<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai on coles.codes</title><link>https://coles.codes/tags/ai/</link><description>Recent content in Ai on coles.codes</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 12 Jun 2026 00:00:00 +1000</lastBuildDate><atom:link href="https://coles.codes/tags/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>Local models in mid-2026: the engineering that closed the gap</title><link>https://coles.codes/posts/local-models-mid-2026/</link><pubDate>Fri, 12 Jun 2026 00:00:00 +1000</pubDate><guid>https://coles.codes/posts/local-models-mid-2026/</guid><description>&lt;p>The 2026 local-model story is quieter than the headlines suggest. Open weights did not catch up to the frontier, but they got close enough on the work most of us do day to day. Running LLM&amp;rsquo;s locally yourself isn&amp;rsquo;t just a hobby project anymore and turned into a reasonable choice if you&amp;rsquo;re after a basic model for writing and research, or running as a specialised agent.&lt;/p>
&lt;p>What I find interesting is the engineering that got us here, and the progress didn&amp;rsquo;t just mean we had to get more RAM to run bigger models. If anything it was the reverse: people figured out how to spend less compute and less memory per token without losing quality.&lt;/p></description></item></channel></rss>