During January 2025, Hroth enhanced experiment tracking in the timholy/boltz repository by introducing a Run Name parameter to the Weights & Biases logger. This feature, implemented in Python, allows users to assign specific identifiers to training runs, improving the organization and traceability of machine learning experiments. By integrating this parameter, Hroth addressed challenges in experiment observability and reproducibility, enabling more accurate reporting and streamlined data governance. The work demonstrated practical application of experiment tracking and machine learning skills, focusing on version-controlled development. While the contribution was focused and limited in scope, it addressed a concrete need for better experiment management.