https://macworks.dev/tags/memory-safety/Recent content in Memory Safety on MacWorksHugoenhttps://macworks.dev/docs/today/engineer-blogs-2026-05-20/Mon, 01 Jan 0001 00:00:00 +0000https://macworks.dev/docs/today/engineer-blogs-2026-05-20/<h1 id="engineering-reads--2026-05-20">Engineering Reads — 2026-05-20<a class="anchor" href="#engineering-reads--2026-05-20">#</a></h1> <h2 id="the-big-idea">The Big Idea<a class="anchor" href="#the-big-idea">#</a></h2> <p>The boundaries of software engineering are being tested by the limits of strict specification: agentic coding tools fail when we cannot mathematically define our intent, while memory-unsafe languages continue to fail because we expect human discipline to substitute for structural guarantees.</p> <h2 id="deep-reads">Deep Reads<a class="anchor" href="#deep-reads">#</a></h2> <p><strong>Three more static code analysis sensors</strong> · Birgitta Böckeler · <a href="https://martinfowler.com/articles/sensors-for-coding-agents.html#StaticCodeAnalysisDependencyRules">Source</a> Birgitta Böckeler explores the effectiveness of using computational versus inferential sensors to evaluate software modularity. She observes that while traditional computational sensors are adequate for enforcing strict, rule-based dependency checks, they fall short when analyzing complex coupling data. Instead, utilizing an inferential sensor—essentially prompting an LLM to evaluate architectural boundaries—proves much more effective for nuanced reviews of system modularity. This highlights a compelling tradeoff: strict deterministic checks are brittle for high-level architectural constraints, whereas probabilistic inference can better grasp design intent. Engineers building or integrating AI coding agents should read this to understand where deterministic rules end and inferential checks must begin.</p>