Yho contributed to both AI-Hypercomputer/torchprime and pytorch/xla by building tools and improving workflows for distributed machine learning. They centralized performance metrics tracking using Python and CSV, enhancing reproducibility and documentation. In torchprime, Yho developed a diagnostics toolkit to validate distributed training setups, logging environment details and providing troubleshooting guides in Markdown. For pytorch/xla, they authored a tutorial on TPU matrix multiplication precision and stabilized gradient checkpointing tests by refining numeric tolerances for XLA optimizations. Their work emphasized code readability through targeted refactoring and improved test reliability, resulting in more robust, maintainable systems and a smoother developer experience across repositories.