<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Artificial Intelligence on Code Mirroring</title><link>https://www.codemirroring.com/artificial-intelligence/</link><description>Recent content in Artificial Intelligence on Code Mirroring</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://www.codemirroring.com/artificial-intelligence/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Fundamentals: A Practical Guide for Engineers and Interviews</title><link>https://www.codemirroring.com/artificial-intelligence/ai-fundamentals/</link><pubDate>Thu, 06 Aug 2026 00:00:00 +0000</pubDate><guid>https://www.codemirroring.com/artificial-intelligence/ai-fundamentals/</guid><description>This guide is for software engineers who want a solid mental model of artificial intelligence — whether you are refreshing for interviews, joining an AI feature team, or reading production LLM code with less confusion.
Read it top to bottom once (~20–30 minutes). Use the table of contents later as a cheat sheet.
The map in one minute Term Plain meaning AI Broad umbrella: machines performing tasks that usually need human intelligence Machine learning (ML) Systems that learn patterns from data instead of only hard-coded rules Deep learning (DL) ML using multi-layer neural networks Generative AI Models that produce new content (text, code, images, audio) LLM Large language model — a generative model specialized for language/tokens Interview one-liner: AI ⊃ ML ⊃ DL; modern GenAI/LLMs sit mostly inside deep learning.</description></item></channel></rss>