Santosh Thoduka focused on improving the reliability of machine learning experiment workflows in the Modalities/modalities repository by addressing a checkpoint integrity issue within the TrainingProgress save process. Using Python and leveraging dataclasses, Santosh implemented a solution that clones the TrainingProgress object before persisting, preventing mutation or overwrite of previously saved checkpoints. This debugging and software development effort enhanced data integrity and reduced the risk of checkpoint corruption, ensuring safer experiment state management. The work did not introduce new features but provided depth by increasing reproducibility and stability for users running experiments, reflecting strong skills in debugging and testing complex workflows.