People also tend to forget that these ML packages are ridiculously complicated, and have a lot of dependencies not just on other libraries but on particularities of your system.
That, and ML researchers can't also be expected to be good at everything. They are busy doing ML research and waiting to be given a recipe to follow.
Meanwhile I can put together a Python package in my sleep that works perfectly on pretty much any system, but I don't know a damn thing about Autotools and would probably make a total mess if I tried use it. Or CMake. Or whatever Java uses.
This "look Python bad!!" stuff has some merit (if only historical), but it mostly amounts to FUD and does a big disservice to the people who have worked hard over the past few years to get everything fixed up.
Plus at least one of the issues I see here is because people disregarded the instructions and used Python 3.9 even though it says to use 3.10 because the code assumes it's running under 3.10.
Firstly, non-experiences Python users (the kind who need recipes to follow) will also blindly follow installation instructions on websites. These all say "pip install xxx" and users will inevitably forget they need to change the path to make that work for their environment. By getting into the habit of activating the environment this problem goes away.
Secondly, you do need to activate the venv so that command line tools in ./venv/bin are used (instead of whatever is in your default path).
This includes both the correct version of python (if you set it when you setup the virtual environment) and importantly jupyter. From personal experience there's a whole set of pain when you don't realize your jupyter isn't the one inside the virtual environment and so is using other libraries.
Because many shells cache the location of executables, if you don't deactivate and reactivate your virtual environment after installing Jupyter into it then it may (in some circumstances) use a globally installed version.
I do think that experience helps here. I have a recipe for installing Python that works on most python projects most of the time.
Done.On a Linux or Intel Mac system this works with pretty much every reasonable Python project.
On M1 Macs the situation isn't great at the moment, though.