<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adaptive Algorithms on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/adaptive-algorithms/</link><description>Recent content in Adaptive Algorithms on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 26 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/adaptive-algorithms/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 8: Optimizing Compression Plans: Training and Adaptation</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/optimizing-compression-plans/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/optimizing-compression-plans/</guid><description>&lt;h2 id="chapter-8-optimizing-compression-plans-training-and-adaptation"&gt;Chapter 8: Optimizing Compression Plans: Training and Adaptation&lt;/h2&gt;
&lt;p&gt;Welcome back, compression adventurers! In the previous chapters, we&amp;rsquo;ve explored the foundational concepts of OpenZL, how to define your data&amp;rsquo;s structure, and even built our first basic compression plans. You&amp;rsquo;re becoming quite the data whisperer!&lt;/p&gt;
&lt;p&gt;But here&amp;rsquo;s a secret: data rarely stays perfectly static. Whether it&amp;rsquo;s evolving sensor readings, changing user behavior logs, or new features in a dataset, data characteristics can subtly shift over time. A compression plan that was perfect yesterday might be merely &amp;ldquo;good enough&amp;rdquo; today, leaving valuable compression ratios on the table.&lt;/p&gt;</description></item></channel></rss>