Prasant Kumar developed a Linear Predictor Sample Demo for the daytonaio/daytona repository, expanding its sample library with a practical, end-to-end example. He used Python and Streamlit to demonstrate how linear predictor workflows can be integrated within the Daytona ecosystem, providing a ready-to-run reference for other developers. The sample showcases real-world usage patterns and emphasizes clear commit traceability, supporting onboarding and knowledge sharing. While the work focused on feature development rather than bug fixes, it highlighted Prasant’s skills in data science, machine learning, and sample integration. The contribution was well-scoped, delivering depth in both technical implementation and documentation.