The unreasonable effectiveness of the Julia programming language
Since everybody is so hyped about Julia, I took on learning it and using it in one of my pet projects. The use case seemed to be perfect for Julia: financial portfolio optimization.<p>But honestly, it is a royal pain in the ass to use. Mostly because tooling is so crappy (no decent IDE).<p>For example, I&#x27;ve made these notes on my journey with Julia:<p>- Juno and VSCode julia is a pain<p>- Debugging is pain - no watch expressions (but debug&gt; works)<p>- Works like with matlab (workspaces, designed for the single script use-cases)<p>- Long registry lookup times (2-5mins) when starting a new session<p>- No hints from IDE (Juno) on what methods are available for which types<p>- Ctrl-click navigation does not work to lookup definitations in libraries<p>- Debugger does not show the full list of elements - there are  triple dots in the middle<p>- Filters and maps are not lazy<p>- Arrays start at one<p>- Precompilation times can take 1-2min<p>- Package management is done as a part of script execution<p>- Delay (0.5-1s) when processing commands (compilation?)<p>- Editors (Juno) do not check types at runtime when the information is readily available<p>- Include() includes (is this PHP all over again?) a file into the script!<p>- Reuses same REPL between different files<p>- DataFrames.jl: Transform does nonsense (use map instead)<p>- DataFrames.jl: No transpose<p>Obviously, there is a bunch of good things like freakin insane speed and vectorized method calls are awesome, but I struggle seeing myself becoming more producting than Python + Pandas.<p>It&#x27;s really a shame that language is strongly and semi-statically typed yet no tooling is making a use of that.