Julia Draft, quick review, guesses. Website: https://julialang.org/ Philosophy: speed + scriptability, dynamic typing, reproducible output Compiled: no, use JIT at runtime Package database integration: bundled with language Embed libraries from other languages: Python, C-family, Java Support for Mobile: very immature, hacks needed REPL: yes, with inline help Documentation: https://docs.julialang.org Quote from docs: For large scale numerical problems, speed always has been, continues to be, and probably always will be crucial: the amount of data being processed has easily kept pace with Moore's Law over the past decades. Novel Philosophy: julia observes that objected oriented code is a special case of function overloading, and makes a focus on generalising this overloading concept as "multiple dispatch", organising code around functions or types as the user sees fit. a function overload is hence called a "method" (of the types it takes). This may provide for more flexibility around which larger concepts shared smaller concepts are associated with. Julia is for mathematicians, and latex/unicode symbols are used in source code. Julia has support for writing native GPU code, and provision for metaprogramming. I learned of Julia in a chatroom around data science and AI theory research. Pretrained transformer models are available for direct inclusion in software in the official package repository. There are also packages for advanced scientific computing # apt-get install julia hello_x.jl hello_x(x) = println("hello " * x) hello_x("world") function hello_x(x) println(string("hello ", x)) end hello_x("world") $ julia hello_x.jl