<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Set Theory on Signal &amp; Syntax</title><link>https://signal-and-syntax.com/tags/set-theory/</link><description>Recent content in Set Theory on Signal &amp; Syntax</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 31 Mar 2026 06:00:00 -0700</lastBuildDate><atom:link href="https://signal-and-syntax.com/tags/set-theory/index.xml" rel="self" type="application/rss+xml"/><item><title>The Discrete Mathematics Hiding Inside LLMs</title><link>https://signal-and-syntax.com/posts/discrete-math-in-large-language-models/</link><pubDate>Tue, 31 Mar 2026 06:00:00 -0700</pubDate><guid>https://signal-and-syntax.com/posts/discrete-math-in-large-language-models/</guid><description>A recent LinkedIn post from Michael Palmer described how discrete mathematics is the foundation for how computers reason about problems. That thread got me thinking about just how many discrete math concepts show up inside systems that seem purely statistical. LLMs are often described in terms of neural networks, gradient descent, and probability distributions. If you&amp;rsquo;ve taken discrete mathematics and wondered what it has to do with modern AI, the answer is: more than you&amp;rsquo;d expect.</description></item></channel></rss>