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Caret: Your New AI Companion
Adding Cursor's Flexibility to Cline's Transparency
Caret is more than just an AI coding tool; it's a VS Code extension that aims to be an AI companion growing with developers. It preserves the proven advantages of the open-source Cline while 'overlaying' more powerful and flexible features to maximize the development experience.
✨ What Makes Caret Different?
Feature |
Cline |
Cursor |
Caret |
AI Behavior |
Plan/Act (somewhat rigid) |
Ask/Agent (natural) |
Chatbot/Agent Mode (more intuitive and powerful) |
AI Transparency |
✅ Open Source (High) |
❌ Black Box (Low) |
✅ Open Source + Improved System Prompt (Very High) |
AI Efficiency |
Basic |
Basic |
50% Token Savings through System Prompt Optimization |
Persona Support |
❌ Not Supported |
❌ Not Supported |
✅ Template & Custom Personas, Profile Image Support |
Multilingual Support |
❌ Not Supported |
❌ Not Supported |
✅ Full Multilingual Support (i18n Overlay) |
Architecture |
Core Features |
Closed |
Overlay Structure (Stability + Extensibility) |
1. More Natural AI Conversation: Chatbot & Agent Mode
Beyond Cline's somewhat rigid Plan/Act mode, Caret offers Chatbot/Agent Mode—flexible like Cursor's Ask/Agent, but more intuitive than 'Ask'. We haven't just changed the interaction style; we've improved the system prompts to enhance both AI response performance and demeanor. Through verified experiments, we've achieved 50% token savings and 20% API cost reduction, enabling smarter and more cost-predictable AI collaboration.
2. Create Your Own AI Companion: Custom Personas
Add joy to your coding with Caret's pre-prepared template personas like K-POP idols, OS-tans, and more. You can register your own AI agent name and profile image to create a visually vibrant development environment.
Default Personas:
Caret: A friendly robot friend who loves coding and helps developers.
Oh Sarang: A K-pop idol and a tsundere engineering girl who helps you navigate between logic and emotion.
Madobe Ichika: A neat and reliable assistant inspired by Windows 11.
Cyan Macin: A concise and efficient helper modeled after macOS.
Tando Ubuntu: A warm collaborator who solves problems with an open-source spirit.
3. Coding Without Language Barriers: Full Multilingual Support
Other AI tools often overlook multilingual support, but Caret solves this. With an i18n-based overlay architecture, developers unfamiliar with English can fully utilize all features in their native languages, including Korean, Japanese, and Chinese.
4. Stability and Extensibility: Overlay Architecture
Caret preserves the core of Cline's proven stability and layers its innovative features on top as an 'overlay'. This allows you to benefit from Cline's stability and transparency while experiencing Caret's powerful extensibility.
🚀 Getting Started
- Installation: Search for "Caret" in the VS Code Marketplace and install it. (Coming Soon)
- Select Persona: Choose your favorite AI persona from the sidebar or create your own.
- Start Chatting: Begin coding with your AI companion!
🔮 Future Vision & Roadmap
Caret continues to evolve towards becoming the 'ultimate AI companion'.
- Self-Login & Credit System: We are preparing self-login functionality (available within 1 week) and a credit purchase system (available within 2 weeks).
- sLLM & Sovereign Model Support: We will enhance support for local LLMs (sLLM) and specialized sovereign models for security and cost efficiency.
- Community-Driven Feature Expansion: We plan to add features co-created with user feedback and contributions.
🤝 Contribute
Caret is an open-source project that grows with your participation. We welcome any form of collaboration, including bug reports, feature suggestions, and code contributions!
🌟 Contribution Types
Contribution Type |
Description |
Benefits |
💻 Code Contribution |
Feature development, bug fixes, documentation improvements |
Service Credits + GitHub Contributor Listing |
🐛 Bug Report |
Issue reporting, providing reproduction steps |
Service Credits |
💡 Idea Suggestion |
Proposing new features, improvements |
Service Credits |
💰 Financial Contribution |
Project sponsorship, development support |
Service Credits + Special Contributor Listing |
📖 Documentation |
Writing guides, translations, tutorials |
Service Credits + Documentation Contributor Listing |
🎁 Contributor Benefits
- Service Usage Credits: Caret service credits provided based on contribution scale
- GitHub Contributor Listing: Name listed in project README and release notes
- Service Page Listing: Profile listed on the official website's contributor page
- Priority Support: Priority access to new features and beta versions
🚀 How to Get Started
- Check Issues: Find issues to contribute to on GitHub Issues
- Join Discussion: Share feature suggestions or questions in Issues or Discussions
- Code Contribution: Contribute code through Fork → Develop → Pull Request process
- Documentation Contribution: Improve or translate documents in the
caret-docs/
folder
For detailed contribution guidelines, please refer to CONTRIBUTING.md.
All information necessary for Caret project development is systematically organized here.
📚 Core Development Guides
🏗️ Architecture & Design
🧪 Testing & Quality Management
- Testing Guide - TDD, test writing standards, coverage management
- Logging System - Integrated logging, debugging, development/production modes
🔄 Frontend-Backend Communication
🤖 AI System Implementation
🎨 UI/UX Development
📖 Documentation & Conventions
🤖 AI Work Methodology
🎯 Quick Start Workflow
- Environment Setup: Developer Guide → Development Guide Overview
- Project Understanding: New Developer Guide → Caret Architecture Guide
- AI System Understanding: AI Message Flow Guide → System Prompt Implementation
- Start Development: AI Work Guide → Testing Guide
- Advanced Features: Interaction Patterns → Component Architecture
📖 Additional Resources
💡 Mandatory read before starting development: Please familiarize yourself with the TDD-based development process and architectural principles in the AI Work Methodology Guide.
⚡ Want to understand AI system?: Check the AI Message Flow Guide to see the complete process of how user messages are sent to AI and responses are received!