29 interactive labs from RAIN: write Arduino code for a virtual kit, tune controllers, plan robot paths, and train machine learning models. Once opened, the labs keep working without the internet.
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Step-by-step missions from absolute beginner to expert in Python, AI and machine learning, and robotics. Python challenges are marked instantly in your browser; you earn points, badges and streaks and can see where you stand on the leaderboard.
Code a virtual Uno and test it on the RAIN kit
Write Arduino code, upload it to a virtual Uno wired to the RAIN kit and test it: LEDs, buttons, sensors, a servo valve on a water tank, a robot car that avoids obstacles and follows a line, and several boards talking to each other. Then upload it to a real board.
Wire a Raspberry Pi and code it in Python
A Raspberry Pi 4 or 5 with its 40-pin header on a breadboard: wire LEDs, a button, a buzzer, a servo, sensors, an MCP3008 and an LCD, and write Python with gpiozero and RPi.GPIO that runs unchanged on a real Pi. Thirteen lessons, from blink to a web dashboard, then download the project with its wiring.
Build circuits on a breadboard and measure them
Build circuits on a virtual breadboard and measure them: Ohm's law, voltage dividers for sensors, LEDs and resistors, RC timing, transistors as switches and op-amps.
Block diagrams, PID, Bode and root locus
Block diagrams, transfer functions, open and closed loop, PID, disturbances and noise, with time response, poles and zeros, Bode, root locus and the Routh table.
Tune PID and on-off control on real plants
Tune P, PI and PID controllers and on-off control with hysteresis on motors, heaters, tanks and a drone, with anti-windup, noise, and Ziegler-Nichols tuning.
Run an industrial plant from a control room
An industrial plant on a control-room screen: tank level and temperature loops, cascade, feedforward, valves, alarms and an IoT dashboard.
State space, LQR and the Kalman filter
State-space models, controllability, pole placement, LQR, observers and the Kalman filter, ending with balancing an inverted pendulum.
Move robot arms in 3D, forwards and back
Frames, rotations and transforms in 3D, robot arms with DH parameters, forward and inverse kinematics, the Jacobian, workspaces and mobile robot motion.
Design a robot from real parts
Design a robot from real parts against a specification: speed, torque, battery life and cost, compare concepts, then test it in an arena.
Watch A*, Dijkstra and RRT find a way
Draw a map and watch BFS, Dijkstra, A*, RRT and potential fields find a way, step by step, then drive the path with a robot.
Plan routes over slopes, mud and sand
Outdoor robots on real terrain: slopes, mud and sand, routes that save time or energy, replanning as the robot discovers the land, and farm and survey coverage.
Help a robot find itself and map the room
Odometry drift, LiDAR, Kalman and particle filters, occupancy grid maps and SLAM with loop closure.
Simple rules, surprising group behaviour
Flocking, consensus, formations, foraging, coverage and task allocation: simple local rules, surprising group behaviour.
Junctions, jams and self-driving cars
Traffic jams from nowhere, junctions and signals, and what an autonomous car sees and decides on a busy Nigerian road.
Design a drone, tune it and fly it
Design a drone and see if it will fly, then tune its cascaded flight controller in wind, fuse its sensors, fly it yourself, plan a crop survey with failsafes and compare it with a fixed-wing VTOL.
Twin a real system and catch faults early
A pumping station, a robot cell and a solar microgrid with their digital twins: calibrate the model from live data, keep it in step with a Kalman filter, catch faults early, predict failures and test changes first.
See the world with laser scans
Time a laser pulse, see what fools a LiDAR, drive a robot with a 2D scanner, then take a 3D street scan apart: ground removal, clustering, boxes, tracking, scan matching and fusion with a camera.
Drive DC, stepper, servo and brushless motors
DC motors through an H-bridge with PWM and a speed PID, steppers and their drivers, hobby and industrial servos, brushless motors with FOC, and sizing a motor, gearbox and supply for a real robot.
3D print, CNC and laser cut with real G-code
Slice a part for 3D printing and download real G-code, plan a CNC job with feeds and speeds and watch the tool cut, make a laser-cut box, and read any G-code file line by line.
Balance a humanoid and make it walk
Pose a 24-joint humanoid, keep its centre of mass over its feet, recover from pushes, plan walks over crates, stairs and slopes with the ZMP, and teach it to balance by trial and error.
Design robot parts in 3D, ready to print
Design robot parts from a first sketch: constraints, extrude and revolve, holes, fillets and booleans, motor mounts and enclosures from templates, print checks, assemblies, drawings and STL ready to print.
Clean, explore and chart data
Clean, explore and chart real-style data with the pandas code shown alongside, then measure uncertainty with samples, intervals and tests.
Algebra, calculus and matrices you can see
Symbolic algebra and calculus, linear algebra as transformations, gradient descent, probability, complex numbers, Laplace and Fourier.
Train your first machine learning models
Linear and logistic regression trained by gradient descent, honest evaluation, bias and variance, k-nearest neighbours and decision trees.
Forests, boosting, clustering and PCA
Random forests and boosting, support vector machines, clustering, PCA, anomaly detection, recommendations and time series.
Build and train neural networks
From one neuron to deep networks: backpropagation step by step, optimisers, regularisation, convolutions and a CNN you train in the browser.
Tokens, embeddings and transformers
Tokens, embeddings, n-grams, RNNs and LSTMs, and attention and transformers taken apart one step at a time.
Teach a camera to see edges, lanes and objects
Pixels, filters and Canny edges, then lanes on a Nigerian road, a camera that tracks a ball, parts counted on a conveyor, a CNN trained in the browser, detection and segmentation, and seeing in harmattan haze.
Teach a computer to win at games
How computers learn to win at games: minimax and alpha-beta, Q-learning and SARSA, Monte Carlo Tree Search, self-play from random to never losing, a mini chess engine, learning agents in a strategy arena and game theory.
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