<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Performance Improvement on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/performance-improvement/</link><description>Recent content in Performance Improvement 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/performance-improvement/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic Optimization: Training Compression Plans</title><link>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/dynamic-optimization-training-plans/</link><pubDate>Mon, 26 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/openzl-mastery-2026/dynamic-optimization-training-plans/</guid><description>&lt;h2 id="dynamic-optimization-training-compression-plans"&gt;Dynamic Optimization: Training Compression Plans&lt;/h2&gt;
&lt;p&gt;Welcome back, future data compression wizard! In our previous chapters, we explored how OpenZL intelligently uses data schemas to create highly efficient, format-aware compression plans. We learned how to define your data&amp;rsquo;s structure and generate static plans. But what if your data isn&amp;rsquo;t perfectly static? What if its characteristics subtly shift over time, or you want to squeeze out every last drop of performance for a specific dataset?&lt;/p&gt;</description></item></channel></rss>