"While JupyterLab uses about 104.8 MB of RAM and 0.8 CPUs, Zasper uses 26.7 MB and 0.2 CPUs," Anand said. The goal is to provide an increasingly efficient, complete IDE that will become separate from Jupyter Notebooks and easy integration for existing tooling.
The chance of success of such projects is always difficult to assess, as the involvement of others tends to encourage the adoption of new tools and does not arise predictably.
Anand admits that there are already alternatives to the frontend tool JupyterLab, such as Databricks Notebooks and Deepnote Notebooks... However, what sets Zasper apart is that it is entirely open-source on an AGPL-3.0 basis and runs a lot more efficiently locally than JupyterLab.
While it seems like a lot of effort has gone into this, an alternative should support the same platforms, languages and related tooling, not just run on macOS, partial support on Linux and IPython.
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