I optimized Labelbox's image annotation tool by implementing a crucial auto-save feature that automatically saves progress during manual labeling sessions. This enhancement ensured that users wouldn’t lose their work due to unexpected interruptions, significantly reducing data loss risks and improving overall user efficiency.

Auto-Save Functionality: Designed and implemented an auto-save mechanism using JavaScript, React.js, and Redux, ensuring users’ work is continuously saved and retrievable, even in the event of interruptions.

Real-Time Syncing: Integrated WebSockets for real-time synchronization between the front-end and back-end, ensuring the latest progress was always reflected across devices.

Cloud Storage: Utilized AWS S3 to securely store image labeling data and progress, providing scalable and reliable cloud storage.

Database Integration: Employed PostgreSQL for efficient storage and retrieval of annotation data, ensuring smooth and consistent access to user progress.

User Interface: Enhanced the UI with React.js to create a responsive and intuitive experience for users while labeling images.

JavaScript, React.js & Redux: Front-end development for implementing auto-save functionality and managing state.

Node.js: Backend for handling real-time synchronization and interactions.

AWS S3: Cloud storage for secure data management and progress retention.

PostgreSQL: Database for efficient data storage and retrieval.

WebSockets: Real-time communication for syncing annotation data across sessions.

Impact: By implementing the auto-save feature, I enhanced the reliability of the image annotation tool, reducing the potential for data loss and boosting user efficiency. This contributed directly to Labelbox’s goal of providing a seamless, user-friendly data labeling experience, helping clients work more confidently and efficiently without the worry of losing their valuable progress.