At the recent JupyterCon 2017 NYC, there were a few presentations that provided an update on development of JupyterLab.
The Next-Generation Jupyter Frontend
Brian Granger (Cal Poly San Luis Obispo), Chris Colbert (Project Jupyter), Ian Rose (UC Berkeley) offer an overview of JupyterLab, which enables users to work with the core building blocks of the classic Jupyter Notebook in a more flexible and integrated manner.
Building a Powerful Data-Science IDE
JupyterLab provides a robust foundation for building flexible computational environments. Ali Marami explains how R-Brain leveraged the JupyterLab extension architecture to build a powerful IDE for data scientists, one of the few tools in the market that evenly supports R and Python in data science and includes features such as IntelliSense, debugging, and environment and data view.