<story><title>Data Science in Julia for Hackers</title><url>https://datasciencejuliahackers.com/</url></story><parent_chain><item><author>kasperset</author><text>I really want to love Julia language but the overhead of package loading annoys me. Perhaps this may be just me being lazy or spoiled after using R packages or Python libraries.</text></item></parent_chain><comment><author>lasagnaphil</author><text>Yeah, it&amp;#x27;s still dissapointing how much system resources it takes to get a plot running (even with all the improvements in 1.6, benchmark taken from &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.oxinabox.net&amp;#x2F;2021&amp;#x2F;02&amp;#x2F;13&amp;#x2F;Julia-1.6-what-has-changed-since-1.0.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.oxinabox.net&amp;#x2F;2021&amp;#x2F;02&amp;#x2F;13&amp;#x2F;Julia-1.6-what-has-chang...&lt;/a&gt;).&lt;p&gt;&lt;pre&gt;&lt;code&gt;    julia&amp;gt; @time (using Plots; display(plot(1:0.1:10, sin.(1:0.1:10))))
      9.694037 seconds (18.29 M allocations: 1.164 GiB, 4.17% gc time, 0.40% compilation time)
&lt;/code&gt;&lt;/pre&gt;
To be honest, this isn&amp;#x27;t really something the devs should be proud about. 10 seconds, 18.29 M allocations and 1.164 GiB of memory just to render a simple static image is just unacceptable. In order for Julia to be a good-enough language for general scientific usage the current slow LLVM-based JIT compiler sadly isn&amp;#x27;t enough. I really want Julia to succeed, but this one problem is triumphing over every good language feature.&lt;p&gt;In an ideal world Julia would have two backends:&lt;p&gt;- A fast, hand-made JIT backend (something like LuaJIT) which is mostly interpreted and only JIT-compiled in frequently run code paths.&lt;p&gt;- The current slow, almost-AOT-like LLVM backend which is only used for precompilation of packages&lt;p&gt;It&amp;#x27;s just like Debug&amp;#x2F;Release bulids in C++, except that the Debug builds are lightning fast to compile (but slower at runtime) and Release builds are slower to compile (but hyper-optimized for runtime speed). I guess there&amp;#x27;s going to be an immense engineering effort if something like this happens, given that Julia today is immensely coupled with the LLVM compiler&amp;#x2F;runtime. But that&amp;#x27;s just my pipe dream.</text></comment>
<story><title>Data Science in Julia for Hackers</title><url>https://datasciencejuliahackers.com/</url></story><parent_chain><item><author>kasperset</author><text>I really want to love Julia language but the overhead of package loading annoys me. Perhaps this may be just me being lazy or spoiled after using R packages or Python libraries.</text></item></parent_chain><comment><author>alpaca128</author><text>One of the things I took away from trying Julia was that unicode is incredibly dumb in some aspects. Unicode lacks support for parts of the latin alphabet in subscript and superscript formatting(similar for the greek alphabet)[0].&lt;p&gt;As Julia relies on Unicode for its neatly formatted variable names this is an annoying limitation that the developers can&amp;#x27;t even do much about.&lt;p&gt;Unicode has over 100k symbols, including about 3500 emoji. But a continuous set of 26 small letters, either in lowercase or uppercase(or both) is too much bloat apparently.&lt;p&gt;[0]: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;stackoverflow.com&amp;#x2F;questions&amp;#x2F;17908593&amp;#x2F;how-to-find-the-unicode-of-the-subscript-alphabet&amp;#x2F;17909597#17909597&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;stackoverflow.com&amp;#x2F;questions&amp;#x2F;17908593&amp;#x2F;how-to-find-the...&lt;/a&gt;</text></comment>