<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Guide on Byte-sized chunks</title><link>https://bytesizedchunks.net/tags/guide/</link><description>Recent content in Guide on Byte-sized chunks</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 06 Aug 2026 09:10:58 +0100</lastBuildDate><atom:link href="https://bytesizedchunks.net/tags/guide/index.xml" rel="self" type="application/rss+xml"/><item><title>We got model fusion at home</title><link>https://bytesizedchunks.net/blog/20260806/</link><pubDate>Thu, 06 Aug 2026 09:10:58 +0100</pubDate><guid>https://bytesizedchunks.net/blog/20260806/</guid><description>&lt;p&gt;In this chunk I&amp;rsquo;m going to document a little OpenCode&lt;sup id="fnref:1"&gt;&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref"&gt;1&lt;/a&gt;&lt;/sup&gt; setup that uses multiple (well, two in this case but your plan is your limit) LLMs to review a GitHub pull request.&lt;/p&gt;
&lt;p&gt;It&amp;rsquo;s a simple exercise in fusing multiple different models to perform a task and can be customized for any given LLM that you have access to and used for any task that benefits from this kind of multi-llm analysis.&lt;/p&gt;</description></item></channel></rss>