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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).

  1. 01 · 5×

    Foundations

    Model, harness, agent — and why the question moved from “AI yes or no” to “what runs where”.

  2. 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.

  3. 03 · 5×

    Local AI

    Your first model on your own machine: Mac mini, Windows with an Nvidia card, memory, and when local is enough.

  4. 04 · 6×

    Orchestration

    One gateway in front of every model, API keys in one place, and routing each job to what handles it best.

  5. 05 · 8×

    Control

    A control room in your pocket: Telegram as the panel, a safe route home, and a queue you approve yourself.

  6. 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.

  7. 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.

Tags

12 parts tagged “Telegram” · Clear filter

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