Christian contributed to the ml-explore/mlx-lm repository by enhancing Gemma3n inference cache handling, updating the model version and refining cache logic to improve performance and reliability during cache-miss scenarios. This work reduced cold-start latency and strengthened deployment resilience, particularly in environments with limited caching. In the piotrplenik/pandas repository, Christian focused on documentation quality, correcting a typo in the .astype() method reference across PDF and PPTX formats to ensure consistency and reduce user confusion. Throughout both projects, Christian applied skills in Python, PyTorch, and deep learning, demonstrating careful attention to maintainability and traceability in code and documentation.