TabHive is a powerful browser extension that helps you organize tabs by clustering similar content together, making your browsing experience more productive and less cluttered.
- Smart Clustering: Automatically groups similar tabs based on content
- Course Code Detection: Recognizes academic course codes like "MATH 118" and groups related tabs
- Advanced Algorithm: Uses sophisticated text analysis to understand tab content
- Privacy Focused: All processing happens locally in your browser
- Time Saving: Reduces time spent searching through tabs, increasing productivity
- User Friendly: Simple interface that makes organizing tabs effortless with one click
This repository contains two main components:
- Browser Extension (
/tabhive): The core extension that runs in Chrome and other Chromium-based browsers - Marketing Website (
/website): The promotional website for the extension
The extension uses a clustering algorithm to analyze tab content and group similar tabs together. It's built with TypeScript and uses modern browser APIs.
Key technical features:
- Content-based clustering using K-means algorithm
- Course code detection with regular expressions
- Chrome Tab Groups API integration
- No external API dependencies - all processing is local
The website showcases the extension's features and provides download links. It's built with React, TypeScript, and Styled Components.
- Node.js (v14 or later)
- npm or yarn package manager
- Chrome browser (for testing the extension)
- Clone this repository:
git clone https://github.com/your-username/tabhive.git
cd tabhive
- Install dependencies for both projects:
# For the extension
cd tabhive
npm install
# For the website
cd ../website
npm install
- Build the extension:
cd tabhive
npm run build:complete
-
Load the extension in Chrome:
- Open Chrome and navigate to
chrome://extensions/ - Enable "Developer mode" (toggle in the top right)
- Click "Load unpacked" and select the
tabhive/distdirectory
- Open Chrome and navigate to
-
Run the website locally:
cd website
npm start
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.