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
9 parts tagged “Hardware” · Clear filter
A specific person, specific hardware, specific accounts. What runs where, what can be cancelled, when it pays to move from a subscription to the API — and in what order to build it so that each step makes sense on its own.
The second setup adds one thing: a machine that's always on. That opens up work that gets done without you — nightly batches, website monitoring, a morning summary to Telegram. And it adds one obligation the gaming PC didn't have.
You don't need a Mac mini to have your own agent. The NAS you keep your photos on is enough: the agent is lightweight, because the thinking is done by a model in the cloud. A half-hour installation and a hundred concrete tasks, grouped by how much you have to entrust to it.
A quiet box next to the router that runs all the time and answers your questions from your phone and your laptop. How to turn a Mac mini into an inference server, how much memory to buy and what to do so it doesn't switch itself off.
A gaming PC is often a better machine for local AI than a Mac — as long as the model fits into the graphics card's memory. How to find out what your rig can handle, what to set up in Windows, and why VRAM is the only number that matters.
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.
Your NAS now runs an agent that reads your messages. That turns a box for photos into a machine worth locking properly. A concrete procedure for DSM: Tailscale, what to switch off, the two scheduled tasks people forget, and a test you can trust.
The morning brief did not arrive. The agent does not react to a message. Before you start reinstalling, go through six questions in order from the most common cause to the rarest. With the commands that answer them.
After three months the agent has your memory, fine-tuned procedures and the keys to everything in a single folder. Which of that to back up, where, how often — and why the backup has to sit somewhere the agent itself can't reach.
Get the next part
New parts and what matters in the world of agents go out in the weekly newsletter.
The Top 30 tips e-book, free — it lands in your inbox right away.
Then 1 tip a week · no spam · unsubscribe in one click