Contributed to the mcgill-robotics/rover-2025 repository by integrating ArUco marker detection into the WebRTC video pipeline, enabling real-time marker-based testing and visualization for robotics applications. Leveraged Python and computer vision techniques to process video streams, while also expanding CORS configuration to support multi-computer access, facilitating broader collaboration and easier troubleshooting in lab environments. The work focused on backend development, ensuring seamless integration of new features without introducing bugs. By addressing both the technical and collaborative needs of the project, the changes reduced setup friction for users and improved the overall testing workflow within the robotics development process.