How does Elixir stack up to Julia in the future of writing machine-learning software?

This page summarizes the projects mentioned and recommended in the original post on /r/Julia

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  • nx

    Multi-dimensional arrays (tensors) and numerical definitions for Elixir (by elixir-nx)

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • julia

    The Julia Programming Language

  • Lots of this is in progress and doesn't require a 2.0. Better error messages landed last week, see https://twitter.com/ChrisRackauckas/status/1661014235466563591 which was https://github.com/JuliaLang/julia/pull/49795. That makes them a ton more readable, and there's a few more of these kinds of things in process. The parser change is set to make it into v1.10, https://github.com/JuliaLang/julia/pull/46372. v1.10 is supposed to branch in just a few weeks and will possibly be LTS. With that all in mind, there's a time to take stock of what has been a large amount of pretty huge changes and seeing what's next to help Julia improve static compilation.

  • carbon-lang

    Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)

  • Carbon at least open sourced (https://github.com/carbon-language/carbon-lang) and can be tested on their experimental compiler w/o sign up. Their ethos and whole approach feels a lot less 'scammy' than Mojo does to me at the moment.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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