<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine-Learning on Greycloak</title><link>https://greycloak.com/tags/machine-learning/</link><description>Recent content in Machine-Learning on Greycloak</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>Copyright © 2023, Vince Wadhwani; all rights reserved.</copyright><lastBuildDate>Thu, 17 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://greycloak.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>When to Reach for a Judgment Model Instead of an LLM</title><link>https://greycloak.com/post/2026-09-17-a-new-class-of-ai-judgment-models-arrives/</link><pubDate>Thu, 17 Sep 2026 00:00:00 +0000</pubDate><guid>https://greycloak.com/post/2026-09-17-a-new-class-of-ai-judgment-models-arrives/</guid><description>
&lt;p&gt;If you have been wiring an LLM into every classification and routing decision in your product, there is now a reason to reconsider the pattern. A new model called JEV, from a company named Typesafe, is built to answer narrow questions with probabilities rather than prose. The practical consequence is that a check you could previously afford to run only on selected cases, or at the end of a task, becomes cheap and fast enough to run on every incoming request, after every draft, across every candidate document. That changes the economics of self-checking, ticket routing, and agent orchestration, which is where a lot of real automation quietly succeeds or fails.&lt;/p&gt;</description></item></channel></rss>