<?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>MeshNetworking on</title><link>https://geekyschmidt.com/tags/meshnetworking/</link><description>Recent content in MeshNetworking on</description><image><title/><url>https://geekyschmidt.com/images/papermod-cover.png</url><link>https://geekyschmidt.com/images/papermod-cover.png</link></image><generator>Hugo</generator><language>en</language><copyright>Copyright ©2002-2026, Nicholas Schmidt; all rights reserved.</copyright><lastBuildDate>Sun, 14 Jun 2026 08:00:00 +0000</lastBuildDate><atom:link href="https://geekyschmidt.com/tags/meshnetworking/index.xml" rel="self" type="application/rss+xml"/><item><title>128GB of Local VRAM vs The Cloud: Cost Optimisation, Zero Telemetry, and What’s Possible with Local Dev Stacks</title><link>https://geekyschmidt.com/post/2026-06-15-localllmdevelopmentpebble/</link><pubDate>Sun, 14 Jun 2026 08:00:00 +0000</pubDate><guid>https://geekyschmidt.com/post/2026-06-15-localllmdevelopmentpebble/</guid><description>&lt;p&gt;Token counts, cloud subscriptions, and API rate limits are a constant drain. Beyond the monthly line items and the continuous cost optimisation of running infrastructure, the primary driver for me is data containment. Ensuring that not a single packet of proprietary code or infrastructure configuration leaks into an outbound telemetry stream to an external corporate LLM provider is a massive win.&lt;/p&gt;
&lt;p&gt;As a follow-up to my last post exploring the trade-offs of RSS feeds versus heavy-handed cloud AI agents, I decided to run an architectural experiment. Can you build a complete, functional smartwatch application end to end inside a strictly self-contained, local AI stack?&lt;/p&gt;</description></item></channel></rss>