<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Working Theories</title><link>https://workingtheories.io/</link><description>Recent content on Working Theories</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 13 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://workingtheories.io/index.xml" rel="self" type="application/rss+xml"/><item><title>Software Development Did It Backwards. We Scaled the Machine First.</title><link>https://workingtheories.io/articles/post-cnc-standard/</link><pubDate>Sat, 13 Jun 2026 00:00:00 +0000</pubDate><guid>https://workingtheories.io/articles/post-cnc-standard/</guid><description>&lt;p>Software development did it backwards. We scaled the machine first.&lt;/p>
&lt;p>I&amp;rsquo;ve been helping customers measure their AI toolchain. And the same problem keeps showing up.&lt;/p>
&lt;p>They want to know if their AI investment is working. So we look at the metrics. Token count. Utilization rates. PR volume. And almost every time, the same question surfaces — not from me, but from them:&lt;/p>
&lt;p>&lt;em>&amp;ldquo;We&amp;rsquo;re shipping more. But are we reviewing less carefully?&amp;rdquo;&lt;/em>&lt;/p></description></item><item><title>Failing Forward: Structured Reflection in the Age of AI</title><link>https://workingtheories.io/articles/post-failing-forward/</link><pubDate>Mon, 08 Jun 2026 00:00:00 +0000</pubDate><guid>https://workingtheories.io/articles/post-failing-forward/</guid><description>&lt;p>Most teams finish a project and immediately start the next one. The lessons evaporate. The same mistakes repeat.&lt;/p>
&lt;p>Structured reflection is the thing that breaks that cycle. AI just made it faster.&lt;/p>
&lt;h2 id="the-framework-we-use">The framework we use&lt;/h2>
&lt;p>For years, my colleague Lynn Kreun and I have run retros together across government and education — complex, high-stakes environments where the cost of repeating mistakes is high and the appetite for slowing down is low. We call it failing forward.&lt;/p></description></item><item><title>Metrics Are the Beginning. Go Ask Why.</title><link>https://workingtheories.io/articles/post-metrics-why/</link><pubDate>Sat, 06 Jun 2026 00:00:00 +0000</pubDate><guid>https://workingtheories.io/articles/post-metrics-why/</guid><description>&lt;p>The real AI code review problem has nothing to do with tokens.&lt;/p>
&lt;p>I&amp;rsquo;ve been helping customers measure their AI toolchain. And the same problem keeps showing up.&lt;/p>
&lt;p>They want to know if their AI investment is working. So we look at the metrics. Token count. Utilization rates. PR volume. And almost every time, the same question surfaces — not from me, but from them:&lt;/p>
&lt;p>&lt;em>&amp;ldquo;We&amp;rsquo;re shipping more. But are we reviewing less carefully?&amp;rdquo;&lt;/em>&lt;/p></description></item></channel></rss>