This month in Julia world - 2023-11

A monthly newsletter, mostly on julia internals, digestible for casual observers. A biased, incomplete, editorialized list of what I found interesting this month, with contributions from the community.

“Internals” Fora and Core Repos (Slack/Zulip/Discourse/Github):

Core Julia Repos:

Dustbin of History:

Ecosystem Fora, Maintenance, and Colab Promises (Slack/Zulip/Discourse/Github):

Soapboxes (blogs/talks):

  • Consider subscribing to the French community newsletter (much of the shared materials are in English).
  • Consider subscribing to the community calendar to be informed of upcoming virtual meetings and talks.
  • Consider attending the triage meetings of the julia core contributors (organized on slack) – being a fly on the wall can be a great way to learn the nitty-gritty details of current priorities and development work. These are organized on the triage channel in slack.

I have started linking to fewer slack threads, as slack references are starting to be more and more unreliable on my end (threads getting lost, etc).

Please feel free to post below with your own interesting finds, or in-depth explanations, or questions about these developments.

If you would like to help with the draft for next month, please drop your short, well formatted, linked notes in this shared document. Some of it might survive by the time of posting.


I’d add that there were a few major workshops producing a lot of online content. One was the JuliaHEP workshop:

Which adds:

  • Maintaining large-scale Julia ecosystems - Chris Rackauckas
  • What is different about the Julia programming language? - Stefan Karpinski
  • Automatic Differentiation and SciML: what can go wrong, and what to do about it - Chris Rackauckas
  • Reproducible Science: Why it matters and how to achieve it - George Datseris

And a new iteration of the DigiWell series:

  • Handling Temporal Data with DataFrames.jl - Bogumił Kamiński
  • Auto-Completing Models to Uncover Missing Physics - Vinicius Santana
  • Machine Learning in Differential Equations for Optimal Control - Frank Schaefer
  • Clapeyron.jl: An extensible, open-source fluid-thermodynamics toolkit - Andrés Riedemann

Also, people seemed to like this blog post: