direnv VS pyenv

I also made the export diff configurable, motivated by this post: https://github.com/direnv/direnv/pull/1233

Nice.

For years I've been using [direnv](https://direnv.net/) for this, setting environment variables which git picks up. This looks like a more feature complete equivalent, although to be honest I only really need switching of committer email and the SSH key used.

Direnv, for the uninitiated, loads and unloads environment variables when directories are entered and exited. Under every project folder there is a `$PROJ_DIR/.envrc` which contains:

buffer-env: A pure-Elisp version of the direnv utility. Useful to make Emacs aware of Python virtualenvs (which, judging by the questions posted here, is unfortunately still a complication for a lot of people). Similar to (and inspired by) envrc, but doesn't require the direnv program.

You can look at direnv to see this in action as they wrote shell hooks that get loaded into the shell profile and are executed on every prompt. https://direnv.net/

Yes, most Nix users employ https://direnv.net or the equivalent for your IDE of choice. Emacs for instance has https://github.com/purcell/envrc which set per-buffer variables.

The more applications I write, the more I tend to avoid typical config files in favor of using direnv [0] to set environment variables.

[0] https://direnv.net/

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.