(i assume with "The languages are in much flux" you meant python and not c/c++ because these aren't in flux)
Yeah i get why Python is currently used[0] and for a theory-focused book Python would still work to outline the algorithms - worst case you boot up an old version of Python in Docker or a VM, but it'd still require using only what is available out of the box in Python. And depending on how the book is written, it may not even be necessary.
That said there are other alternatives nowadays and when trying to learn the theory you may not need to use the most efficient stuff. Using C, C++, Go, Java, C# or whatever other language with a decent backwards compatibility track record (so that it can work in 5-10 years) should be possible and all of these should have some small (if not necessarily uberefficient) library for the calculations you may want to do that you can distribute alongside the book for those who want to try the code out.
[0] even if i wish people would stick on using it only for the testing/experimentation phase and move to something more robust and future proof for stuff meant to be used by others
"The languages are in much flux" you meant python and not c/c++ because these aren't in flux
No I meant C++.
2011 14882:2011[44] C++11
2014 14882:2014[45] C++14
2017 14882:2017[46] C++17
2020 14882:2020[47] C++20
2024 14882:2024[17] C++23
That is 4 major language changes in 10 years.
As a S/W manager in an enterprise context having to coordinate upgrades of multi-million LOC codebases for mandated security compliance, C++ is not the silver bullet in handling the version problem that exists in every eco system.
As said, the compilers/linkers allow you to run in compatibility mode so as long as you don't care about the new features (and the company you work for doesn't) then, yes, C/C++ is easier for managing legacy code.
These are new features. Many of them are part of the library not the language. Generally speaking what you do is enable the new features in your compiler, you don't need to disable that to compile old code. It's not a problem to work on legacy code and use new features for new code either.
C++ has issues with becoming more bloated and complicated but it does not have issues with having existing code breaking due to these changes (aside perhaps some very edgy edge cases).
Yeah i get why Python is currently used[0] and for a theory-focused book Python would still work to outline the algorithms - worst case you boot up an old version of Python in Docker or a VM, but it'd still require using only what is available out of the box in Python. And depending on how the book is written, it may not even be necessary.
That said there are other alternatives nowadays and when trying to learn the theory you may not need to use the most efficient stuff. Using C, C++, Go, Java, C# or whatever other language with a decent backwards compatibility track record (so that it can work in 5-10 years) should be possible and all of these should have some small (if not necessarily uberefficient) library for the calculations you may want to do that you can distribute alongside the book for those who want to try the code out.
[0] even if i wish people would stick on using it only for the testing/experimentation phase and move to something more robust and future proof for stuff meant to be used by others