Worked on enhancing debugging and documentation for TPU workloads, focusing on improving diagnosability across the Intel-tensorflow/tensorflow and openxla/xla repositories. Developed standardized guidance for interpreting TPU state reports, which streamlines error reporting and accelerates issue triage for MXLA-related hangs. Leveraged Markdown for technical writing, ensuring documentation was clear and accessible to engineering teams. Emphasized cross-repository alignment by introducing a structured approach to TPU state interpretation, directly supporting reduced mean time to resolution for customers using TPU acceleration. The work combined debugging expertise with strong documentation skills, resulting in more reliable workflows and improved support for diagnosing complex TPU issues.