Sunday, June 28, 2020

Carbon Copy Cloner was quietly excluding 1Password stores from backup (Corrected: app, not data stores)

This morning's heart attack:

"CCC no longer excludes 1Password by default."

What the fork were they thinking?!

Update: OK, looks like they did this briefly in 5.1.18 and, even though their language is sadly unclear, it was the the 1Password app rather than the 1Password credentials that were not being backed up. That link lists all the files not backed up, though as of today it's not been corrected for 5.1.19.

So I still have chest pain, but not a heart attack.

Wednesday, June 24, 2020

Python macOS environments for learners in 2020

My daughter is auditing Coursera's Intro to Python class. It's pretty standard stuff, but I was surprised by the development environment. For macOS there's a non-trivial Python install that requires some unix knowledge, use of the Homebrew package manager, dealing with admin vs non-admin user issues, consideration of pyenv, editing the path, and finally installing Python.

That's a long way from the ease of, say, TurboPascal circa 1983.

I figured there had to be a better way, but Google only found me some pro-level IDEs. It fell to Twitter to clue me in to the modern scene. The 4 good modern options turn out to be:
  • Google Colab: absolute easiest and least painful. I believe the Python code executes in the browser, so it's substantially slower than execution directly in macOS.
  • Microsoft Visual Studio Code for macOS: this does require the traditional Python install with Homebrew, but it's a very beginner friendly environment. The Python plugin provides Jupyter support.
  • Homebrew Jupyter: similar to Colab but like Visual Studio is part of the Homebrew/Python path.
  • Azure does Jupyter Notebooks (via @jhovland) at
Years ago I ran into iPython as a novice environment; turns out it morphed into Juypter.

It's a sign of the times that Google search didn't turn up a blog post with these options. (It won't find this one either, I'm way off Google's radar now.) Once I'd identified the above options however I could do a Google search to find an educational resource that did mention then:

There are many ways to write and execute Python code:

Python tutor (online, visual debugger)
Python interpreter (command line)
Visual Studio Code (editor, good debugger)
Jupyter (notebook)
Google Colab (online, collaborative)
During this lab we see all of them and familiarize with the exercises format. For now ignore the exercises zip and proceed reading.

That site is the University of Trento's data science lab course, updated 2019/2020.  The U of Trento was founded in 1962. Reading the wikipedia page it seems to have started out focusing on sociology (and, given the era, was likely a wee bit Left) but now seems to be very tech.

The course material is presumably translated from Italian. It's quite readable though it would benefit from a native speaker updating the GitHub content. Judging by my little test it may be one of the best resources of its kind.

See also:

I came back to Python for course on working with the OpenAI ChatGPT LLM. This time around I used Visual Studio Code with the Jupyter support. I use the default Python PIP package manager but I think Microsoft favors Conda. As of 2024 CoPilot is an option but it is not free.