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Courses

Course catalog

One course, taught as a route you actually walk. Below is the whole thing: every path, every lesson title, and the honest time each one takes.

5 stages, beginner to expert, plus 8 role tracks.

  • 32learning paths
  • 546lessons
  • 7,696screens in total
  • 120–185hours, end to end

Where do you want to get to?

Pick a destination, not a catalog. Each one is a handful of courses with an honest estimate; everything below stays here for when you want it.

Foundations that endure, tools that evolve

We teach the principles slowly and update the tools quickly. 19 of the 32 courses teach things that were true before this generation of AI and will be true after it: computation, math, control theory, systems, how a model actually decides. The other 13 track what is current — agents, AI coding tools, the techniques that move with the models, and the shape of a job this year. When the tools change we rewrite that layer. What is underneath keeps its value.

How long it takes

About 120–185 h to work through all of it.

Where the time goes. Each path carries its own figure on its card.
StagePathsLessonsScreensTime
Understand AI38177111–17.5 h
Computing & Math Foundations45099716.5–25.5 h
Machine Learning & Modern AI6771,17419–29.5 h
Building & Shipping AI5821,18419.5–30 h
Robotics & Physical AI61782,57140–65 h
Role tracks87899915.5–23.5 h
Whole catalog325467,696120–185 h

Nobody works through the whole catalog in one run, and nobody has to. The paths are independent, and the shortest of them is about an hour.

What a screen is

A lesson is a run of screens, and they are not all the same thing. This is the whole catalog broken down by what each screen asks of you.

  • 3,513 reading cardsOne idea each, in plain words, with a deeper layer folded underneath for anyone who wants it. 518 of them carry a figure drawn by the app and not a picture of one — fire a projectile and watch gravity bend it, turn one of 37 3D scenes with a finger. We are still working through the catalog adding them.
  • 2,306 questionsMultiple choice, asked before the teaching rather than after. A wrong answer gets the reason it was tempting, not a cross.
  • 826 exercisesSomething on the screen you work rather than an option you tick: a robot to program and run, a circuit to find the break in, a program to assemble line by line. Where one of these needed a paragraph of instructions before it taught anything, we are replacing it with a plain question and a figure worth looking at.
  • 701 written challengesYou write the answer in your own words first, then read a worked one and mark your own against it.
  • 350 reflectionsA question with no right answer. What you write goes in a journal nobody else can reach.
One exercise, start to finish

In Circuits and Open Loops the flashlight is dark. Its battery is fresh and its bulb is good, so the circuit must be open somewhere. Four joints look loose and only one really is, and a meter beside the bulb reads the current through it. Every tap re-solves the whole network by modified nodal analysis, so the meter reads what a meter would read — which is the reason it is an exercise and not a paragraph.

Stage 1 of 5

Understand AI

Start from zero and build a real mental model of how AI works.

  • Beginner → Advanced
  • 3 paths
  • 81 lessons
  • 771 screens
  • 11–17.5 h

AI Literacy

Understand AI, and use it well.

Start here. What AI actually is, what it's good and bad at, how to check an answer you can't verify yourself, and what you should never paste into it.

  • Beginner
  • 51 lessons
  • 366 screens
  • 4.5–7.5 h
Lessons (51)
  1. What Is AI? 6 min
  2. AI for Images and Text 6 min
  3. One Model for Each Job 6 min
  4. Why AI Took Off 5 min
  5. Rules or Learning 6 min
  6. What's Inside a Trained Model 4 min
  7. Weights and Bias 6 min
  8. Scalars, Vectors, Matrices, Tensors 4 min
  9. How a Chatbot Writes a Reply 6 min
  10. How a Chatbot Learns From Text 7 min
  11. Writing a Clear Prompt 6 min
  12. Safe Replies or Creative Ones 5 min
  13. Tokens: How AI Reads Text 6 min
  14. From Tokens to Meaning 6 min
  15. What Tokens Cost 6 min
  16. When Text Doesn't Fit 6 min
  17. One Neuron, Up Close 5 min
  18. Layers of Neurons 4 min
  19. How a Network Learns 4 min
  20. What AI Does Well 5 min
  21. When AI Makes Things Up 6 min
  22. When AI Copies Unfair Patterns 5 min
  23. When the World Changes 8 min
  24. Making Pictures From Static 4 min
  25. How Words Steer a Picture 5 min
  26. Why Generators Shrink the Picture 6 min
  27. Other Ways to Generate 6 min
  28. Three Ways to Work With AI 6 min
  29. Two Questions Before You Hand It Over 6 min
  30. Good at One Task, Not the Next 5 min
  31. Keep the Skills You Want 6 min
  32. Say What You Want From AI 6 min
  33. Give AI the Context It Needs 4 min
  34. Improve AI Answers in Rounds 5 min
  35. Confident Isn't the Same as Correct 5 min
  36. Check the Facts an Answer Rests On 6 min
  37. Check More When More Is at Stake 6 min
  38. What Happens to What You Paste 6 min
  39. You're Responsible for What You Send 5 min
  40. When to Say AI Helped 6 min
  41. Where Your Pasted Text Goes 6 min
  42. Hiding Personal Details 6 min
  43. Safer Setups and the Law 5 min
  44. When Hidden Text Tricks an AI 6 min
  45. When AI Takes Actions 5 min
  46. Can It Be Undone? 6 min
  47. Checking an Agent's Work 4 min
  48. What an Eval Is 5 min
  49. Building an Answer Key 5 min
  50. Using AI to Grade AI 6 min
  51. Test Again After Every Change 6 min

Machine Learning, Demystified

How machines actually learn.

How a model turns examples into skill. You train one, watch it overfit, and find out why similar things end up near each other inside an embedding.

  • Intermediate
  • 10 lessons
  • 142 screens
  • 2–3.5 h
Lessons (10)
  1. How Machines Learn 15 min
  2. Learning From Examples 13 min
  3. Getting It Right 13 min
  4. Finding Hidden Structure 10 min
  5. Learning by Trial and Error 10 min
  6. Embeddings and Vector Search 14 min
  7. Deep Learning and Backpropagation 14 min
  8. Tensors and How Models Train 13 min
  9. Computer Vision and CNNs 16 min
  10. Bias, Variance, and Regularization 16 min

How AI Really Works

How it works under the hood.

How AI learns from data, decides what matters, and gets tested before anyone trusts it. Plus the problem nobody has solved: getting a model to do what you meant rather than what you said.

  • Advanced
  • 20 lessons
  • 263 screens
  • 4–7 h
Lessons (20)
  1. Data Is the Teacher 16 min
  2. Attention, Simply 16 min
  3. From Predictor to Assistant 11 min
  4. Measuring Intelligence 13 min
  5. What We Said vs. What We Meant 15 min
  6. The Transformer, In Depth 13 min
  7. Scaling Laws and Emergence 13 min
  8. Fine-Tuning, LoRA, and Adaptation 12 min
  9. Tool Use and Function Calling 15 min
  10. The Hugging Face Hub 11 min
  11. The Core Libraries 12 min
  12. Fine-Tuning on One GPU 13 min
  13. Training and Shipping It 15 min
  14. How Reasoning Models Think 11 min
  15. Learning That Never Stops 14 min
  16. Knowing What You Don't Know 14 min
  17. Free Energy and Active Inference 13 min
  18. The Bitter Lesson 15 min
  19. The Open Frontier 13 min
  20. Final Exam: Understand AI 14 min

Stage 2 of 5

Computing & Math Foundations

The code and math every serious AI builder leans on.

  • Beginner → Intermediate
  • 4 paths
  • 50 lessons
  • 997 screens
  • 16.5–25.5 h

Coding Foundations

Think like a builder.

AI writes most of the code now, so the job is reading it and judging it. Learn how a program is put together well enough to review what a model hands you.

  • Beginner
  • 10 lessons
  • 177 screens
  • 2.5–4.5 h
Lessons (10)
  1. Variables and Decisions 23 min
  2. Loops and Patterns 20 min
  3. Functions as Building Blocks 20 min
  4. APIs and Apps 15 min
  5. Debugging Like a Builder 12 min
  6. Reading Code You Didn't Write 14 min
  7. Specifying and Verifying Work 16 min
  8. The Practical Dev Toolkit 17 min
  9. Full-Stack, End to End 18 min
  10. Frontend with TypeScript and React 24 min

Python, Properly

The one language AI work runs on.

Real Python, hands-on: types and collections, files and JSON, NumPy and pandas, calling an API, writing a test. You build things rather than read about them.

  • Beginner
  • 12 lessons
  • 282 screens
  • 4.5–7.5 h
Lessons (12)
  1. Why Python (and How to Read It) 27 min
  2. Values, Variables, and Types 26 min
  3. Lists, Dicts, Sets, and Tuples 25 min
  4. Comprehensions and Pythonic Style 24 min
  5. Functions, Arguments, and Modules 25 min
  6. Files, JSON, and the Standard Library 23 min
  7. Errors, Exceptions, and Tracebacks 25 min
  8. Virtual Environments and pip 20 min
  9. NumPy and pandas: the Data Duo 24 min
  10. Calling APIs in Python: requests and async 24 min
  11. Classes, Type Hints, and Testing 23 min
  12. Building APIs with FastAPI 21 min

Computer Science Foundations

The bedrock under all of it.

Data structures, algorithms, operating systems, databases, concurrency, and Git. The durable half of computing, which is what makes you a sharp reviewer of anything AI writes.

  • Intermediate
  • 18 lessons
  • 262 screens
  • 4–6.5 h
Lessons (18)
  1. How Computers Actually Work 14 min
  2. Data Structures 17 min
  3. Memory, Caches, and Locality 14 min
  4. Algorithms and Big-O 14 min
  5. Dynamic Programming 14 min
  6. Graph Algorithms 16 min
  7. Operating Systems 15 min
  8. How the Internet Works 12 min
  9. Databases and SQL 12 min
  10. Concurrency and Parallelism 16 min
  11. Version Control with Git 15 min
  12. Software Engineering Practices 13 min
  13. Classical AI: Search and Planning 13 min
  14. Logic and Knowledge Representation 13 min
  15. Computability and P vs NP 14 min
  16. Compilers and Interpreters 12 min
  17. Coding Interview Patterns 18 min
  18. Coding Interview Problems, Worked 18 min

Math for Machine Learning

The language under the hood.

Vectors, derivatives, probability, and optimization, taught with pictures and plain words. Enough to read a paper or a loss function without flinching.

  • Intermediate
  • 10 lessons
  • 276 screens
  • 4.5–7.5 h
Lessons (10)
  1. Vectors and Vector Spaces 27 min
  2. Matrices as Transformations 31 min
  3. Derivatives and Gradients 33 min
  4. Why Small Samples Lie 26 min
  5. Probability Essentials 33 min
  6. Statistics and Distributions 36 min
  7. Optimization: Finding the Bottom 26 min
  8. Entropy and Cross-Entropy 27 min
  9. Differential Equations and Numerical Methods 29 min
  10. Final Exam: Computing and Math Foundations 15 min

Stage 3 of 5

Machine Learning & Modern AI

From classic algorithms to language, vision, and generative models.

  • Intermediate
  • 6 paths
  • 77 lessons
  • 1174 screens
  • 19–29.5 h

Classical Machine Learning

The algorithms that still win.

Regression, trees, forests, boosting, SVMs, clustering. The models that quietly beat deep learning on most real tables, and the feature work that decides it.

  • Intermediate
  • 9 lessons
  • 141 screens
  • 2.5–4 h
Lessons (9)
  1. Linear Regression 22 min
  2. Logistic Regression and Classification 22 min
  3. Working with Data 14 min
  4. Evaluating a Model 17 min
  5. Decision Trees 17 min
  6. Random Forests and Gradient Boosting 16 min
  7. SVMs and k-Nearest Neighbors 14 min
  8. Clustering and PCA 15 min
  9. Feature Engineering and Model Selection 15 min

Machine Learning Deep Dive

The deep end.

Graduate-level ML: learning theory, Bayesian methods, reinforcement learning, time series, causal inference, and how to evaluate a model so the number means something.

  • Advanced
  • 19 lessons
  • 295 screens
  • 4.5–7.5 h
Lessons (19)
  1. Learning Theory and Generalization 17 min
  2. Loss Functions and What They Encode 19 min
  3. Regularization, In Depth 15 min
  4. Model Evaluation, Rigorously 14 min
  5. Probabilistic and Bayesian ML 16 min
  6. Reinforcement Learning, In Depth 18 min
  7. Recommender Systems 16 min
  8. Time Series and Forecasting 16 min
  9. Interpretability and Explainability 20 min
  10. Hyperparameter Tuning and AutoML 14 min
  11. Graph Machine Learning 14 min
  12. Causal Inference 17 min
  13. Weight Initialization 14 min
  14. Batch and Layer Normalization 13 min
  15. Variational Autoencoders 14 min
  16. Algorithmic Fairness and Bias Auditing 14 min
  17. Bayesian Networks and Graphical Models 13 min
  18. Mechanistic Interpretability 15 min
  19. Neural Operators 13 min

Natural Language Processing

Teaching machines to read and write.

How machines handle language, from counting words to attention. You see why word vectors work, what an LSTM was for, and where BERT and GPT part ways.

  • Intermediate
  • 8 lessons
  • 124 screens
  • 2–3 h
Lessons (8)
  1. How Language AI Works 14 min
  2. Text as Data 16 min
  3. Word Vectors: word2vec and GloVe 15 min
  4. Language Modeling and Perplexity 13 min
  5. RNNs, LSTMs, and Memory 14 min
  6. Seq2Seq, Attention, and Translation 17 min
  7. BERT vs GPT: Two Ways to Pretrain 14 min
  8. NLP Tasks and How to Score Them 17 min

Computer Vision

Teaching machines to see.

From a raw pixel to a model in production: filters, CNNs, detection and segmentation, vision transformers, and the geometry that turns two photos into depth.

  • Intermediate
  • 17 lessons
  • 258 screens
  • 4–6.5 h
Lessons (17)
  1. How Computers See 15 min
  2. Image Formation and Color 15 min
  3. Edges, Filters, and Classic Features 16 min
  4. Convolutional Networks, In Depth 13 min
  5. CNN Architectures: AlexNet to ResNet 14 min
  6. Image Classification in Practice 12 min
  7. Object Detection: R-CNN to YOLO 17 min
  8. Segmentation: Labeling Every Pixel 15 min
  9. Pose Estimation and Tracking 18 min
  10. Vision Transformers 20 min
  11. 3D Vision and Geometry 14 min
  12. Generative Vision: GANs, VAEs, Diffusion 13 min
  13. Self-Supervised and Multimodal Vision 13 min
  14. Video Understanding 13 min
  15. Computer Vision in Production 13 min
  16. Signals and the Fourier View 11 min
  17. Bird's-Eye-View and Occupancy Networks 18 min

Video Generation AI

How AI dreams up moving pictures.

How video models work, from diffusion up to Sora-style world models, and why keeping one character the same from shot to shot is still hard.

  • Advanced
  • 9 lessons
  • 127 screens
  • 2–3.5 h
Lessons (9)
  1. How AI Generates Video 14 min
  2. Diffusion Models, Properly 16 min
  3. Flow Matching and Rectified Flow 10 min
  4. Latent Diffusion and Conditioning 11 min
  5. Text-to-Video: Adding Time 12 min
  6. Video Diffusion Architectures 18 min
  7. Diffusion Transformers and Sora 13 min
  8. Controlling Generated Video 15 min
  9. Evaluating Video AI and Its Limits 17 min

LLMs & Multimodal AI

Building and steering the big models.

Inside a modern LLM, and how a team shapes one: fine-tuning, RLHF and DPO, quantization, retrieval, and keeping it from making things up.

  • Advanced
  • 15 lessons
  • 229 screens
  • 3.5–6 h
Lessons (15)
  1. Inside a Modern LLM 14 min
  2. Training a Model From Scratch 13 min
  3. Supervised Fine-Tuning 15 min
  4. RLHF, DPO, and Preference Tuning 15 min
  5. PEFT, LoRA, and Quantization 16 min
  6. Advanced RAG 17 min
  7. Hallucination Control and Grounding 16 min
  8. Multimodal LLMs 15 min
  9. Audio and Speech AI 13 min
  10. Building Voice Interfaces: Wake Word to Reply 16 min
  11. Any-to-Any Multimodal AI 17 min
  12. Mixture of Experts 15 min
  13. Speculative Decoding 15 min
  14. Project: Fine-Tune a Small Model 15 min
  15. Final Exam: Machine Learning and Modern AI 15 min

Stage 4 of 5

Building & Shipping AI

Turn models into real, safe, production-grade products.

  • Advanced
  • 5 paths
  • 82 lessons
  • 1184 screens
  • 19.5–30 h

Prompt Engineering & AI Collaboration

Get the most out of any model.

Prompting that survives contact with real work: few-shot, chain-of- thought, structured output, tool calls, and testing a prompt instead of guessing at it.

  • Advanced
  • 9 lessons
  • 125 screens
  • 2–3.5 h
Lessons (9)
  1. Prompting Foundations 13 min
  2. Few-Shot and Chain-of-Thought 13 min
  3. Structured Outputs and Contracts 13 min
  4. Prompt Patterns and Anti-Patterns 15 min
  5. Designing Tools and Function Calls 12 min
  6. Context Engineering 13 min
  7. Eval-Driven Prompt Development 14 min
  8. Designing Agentic Workflows 13 min
  9. Working With AI Systems in Production 16 min

Building With AI

From prompts to a fleet of agents.

Write prompts that hold up, brief an agent that runs for an hour without you, then build one yourself. It ends with using AI coding tools the way people who ship with them do.

  • Advanced
  • 18 lessons
  • 250 screens
  • 4–6.5 h
Lessons (18)
  1. Prompt Engineering, Properly 11 min
  2. Briefing a Long-Running AI 10 min
  3. Build an Agent From Scratch 16 min
  4. Context Mastery 12 min
  5. Giving AI Memory: Context and State 14 min
  6. Claude Code, Fundamentals 16 min
  7. The Everyday AI Dev Workflow 12 min
  8. Parallel Agents and Worktrees 14 min
  9. Multi-Agent Orchestration 16 min
  10. RAG in Practice 11 min
  11. MCP and Tool Integration 13 min
  12. Build Your First MCP Server 16 min
  13. Wiring AI Into the World: APIs and Automation 13 min
  14. Connecting Accounts: OAuth and Integrations 15 min
  15. Choosing and Routing Models 13 min
  16. Defending Your AI App 16 min
  17. Code Review in the AI Age 12 min
  18. Agent-First Software Design 14 min

Production AI & MLOps

From notebook to product.

What happens after training: pipelines, deployment, monitoring for drift, and the tracking that tells you which model is actually live right now.

  • Advanced
  • 22 lessons
  • 332 screens
  • 5–8 h
Lessons (22)
  1. The ML Lifecycle 13 min
  2. Data Engineering and Pipelines 13 min
  3. Training at Scale 12 min
  4. Serving and Deployment 16 min
  5. Monitoring and Drift 14 min
  6. Experiments, Versioning, and CI/CD 15 min
  7. LLMOps: Serving LLMs in Production 15 min
  8. A/B Testing and Online Experiments 13 min
  9. The Economics of AI Systems 15 min
  10. The Data Engine 13 min
  11. The Reliability Gap 12 min
  12. Designing for Failure 12 min
  13. Inference Optimization 19 min
  14. From Demo to Fleet 14 min
  15. When One Machine Runs Out 11 min
  16. The Flavors of Parallelism 11 min
  17. Talking Between GPUs 14 min
  18. Training at Scale: Internals 16 min
  19. Keeping It Alive at Scale 13 min
  20. Kubernetes: Who Gets the GPU 18 min
  21. Serving at Scale 20 min
  22. ML System Design 16 min

AI Security & Privacy

Build it safe, keep it private.

How AI systems get attacked and how to defend them: prompt injection, adversarial examples, data that leaks, and guardrails that hold when someone is trying.

  • Advanced
  • 11 lessons
  • 158 screens
  • 3–5 h
Lessons (11)
  1. Data Privacy Foundations 17 min
  2. Differential Privacy 16 min
  3. Federated Learning 14 min
  4. Adversarial Machine Learning 16 min
  5. LLM Security: Prompt Injection and Jailbreaks 18 min
  6. Privacy-Preserving Inference 17 min
  7. Securing Deployed AI Systems 19 min
  8. Guardrails and Abuse Prevention 18 min
  9. Data Retention, Permissioning, and Governance 15 min
  10. AI Security Architecture 18 min
  11. AI Regulation and Governance 17 min

Systems Design & Engineering

Design choices, and what each one costs.

How real systems are put together: scaling, databases, caching, queues, APIs, and what every choice costs you later. Here the trade- offs are the whole job.

  • Advanced
  • 22 lessons
  • 319 screens
  • 5–8 h
Lessons (22)
  1. Thinking in Systems 16 min
  2. Requirements and Systems Engineering 13 min
  3. System Design Fundamentals 12 min
  4. Performance: Latency, Throughput, and Capacity 14 min
  5. Designing for Scale 15 min
  6. Caching, CDNs, and DNS 16 min
  7. Databases: SQL, NoSQL, and Storage Engines 18 min
  8. Scaling Data: Replication, Partitioning, and Sharding 14 min
  9. Distributed Systems 15 min
  10. APIs, Services, and Architecture 15 min
  11. API Styles: REST, GraphQL, gRPC and Serialization 14 min
  12. The Edge: API Gateway, Auth, and Rate Limiting 17 min
  13. Messaging and Event-Driven Architecture 13 min
  14. Reliability and SRE 15 min
  15. Resilience Patterns: Failing Gracefully 14 min
  16. Observability: Logs, Metrics, and Traces 12 min
  17. Shipping Safely: Deploys, Autoscaling, and Flags 14 min
  18. Security by Design 14 min
  19. Data Engineering for AI: Pipelines, Lakes, and Vector DBs 16 min
  20. Tradeoffs, Reviews, and Judgment 12 min
  21. System Design Interviews 14 min
  22. Final Exam: Building and Shipping AI 14 min

Stage 5 of 5

Robotics & Physical AI

Give intelligence a body: sense, plan, and act in the real world.

  • Beginner → Expert
  • 6 paths
  • 178 lessons
  • 2571 screens
  • 40–65 h

Electronics, From the First Spark

Wire it, measure it, make it work.

Electronics from nothing. Current and voltage, then resistors, capacitors, diodes and transistors, each on a live circuit you can break and fix. You light an LED, power an Arduino, and drive a motor.

  • Beginner
  • 58 lessons
  • 430 screens
  • 5.5–9 h
Lessons (58)
  1. Charge and Current 6 min
  2. Voltage: The Push 6 min
  3. Circuits and Open Loops 5 min
  4. Ground and Everyday Voltages 6 min
  5. Resistance and Resistors 6 min
  6. Ohm's Law and Power 6 min
  7. A Resistor for an LED 6 min
  8. Reading Resistor Stripes 5 min
  9. Parts in Series 6 min
  10. Parts in Parallel 6 min
  11. Kirchhoff's Two Rules 6 min
  12. Build 1: Parts and the Breadboard 7 min
  13. Build 1: Wire Up the LED 6 min
  14. Build 1: When the LED Stays Dark 4 min
  15. Switches and Buttons 6 min
  16. A Chip That Reads a Button 4 min
  17. The Pull-Up Resistor 5 min
  18. Knobs and Potentiometers 6 min
  19. Meet the Multimeter 5 min
  20. Measuring Voltage 6 min
  21. Measuring Current and Resistance 6 min
  22. What a Capacitor Stores 5 min
  23. How Fast a Capacitor Fills 6 min
  24. Capacitors on a Real Board 6 min
  25. Big Capacitors, Handled Safely 6 min
  26. Diodes: One Way Only 6 min
  27. The Voltage a Diode Uses 6 min
  28. From Back-and-Forth to One Way 6 min
  29. Protecting a Chip From a Motor 8 min
  30. Why a Pin Needs a Transistor 7 min
  31. Choosing the Base Resistor 6 min
  32. The MOSFET: A Switch Run by Voltage 7 min
  33. Transistors as Amplifiers 5 min
  34. Low, High, and the NOT Gate 6 min
  35. AND and OR From Switches 8 min
  36. Batteries and Voltage Sag 6 min
  37. Battery Life and LiPo Safety 6 min
  38. USB Power and Voltage Regulators 4 min
  39. Wiring a Robot's Power Safely 5 min
  40. Analog, Digital, and PWM 6 min
  41. Reading a Knob With an ADC 3 min
  42. Flicker, Sampling, and Noise 7 min
  43. Build 2: Set Up an Arduino 7 min
  44. Blink Your Own LED 7 min
  45. Read a Button 6 min
  46. Read a Knob and Check the Build 6 min
  47. Build 3: Why a Motor Needs a Driver 5 min
  48. Wire the Motor and MOSFET 6 min
  49. Protect the Motor Driver 7 min
  50. Run the Motor, Then Reverse It 7 min
  51. Read a Schematic 7 min
  52. Start a Board in KiCad 6 min
  53. Place Parts and Route Copper 6 min
  54. Check the Board and Order It 6 min
  55. Solder and a Safe Iron 7 min
  56. Make a Good Solder Joint 4 min
  57. Find Why It Doesn't Light 5 min
  58. Fix It and Write It Down 5 min

Physics for Robotics

The rules every robot obeys.

Forces, motion, energy, torque, friction, and the wobble a control loop has to settle. The mechanics behind every machine that moves.

  • Intermediate
  • 8 lessons
  • 225 screens
  • 3.5–6 h
Lessons (8)
  1. Forces and Newton's Laws 32 min
  2. Motion: Velocity and Acceleration 29 min
  3. Energy, Work, and Power 27 min
  4. Rotation, Torque, and Momentum 28 min
  5. Friction, Contact, and Traction 27 min
  6. Electricity, Magnetism, and Motors 26 min
  7. Oscillation, Resonance, and Damping 28 min
  8. Electronics That Move a Robot 29 min

Robotics Foundations

Sense. Decide. Act.

How a robot senses the world, decides what to do, and moves. Written for someone who has never touched one.

  • Intermediate
  • 39 lessons
  • 303 screens
  • 3.5–6 h
Lessons (39)
  1. What Makes a Robot a Robot? 6 min
  2. How a Robot Vacuum Avoids the Stairs 5 min
  3. How Much a Robot Can Notice 6 min
  4. Sensors and Actuators 6 min
  5. Each Sensor Answers One Question 5 min
  6. How Much a Robot Arm Can Hold 5 min
  7. How a Robot Gripper Holds Things 6 min
  8. Robot Brains: Reflexes and Plans 6 min
  9. The Three Layers of Robot Software 6 min
  10. How Robots Learn from Examples 4 min
  11. Build 4: Power Your Rover's Motors 5 min
  12. Build 4: Signal Wires and the Sensor 4 min
  13. Build 4: The First Drive 6 min
  14. Build 4: Stop Before the Wall 5 min
  15. Why Moving Is Harder Than Deciding 6 min
  16. How Hard a Robot Corrects 6 min
  17. Delays, Brakes, and Balance 6 min
  18. How Robots See: Pixels to Objects 7 min
  19. Following Objects Over Time 5 min
  20. Labels, Boxes, and Outlines 5 min
  21. What Each Sensor Can't See 6 min
  22. Why Easy Things Are Hard for Robots 7 min
  23. Why Standing Still Is Hard 6 min
  24. What Every Joint Costs 6 min
  25. The Battery Problem 6 min
  26. What a Coordinate Frame Is 6 min
  27. Slide and Turn: Changing Frames 5 min
  28. The Family Tree of Frames 6 min
  29. Counting Wheel Turns 7 min
  30. Fixing Drift with Landmarks 6 min
  31. Doubt, GPS, and Drawing a Map 6 min
  32. Routes and Poses 6 min
  33. Searching for a Route 6 min
  34. A*: Searching Toward the Goal 6 min
  35. Following the Route 6 min
  36. Which Way a Robot Faces 6 min
  37. Gimbal Lock 7 min
  38. One Axis, One Angle 6 min
  39. Quaternions: Four Numbers for a Turn 7 min

Physical AI & Robotics Engineering

Robots that work outside the lab.

The hard half of robotics: where the robot thinks it is, what it can see in 3D, how it plans a path, and how it keeps its grip or its balance while things go wrong.

  • Advanced
  • 24 lessons
  • 656 screens
  • 9.5–15 h
Lessons (24)
  1. Kinematics: Where the Arm Goes 30 min
  2. Inverse Kinematics and Jacobians 26 min
  3. Dynamics: Forces and Motion 20 min
  4. Control: From PID to MPC 28 min
  5. Impedance and Force Control 25 min
  6. State Estimation and the Kalman Filter 22 min
  7. Sensor Fusion 22 min
  8. SLAM: Mapping While Moving 28 min
  9. Real-Time Perception 22 min
  10. Depth, Point Clouds, and 3D Vision 27 min
  11. Motion Planning: A* to RRT* 24 min
  12. Manipulation and Grasping 23 min
  13. Legged Locomotion 26 min
  14. RL for Robots and Sim-to-Real 25 min
  15. Physical AI: Foundation Models for Robots 22 min
  16. Real-Time Systems, ROS 2, and the Edge 28 min
  17. Safety and Reliability in Physical Systems 29 min
  18. Sim-to-Real, Deeper 29 min
  19. Imitation Learning and the Data Engine 21 min
  20. Vision-Language-Action Models 21 min
  21. World Models and JEPA 19 min
  22. Spatial Intelligence 22 min
  23. Tactile Intelligence and the Force Bottleneck 23 min
  24. Cross-Embodiment and the Road Ahead 21 min

Robotics Systems & Integration

Turning parts into a working robot.

The engineering around the robot: sensors and buses, ROS 2, simulation, hardware design, and running a fleet that has to keep working.

  • Advanced
  • 34 lessons
  • 761 screens
  • 14–21.5 h
Lessons (34)
  1. Embedded Systems and Microcontrollers 24 min
  2. Sensors, Devices, and Buses 24 min
  3. Talking to Motors 19 min
  4. Latency and the PID Loop 21 min
  5. Embedded Hardware, Hands-On 25 min
  6. Why Robots Run C++ 17 min
  7. Copies, References, and Pointers 19 min
  8. Memory and Who Owns It 19 min
  9. Reading Real Robot Code 16 min
  10. C++ and Real-Time Systems 35 min
  11. Integrating a Robot with ROS 2 46 min
  12. Robot Simulation and Digital Twins 37 min
  13. Humanoid Robotics 37 min
  14. Robot Hardware Design 38 min
  15. Voice UX and Human-Robot Interaction 33 min
  16. The Robotics Supply Chain 30 min
  17. Raw Materials and Manufacturing Hubs 32 min
  18. Product Reliability and Fleet Deployment 17 min
  19. The Compute Ecosystem 19 min
  20. AI Accelerators and Novel Hardware 38 min
  21. Edge Compute for Robots 18 min
  22. Same Model, Different Chip 18 min
  23. The Edge-Cloud Hybrid 17 min
  24. Event-Based Vision 15 min
  25. Power, Brains, and One Island 21 min
  26. The Hardwired Safety Tier 24 min
  27. Robots Among People 24 min
  28. Fleet Orchestration 25 min
  29. The Human in the Loop 19 min
  30. CapEx vs Robotics-as-a-Service 25 min
  31. Brownfield vs Greenfield 22 min
  32. Reverse Logistics and OTA Risk 17 min
  33. Black Box, Isolation, and Liability 23 min
  34. The Founder's Bet 27 min

Future Builder

See how it all connects.

How AI, robots, chips, and automation fit together, and where the next ten years of it are likely to go.

  • Intermediate
  • 15 lessons
  • 196 screens
  • 3–5 h
Lessons (15)
  1. The AI Stack 8 min
  2. Data Centers and GPUs 9 min
  3. Powering AI: The Power Wall 12 min
  4. Edge AI 11 min
  5. Robots in the Real World 10 min
  6. Building Your First AI Product 9 min
  7. Careers in AI and Robotics 17 min
  8. Safety, Alignment, and Policy 13 min
  9. Reading AI Research 11 min
  10. The Honest Limits of AI & Robotics 13 min
  11. Building a Career in the AI Age 14 min
  12. Build Your Project Portfolio 15 min
  13. Behavioral Interviews 15 min
  14. Your Resume and Job Search 16 min
  15. Final Exam: Robotics and Physical AI 15 min

Role tracks

Job-shaped routes through the same catalog, for people learning AI for their work rather than to become an engineer.

  • 8 paths
  • 78 lessons
  • 999 screens
  • 15.5–23.5 h

AI at Work: Core

The foundation every role shares.

The six moves that matter whatever your job: pick the right tool, fix a prompt that's almost working, know when to skip AI entirely, automate the thing you do every week, and put an agent on real work.

  • Intermediate
  • 5 lessons
  • 56 screens
  • 55–85 min
Lessons (5)
  1. Improve the Prompt 13 min
  2. Choose When NOT to Use AI 11 min
  3. Automate the Workflow You Run Every Week 10 min
  4. Put an Agent on Real Work 12 min
  5. Break the Agent 9 min

AI for Finance & Professional Services

Finance work, with AI on the grunt work.

Research a company, analyze the numbers, make the spreadsheet do the work, build the deck, and run the diligence pass. Including how to catch a number the model invented before it reaches a client.

  • Intermediate
  • 10 lessons
  • 115 screens
  • 115–180 min
Lessons (10)
  1. Research a Company in Fifteen Minutes 12 min
  2. Analyze the Numbers You Were Handed 10 min
  3. Make the Spreadsheet Do the Work 11 min
  4. Build the Deck Before the Meeting 9 min
  5. Read 200 Pages You Do Not Have Time For 14 min
  6. Run the Diligence Pass 15 min
  7. Walk In Prepared 10 min
  8. Catch Your Own Invented Number 10 min
  9. Decide What Leaves the Building 12 min
  10. Prove It: A Week on the Desk 16 min

AI for Marketing

Briefs, campaigns, and brand voice.

Turn a vague brief into a working one, make copy sound like your brand, ship the campaign set, make the image and the video, and read what it actually did. Plus how to catch a claim you can't back.

  • Intermediate
  • 10 lessons
  • 128 screens
  • 115–180 min
Lessons (10)
  1. Turn a Vague Brief Into a Working One 11 min
  2. Find Out Who You Are Talking To 11 min
  3. Make It Sound Like Your Brand 12 min
  4. Ship the Whole Campaign Set 8 min
  5. Make the Image and the Video 13 min
  6. Get Found When the Answer Is the Engine 10 min
  7. Read What the Campaign Actually Did 11 min
  8. Catch the Claim You Cannot Back 15 min
  9. Decide What You Are Allowed to Publish 13 min
  10. Prove It: A Week on the Team 14 min

AI for Sales

From the first call to a number you sign.

Know the account before the call, write a message that gets a reply, run discovery, update the CRM without typing, and prepare for the objection. It ends with committing a number you can defend.

  • Intermediate
  • 10 lessons
  • 129 screens
  • 2–4 h
Lessons (10)
  1. Know the Account Before the Call 15 min
  2. Write the Message That Gets a Reply 15 min
  3. Run the Discovery Call 12 min
  4. Update the CRM Without Typing 13 min
  5. Write the Proposal They Will Read 18 min
  6. Prepare for the Objection You Will Get 14 min
  7. Commit a Number You Can Defend 13 min
  8. Check It Before You Say It 11 min
  9. Decide Where Customer Data May Go 12 min
  10. Prove It: A Week in the Pipeline 19 min

AI for Every Role

For everyone whose job is the inbox.

Clear the inbox without losing the one that mattered, turn meetings into actions somebody owns, draft the document and the deck, and ask the spreadsheet a question. The everyday work, faster and still correct.

  • Intermediate
  • 10 lessons
  • 110 screens
  • 100–155 min
Lessons (10)
  1. Clear the Inbox Without Losing the One That Mattered 12 min
  2. Turn the Meeting Into Owned Actions 12 min
  3. Get the Document and the Deck Drafted 9 min
  4. Read the Sixty Pages You Were Sent 11 min
  5. Ask the Spreadsheet a Question 11 min
  6. Produce a Brief Someone Can Check 10 min
  7. Run the Project Without the Status Meeting 9 min
  8. Find the Part That Is Not True 9 min
  9. Decide What You May Paste 10 min
  10. Prove It: An Ordinary Week 8 min

AI for Team and Business Leaders

Rolling AI out to a team.

For whoever has to make this work across a team: pick the work worth changing, run a pilot that's allowed to fail, write a policy people actually follow, and know what to do in the first hour after something goes wrong.

  • Intermediate
  • 10 lessons
  • 111 screens
  • 100–150 min
Lessons (10)
  1. Map the Week Your Team Actually Has 12 min
  2. Price the Hour Before You Promise the Saving 8 min
  3. Run the Pilot That Proves Something 11 min
  4. Write the Policy People Will Actually Follow 9 min
  5. Decide the Route for a New Tool 9 min
  6. Buy It Without Being Sold To 10 min
  7. Teach the Team, Not the Tool 9 min
  8. Review Work You Did Not Watch Being Made 9 min
  9. The First Hour After It Goes Wrong 9 min
  10. Prove It: A Quarter of Rollout 14 min

Earning With AI

Sell the result, not the tool.

For freelancers and one-person shops: what you may legally sell, what you can honestly promise a client, how to get real work out of an agentic coding tool, and how to price a job so that getting faster doesn't cut your own pay.

  • Intermediate
  • 10 lessons
  • 125 screens
  • 2–3 h
Lessons (10)
  1. Measure the Gain, Do Not Feel It 10 min
  2. Read the License Before You Quote 12 min
  3. What You Can Honestly Promise 10 min
  4. Get Real Work Out of a Coding Agent 13 min
  5. Review What You Did Not Write 12 min
  6. Generate Video a Client Will Accept 11 min
  7. Scope a Job You Can Deliver 11 min
  8. Price the Outcome, Not the Hour 10 min
  9. Find the First Client 12 min
  10. Prove It: Working On Your Own Account 19 min

AI for Business

Put AI to work in any company.

The tools you can use today and where they pay off, function by function: marketing, sales, support, finance, HR, operations, legal. Plus how to roll them out with a person still checking the work.

  • Beginner
  • 13 lessons
  • 225 screens
  • 3–5 h
Lessons (13)
  1. Where AI Actually Helps Your Business 18 min
  2. The Generative AI Toolbox 19 min
  3. AI Images and Video: Tools You Can Use Today 14 min
  4. AI for Marketing and Content 12 min
  5. AI for Sales 12 min
  6. AI for Customer Support 12 min
  7. AI for Finance and FP&A 16 min
  8. AI for Accounting 12 min
  9. AI for HR and Recruiting 13 min
  10. AI for Operations and Supply Chain 12 min
  11. AI for Legal and Compliance 13 min
  12. Industry Tour: Healthcare, Retail, Manufacturing and More 15 min
  13. Rolling Out AI in Your Organization 14 min

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