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, and CPU fallback. This was implemented in Python, with comprehensive unit tests ensuring robust cross-hardware support and reducing manual configuration for developers and CI environments. In lightning-thunder, he addressed a critical static analysis issue by correcting the mypy ignore_errors setting in the pyproject.toml, improving type-checking accuracy and CI reliability. His work demonstrated depth in CLI development, configuration management, and Python-based testing practices.