In March 2026, Machc enhanced the evaluation metrics pipeline for the google-research/swirl-dynamics repository by introducing support for nested dictionaries and improving result logging. Using Python, Machc restructured the metrics processing logic to handle complex, hierarchical data structures, enabling richer analytics and more detailed experiment tracking. The updated logging patterns provided clearer traceability and facilitated faster debugging, directly supporting more reliable model evaluation and streamlined iteration cycles. This work demonstrated skills in data analysis, machine learning, and robust Python development, laying a foundation for scalable reporting and maintainable evaluation workflows within the research codebase. The contribution was focused and technically sound.