If you’ve been using AI coding tools long enough, you’ve probably noticed that they all start to feel the same. Cursor, Claude Code, Codex, Amp — they’re converging on a set of common features: plan modes, permission popups, sub-agents, MCP integration, to-do tracking. More surface area, more abstraction, more decisions made for you. Pi bets against […]
Read More →Sharing local LLM models between Ollama and llama.cpp seems like a niche concern until you’ve burned through tens of GB of disk space on duplicate copies of the same model. The two tools use completely different storage formats by default, but you can configure them to share one file. Table of contents The problem: data […]
Read More →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 →Your brain is a muscle. And AI is offering to carry all the weight. Table Of Contents Introduction In 2000, Eleanor Maguire and her colleagues at University College London scanned the brains of licensed London taxi drivers — people who had spent years memorizing 25,000 streets and thousands of landmarks through a grueling training process […]
Read More →Large language models are increasingly extensible. Whether they’re called “skills,” “plugins,” “tools,” or “MCP servers,” the core idea is the same: let an LLM invoke external code, read external data, and act on external instructions. It’s also one of the most serious attack surfaces in modern AI systems. Table Of Contents What’s the problem? Prompt […]
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