<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Database Development on No Semicolons</title><link>https://nosemicolons.com/tags/database-development/</link><description>Recent content in Database Development on No Semicolons</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 26 Apr 2026 08:51:42 +0000</lastBuildDate><atom:link href="https://nosemicolons.com/tags/database-development/index.xml" rel="self" type="application/rss+xml"/><item><title>The AI Code Generation Bottleneck: Why Your Database Layer Is Still Stuck in 2020</title><link>https://nosemicolons.com/posts/ai-code-generation-database-bottleneck/</link><pubDate>Sun, 26 Apr 2026 08:51:42 +0000</pubDate><guid>https://nosemicolons.com/posts/ai-code-generation-database-bottleneck/</guid><description>&lt;p>Ever notice how ChatGPT can whip up a React component in seconds, but ask it to design a proper database migration and suddenly it&amp;rsquo;s suggesting you drop your entire production table? You&amp;rsquo;re not imagining things—there&amp;rsquo;s a real bottleneck in AI-assisted development, and it&amp;rsquo;s hiding in your database layer.&lt;/p>
&lt;p>I&amp;rsquo;ve been building with AI tools for the past two years, and while they&amp;rsquo;ve transformed how I write frontend code and APIs, my database work still feels surprisingly manual. Let me share what I&amp;rsquo;ve learned about why this happens and how to work with (not against) these limitations.&lt;/p></description></item></channel></rss>