eqwalizer VS nx

I don't think the implementation itself is at fault, but yes, I do think that the design of dialyzer makes it an (at times) faulty type checker. The unfortunate reality of a type checker that fails sometimes is that it makes it mostly useless because you can never trust that it'll do the job.

To be clear, I've had it fail in a function where I've literally specced that very function to return a `binary` but I'm returning an `integer` in one of the cases. This is a very shallow context but it can still fail. Now add more functions, maybe one more `case`.

I think an entire rethink of type checking on the BEAM had to be done and that's why eqWalizer[0] was created and why Elixir is looking to add an actual sound, well-developed type checker. Gleam[1] I would assume is just a Hindley-Milner system so that's completely solid. `purerl`[2] is just PureScript for the BEAM so that's also Hindley-Milner, meaning it's solid. `purerl` has some performance issues caused by it compiling down to closures everywhere but if you can pay that cost it's actually pretty fantastic. With that said my bet for the best statically typed experience right now on the BEAM would be `gleam`.

0 - https://github.com/WhatsApp/eqwalizer

1 - https://gleam.run

2 - https://github.com/purerl/purerl

While it's not static-typing, compile-time type checking for Erlang have come a long way: Eqwalizer works pretty well - but I may be biased since my employer sponsors the project.

1. https://github.com/WhatsApp/eqwalizer

Note there is also research happening in this area by the Elixir team. The WhatsApp is also working on static types for Erlang, which I am certain will be available for Elixir too at some point.

From the discord blog posts it seems that elixir powers the chat system, with rust and python as the other two main languages in their stack.

As for whatsapp, they are mainly a erlang shop and yesterday they open sourced a type checker for erlang:

https://github.com/WhatsApp/eqwalizer

Does nx not work for you? https://github.com/elixir-nx/nx/tree/main/nx#readme

It is historically not great at number computing. This is being addressed by a relatively new project called Nx. https://github.com/elixir-nx/nx

It is not the right choice for CPU intensive tasks like graphics, HFT, etc. Some companies have used Rust to write native extensions for those kinds of problems. https://discord.com/blog/using-rust-to-scale-elixir-for-11-m...

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

> I guess, why not use Rust entirely instead of as a FFI into Elixir or other backend language?

Because Rust brings none of the benefits of the BEAM ecosystem to the table.

I was an early Elixir adopter, not working currently as an Elixir developer, but I have deployed one of the largest Elixir applications for a private company in my country.

I know it has limits, but the language itself is only a small part of the whole.

Take ML, Jose Valim and Sean Moriarity have studied the problem, made a plan to tackle it and started solving it piece by piece [1] in a tightly integrated manner, it feels natural, as if Elixir always had those capabilities in a way that no other language does and to put the icing on the cake the community released Livebook [2] to interactively explore code and use the new tools in the simplest way possible, something that Python notebooks only dream of being capable of, after a decade of progress

That's not to say that Elixir is superior as a language, but that the ecosystem is flourishing and the community is able to extract the 100% of the benefits from the tools and create new marvellously crafted ones, that push the limits forward every time, in such a simple manner, that it looks like magic.

And going back to Rust, you can write Rust if you need speed or for whatever reason you feel it's the right tool for the job, it's totally integrated [3][4], again in a way that many other languages can only dream of, and it's in fact the reason I've learned Rust in the first place.

The opposite is not true, if you write Rust, you write Rust, and that's it. You can't take advantage of the many features the BEAM offers, OTP, hot code reloading, full inspection of running systems, distribution, scalability, fault tolerance, soft real time etc. etc. etc.

But of course if you don't see any advantage in them, it means you probably don't need them (one other option is that you still don't know you want them :] ). In that case Rust is as good as any other language, but for a backend, even though I gently despise it, Java (or Kotlin) might be a better option.

[1] https://github.com/elixir-nx/nx https://github.com/elixir-nx/axon

[2] https://livebook.dev/

[3] https://github.com/rusterlium/rustler

[4] https://dashbit.co/blog/rustler-precompiled

(including docs and tests!): https://github.com/elixir-nx/nx/pull/1090

I'll be glad to answer questions about Nx or anything from Livebook's launch week!

I think that experiment is taking shape with Elixir:

https://github.com/elixir-nx/nx

However the issue here at Nx https://github.com/elixir-nx/nx/issues/490 is already closed.

Outside of that, Elixir can be used for data pipelines, audio-video processing, and it is making inroads on Machine Learning with projects like Livebook, Nx, and Bumblebee.