Jupyter's Architecture Unpacked (with Afshin Darian & Sylvain Corlay)

Jupyter’s become an incredibly popular programming and data science tool, but how does it actually work? How have they built an interactive language execution engine? And if we understand the architecture, what else could it be used for?

Joining me to look inside the Jupyter toolbox are Afshin Darian and Sylvain Corlay, two of Jupyters long-standing contributors and project-steerers. They’ve going to take us on a journey that starts with today’s userbase, goes through the execution protocol and ends with a look at what Jupyter will be in the future - an ambitious framework for interactive, collaborative applications and more.

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Jupyter Homepage: https://jupyter.org/

Jupyter Xeus: https://github.com/jupyter-xeus/xeus

Jupyter AI: https://github.com/jupyterlab/jupyter-ai

Jupyter CAD: https://github.com/jupytercad/JupyterCAD

Jupyter GIS: https://github.com/geojupyter/jupytergis/

Jupyter GIS Announcement: https://blog.jupyter.org/real-time-collaboration-and-collaborative-editing-for-gis-workflows-with-jupyter-and-qgis-d25dbe2832a6

QGIS: https://qgis.org/

ZeroMQ: https://zeromq.org/

Sylvain on LinkedIn: https://www.linkedin.com/in/sylvaincorlay

Darian on LinkedIn: https://www.linkedin.com/in/afshindarian

Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/