single_file_libs VS pyenv

Yep: https://github.com/nothings/single_file_libs

Also sometimes I use only header (.h) with all functions included wrapped by #ifndef and #endif. When I use these? for code that I always reuse to simplify things and some data stucture handling (to implement dynamic arrays). A good example list of these (not mine) are https://github.com/nothings/single_file_libs

Also, there are many libraries that are much bigger (little list I found) but are implemented in a single header file.

If others have a similiar problem, I found a great github page that has lots of single header libraries, including data structures.

> Like, why don't we just let projects be "done"? Things don't need to be maintained and updated for eternity.

This is generally why I opt for "single-file" libraries that do one simple task well. The smaller the library, the more likely it is "done". For example, do I want some insanely complex image library that handles every file format under the sun, or do I just want some basic one that allows me to output a simple JPEG?

I often find myself referring to "single_file_libs" repository: https://github.com/nothings/single_file_libs

Looking at the open issues, it doesn't appear to be actively maintained but it's still an incredibly good resource for "completed" projects.

Steamworks is the easy option, but if you're programming-savvy you could also use a networking library (some lists for C and C++: 1 2 3) to accomplish the same thing.

Have a look at these. https://github.com/nothings/single_file_libs#geometry-file

If you have a requirement for multiple, specific Python versions, why not just use pyenv?

https://github.com/pyenv/pyenv

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.