Feidias focused on stabilizing and enhancing scalable workload capabilities within the AI-Hypercomputer/xpk repository, balancing risk reduction with new feature delivery. He implemented Task-as-a-Service support for DWS clusters, updating workload annotation handling and Kueue configuration to enable dynamic pod set resizing. To improve deployment reliability, he reverted previous TAS integration and refined conditional logic across Python modules. Feidias also addressed robustness in the tcpx_decorator by introducing a volume mount and comprehensive unit tests, refactoring code for idiomatic Python and better testability. His work leveraged Python, Kubernetes, and CI/CD practices, resulting in safer, faster iteration cycles and improved test coverage.