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.
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13 parts tagged “Cost” · Clear filter
Most of this series assumes a box that runs at home around the clock. If you don't have one and don't want one, you can get a large share of the benefit from the app alone. An honest comparison: what works without a server, what doesn't, and how to decide.
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.
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.
A local model isn't free and the cloud isn't expensive — until you start counting volume. How to work out your own break-even point, where the costs nobody tells you about are hiding, and why money isn't the main reason for going local anyway.
One account, one key, one invoice — and access to models from dozens of providers. What it really costs, how to set a spending cap, when it suits as your main route and when only as a safety net for an agent.
An agent doesn't have one price. It has four components, three of which are predictable while the fourth can spring a surprise. How to measure them, a table to fill in, where the money most often goes missing, and five levers for cutting the bill without losing the benefit.
Ten scheduled tasks mean ten messages you'll stop reading within a week. How to fold them into a single overview that takes a minute to read, what doesn't belong in it, and how to tell it's time to cut it down.
The plan from January: let agents take over routine work and slim teams down by up to 60%. The reality from August: 220% more code, 36% more finished features, 40% more incidents. A case study you can learn more from than from ten how-to guides.
An agent for one person is a tool. An agent for five people is a colleague, and it has to be clear who it answers to, who may do what and who pays for it. A three-role model, a shared channel instead of private messages, and five decisions to make before you switch it on.
In the first month you add things. By the third you have twenty tasks, read three and pay for the rest out of habit. A one-hour audit with four questions for every task, a table to fill in, and five patterns that tell you which task won't survive.
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