During May 2025, Bence focused on enhancing the stability of the ml-explore/mlx-lm repository by addressing a critical issue in the tokenizer component. He implemented a targeted fix in Python to ensure the tokenizer could gracefully handle cases where the tools input array was empty, thereby preventing potential runtime errors and improving the reliability of downstream machine learning workflows. By applying defensive programming techniques and strengthening input validation, Bence reduced error rates in edge scenarios without impacting performance. His work demonstrated a strong command of backend development and API design, contributing to the overall robustness of the project’s infrastructure.