MichaelJamieson.

How AI Generates Text vs Images

Text and image generation look like the same trick from the outside, but under the hood they are two completely different machines: a language model that writes one token at a time like a typewriter, and an image model that sculpts a picture out of noise. Different math, different training, different failure modes.

Headroom: Your AI Agent Is Wasting Money On Tokens

The most expensive part of an AI agent is usually not the answer - it is everything the agent reads on the way there. Headroom compresses that context before the request leaves your machine, keeps the original nearby, and lets the model ask for the full detail only when it needs it.

Can Self-Hosted Models Do Real Agentic Work?

The honest, fast-changing answer to whether an open model running on hardware you own can do genuinely useful agentic work inside a real codebase - what you trade away by renting a cloud frontier model, and what self-hosting actually buys back.

What Actually Fits on 128 GB (Quantization Explained)

Quantization is the single most important lever for fitting large models onto hardware you actually own. What a number really is inside a model, how quantization shrinks it, and what genuinely fits in 128 GB of unified memory.

The Local AI Loop the Cloud Can't Afford

A small box on your desk can now run a whole family of open models at once - and do the one move the cloud quietly will not let you afford: let the models loop on their own outputs. Why raw speed is the wrong scoreboard for local AI.