<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Ollama - Tag - vo.rs</title><link>https://vo.rs/tags/ollama/</link><description>Ollama - Tag - vo.rs</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Wed, 29 Apr 2026 14:30:00 +0000</lastBuildDate><atom:link href="https://vo.rs/tags/ollama/" rel="self" type="application/rss+xml"/><item><title>Your First Local AI Coding Assistant: Wiring Ollama into Your Editor</title><link>https://vo.rs/story/your-first-local-ai-coding-assistant-ollama-in-your-editor/</link><description>&lt;p&gt;Cloud coding assistants are wonderful right up until you remember where your code is going. Every keystroke, every half-finished function, every comment grumbling about a colleague&amp;rsquo;s API design is shipped off to someone else&amp;rsquo;s server. For a side project that scarcely matters; for proprietary code under a strict NDA it can be a genuine problem. The good news is that you can run a capable coding assistant entirely on your own machine, with no network round-trips and no data leaving the building. If you have already met Ollama in our introductory piece, this guide takes the next step: wiring a local model directly into your editor so it suggests code as you type.&lt;/p&gt;</description><pubDate>Wed, 29 Apr 2026 14:30:00 +0000</pubDate></item><item><title>Local AI on Your Own Metal: Running LLMs Offline with Ollama</title><link>https://vo.rs/story/local-ai-on-your-own-metal-running-llms-with-ollama/</link><description>&lt;p&gt;Not so long ago the idea of a capable language model running on the computer under your desk, with no internet connection and no monthly bill, sounded faintly absurd. We have written before about the leap from the stumbling early days of GPT-2 to the polished conversations of modern chatbots, and the assumption baked into all of it was that the clever part lived in someone else&amp;rsquo;s datacentre. That assumption no longer holds. A tool called Ollama has made running open-weight language models on your own hardware about as difficult as installing a music player. This guide shows you how to do it, what to expect from the machine you already own, and where the honest limits lie.&lt;/p&gt;</description><pubDate>Tue, 24 Feb 2026 11:00:00 +0000</pubDate></item></channel></rss>