Learning Area

The central entry point to all learning content — guided or free, whatever you prefer.

Learning PathsContent

The Foundation

For users with no prior knowledge. If you can operate a computer, start here.

Operation & Command Line

How a computer works, what APIs are, how to use a terminal, Git, and understand open source.

Beginner 0 of 5

You understand how computers work, navigate the terminal confidently, use Git and understand open source.

5 articles Absolute beginners None
1

How a Computer "Thinks"

CPU, GPU, RAM, hard drive and binary code — the hardware basics.

2

The Internet & APIs

Client, server, HTTP requests and APIs as interfaces.

3

The Terminal

GUI vs. CLI, paths and the most important commands.

4

Version Control with Git

Repository, commit, branch and GitHub as cloud storage.

5

Open Source vs. Closed Source

Source code, licenses (GPL, MIT) and why transparency matters.

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Python & Programming Logic

From variables through control structures and functions to classes, packages and virtual environments.

Beginner

You program in Python, understand data types, control structures, OOP and set up clean development environments.

8 articles Beginners with basic computer knowledge Prerequisite: Operation & Command Line
1

Variables & Data Types

String, integer, float, boolean and assignments.

2

Control Structures

If/else branches and loops (for, while).

3

Functions

Parameters, return values and modular code.

4

When Code Breaks (Errors & Debugging)

Syntax error vs. logic error, try/catch and reading stack traces.

5

Lists & Dictionaries

Arrays, key-value stores and index-based access.

6

Object-Oriented Programming (Classes & Objects)

Class, object, method and attribute — the blueprint for complex code.

7

External Code Libraries (Packages)

Pip, dependencies, import — don't reinvent the wheel.

8

The Isolation Bubble (Virtual Environments / Docker)

venv, containers and reproducible environments.

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The Workshop

Practice for makers. Focus on application, tools and modern workflows.

Generative Art (Images & Video)

Image generation, Stable Diffusion, LoRA, ControlNet and workflow optimization.

Beginner Available from Aug 26, 2026

You generate your own images with Stable Diffusion, understand text-to-image and master LoRA, ControlNet and upscaling.

4 articles + 1 demo Creatives, designers, hobbyists Prerequisite: Operation & Command Line
1

Image Generation (From Text to Pixel)

Available from Aug 26, 2026

2

The Tool (Stable Diffusion Forge)

Available from Aug 26, 2026

3

Control & Finetuning

Available from Aug 26, 2026

4

Workflow Optimization

Available from Aug 26, 2026

5

GANs - Interactive Demo

Experience interactively how GANs generate images.

Optional

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Local Language Models

The Hugging Face ecosystem, running models locally and understanding quantization.

Intermediate Available from Sep 9, 2026

You understand Hugging Face, bring models to your PC and understand quantization.

3 articles Privacy-conscious users, tinkerers Prerequisite: Operation & Command Line
1

The Ecosystem (Hugging Face)

Available from Sep 9, 2026

2

Bringing Models to Your PC

Available from Sep 9, 2026

3

Quantization (Shrinking Models)

Available from Sep 9, 2026

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Agents & Automation

System prompts, prompt engineering, RAG, function calling, APIs and no-code workflows.

Intermediate

You create system prompts, build RAG pipelines, use function calling and automate workflows with n8n.

6 articles Makers, automators Prerequisite: Local Language Models Python & Programming Logic
1

Giving the Model a Task (System Prompts)

System prompt, roleplay, one-shot vs. few-shot.

2

Prompt Engineering (Chain of Thought)

Zero-shot, chain-of-thought and hallucination avoidance.

3

Connecting Your Own Data (RAG)

Retrieval-augmented generation, chunking, embeddings and vector database.

4

Function Calling (When AI Pushes Buttons)

JSON output, tool use — the AI decides which tool to execute.

5

Using Interfaces (APIs & MCP)

API key, Model Context Protocol and server architecture for AI agents.

6

No-Code Workflows (n8n)

Trigger, node, workflow automation and webhooks.

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AI in Software Development

Vibecoding, context engineering and agentic coding — the new way to program.

Intermediate Available from Oct 7, 2026

You program with AI, master context engineering and use autonomous coding agents.

3 articles Developers, makers Prerequisite: Python & Programming Logic Agents & Automation
1

Natural Language Programming (Vibecoding)

Available from Oct 7, 2026

2

Context Engineering

Available from Oct 7, 2026

3

Agentic Coding (AIs That Type Themselves)

Available from Oct 7, 2026

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The Academy

The in-depth computer science track. Equivalent to AI modules in a CS degree, but accessible.

Computer Science Structures & Algorithms

What algorithms are, how they scale and which data structures drive computer science.

Intermediate

You understand algorithms, Big-O notation, recursion and the key data structures (linear, hierarchical, key-based).

6 articles + 1 demo Learners with Python basics Prerequisite: Python & Programming Logic
1

What is an Algorithm?

Determinism, finiteness, input/output — the recipe of the machines.

2

Algorithmic Complexity (Big-O)

Understanding scaling: linear, quadratic, exponential.

3

Data Structures I (Linear)

Array, linked list, stack, queue.

4

Recursion

Base case, recursive call and stack overflow — loops on steroids.

5

Data Structures II (Hierarchical & Networked)

Tree, binary search tree, graph, node, edge.

6

Data Structures III (Key-Based)

Hash table, key-value pair and caching.

7

Travelling Salesman — When Trying Everything Takes Too Long

This path's first demo: find the shortest tour through many cities — an NP-hard problem that exposes the limits of brute force and why we need clever heuristics like simulated annealing.

Optional

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Mathematics of Data

Statistics and probability as the foundation for machine learning.

Intermediate

You understand measures of central tendency, dispersion, distributions, correlation, probability and Bayes' theorem.

6 articles + 1 demo Learners with algorithm basics Prerequisite: Computer Science Structures & Algorithms
1

Measures of Central Tendency (Where's the Middle?)

Mean, median and outliers.

2

Dispersion (How Far from the Middle?)

Variance, standard deviation and range.

3

Distributions (The Shape of Data)

Normal distribution, skewness and long-tail.

4

Connections Between Data

Correlation, causation and confounders.

5

Probability & Expected Value

Probability, expected value and risk/reward calculations.

6

Bayes & Conditional Probability

Bayes' theorem: updating beliefs with new evidence.

7

Naive Bayes - Interactive Demo

Classify texts live with the Naive Bayes algorithm.

Optional

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Linear Algebra & Calculus

Vectors, matrices, tensors, derivatives and the chain rule — the language of deep learning.

Intermediate

You understand vectors, matrices, tensors, derivatives, partial derivatives and the chain rule as the foundation for backpropagation.

6 articles Learners with statistics basics Prerequisite: Mathematics of Data
1

Spaces and Directions (Vectors)

Scalar, vector, dimensionality and vector space.

2

Data Tables & Transformations (Matrices)

Matrix, matrix multiplication, transformation and dot product.

3

Tensors (The Language of GPUs)

Tensor as n-dimensional array, batching and shape.

4

Measuring Change (Derivatives)

Slope, rate of change and minima/maxima.

5

Partial Derivatives & The Gradient

Multi-dimensional slope and the gradient vector.

6

Nested Functions (Chain Rule)

Function composition and the chain rule — the precursor to backpropagation.

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Classical, Symbolic AI

From Turing through search, heuristics and game theory to expert systems and the AI winter.

Intermediate

You understand the roots of AI, navigate state spaces, master pathfinding and game theory, and know the lessons of the AI winter.

7 articles + 4 demos Learners with algorithm basics Prerequisite: Computer Science Structures & Algorithms
1

The Birth of AI (From Turing to Dartmouth)

Turing test, Dartmouth Conference (1956), ELIZA — the historical framework.

2

Graph Search

State space, breadth-first search (BFS) and depth-first search (DFS).

3

Heuristics & Pathfinding

Heuristic, cost function, Dijkstra and the A* algorithm.

4

A* Pathfinding - Interactive Demo

Watch A* and Dijkstra in action navigating through a maze.

Optional
5

Agents in Conflict (Game Theory)

Multi-agent systems, zero-sum games and Nash equilibrium.

6

MinMax & Pruning

Decision tree, MinMax strategy and alpha-beta pruning.

7

MinMax - Interactive Demo

Play Tic-Tac-Toe against an unbeatable MinMax AI.

Optional
8

Rules & Logic (Expert Systems)

Expert system, inference engine, if-then rules and combinatorial explosion.

9

Rule-Based AI - Interactive Demo

Experience interactively how an expert system forms logical causal chains.

Optional
10

ELIZA Chatbot - Interactive Demo

Chat with the first rule-based therapist bot in history.

Optional
11

The AI Winter (Failure & Lessons)

Lighthill Report, overhyped promises and why symbolic AI failed at complexity.

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Machine Learning Fundamentals

The paradigm shift: from programming to training — supervised, unsupervised and evaluation tools.

Intermediate 0 of 4

You understand ML paradigms, train supervised learning models, evaluate with metrics and know overfitting and unsupervised learning.

8 articles + 3 demos Learners with all foundation paths Prerequisite: Computer Science Structures & Algorithms Mathematics of Data Linear Algebra & Calculus Classical, Symbolic AI
1

Programming vs. Training

Machine learning, training data, model and inference.

2

Supervised Learning (Learning with a Teacher)

Label, features, target, classification vs. regression.

3

Linear & Logistic Regression

Line of best fit, sigmoid and error function (MSE).

4

The Raw Material (Data Engineering)

Feature engineering, missing values and normalization.

5

Parameters vs. Hyperparameters

What the AI learns itself vs. what humans set.

6

When the Student Memorizes

Overfitting, underfitting, train-test split and validation set.

7

Evaluating Models

Accuracy, precision, recall, F1-score and confusion matrix.

8

Unsupervised Learning (Structure in Chaos)

Clustering (k-means), dimensionality reduction (PCA) and anomaly detection.

9

Supervised Learning - Interactive Demo

Train a model and watch the decision boundary.

Optional
10

K-Means Clustering - Interactive Demo

Watch K-Means sort data points into groups.

Optional

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Deep Learning (The Neural Revolution)

From the artificial neuron through activation, hidden layers and loss functions to gradient descent and backpropagation.

Advanced Available from Jul 15, 2026

You understand neural networks from the perceptron to backpropagation and can follow the complete training process.

6 articles + 3 demos Learners with ML basics Prerequisite: Machine Learning Fundamentals
1

The Artificial Neuron

Available from Jul 15, 2026

2

Perceptron - Interactive Demo

Train the simplest neural network step by step.

Optional
3

The Spark (Activation)

Available from Jul 15, 2026

4

The Network (Hidden Layers)

Available from Jul 15, 2026

5

How AI Calculates How Wrong It Is

Available from Jul 15, 2026

6

The Path to the Valley (Gradient Descent)

Available from Jul 15, 2026

7

The Heart of Learning (Backpropagation)

Available from Jul 15, 2026

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Modern Architectures (GenAI)

From tokenization through embeddings, CNNs, RNNs and transformers to LLMs, transfer learning and diffusion.

Advanced Available from Aug 12, 2026

You understand tokenization, embeddings, CNNs, RNNs, transformers, LLMs, transfer learning, sampling and diffusion.

9 articles + 4 demos Learners with deep learning basics Prerequisite: Deep Learning (The Neural Revolution)
1

Tokenization (The Alphabet of Machines)

Available from Aug 12, 2026

2

Embeddings & Latent Space

Available from Aug 12, 2026

3

Computer Vision (CNNs)

Available from Aug 12, 2026

4

Time & Sequences (RNNs)

Available from Aug 12, 2026

5

The Breakthrough (Transformers & Attention)

Available from Aug 12, 2026

6

Large Language Models (LLMs)

Available from Aug 12, 2026

7

Transfer Learning & Fine-Tuning

Available from Aug 12, 2026

8

Sampling & Temperature

Available from Aug 12, 2026

9

Generative Image Models (Diffusion)

Available from Aug 12, 2026

10

GANs - Interactive Demo

Experience interactively how GANs generate images.

Optional

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Reinforcement Learning

Learning through reward: from the basic principle through Q-Learning to RLHF.

Advanced Available from Sep 23, 2026

You understand reinforcement learning, MDP, Q-learning and RLHF — how ChatGPT learns politeness.

3 articles + 2 demos Learners with deep learning basics Prerequisite: Deep Learning (The Neural Revolution)
1

Carrot and Stick (The Principle)

Available from Sep 23, 2026

2

Developing Strategies (Q-Learning & MDPs)

Available from Sep 23, 2026

3

Q-Learning - Interactive Demo

Watch an agent find the exit using Q-Learning.

Optional
4

RLHF (How LLMs Learn Politeness)

Available from Sep 23, 2026

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Society & Inclusion

Societal and legal implications, tailored to specific target groups.

Ethics & Myths

Consciousness, bias, copyright and prompt injections — the critical questions around AI.

Beginner Available from Jul 29, 2026

You distinguish AGI from stochastics, understand training bias analytically, know copyright questions and prompt injection risks.

4 articles Everyone interested None
1

Consciousness vs. Probability

Available from Jul 29, 2026

2

Bias & Data Quality

Available from Jul 29, 2026

3

Copyright & Data Theft

Available from Jul 29, 2026

4

Security & Prompt Injections

Available from Jul 29, 2026

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Europe & Law

EU AI Act, regulation of foundation models and data protection in AI applications.

Beginner Available from Oct 21, 2026

You understand the EU AI Act, transparency obligations for foundation models and GDPR implications for AI.

3 articles Everyone interested None
1

The EU AI Act

Available from Oct 21, 2026

2

Regulation of Foundation Models

Available from Oct 21, 2026

3

Data Protection & AI (GDPR)

Available from Oct 21, 2026

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For Seniors (AI in Everyday Life)

A digital helper, online safety and maintaining sovereignty.

Beginner 0 of 1

You use AI as an everyday helper, recognize fraud schemes and maintain control over your data.

3 articles + 2 demos Seniors, late adopters None
1

A Digital Helper

Voice assistant, translation and reading aloud.

2

First AI Contact - Interactive Demo

A safe first conversation with a simple chatbot.

Optional
3

Safety & Fraud Protection

Grandchild scam 2.0, voice cloning and phishing detection.

4

Email Shield - Interactive Demo

Experience how a spam filter detects suspicious emails.

Optional
5

Maintaining Sovereignty

Opting out from providers and controlling data sharing.

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For Students

Learning with AI instead of cheating, recognizing deepfakes and critically checking sources.

Beginner

You use AI as a learning tutor, recognize deepfakes and synthetic media and critically verify sources.

2 articles + 3 demos Students None
1

Learning with AI (Instead of Cheating)

Socratic dialogue, learning tutor and critically questioning hallucinations.

2

Truth on the Internet

Deepfakes, source verification and synthetic media.

3

Cars Learn to Drive - Interactive Demo

Watch cars learn to navigate a racetrack through neuroevolution. Drive yourself and compare with the AI.

Optional
4

Play Against the Computer - Interactive Demo

Compete against an AI that selects the best move using MinMax.

Optional
5

The Oldest Chatbot - Interactive Demo

Chat with ELIZA, the first chatbot in history.

Optional

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Curiosities

A growing showcase of demos that don't fit any single subject path — classics, curiosities, charming outliers.

Curiosities

A home for demos that don't belong to any other path — classics, curiosities, charming outliers. A growing showcase without a shared curriculum. Sometimes one moves into a subject path, sometimes a new one joins.

Beginner

A curated handful of standalone demos — a stroll for the curious. No homework, just exploration.

2 demos The curious None
1

Boids — flocking behaviour

Three simple rules per bird produce a flock without a leader.

Optional
2

Evolution — mutation and selection

Tiny creatures are randomly mutated across generations — evolution to watch.

Optional

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