<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Compression Ratio on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/compression-ratio/</link><description>Recent content in Compression Ratio 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/compression-ratio/index.xml" rel="self" type="application/rss+xml"/><item><title>Performance Profiling and Tuning OpenZL</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/performance-profiling-tuning-openzl/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/performance-profiling-tuning-openzl/</guid><description>&lt;h2 id="performance-profiling-and-tuning-openzl"&gt;Performance Profiling and Tuning OpenZL&lt;/h2&gt;
&lt;p&gt;Welcome back, compression enthusiast! In the previous chapters, you&amp;rsquo;ve mastered the basics of OpenZL, from setting it up to crafting your first compression plans for various structured data types. You&amp;rsquo;re now a wizard at making data smaller! But what if &amp;ldquo;smaller&amp;rdquo; isn&amp;rsquo;t enough, or what if it&amp;rsquo;s taking too long? This chapter is all about taking your OpenZL skills to the next level: understanding, measuring, and optimizing its performance.&lt;/p&gt;</description></item></channel></rss>