<?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>Artificial Intelligence on Dominic Keone | Cyber Portfolio</title><link>https://notdomquanmbs.github.io/tags/artificial-intelligence/</link><description>Recent content in Artificial Intelligence on Dominic Keone | Cyber Portfolio</description><generator>Hugo -- 0.163.3</generator><language>en-us</language><lastBuildDate>Sun, 08 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://notdomquanmbs.github.io/tags/artificial-intelligence/index.xml" rel="self" type="application/rss+xml"/><item><title>Project: Training &amp; Fine Tuning a LLM</title><link>https://notdomquanmbs.github.io/posts/my-first-project/</link><pubDate>Sun, 08 Mar 2026 00:00:00 +0000</pubDate><guid>https://notdomquanmbs.github.io/posts/my-first-project/</guid><description>&lt;h2 id="executive-summary"&gt;Executive Summary&lt;/h2&gt;
&lt;p&gt;For this project, I built a fully offline, privacy-focused AI coding assistant tailored specifically for Swift development. Base AI models often lack the newest programming rules, I used a Retrieval-Augmented Generation (RAG) pipeline to feed the AI static, up-to-date Swift developer guides. This allowed me to safely bridge the gap between hardware constraints and the need for accurate, current coding assistance.&lt;/p&gt;
&lt;h2 id="hardware-architecture"&gt;Hardware Architecture&lt;/h2&gt;
&lt;p&gt;Running a Retrieval-Augmented Generation (RAG) pipeline locally requires balancing compute power with strict memory constraints. The foundation of this localized AI environment relies on consumer-grade hardware optimized for parallel processing:&lt;/p&gt;</description></item></channel></rss>