<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI Tech Blog (English)</title><description>A multilingual technical blog covering AI, agentic coding, AI systems, and SEO/AEO/GEO from hands-on experience.</description><link>https://warren-ai-tech-blog.pages.dev/en/</link><language>en</language><atom:link href="https://warren-ai-tech-blog.pages.dev/en/rss.xml" rel="self" type="application/rss+xml"/><item><title>An AI Learning Map — Orientation: The Whole Picture Before the Details</title><link>https://warren-ai-tech-blog.pages.dev/en/blog/ai-orientation/</link><guid isPermaLink="true">https://warren-ai-tech-blog.pages.dev/en/blog/ai-orientation/</guid><description>An orientation that maps how AI, machine learning, deep learning, and LLMs relate, how AI products travel from data to the user, and how the field arrived where it is today.</description><pubDate>Thu, 16 Jul 2026 00:00:00 GMT</pubDate><content:encoded>&lt;p&gt;An orientation that maps how AI, machine learning, deep learning, and LLMs relate, how AI products travel from data to the user, and how the field arrived where it is today.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;This aims for conceptual understanding to judge AI conversations yourself, not formula derivation.&lt;/li&gt;&lt;li&gt;It surveys the terrain of AI/ML/DL/LLM, the pipeline of AI products, and the history from an observation deck.&lt;/li&gt;&lt;/ul&gt;</content:encoded><category>ai</category><category>machine-learning</category><category>llm</category><category>getting-started</category></item></channel></rss>