<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Conceptual Modeling on MacWorks</title><link>https://macworks.dev/tags/conceptual-modeling/</link><description>Recent content in Conceptual Modeling on MacWorks</description><generator>Hugo</generator><language>en</language><atom:link href="https://macworks.dev/tags/conceptual-modeling/index.xml" rel="self" type="application/rss+xml"/><item><title>Week 19 Summary</title><link>https://macworks.dev/docs/month/blogs/weekly-2026-W19/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/month/blogs/weekly-2026-W19/</guid><description>&lt;h1 id="engineering-reads--week-of-2026-04-17-to-2026-05-01"&gt;Engineering Reads — Week of 2026-04-17 to 2026-05-01&lt;a class="anchor" href="#engineering-reads--week-of-2026-04-17-to-2026-05-01"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;h2 id="week-in-review"&gt;Week in Review&lt;a class="anchor" href="#week-in-review"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;This week&amp;rsquo;s reading fundamentally re-evaluates the role of the software engineer in an era where text and code generation are practically free. The dominant debate has shifted from how to generate logic faster to how we deterministically verify it, forcing a transition toward strict mechanical guardrails and &amp;ldquo;agentic engineering&amp;rdquo;. Alongside this technical shift, there is a fierce resurgence in confronting the sociopolitical reality of our craft, reminding us that architectural choices—from open-source licenses to structural capability boundaries—never exist in a moral vacuum.&lt;/p&gt;</description></item><item><title>2026-04-29</title><link>https://macworks.dev/docs/archives/blogs/engineer-blogs-2026-04-29/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://macworks.dev/docs/archives/blogs/engineer-blogs-2026-04-29/</guid><description>&lt;h1 id="engineering-reads--2026-04-29"&gt;Engineering Reads — 2026-04-29&lt;a class="anchor" href="#engineering-reads--2026-04-29"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;h2 id="the-big-idea"&gt;The Big Idea&lt;a class="anchor" href="#the-big-idea"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;As AI tools accelerate code generation, the primary engineering bottleneck shifts from writing implementation logic to verifying it and providing structural intent. The high-leverage work of a senior engineer is evolving from writing instructions to building deterministic verification harnesses and formalizing clear conceptual boundaries.&lt;/p&gt;
&lt;h2 id="deep-reads"&gt;Deep Reads&lt;a class="anchor" href="#deep-reads"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;[On Agentic Programming and Verification]&lt;/strong&gt; · Chris Parsons · &lt;a href="https://martinfowler.com/fragments/2026-04-29.html"&gt;Fragments: April 29&lt;/a&gt;
Chris Parsons argues that as AI throughput scales, verification can no longer rely purely on human reading. Instead, modern verification must rely on tests, type checkers, and automated gates to handle the volume. The core bottleneck in software engineering is no longer how fast we can generate code, but how fast we can determine if that generated code is correct. He contrasts &amp;ldquo;vibe coding&amp;rdquo; with rigorous &amp;ldquo;agentic engineering,&amp;rdquo; where shaping the inner harness is a distinct advantage. For senior engineers, reviewing endless AI diffs is a dead end; the real compounding value lies in training the AI to get it right the first time and shaping the review surfaces. Read this if you are a senior engineer trying to figure out how your role scales in an AI-heavy workflow.&lt;/p&gt;</description></item></channel></rss>