During December 2024, ec2-user developed a notebook-driven random walk simulation demo for the liquidinstruments/moku-examples repository. The work centered on integrating a 500-epoch random walk function into the Emitter_control.ipynb notebook, providing a clear demonstration of stochastic processes within a reproducible Jupyter Notebook workflow. Using Python and leveraging data science and machine learning techniques, ec2-user also contributed test code to ensure the reliability of the new feature. While the focus remained on feature delivery rather than bug fixes, the contribution advanced the repository’s support for reproducible experimentation, reflecting a targeted and technically sound approach within a short engagement period.