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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11 parts tagged “Your data” · Clear filter
Five accounts, three subscriptions, every model in a different tab and the files still sitting on disk. This part is the map of the whole series: what a control room is, which four layers it consists of and in what order to build it.
Two containers on a home NAS: one holds all your chats, the other is an agent that reads them continuously, writes promises into Notion and answers you in Telegram. Claude prepares the containers; you just log in twice in the terminal.
Three devices, three different roles and one shared memory. How to divide up what gets done where, so that work in progress does not get left on the machine that happens to be shut in your backpack.
“AI sees my files” means five different things with very different consequences for privacy. The difference between attaching, an index, a connector and direct access — and why you should know it before you upload a contract anywhere.
Uploading a thousand documents to a model and expecting it to know them doesn't work. How search over your own material really operates, why it most often fails on bad data, and what to do to make it answer correctly.
Connectors aren't only for cloud services. A local MCP server can give the model access to a folder, a database or the command line — and the difference between a useful hook-up and a dangerous one is a single parameter: how much you fence off for it.
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.
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.
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.
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