<?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>Techniques on J()s≡ph W()rksh()p</title><link>https://jsphwrkshp.com/techniques/</link><description>Recent content in Techniques on J()s≡ph W()rksh()p</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 04 Jul 2026 08:04:00 -0400</lastBuildDate><atom:link href="https://jsphwrkshp.com/techniques/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Productivity</title><link>https://jsphwrkshp.com/techniques/llm-productivity/</link><pubDate>Fri, 03 Jul 2026 23:05:37 -0400</pubDate><guid>https://jsphwrkshp.com/techniques/llm-productivity/</guid><description>&lt;p&gt;This is a non-static page for describing how I am thinking about using LLMs.&lt;/p&gt;
&lt;h1 id="harness"&gt;Harness&lt;/h1&gt;
&lt;p&gt;When you start working with language models to generate code, you want whatever is produced to work. This requires
some additional harnesses that both guide the agent and also give the agent a way to verify what it is doing. The
tools that were helpful for humans are mostly useful for agents as well. They may require a bit of editing and
changing of format to be more useful for an agent than a human.&lt;/p&gt;</description></item></channel></rss>