, Sublime, JetBrains, Visual Studio
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦Natural-language to SQL with instant charts
- ✦Semantic modeling layer (MDL)
- ✦Row-level and column-level data policies
- ✦20+ connectors (BigQuery, PostgreSQL, ClickHouse, Redshift)
- ✦Auto-generated GenBI dashboards
- ✦Embedded AI API with agent skills and memory
- ✦Cloud and self-hosted deployment
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Building apps from anywhere in the browser
- →Prototyping with AI assistance
- →Developing cross-platform applications
- →Generating unit tests for existing functions
- →Refactoring legacy code to modern practices
- →Producing inline documentation automatically
- →Learning new programming languages or concepts via AI explanations
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Self-serve analytics for non-technical teams
- →Building governed dashboards from a prompt
- →Embedding AI analytics into products
- →Cutting ad-hoc SQL requests to data teams