Hirwan Shuti focused on enhancing hardware portability and configuration reliability across Lightning-AI repositories. For pytorch-lightning, he developed an automated hardware accelerator detection feature for the Fabric CLI, enabling seamless selection between TPU, MPS, CUDA, or CPU based on availability. This was implemented in Python and validated through expanded unit tests, reducing manual configuration and improving developer experience in diverse environments. In lightning-thunder, he addressed a critical static analysis issue by correcting the mypy ignore_errors setting in the TOML configuration, ensuring accurate type checking and maintainable code. His work demonstrated depth in CLI development, device management, and configuration management.