What runs where, and who may start it
The question is no longer “ChatGPT, yes or no”. The question is what runs where and who may start it. Local and in-house AI is growing alongside the cloud: a Mac mini handles inference, open weights keep closing the gap, and agents are the product of the year and the risk of the year. This series takes you from your first local model to a control room you run the whole thing from — and to the rules without which it all falls apart.
45 parts · six tracks · more coming
Read it front to back like a course, or jump around by tag. If in doubt, start with the first part of each track — they assume you have installed nothing yet.
Prefer a fixed order to a signpost? Follow the path An agent on your NAS from scratch (13 steps).
01 · 5×
Model, harness, agent — and why the question moved from “AI yes or no” to “what runs where”.
02 · 5×
Two complete setups from hardware to invoice: what runs where, what it costs and why it is put together this way.
03 · 5×
Your first model on your own machine: Mac mini, Windows with an Nvidia card, memory, and when local is enough.
04 · 6×
One gateway in front of every model, API keys in one place, and routing each job to what handles it best.
05 · 8×
A control room in your pocket: Telegram as the panel, a safe route home, and a queue you approve yourself.
06 · 8×
Files from your disk into the chat: how it really works, what RAG is, what MCP does, what stays off the cloud.
07 · 8×
Agents are the product of the year and the risk of the year. One page of rules and the case studies behind them.
Tags
4 parts tagged “Open weights” · Clear filter
No account, no card, no data sent to anyone. Install one app, run one command, download one model — and an hour from now you have AI on your own machine that works even on a plane.
Why the same model comes in five different sizes in the library, what Q4 in the file name means and how to work out whether it will fit in your machine. One table, one formula, no maths.
Open models are no longer the poor man's option. For half of everyday work you won't notice the difference — and for the other half you'll notice it a lot. How to decide task by task instead of picking a camp.
Sending everything to the smartest model is like driving a lorry to the corner shop. How to split work into three tiers, set up a fallback route and stop paying for work the box in the living room can handle.
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