What is this website about?
Robotics, microcontrollers & industrial automation, AI & machine learning — and the maths & algorithms underneath it all. Learned from first principles, documented end to end.
Self-driving robots, smart factories, and the maths underneath — studied in the open, one layer at a time.
Probability, algorithms, cryptography, operating-system internals, machine learning, systems thinking — different vocabularies, same machinery. A PID controller and a neural-network training loop both work the same way: measure an error, feed it back, shrink it. A language model and a zip file are both bets about which symbol comes next. Underneath, it's one subject: mathematics, wearing different clothes — and every course here climbs down to the layer where they meet.
For as long as I can remember, I've needed to know how things work — really work, the layer beneath the layer, until there's nothing left to take apart. This site is that habit written down: I pick a concept, learn it until it stops being fog, and document the path while the climb is still fresh.
Robotics, factory automation, AI, the maths underneath — one story told at different depths. A sensor reading is a voltage; an ADC turns it into an integer; firmware turns the integer into a message on a bus; a filter turns noisy messages into a state estimate; a planner turns the estimate into a decision — and an equation at every joint says why the hand-off is legal. The courses here walk stretches of that chain and refuse to hand-wave the joints, because the joints are where understanding actually lives.
The projects run end to end. A small self-driving robot — perception, planning, control — trained in simulation, then debugged on real hardware, where timing jitter, sensor noise and a sagging battery teach things no simulator will. Industrial systems where microcontrollers and PLCs meet the right comms protocols until IT and OT share one picture of the plant. The write-ups keep the failures in, because the failure is usually the moment the concept clicked.
Under the builds sit the ideas I keep returning to: the algorithms, transformers and RAG, the way a quant prices risk, the mathematics holding the structure up. ROS, STM32, Isaac Lab, training loops — good tools, but tools age fast and ideas don't: attention is from 2017, the Kalman filter from 1960, Bayes' rule from 1763 — and all three are running in production right now. These pages spend their effort on the part that lasts.
Since so much of this site orbits AI, here is what the word means, mechanically. A neural network is a huge pile of numbers, tuned by showing it examples and nudging every number in the direction that shrinks the error — the nudge is a gradient, and repeating it billions of times is all "training" is. Generative AI is a model that has learned the shape of its data well enough to sample from it: a language model does one thing — it predicts the next token — and attention is the mechanism that lets every word weigh every other word while it decides. Chat, reasoning, working code: all of it falls out of that one loop run at scale. Agentic AI puts the model inside a loop — a goal, tools it can call, memory of what just happened — so prediction becomes behaviour. And look closely at that loop: sense → decide → act. It's a control system. Control engineers have been shipping that loop since 1922, when it first steered ships — the plant just got stranger.
People sometimes ask why one site covers circuits, factories, models and markets. Because the walls between them are thinner than the job titles suggest. Feedback, probability, optimisation, information — the same four ideas run the robot, the plant, the model and the trading book, and each field keeps rediscovering them under new names. I didn't set out to connect these subjects; the connections were already there. Writing is how I catch them in the act.