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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
AI content-workflow platform helping marketing teams create and refresh SEO/AEO content at scale with human review.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
Google's asynchronous AI coding agent that autonomously fixes bugs and builds features in GitHub repos, powered by Gemini.
No public pricing
Free trial available
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
- ✦AI workflows for content creation, optimization and refresh
- ✦AI and traditional search visibility insights
- ✦Brand Kit for voice and style grounding
- ✦Power Agents and no-code workflow builder
- ✦Human review checkpoints
- ✦Integrations with WordPress, Notion and Semrush
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Autonomous coding agent
- ✦GitHub repository integration
- ✦Runs in a cloud VM
- ✦Multi-step task planning
- ✦Opens pull requests with changes
- ✦Powered by Gemini
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Producing SEO and AEO content at scale
- →Refreshing old content to regain traffic
- →Tracking brand visibility in AI answers
- →Agency content production
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Fixing bugs asynchronously
- →Adding features to a codebase
- →Writing and updating tests
- →Automating routine development tasks