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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Autonomous AI software engineer by Cognition that plans and completes full coding tasks from a natural-language brief.
AI app builder that turns chat prompts into working web apps and sites, with credit-based build and deploy.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
No public pricing
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
- ✦Autonomous end-to-end task execution
- ✦Planning and multi-step reasoning
- ✦Code writing, running and debugging
- ✦Integrated shell, editor and browser
- ✦Application building and deployment
- ✦Chat-to-app and website generation
- ✦Real-time prototype building
- ✦One-click deploy and hosting
- ✦Templates to start projects
- ✦Credit-based building with shared workspaces
- ✦You own your code and data
- ✦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
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Automating software engineering tasks
- →Building apps from a brief
- →Debugging and fixing code
- →Assisting development teams
- →Build web apps without coding
- →Prototype product ideas quickly
- →Create landing pages and sites
- →Ship internal tools
- →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
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks