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
16 parts tagged “Governance” · Clear filter
In August 2026 alone, Grok Bot, Cursor Origin, Slack Code and Claudeforce all came out. They all solve the same thing: letting AI out of the chat and into your work. And they all run into the same question that nobody can answer elegantly — who vouches for it.
“Do we use AI?” is a question from 2023. Today everyone gives the same answer and it tells you nothing. The useful question has two halves — where that particular task runs and who is allowed to start it — and the answer fits on one page.
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.
An account, a connected GitHub, a first brief — and within a few weeks a website that finds its own topic every morning, writes an article and waits for you to approve it. That's exactly how the site you're reading came about.
The difference between a useful agent and a dangerous one is not in the model but in a single line: where preparation ends and action begins. How to draw that line so that it holds — with a folder, a queue and one button on your phone.
A short list of things that shouldn't leave your machine, and three technical measures that mean it doesn't depend on your memory. Because a rule you have to remember will fail sooner or later.
Nobody reads the brand manual in PDF, least of all an AI. Put the rules, texts and approved photos on one server and every tool will request them itself — Claude, the agent on your NAS and the colleague in another department. What belongs on such a server and how to build it.
How to build a company knowledge base from the first folder to deployment is covered by the big guide in Tips. This part is a short companion for readers of the series: four rules that tell you a base is sound, trust levels the guide does not cover, and connecting it to an agent on your own machine.
A model follows the rules most of the time. A check written as ordinary code follows them every time. What belongs in it, what does not, how to offer it to an agent as a tool and why it should stand at the end of every text that goes out.
It isn't a bug in the code that someone will fix. It's a property of the way models work — and the only defence is to take one of three capabilities away from the agent. Which three they are, and how to check your own setup.
A twenty-page policy nobody reads protects less than a single page that everyone has on the noticeboard. A complete template with eight points that takes five minutes to read and can be adopted this week.
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 you have to explain everything to again is just a more expensive chat. A skill is a written-down procedure that it loads by itself next time. How one comes about, what it looks like inside, how to fix it — and why an agent's learning should pass your review just like everything else.
Your agent knows your promises, your mail and your calendar. You can't lend it to the children. A second profile has its own memory, its own bot and its own personality — and this part also says where a profile stops being enough and you need a second container.
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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