Amanda Liang focused on improving the reliability and performance of WAN quantization workflows in the AI-Hypercomputer/maxdiffusion repository. She addressed a critical bug in the quantization process by ensuring transformers were correctly assigned to pipeline objects, which stabilized the quantization flow and preserved model inference accuracy. Using Python and leveraging her skills in data processing and machine learning, Amanda delivered a targeted, auditable fix that mitigated production risk and maintained system stability. Her work was concentrated in a single, well-documented commit, demonstrating a precise and methodical approach to resolving complex issues in high-performance AI infrastructure.