Running LLMs locally has become a normal part of how developers work. Two tools dominate this space: llama.cpp and Ollama. They look like competitors, but the relationship is more direct — Ollama is built on top of llama.cpp. This post covers the technical differences, where each performs better, and when to use one versus the other. Table of […]
Read More →Ranking repos by stars favors visibility over fit. That can be a fair first filter—stars are cheap and usually mean someone noticed the project—but treating the count as proof of quality, maintenance, or production readiness is the common mistake. What follows is what stars actually measure, where they mislead, and what to check on the […]
Read More →The ecosystem of “AI skills” — modular instruction packs that extend an LLM with task-specific know-how, whether they’re called Skills, plugins, agents, MCP servers, system-prompt templates, or tool bundles — has exploded fast enough that “which one should I use?” has become the dominant question. The answer is rarely obvious from a README, and almost […]
Read More →AI coding agents are fast, but they cut corners. Agent Skills is an open-source project by Addy Osmani that gives agents the same structured workflows senior engineers follow, from spec to ship. This post breaks down how it works, explains the Google engineering principles it builds on (Hyrum’s Law, Chesterton’s Fence, the Beyonce Rule, Shift […]
Read More →Getting productive in an open source project usually means understanding someone else’s repository before you can ship a useful issue or PR. The default playbook is familiar: clone the repo, read whatever README or contributing guide exists, search the tree, skim recent commits, and hope the architecture becomes clear before you lose momentum. That path […]
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