node-zwave-js VS pyenv

There's also the Z-Wave JS command-line flash utility (same code used by ZUI). Still requires node.js, but easier than installing ZUI, if the latter is not in your plans. https://github.com/zwave-js/node-zwave-js/tree/master/packages/flash

First, it's not in the ZWaveJS database yet, so I can't reconfigure anything for it (via Home Assistant). The dimmer/on/off all work, but I'm unable to change the device settings (like dim time, scene triggers, etc). I've opened a request about it, but have no idea what that time frame looks like. I've pinged the company about this and they're going to look into it.

I found the solution, the problem was with z-wave-js and the device, here is the resolved issue in GitHub for future reference https://github.com/zwave-js/node-zwave-js/issues/5599

Responding to a merch message related to Z-Wave problems. If they are using a 700 series stick then that is your network problem. You'll need to update the firmware to fix it https://github.com/zwave-js/node-zwave-js/issues/3906

Be careful using RGBGenie z-wave stuff. I have one of their touch panels, and it doesn't work in Home Assistant with zwave-js. Likely never will unless RGB Genie releases a firmware update for it. It works fine in Hubitat though, where a custom driver was written.

The Z-Wave JS author is on Reddit sometimes, so maybe he'll see this, but you'd get a better answer on GitHub. https://github.com/zwave-js/node-zwave-js/discussions

I checked the zwavejs device database, and it looks like the device doesn't have anything configured.

I just checked this thread - not sure if Aeotec has a different firmware then the 700 series zooz, but looks like the new firmware is markedly better but still has some issues as of 10 days ago: https://github.com/zwave-js/node-zwave-js/issues/3906

I second this - I've been doing it for the last 4 years in a library with over 10k active users. Node.js might not be as fast as C for some tasks, but when the hardware you're talking to is the limiting factor, it just doesn't matter that the driver could be 4x as fast.

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.