mise VS pyenv

Where are you getting "mise uses asdf" from? mise is simply compatible with all asdf plugins. Not the same thing.

It's even said almost at the top of the README.md in the "30 seconds demo" section:

"The following shows using mise to install different versions of node. Note that calling which node gives us a real path to node, not a shim."

https://github.com/jdx/mise?tab=readme-ov-file#30-second-dem...

So yes, mise does not use shims. It only manipulates $PATH. I did benchmarks a while ago and that definitely and consistently has shaved some milliseconds off of the startup times of my tools.

mise borrows the plugins from asdf, which also makes it non-cross platform. Interesting discussion on this topic on their GitHub: https://github.com/jdx/mise/discussions/66

Solutions considered include adopting the vfox plugin system or transpiling all asdf plugins to ShellJs.

Now I know that vfox exists.

Why not just use a tool like asdf (https://asdf-vm.com/) or mise (https://mise.jdx.dev/)?

These tools have the advantage of not being multi-taskers and can manage version for all your tools. You wouldn’t need pyenv and npm and rvm and…

We’ve even started committing the .mise.toml files for projects to our repos. That way, since we work on multiple projects that may need multiple versions of the same tool, it’s handled and documented.

direnv + mise does exactly that. When I cd to various directories I get different env vars, it's pretty neat. Setting aliases would just be a case of adding them.

https://github.com/jdx/mise/discussions/1525 for an example of how I use direnv with mise.

https://mise.jdx.dev/direnv.html

https://mise.jdx.dev/templates.html

Installed bun js runtime (I used mise, btw)

Not nix based, but I really like https://github.com/jdx/mise too to manage dev tools.

It’s a modern version of https://github.com/asdf-vm/asdf written in Rust.

The purpose of a version manager is to help you navigate or install any tools for development easily. Version Manager can be one tool for each dependency (e.g. NVM, g) or One tool for all dependencies (e.g. asdf, mise).

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.