Heidelberg AI Curriculum

5books in this track

L01 · User

Who is affectedOnly you

Run an AI Model on Your Own Laptop, Offline

Install LM Studio, download a small open model that fits your laptop's memory, and get an answer with the network switched off. Learn what running locally gains you, what it costs, and which tasks a small model handles well enough.

Outcome: A screenshot of LM Studio answering your own prompt while your laptop shows no network connection, plus three lines (model and download size, your installed RAM, one task you'll use it for); it passes if the answer was generated after you disconnected.

User14 minLast edited: Sep 23, 2026

L02 · Power user

Who is affectedYour team sees a bad document or a wrong figure

T11-L02

A Local Model Your Tools Can Call

Turn the model in LM Studio into a local server that other programs can use. Connect Open WebUI or n8n, installed through Heidelberg AI Desk, with three settings, check the results with a fixed set of examples, and choose between local, EU-hosted and frontier models task by task.

Outcome: One tool installed through Heidelberg AI Desk (Open WebUI or n8n) that gets its answers from LM Studio's local server, plus a check sheet of five examples with known answers; it passes when all five come out right with the network switched off, and your three lines name the address, the model and the task.

Power user15 minLast edited: Oct 12, 2026

L03 · Builder

Who is affectedColleagues depend on something you built

T11-L03

Local AI Apps Your Colleagues Can Rely On

Run local AI apps with Heidelberg AI Desk so colleagues can depend on the result: install Desk and let it set up Podman, read what an app needs before it changes anything, find where its data lives, back it up, restore it on a second computer, update it safely, and know when a laptop is no longer enough.

Outcome: A one-page run note for one app (what it produces, who reads it, how to start, stop, back up and restore it, and what happens when it fails), plus a restore of your latest backup on a second computer; it passes when a colleague follows the note without your help and the restored app shows your real data.

Builder24 minLast edited: Oct 12, 2026

L04 · Integrator

Who is affectedCompany or lab systems and real data are in the loop

T11-L04

Put Your Team's Assistant on a Server Colleagues Can Reach

Move a self-hosted assistant from one computer to a server the whole team can reach: one HTTPS address, a firewall that lets nothing else in, the model port closed, named accounts, backups you have restored, and an update you can undo. IT and the data protection officer sign off before the first colleague logs in.

Outcome: A server record for your team's assistant, plus a second-device test: it passes when the HTTPS address works from a colleague's computer, the Open WebUI and model ports don't answer from there, a restore from last night's backup has worked on another machine, and IT (plus the data protection officer if chats hold personal data) has signed the record.

Integrator23 minLast edited: Sep 23, 2026

L05 · Operator

Who is affectedWork stops when it breaks, and it must survive you leaving

T11-L05

Keep a Shared Model Server Fast, Affordable and Running

Run a self-hosted AI server that many people rely on, month after month: watch five signals, measure how many people it serves at once, set a limit below the breaking point, compare its cost per 1,000 answers with an API, and hand it to a second operator with records an auditor can check.

Outcome: An operations pack for a shared model server: a capacity table from a load test with the limit you set, a cost sheet comparing your server and an API per 1,000 answers, a one-page runbook and an operations log. It passes when a second operator has handled a practice outage from the runbook alone and the service owner has signed the cost decision.

Operator21 minLast edited: Sep 23, 2026