<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Code Scalability on No Semicolons</title><link>https://nosemicolons.com/tags/code-scalability/</link><description>Recent content in Code Scalability on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 10 Sep 2026 12:32:33 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/code-scalability/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Prototype Pivot: How to Refactor Demos Into Production Systems Without Starting Over</title><link>https://nosemicolons.com/posts/ai-code-generation-prototype-pivot-refactor-demo-production/</link><pubDate>Thu, 10 Sep 2026 12:32:33 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-code-generation-prototype-pivot-refactor-demo-production/</guid><description>&lt;p>You know that feeling when you&amp;rsquo;ve just cranked out an AI-generated demo that works perfectly, and suddenly everyone wants it in production? Your stomach drops because you know that beautiful prototype is held together with the digital equivalent of duct tape and good intentions.&lt;/p>
&lt;p>I&amp;rsquo;ve been there more times than I care to admit. That moment when stakeholders see your AI assistant churning out a slick proof-of-concept and immediately start talking about user loads, security audits, and deployment timelines. The natural instinct is to throw everything away and start fresh with &amp;ldquo;proper&amp;rdquo; architecture. But here&amp;rsquo;s what I&amp;rsquo;ve learned: you don&amp;rsquo;t have to burn it all down.&lt;/p></description></item></channel></rss>