<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Code-Quality-Monitoring on No Semicolons</title><link>https://nosemicolons.com/tags/code-quality-monitoring/</link><description>Recent content in Code-Quality-Monitoring on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 05 Sep 2026 11:30:28 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/code-quality-monitoring/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Model Degradation Crisis: How to Detect When Your Favorite Model Is Getting Worse (With Performance Tracking Scripts)</title><link>https://nosemicolons.com/posts/ai-code-generation-model-degradation-crisis/</link><pubDate>Sat, 05 Sep 2026 11:30:28 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-code-generation-model-degradation-crisis/</guid><description>&lt;p>Have you ever noticed your go-to AI coding assistant giving you slightly&amp;hellip; off responses? Maybe the code it generates doesn&amp;rsquo;t quite hit the mark like it used to, or you find yourself doing more manual corrections than before. You&amp;rsquo;re not imagining things – AI models can and do degrade over time, and it&amp;rsquo;s happening more quietly than most of us realize.&lt;/p>
&lt;p>I learned this the hard way when my team&amp;rsquo;s productivity started slipping despite using the same AI tools we&amp;rsquo;d been relying on for months. The culprit? Silent model degradation that we didn&amp;rsquo;t catch until it was already impacting our workflow.&lt;/p></description></item></channel></rss>