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Stanford Professor Jon Claerbout, reproducible-research grand master, said that: "interactive programs are slavery unless they include the ability to arrive in any previous state by means of a script.” Jupyter was born out of IPython (where the I stands for “interactive”) to offer a solution for creating “reproducible computational narratives.” The tool is both interactive and supports reproducible research, even if there is tension between the two attributes. In this talk, we explore the question of how we may build into the design of our tools (like Jupyter) an enabling capacity to support reproducible research. With better insights on Design for Reproducibility, we might extend the design to our research workflows, with the machine as our active collaborator.