Worked on the ml-explore/mlx repository to enhance the stability of neural network module updates in production environments. Focused on resolving an out-of-bounds handling issue that occurred during non-strict mode updates when extra weights were present. Implemented a fix in Python that ensures out-of-bounds indices do not trigger errors if strict mode is disabled, thereby supporting more robust model deployment workflows. Collaborated closely with another contributor to co-author the solution, validating the changes through targeted tests and code review. Leveraged expertise in machine learning and neural networks to deliver a targeted bug fix that improved reliability without introducing regressions.