<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Infrastructure as Code on No Semicolons</title><link>https://nosemicolons.com/tags/infrastructure-as-code/</link><description>Recent content in Infrastructure as Code on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 15 Aug 2026 08:17:09 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/infrastructure-as-code/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Kubernetes Cascade Failure: How Generated Helm Charts Are Silently Destroying Production Infrastructure</title><link>https://nosemicolons.com/posts/ai-generated-kubernetes-helm-charts-production-failures/</link><pubDate>Sat, 15 Aug 2026 08:17:09 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-generated-kubernetes-helm-charts-production-failures/</guid><description>&lt;p>Last Tuesday at 3 AM, my phone buzzed with alerts that made my blood run cold. Our production cluster was experiencing cascading failures across multiple services, CPU usage was spiking erratically, and pods were getting evicted left and right. The culprit? A seemingly innocent Helm chart that our team had generated using AI assistance just days before.&lt;/p>
&lt;p>Sound familiar? If you&amp;rsquo;ve been using AI to generate Kubernetes configurations, you might be sitting on a ticking time bomb. After investigating dozens of similar incidents across different teams, I&amp;rsquo;ve discovered a troubling pattern: AI-generated Kubernetes configs contain subtle but dangerous resource management errors that can bring down entire production environments.&lt;/p></description></item></channel></rss>