<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Principle-Driven on Chuanxilu for Skilled Homo sapiens</title><link>https://blog.chuanxilu.net/en/tags/principle-driven/</link><description>Recent content in Principle-Driven on Chuanxilu for Skilled Homo sapiens</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Wed, 20 May 2026 10:00:00 +0800</lastBuildDate><atom:link href="https://blog.chuanxilu.net/en/tags/principle-driven/index.xml" rel="self" type="application/rss+xml"/><item><title>Using the Method to Improve the Method</title><link>https://blog.chuanxilu.net/en/posts/2026/05/tdd-pipeline-v07-refinement-experiment/</link><pubDate>Wed, 20 May 2026 10:00:00 +0800</pubDate><guid>https://blog.chuanxilu.net/en/posts/2026/05/tdd-pipeline-v07-refinement-experiment/</guid><description>I built a ruler. The ruler measured &amp;#39;redundancy is harmful.&amp;#39; Then I used that ruler to trim the ruler&amp;#39;s own redundancy. I deleted the operational steps from my AI skill files, keeping only principles and counterexamples. The model reconstructed the deleted steps on its own — output quality didn&amp;#39;t drop.</description></item></channel></rss>