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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
AI-powered IDE with code completion, generation, explanation and debugging, plus a cloud dev environment, for developers.
Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
Side-by-side arena to compare AI coding models and build multi-file apps, with a public leaderboard and battle mode.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦AI code completion and snippet generation
- ✦Natural-language code generation
- ✦Code explanation and AI Q&A
- ✦Automated bug detection and fixes
- ✦Zero-config cloud development environment
- ✦Project creation from templates or Git
- ✦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
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦Head-to-head model comparison
- ✦Battle mode matchups
- ✦Public model leaderboard
- ✦Multi-file app generation
- ✦File uploads as input
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Writing and completing code faster with AI
- →Onboarding to unfamiliar codebases
- →Debugging and optimizing code
- →Spinning up dev environments in the browser
- →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
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →Choosing the best coding model
- →Benchmarking AI code quality
- →Prototyping small apps