Why Julia's multiple dispatch is so greated explained with Pokemons

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

Our great sponsors
  • SonarQube - Static code analysis for 29 languages.
  • Scout APM - Less time debugging, more time building
  • SaaSHub - Software Alternatives and Reviews
  • pytype

    A static type analyzer for Python code

    i am perhaps biased, since my day job is working on static type inference for python[0], but i genuinely do believe that encoding properties like this into the type system gives you not just an extra level of safety, but an extra level of expressiveness when modelling your data in code. it's the equivalent of having units in physics.

    [0] https://github.com/google/pytype

  • PackageCompiler.jl

    Compile your Julia Package

    Julia is fairly fast, since its type system _only_ does dynamic/runtime typing, the JIT is optimized towards that. You'll experience some minor startup lag, typically due to initial JIT'ing of any new used functions. However, this has largely be remedied with a compiler backend that completely precomputes this behavior. https://julialang.github.io/PackageCompiler.jl/dev/

  • SonarQube

    Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.

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.

Suggest a related project

Related posts