<?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>Software-Engineering on Hojae Lee</title><link>https://hojaeklee.github.io/tags/software-engineering/</link><description>Recent content in Software-Engineering on Hojae Lee</description><generator>Hugo -- 0.160.1</generator><language>en</language><lastBuildDate>Mon, 04 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://hojaeklee.github.io/tags/software-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Float Tolerances in NumPy Testing</title><link>https://hojaeklee.github.io/posts/float-tolerances-in-numpy-testing/</link><pubDate>Mon, 04 May 2026 00:00:00 +0000</pubDate><guid>https://hojaeklee.github.io/posts/float-tolerances-in-numpy-testing/</guid><description>A practical note on float32, float64, rtol, atol, and why testing numerical code near zero is surprisingly easy to get wrong.</description></item></channel></rss>