obelisk VS post-rfc

Hello I remember successfully setting up obelisk a while ago and have gone through the instructions https://github.com/obsidiansystems/obelisk and ensured that everything is installed correctly, when I run the install command fro obelisk it says that it's installed but when I run ob init I get an error of command not found, this is an arch machine not nixOS. Any help would me much appreciated.

If you want to use reflex, the obelisk framework is pretty user friendly. You do have to install nix on your machine, but the ob command handles all the nix interactions for you so you /hopefully/ don't need to know much.

You can make all those things in haskell, and I do professionally. Frontends (entirely in haskell), native IOS and Android applications, Servers, and Games. In fact the framework Obelisk does most of these all out of the box.

Thanks for the comment! We can honestly say that Obelisk is far from perfect, but we're continuously improving the project in our daily basics. And that's why we encourage you to start your adventure with the lib anew. If you still miss a guide to routing with Obelisk, please read this doc. Our team would be happy to answer your further question regarding Obelisk; feel free to email us anytime!

I also had a use case where I needed to extract the nix derivation dependencies of haskell packages: https://github.com/obsidiansystems/obelisk/pull/933

There's also GHCJS, with https://github.com/obsidiansystems/obelisk being (probably) the best choice, but personally I found it extremely tedious to set up a dev environment (not a nix guy) and there's also the learning curve of FRP.

https://github.com/obsidiansystems/obelisk, https://shpadoinkle.org/

I can't speak to the nicest way, as I haven't actually developed any Android apps with Haskell, but I've been meaning to give Obelisk a try.

That's what it's best for, but personally I use it for everything. If I ever get into low-level code I'll probably use Rust though.

You can confirm that parsers/tokenizers is ranked "best in class" here though:

https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

Note that this is ported from here: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md which comes with more exposition.

State of the Haskell Ecosystem

According to State of the Haskell ecosystem, Haskell is THE language of choice for implementing compilers, and THE language of choice for writing parsers. Thus, it is not surprising to see more Haskell projects from those particular categories than from other categories.

This is great for understanding what libraries to use in the Haskell ecosystem: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

In particular, I got comfortable reading hackage documentation to understand quickly how to use libraries (aeson, megaparsec, mtl, pipes, etc), got comfortable with the ecosystem (this helped: https://github.com/Gabriella439/post-rfc/blob/main/sotu.md), got comfortable with the main language idioms and features (https://smunix.github.io/dev.stephendiehl.com/hask/tutorial.pdf) and got comfortable with simple things that for some reason had confused me before (case, \case, let).

Gabriella Gonzalez has a great doc that is reasonably up-to-date, sounds similar to what you're looking for? https://github.com/Gabriella439/post-rfc/blob/main/sotu.md

With that being said, Python is without a doubt the best option, and I'd also be very interested to read the articles you found that say that Python is not a good choice because it's been the industry standard for a long time now. Data science and machine learning are one of the areas where the Haskell ecosystem is not as strong as other languages, but libraries and tools do exist. There's a great list of Haskell resources by domain here, and as you can see, there are Haskell bindings to tensorflow and pytorch, along with other libraries that support common data science programming.