What runs where, and who may start it
Agents and your own AI
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
Where to start
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×
Foundations
Model, harness, agent — and why the question moved from “AI yes or no” to “what runs where”.
02 · 5×
Reference setups
Two complete setups from hardware to invoice: what runs where, what it costs and why it is put together this way.
03 · 5×
Local AI
Your first model on your own machine: Mac mini, Windows with an Nvidia card, memory, and when local is enough.
04 · 6×
Orchestration
One gateway in front of every model, API keys in one place, and routing each job to what handles it best.
05 · 8×
Control
A control room in your pocket: Telegram as the panel, a safe route home, and a queue you approve yourself.
06 · 8×
Your data
Files from your disk into the chat: how it really works, what RAG is, what MCP does, what stays off the cloud.
07 · 8×
Governance
Agents are the product of the year and the risk of the year. One page of rules and the case studies behind them.
5 parts tagged “Reference setups” · Clear filter
Part 01Beginner · 8 min
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.
#Reference setups#Cost#Orchestration#Local AI#Hardware
Part 02Advanced · 6 min
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.
#Reference setups#Hardware#Control#Telegram#Cost
Part 03Beginner · 10 min
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.
#Reference setups#AI agents#Orchestration#Governance
Part 04Beginner · 12 min
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.
#Reference setups#AI agents#Telegram#MCP#Your data#Security
Part 05Beginner · 11 min
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.
#Reference setups#AI agents#Hardware#Telegram#Cost
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