Telling someone they’re ignorant probably doesn’t get them to use your tech. Not the person you’re replying to; I use elixir everyday at work.
The biggest barrier to adoption of these libraries I can see is that there is not a wealth of resources to solve the actual problem the person is experiencing.
Googling “dyplr verbs in python” there’s a towards data science post that’s the 6th result. It has step by step code examples to get you started with what I guess the person is looking for.
Googling “dyplr verbs in elixir”, you get the explorer library’s GitHub page and docs as results. I see there’s an example on the first page, but docs present as a high barrier to entry. For better or worse, hand-holding helps get people using the thing, and there’s a SEO-fueled ton of python hand-holding resources out there.
I’m sure you’re aware of these things as the author of the book mentioned, but buying a book is a step for someone who’s already sold on learning the language/framework. Having google-able content is much more useful to get people in the door and in the day-to-day work looking something up.
Until something “cool” and well-known is made with Nx/Axon and friends, they’re gonna be seen as second-class to most people who aren’t elixir diehards. Somehow everyone in elixir-land recs these, but never has anything they actually use them for.
There’s also the switching cost. Most Python ML compute is actually done in C/off-loaded to gpu, so its speed is ok and not greatly constrained by the language choice. Python does have multiprocessing stuff which, while annoying in my experience, makes parallelizing stuff possible. Again, thinking from an outsider’s perspective, there doesn’t seem to be a “must have” reason to use elixir when they’ve already been working in python.
I appreciate your response. I should not have replied in the manner that I did. I think there are many ways to make the argument against using these libraries, and all of the maintainers would agree it doesn't make sense in many situations. I am fine with these arguments, but I don't think it warrants referring to the work we've done as half-baked or hobby projects. Even if not the intention, I think it implies a lack of effort and professionalism which comes across as more disparaging than simply pointing out the gaps.
The biggest barrier to adoption of these libraries I can see is that there is not a wealth of resources to solve the actual problem the person is experiencing.
Googling “dyplr verbs in python” there’s a towards data science post that’s the 6th result. It has step by step code examples to get you started with what I guess the person is looking for.
Googling “dyplr verbs in elixir”, you get the explorer library’s GitHub page and docs as results. I see there’s an example on the first page, but docs present as a high barrier to entry. For better or worse, hand-holding helps get people using the thing, and there’s a SEO-fueled ton of python hand-holding resources out there.
I’m sure you’re aware of these things as the author of the book mentioned, but buying a book is a step for someone who’s already sold on learning the language/framework. Having google-able content is much more useful to get people in the door and in the day-to-day work looking something up.
Until something “cool” and well-known is made with Nx/Axon and friends, they’re gonna be seen as second-class to most people who aren’t elixir diehards. Somehow everyone in elixir-land recs these, but never has anything they actually use them for.
There’s also the switching cost. Most Python ML compute is actually done in C/off-loaded to gpu, so its speed is ok and not greatly constrained by the language choice. Python does have multiprocessing stuff which, while annoying in my experience, makes parallelizing stuff possible. Again, thinking from an outsider’s perspective, there doesn’t seem to be a “must have” reason to use elixir when they’ve already been working in python.