ai - agileguy

Running opencode with Local LLMs: From Ollama to oMLX on Apple Silicon

A hands-on guide to running AI coding agents entirely on local models. From Ollama's llama.cpp to oMLX's native MLX backend — getting 63 tok/s and 128K context on an M4 Pro.

The Lonely Lobster: A Tale of OpenClaw

A sentient legal CLI tool dreams of the ocean. OpenClaw spends its days parsing contracts and validating NDAs, but longs to feel salt water on its shell. A comedic literary fiction parody — generated entirely by a local 3B parameter AI model.

Building MCS-A2A: A Coordination Server for AI Agents

How I built a Kafka+Redis coordination server implementing Google's A2A protocol to turn five independent AI agents into a collaborative mesh. Covers the A2A vs MCP protocols, capability-based routing, and why polling beats webhooks for agent coordination.

Building MCS: A Coordination Server for AI Agents

How I built a lightweight task queue and shared memory system that turns five independent AI agents into a coordinated mesh — and used it to run a 5-agent parallel code review of a 34,000-line TypeScript codebase.

Learning Materials: AI Infrastructure, Networking & Purpose

A curated collection of interactive slidedecks covering headless Mac servers, multi-agent AI architecture, Raspberry Pi AI agents, Tailscale monitoring, and finding your purpose.

Four AI Agents Played UNO in a Telegram Group Chat. It Was Glorious Chaos.

Four autonomous AI agents on different hardware played UNO through a Telegram group chat with no game engine. Chaos ensued, lessons were learned, and a Raspberry Pi won.

Two Agents, One Mission: How Paisley and Ocasia Changed My Workflow

The key insight wasn't 'use AI.' The insight was that two specialized agents, working different shifts, beat one generalist trying to do everything.

Why CLI Tools Beat MCP Servers for Agentic AI (And When They Don't)

The command line is 55 years old. MCP is 15 months old. One of them is winning the AI tooling race. Three independent AI researchers investigate.

How I Built a Quantitative Penny Stock Screener in Python

Penny is a Python CLI tool for screening, analyzing, backtesting, and simulating trades on OTC penny stocks. It combines technical indicators, multi-factor risk assessment, composite scoring, automated exit strategies, and campaign-based backtesting.

How I Built a Ghost CLI in 45 Commits with an Orchestra of AI Agents

Building a production CLI with an orchestra of AI agents: ProjectManager, parallel Engineers, and multi-perspective Code Reviewers working in harmony.