Over a two-month period, contributed to AI-Hypercomputer/torchprime and pytorch/xla by building tools and documentation that improved distributed training reliability and performance tracking. Developed a diagnostics toolkit in Python to validate distributed setups, logging environment details and integrating PyTorch and XLA checks, while also centralizing performance metrics into a results.csv for reproducibility. Enhanced documentation with troubleshooting guides and tutorials, including a guide on TPU matrix multiplication precision. Refactored code for readability and stabilized gradient checkpointing tests by adjusting tolerances for XLA optimizations. These efforts improved developer experience, test reliability, and maintainability across both repositories, leveraging skills in Python, PyTorch, and documentation.