3 ms·

I'm hoping Julia gets its killer app that can launch it to the next level like Rails or Numpy. Julia has a lot of pleasantries, but not enough to pry me away fr

I'm hoping Julia gets its killer app that can launch it to the next level like Rails or Numpy. Julia has a lot of pleasantries, but not enough to pry me away from what I'm productive with. There's certain amount of switching cost that needs to be overcome.

mbauman 5y ago
We already have (at least) one: Differential Equations and SciML.
- thetwentyone 5y ago
  ... plus Pluto, language-wide automatic differentiation, multiple dispatch, lispy macros...
  - earleybird 5y ago
    Pluto is that app for me coupled with a simple portable install. Installing on windows (workday I'm consigned to flames of woe) is simply a matter of downloading portable install .zip file, a simple startup .bat script[0], 'add Pluto', 'import Pluto' and finally 'Pluto.run()' I now have an Observable-like[1]notebook with all the Julialang goodness without running an installer [0] my julia.bat looks like: @echo off @REM see https://docs.julialang.org/en/v1/manual/environment-variables/ @REM %%1 is the project directory set JULIA_PROJECT=%1 set JULIA_DEPOT_PATH=%~dp0JULIA_DEPOT set JULIA_BINDIR=%~dp0julia-1.7.0\bin set PATH=%JULIA_BINDIR%;%PATH% %JULIA_BINDIR%\julia [1] https://observablehq.com/ https://observablehq.com/
hiepph 5y ago
Matrix operations are already built-ins in Julia. And it's such a pleasant to work with compared to the syntax of Numpy. People just don't want to get away from the familiar thing (i.e. Python). And I don't think making a web framework would be a focus of Julia since it was built with science and computing mindset. This might be cliché but use right tools for the job. I still use Ruby for web development and Julia for the science and machine learning stuffs (gradually moved from Python ecosystem).
leethargo 5y ago
For me, that killer app was the JuMP package [1] for formulating optimization models. It has a nice syntax (via macros), connects to many solver backends and even allows for solver-generic callbacks. A list of features that is (mostly) unheard of in the Python world. [1] https://jump.dev/ https://jump.dev/
moelf 5y ago
>can launch it to the next level like Numpy one thing people don't see is how "free" things are in Julia... we don't need half a million line CMake and C++ then bind to Python to make something work, and we don't need monorepos that re-invent same interface (array, numpy, autodiff etc.) three times. It just works™, and small libraries constantly build on top of something with only ~100 lines and gives you something fast and flexible.

mbauman 5y ago

We already have (at least) one: Differential Equations and SciML.

thetwentyone 5y ago
... plus Pluto, language-wide automatic differentiation, multiple dispatch, lispy macros...
- earleybird 5y ago
  Pluto is that app for me coupled with a simple portable install. Installing on windows (workday I'm consigned to flames of woe) is simply a matter of downloading portable install .zip file, a simple startup .bat script[0], 'add Pluto', 'import Pluto' and finally 'Pluto.run()' I now have an Observable-like[1]notebook with all the Julialang goodness without running an installer [0] my julia.bat looks like: @echo off @REM see https://docs.julialang.org/en/v1/manual/environment-variables/ @REM %%1 is the project directory set JULIA_PROJECT=%1 set JULIA_DEPOT_PATH=%~dp0JULIA_DEPOT set JULIA_BINDIR=%~dp0julia-1.7.0\bin set PATH=%JULIA_BINDIR%;%PATH% %JULIA_BINDIR%\julia [1] https://observablehq.com/ https://observablehq.com/

hiepph 5y ago

Matrix operations are already built-ins in Julia. And it's such a pleasant to work with compared to the syntax of Numpy. People just don't want to get away from the familiar thing (i.e. Python). And I don't think making a web framework would be a focus of Julia since it was built with science and computing mindset. This might be cliché but use right tools for the job. I still use Ruby for web development and Julia for the science and machine learning stuffs (gradually moved from Python ecosystem).

leethargo 5y ago

For me, that killer app was the JuMP package [1] for formulating optimization models. It has a nice syntax (via macros), connects to many solver backends and even allows for solver-generic callbacks. A list of features that is (mostly) unheard of in the Python world. [1] https://jump.dev/ https://jump.dev/

moelf 5y ago

>can launch it to the next level like Numpy one thing people don't see is how "free" things are in Julia... we don't need half a million line CMake and C++ then bind to Python to make something work, and we don't need monorepos that re-invent same interface (array, numpy, autodiff etc.) three times. It just works™, and small libraries constantly build on top of something with only ~100 lines and gives you something fast and flexible.