
Arthur Teng enhanced the KnightHacks/forge repository by delivering a suite of features and reliability improvements focused on hackathon check-in workflows. He implemented responsive toast notifications and automated Discord role assignments, while introducing class-based logic for role and repeat check-in management. Using TypeScript, React, and SQL, Arthur refactored backend and frontend components to support per-class check-in controls and mitigate QR code double scans, improving data integrity and user experience. His work addressed edge cases in repeated check-ins and streamlined participant tracking, demonstrating a strong grasp of full stack development and database schema design within a fast-paced, event-driven environment.

In 2025-09, delivered major improvements to KnightHacks/forge focused on hackathon check-in UX, data integrity, automation, and reliability. Implemented responsive toasts with breakpoint-aware sizing and extended visibility, added class-based repeat controls and per-class logic, introduced HACKER_CLASSES and a class column for hackerattendee to support role assignments, enabled end-to-end check-in enhancements, and automated Discord role assignments via TK bot. Fixed critical reliability gaps in QR scanning and repeated check-ins, reducing friction and data inconsistencies. These changes collectively improve throughput, reduce duplicate scans, and provide clearer participant tracking and business value across the hackathon workflow.
In 2025-09, delivered major improvements to KnightHacks/forge focused on hackathon check-in UX, data integrity, automation, and reliability. Implemented responsive toasts with breakpoint-aware sizing and extended visibility, added class-based repeat controls and per-class logic, introduced HACKER_CLASSES and a class column for hackerattendee to support role assignments, enabled end-to-end check-in enhancements, and automated Discord role assignments via TK bot. Fixed critical reliability gaps in QR scanning and repeated check-ins, reducing friction and data inconsistencies. These changes collectively improve throughput, reduce duplicate scans, and provide clearer participant tracking and business value across the hackathon workflow.
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