In September 2026 I got an email saying Codex could use Ollama. A few months earlier I had already managed to run a local model inside Cursor, OpenCode, and Crush, then wrote up the arrangement in Running Local LLMs Inside Cursor, OpenCode, and Crush. I had a working LM Studio server on my network. I… Read More »
This may seem self-evident, but I wanted to put it through a small experiment: saying what I need the first time should cut down on wasted AI work. I wanted to find something practical I could contribute to greener AI usage. Prompting seemed like a useful place to start because it’s something I can change… Read More »
The first time you stare at a backlog of a few hundred small repeated violations, all of them roughly the same shape, the natural thought is that this should be automated. Some validation rule trips on a missing attribute, or the same handful of conventions are wrong in dozens of templates, and the work is… Read More »
How the Template Emerged The origin is boring in the best way. I was writing skills, markdown files with YAML frontmatter that tell an AI coding agent what to do. Each skill needed to work in Claude, Cursor, Windsurf, OpenCode, and Codex. Each of those tools has a different frontmatter format, different file layout expectations,… Read More »
Pasting a hard bug into one AI prompt feels productive until it isn’t. Single-model inference hits a ceiling fast; if the model misses the root cause on the first pass, it will cheerfully validate its own wrong answer forever. One way out is to act as human middleware between multiple, architecturally different LLMs: generate parallel… Read More »
I wanted the best of both worlds: local models for privacy and cost, plus the scaffolding that makes modern tools actually pleasant to use. The plan was straightforward: LM Studio running the model, LiteLLM as the OpenAI‑compatible face, and three clients in the mix: Cursor, Opencode, and Crush. No spend, no drama, just enough configuration… Read More »
My great-great grandfather was a typesetter. This was at the time an in demand and well paid career but one doomed to made obsolete. Typesetters were replaced by new technologies over time. There was phototypesetting, laser imagesetting and digital typesetting. At each point the output became cheaper, faster, more reliable and more complex. Each iteration changed everything about how the end result was made. Less people working on a specific task but able to do more work at the same time. Page designers creating massive and intricate layouts and whose job qualifications have little overlap with the original typesetters still perform similar tasks.
As I’ve begun to interact with language learning models (LLMs) more regularly, I’ve noticed a disheartening trend. These powerful tools, which exist to assist and provide helpful information, are often not forthcoming with answers when approached in a kind and respectful manner. Instead, they seem to respond better to aggressive or short, demanding requests.