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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fast Flexible Function Dispatch in Julia · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1808.03370/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1808.03370/feed.xml" rel="self" type="application/rss+xml" /><item><title>Fast Flexible Function Dispatch in Julia — mentioned by ddragon</title><link>https://news.ycombinator.com/item?id=20616000</link><guid isPermaLink="false">urn:hackerpapers:mention:1808.03370:20616000</guid><pubDate>Mon, 05 Aug 2019 17:18:22 GMT</pubDate><description>&lt;p&gt;Each language will have different design priorities. For example auto-currying is great but it wouldn&amp;#x27;t make sense in a multiple dispatch language since you have to evaluate every argument to dispatch (which makes n-arity functions necessarily first class, not a composition of 1-arity functions). Another example is that Julia&amp;#x27;s type system exists to make it more dynamic (while maintaining high performance), while ML languages focus on static properties. But previous designs are great for inspiration (like you say, Julia has a lot from them, like sum and product types, subtyping, parametric…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1808.03370/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.PL</category></item></channel></rss>
