<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Local-First on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/local-first/</link><description>Recent content in Local-First on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/local-first/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 2: Setting Up Your Trackio Environment &amp;amp; First Log</title><link>https://ai-blog.noorshomelab.dev/trackio-2026-guide/02-installation-and-first-log/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/trackio-2026-guide/02-installation-and-first-log/</guid><description>&lt;h2 id="chapter-2-setting-up-your-trackio-environment--first-log"&gt;Chapter 2: Setting Up Your Trackio Environment &amp;amp; First Log&lt;/h2&gt;
&lt;p&gt;Welcome back, aspiring ML experimenter! In our previous chapter, we got a high-level overview of Trackio and why it&amp;rsquo;s such a valuable tool for managing your machine learning endeavors. Now, it&amp;rsquo;s time to roll up our sleeves and get our hands dirty!&lt;/p&gt;
&lt;p&gt;This chapter is all about getting you set up for success. We&amp;rsquo;ll walk through setting up a clean Python environment, installing Trackio, and then making your very first experiment log. By the end, you&amp;rsquo;ll have Trackio running on your machine and recording actual data, which is a huge step towards gaining control over your ML experiments. Ready to dive in? Let&amp;rsquo;s get started!&lt;/p&gt;</description></item></channel></rss>