Jupyter notebooks have become a hugely popular tool in research, machine learning, data science, education, and many many areas. Their use and adoption have opened the door to a new paradigm: the emergence of literate programming. And with this new paradigm, the users are not only able to develop quick prototypes but also generate compelling narratives in which the code and its outputs are presented side by side. Hence why big companies such as Google and Microsoft have created their own ports of the notebooks: Google Colab and Azure Notebooks. But how well do notebooks perform in different contexts and standards? Are these suitable for all audiences and applications? This talk will dive into some of the best and lesser known features of the Jupyter notebooks and tools within the Jupyter ecosystem. At the same time, we will explore the limitations and ‘odd behaviors’ of notebooks in a number of contexts while exploring the boundaries of the notebooks and their usefulness under such circumstances.