Worked on the pytorch/pytorch repository to enhance kernel-level stability by addressing a bug in the Repeat Interleave kernel. Focused on improving input validation and error messaging, the contribution provided clearer, more actionable feedback for model authors encountering input inconsistencies. This targeted fix, implemented using CUDA programming and robust debugging techniques, streamlined the developer experience by reducing time spent diagnosing errors. Emphasizing error handling, the work contributed to the long-term maintainability and reliability of PyTorch’s core functionality. Although the period involved a single bug fix rather than new features, the depth of the solution improved both usability and project stability.