beam_languages VS experimental

Languages, and about languages, on the BEAM: https://github.com/llaisdy/beam_languages

PS: You might also find this interesting : https://www.grisp.org/

I thought this was a call for Standard ML or something a rather on the Erlang VM.

(I really enjoyed this article though!)

As far as I know theres a few implementations of ML like languages on the Erlang VM

https://github.com/llaisdy/beam_languages

caramel and alpaca are worth checking out.

Gleam doesn't look like a ML lang but has a lot of the same semantics of a ML lang

Have I got a link for you!

https://github.com/llaisdy/beam_languages

See you down the rabbit hole!

Elixir, LFE, etc. seem to work fine for the people who enjoy them.

https://github.com/llaisdy/beam_languages

LFE is Virding's himself, and has been around for longer than Erlang has been popular: Robert Virding - LFE - a lisp flavour on the Erlang VM (Lambda Days 2016) https://www.youtube.com/watch?v=Br2KY12LB2w

The Beam Languages repo is filled with projects to build on top of Erlang and the BEAM to take inspiration from.

Many years ago I was studying deep learning using this resource:

* http://neuralnetworksanddeeplearning.com/

I decided to try to implement everything from scratch in Elixir (after initially doing all the math with pen and paper on a trivial example to get the feel of it). Obviously pure elixir was extremely slow, so I started creating NIFs to pass over matrix multiplication to OpenBLAS. Then I was thinking more and more of what things I can pass to C code and just have Elixir as a "frontend" for it. My enthusiasm died down when I realised I was simply implementing things in C with the pretext of "doing elixir", a nice learning experience but I could see I was not doing the things that initially got me pumped up.

Don't get me wrong, I loved the discovery part of it, reading research and trying to understand so I can implement the different new (at the time) deep learning techniques, like convolutions, LSTM, and the different nuances of it. I think it gave me a better understanding of how things work and why it works. But it deviated from the initial scope and I lost interest once the learning phase was over and I knew I could simply use tensorflow or pytoorch as I did not actually need the advantages BEAM offers for this type of workload.

Code is still available here:

* https://gitlab.com/sdwolfz/experimental/-/tree/master/exlear...

I'm trying to package a trivial Gnome GJS application, that's just webkit2gtk displaying an index.html file, into an AppImage. You can browse the code here if you'd like: https://gitlab.com/sdwolfz/experimental/-/tree/master/gjs