Plurrrr

In Emacs version 28 Emacs developers introduced so-called read symbol shorthands. If you’re interested in the rationale, feel free to search the Emacs developer mailing list for the discussion. However, it does seem that not everyone likes the idea of shorthands as a substitution for namespaces (or packages, if you’re coming from Common Lisp).

Macpine makes it possible to run LXD/LXC containers on MacOS with support for both amd64 and arm64 processors, through its lightweight virtualisation layer. This workflow makes it easy to develop and test LXD containers locally.

Many users of Python deprioritize performance in favor of soft benefits like ergonomics, business value, and simplicity. Users who prioritize performance typically end up on faster compiled languages like C++ or Java.

One group of users is left behind, though. The scientific computing community has lots of raw data they need to process, and would very much like performance. Yet, they struggle to move away from Python, because of network effects, and because Python’s beginner-friendliness is appealing to scientists for whom programming is not a first language. So, how can Python users achieve some fraction of the performance that their C++ and Java friends enjoy?

My colleagues recently wrote a great post on the Netflix tech blog about a tough performance issue they wrestled with. They ultimately diagnosed the problem as false sharing, which is a performance problem that involves caching.

I’m going to take that post and write a simplified version of part of it here, as an exercise to help me understand what happened. After all, the best way to understand something is to try to explain it to someone else.