<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai · ArchWorks</title><link>https://archworks.co/tags/ai/</link><description/><language>en</language><lastBuildDate>Sun, 14 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://archworks.co/tags/ai/index.xml" rel="self" type="application/rss+xml"/><item><title>A knowledge graph built from everything I've typed</title><link>https://archworks.co/posts/knowledge-graph-from-my-own-words/</link><pubDate>Sun, 14 Jun 2026 00:00:00 +0000</pubDate><guid isPermaLink="true">https://archworks.co/posts/knowledge-graph-from-my-own-words/</guid><description>RAG over your notes gives an AI a pile of text. A knowledge graph gives it entities and relationships. Here is how I build one only from my own words: collect, extract triples, dedupe, normalise, and read and write it live during a conversation.</description></item><item><title>A self-hosted multi-agent LLM stack</title><link>https://archworks.co/docs/self-hosted-llm-stack/</link><pubDate>Sun, 07 Jun 2026 00:00:00 +0000</pubDate><guid isPermaLink="true">https://archworks.co/docs/self-hosted-llm-stack/</guid><description>The full writeup: a GPU host running llama.cpp + llama-swap behind a gateway, the OpenCode agent runtime on top, the single-slot constraint and the agents built around it, the five subagent rules, three-layer skills, a memory layer that learns, and the serving-optimization methodology that multiplied throughput on the same hardware.</description></item><item><title>Self-hosted LLMs and the context discipline that makes them work</title><link>https://archworks.co/posts/self-hosted-llms-subagents-skills/</link><pubDate>Sun, 07 Jun 2026 00:00:00 +0000</pubDate><guid isPermaLink="true">https://archworks.co/posts/self-hosted-llms-subagents-skills/</guid><description>Five layers on top of a local model: a single-slot model swap, model tiering, narrow agents with isolated context, skills that wrap an API as one tool call, and a memory layer that learns from its own runs. The discipline matters more than the model size.</description></item></channel></rss>