<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>machine-learning on Lankan Lion | Tech Blog</title><link>https://blog.lankanlion.com/tags/machine-learning/</link><description>Recent content in machine-learning on Lankan Lion | Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 14 Jan 2026 20:47:19 -0600</lastBuildDate><atom:link href="https://blog.lankanlion.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>AI for Developers: A Practical, No-Hype Primer for AWS &amp; Backend Engineers</title><link>https://blog.lankanlion.com/post/ai-primer/</link><pubDate>Wed, 14 Jan 2026 20:47:19 -0600</pubDate><guid>https://blog.lankanlion.com/post/ai-primer/</guid><description>AI has quietly moved from “interesting experiment” to default tooling for many development teams. If you’re a backend, DevOps, or cloud engineer, you’re already using it—sometimes intentionally, sometimes indirectly—whether it’s in IDE assistants, CI pipelines, log analysis, or documentation workflows.
This article is not a hype piece, a vendor comparison, or a prompt-engineering guide. It’s a practical, architecture-oriented view of AI in 2026: what it really is, where it fits into an AWS-centric stack, and where engineers routinely get burned.</description></item></channel></rss>