I see what you're saying, but looking at the video, which shows playgrounds and notes, I'm quite excited to try this because it looks a lot like jupyterlab. Jupyterlab is familiar to any data scientist, but while it's easy to use, it's quite awkward to extend due to the latter being based on a plugin system (understandably) based on typescript.
Here it's all one system, and thinking of the image as a key-value store feels quite natural too. Finally, the UI with panes that go right also feels natural and looks quite slick. I wonder if it's easy to switch between languages? Like can the key-value store pass data to a python program, or use an Apache arrow table?
A few notes: the moving from left to right allows for a dynamic exploration which is different from the typical defined exploration from a notebook. In Glamorous Toolkit we consider that both are important and complementary.
The dynamic exploration is enabled by the tools following the context. For example, the views in the inspector appear when you get to an object that has those views. You do not call these views by name. Also, choosing a different view allows you to change the course of the exploration midstream. Furthermore, you can create a view right in place, too.
The exploration possibilities are visible, but there are more pieces that are less visible that make the environment interesting. For example, there is a whole language workbench underneath and a highly flexible editor that can also be contextualized programmatically.
If you do give it a try, please let us know what you think of it.
Here it's all one system, and thinking of the image as a key-value store feels quite natural too. Finally, the UI with panes that go right also feels natural and looks quite slick. I wonder if it's easy to switch between languages? Like can the key-value store pass data to a python program, or use an Apache arrow table?