Authorea provides a free-to-use, openly accessible authoring environment where I can blend codes, notes, and serve the article as a preprint to draw comments and critiques from colleagues and readers. Jupyter notebooks provide authoring environments, mostly openly-accessible, and free-to-use (many) where using a range of computer languages (Python, Scala, R, Stata and others) where it is possible to develop analyses, visualisations, and add notes, including references, and annotations. While Authorea provides a one-stop solution to share the authored content to be served as preprints and to journal articles for submission, Jupyter notebooks can also be shared over github and binder and be rendered over the web. When these two authoring environments are put together, they provide a workflow for reproducible research that is both intuitive and useful. In this article, I will discuss the ways these two tools can be integrated to rapidly develop data analyses and visualisations and distribute them across a range of options for readers to interact with them.