https://yetch.store/products/every-day-goal-calendar For a physical/digital device. … way more expensive than I remember it being (especially given it’s single-user), but it’s a pretty good incentive to keep up with less-fun habits

IIRC, the biggest issue with TrueNAS SCALE + Docker is that they really run the containers on a ‘hidden’ kubernetes cluster and obfuscate the standard docker and docker-compose way of doing things behind a gui with limited customization and poor field descriptions.
I found it much easier to spin up a VM on SCALE and run docker through that, although then you have to deal with multilayer networking.

… To be fair, this was when SCALE was still in beta, so it has possibly improved since then.

It’s not just every tech company, it’s every company. And it’s terrifying - it’s like giving people who don’t know how to ride a bike a 1000hp motorcycle! The industry does not have guardrails in place and the public consciousness “chatGPT can do it” without any thought to checking the output is horrifying.

python packaging authority and pytest have pretty good resources on standard repo structure; poetry is a new-kid-on-the-block tool to get started developing packages quickly (i.e., standard repo config, handles dependency environments / works as build tool)

re: formatting & style – others have mentioned black; I also recommend ruff to lint/standardize your code to many accepted best practices.

import is kind of a clusterf, because you can have absolute import packagename and relative from . import x.y.z imports, and importing a project-in-development can depend on the IDE you use (i.e., vscode and pycharm are generally smart enough to figure out that a src/ dir in the workspace should be importable, but not always). Using pip install -e can install your project-in-dev in “editable” mode and make it available for import. The modules docs may help here.

Package management/locking is a a (relatively) rapidly evolving part of the python ecosystem. Because Python can be so dependent on the packages installed in the environment, simply managing the python version (like you would with pyenv) is insufficient, and it is recommended to create pseudo-hermetic virtual environments per project (venv, virtualenv, poetry, or conda help with this). I can’t help with pyenv (I use conda); this might be helpful. I think you would use pyenv to manage the python version and then venv or virtualenv to manage the installed packages. Personally, I would first get used to managing virtual environments with venv and then deal with pyenv later if you decide you need multiple python versions