What 22 CI runs taught me about the gap between local dev and production

<h1> Production Was Down… But Everything Looked Normal 🤯 </h1> <blockquote> <p>A real debugging story about silent failures and why thinking matters more than tools.</p>

<p>TL;DR: I ported Python's natsort library to Rust for Port Mortem 2026.</p> <p>Writing the sorting algorithm turned out to be the easy part.</p> <p>The difficult part was

<p>I built a demo script to show off a project. It failed on step 2 — and in doing so, it found a real bug that had been sitting in the codebase the whole time, invisible to a

<p>Last Friday at 5 PM, right when I was about to close my laptop and sneak out, the alert channel exploded — the online data collection service had a timeout rate spiking to 40%,

<p>Four agents entered an infinite loop in November 2025. They ran for 11 days. The bill was $47,000. Nobody noticed until it was over.</p> <p>The team was running a market

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>Your agent passed all the tests. It's been running in production for three weeks. And it has been quietly wrong the entire time.</p> <p>This is not a hypothetical. Galileo's

<p><em>The AWS Reliability Files, Part 1</em></p> <p>I told an AI a true fact about AWS. Then I had a second AI confidently insist it was wrong. Within one exchange, the first