Chao Jhou contributed to the AMD-AGI/Primus repository by developing two features focused on improving network reliability and job observability for high-performance training workloads. He implemented RDMA adapter filtering in Shell, ensuring that GPU and non-socket interfaces were excluded from InfiniBand HCA selection, which reduced the risk of misconfiguration in RDMA operations. Additionally, he enhanced logging for Kubernetes pretraining jobs by redirecting output streams to log files and ensuring proper log directory setup, facilitating better debugging and monitoring. His work demonstrated depth in Kubernetes, networking, and system administration, addressing core reliability and maintainability challenges in distributed training environments.