<?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>Home-Automation on Rusty Bower</title><link>https://www.rustybower.com/tags/home-automation/</link><description>Recent content in Home-Automation on Rusty Bower</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 28 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.rustybower.com/tags/home-automation/index.xml" rel="self" type="application/rss+xml"/><item><title>Teaching Security Cameras to Describe What They See</title><link>https://www.rustybower.com/posts/frigate-coral-tpu-vision-llm-camera-alerts/</link><pubDate>Tue, 28 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.rustybower.com/posts/frigate-coral-tpu-vision-llm-camera-alerts/</guid><description>&lt;p&gt;A notification that says &amp;ldquo;person detected on doorbell&amp;rdquo; is useful. A notification that says &amp;ldquo;a delivery driver in a brown UPS uniform placed a medium-sized box on the porch and is walking back to the truck&amp;rdquo; is significantly more useful — especially when you&amp;rsquo;re in a meeting and trying to decide whether to get up.&lt;/p&gt;
&lt;p&gt;I wanted that second kind of notification. The pieces were already in my homelab: &lt;a href="https://frigate.video"&gt;Frigate&lt;/a&gt; NVR for camera management, a Coral USB TPU for real-time object detection, and a Mac Studio running &lt;a href="https://github.com/mostlygeek/llama-swap"&gt;llama-swap&lt;/a&gt; for LLM inference. The challenge was wiring them together so that every time Frigate detects a person or package, a vision model looks at the image and writes a one-sentence description.&lt;/p&gt;</description></item></channel></rss>