During October 2025, Vapor focused on improving the accuracy and reliability of training throughput metrics in the karpathy/nanochat repository. They addressed a core issue in the calculation of tokens-per-second by updating the metric to use total_batch_size and properly account for gradient accumulation, ensuring that reported throughput accurately reflected real model performance. Working primarily in Python and leveraging deep learning and performance optimization expertise, Vapor validated these changes across multiple training scripts. This fix resolved misleading performance reporting, providing more truthful data for capacity planning and evaluation. The work demonstrated careful attention to detail and a strong understanding of machine learning workflows.