Simulations
Interactive visualizations of algorithms inspired by nature. Each simulation is built on genuine bio-inspired principles - click a card to launch.
Boids - Flocking Simulation
Hundreds of autonomous agents exhibiting emergent flocking behaviour using just three simple rules: separation, alignment, and cohesion. Based on Craig Reynolds' 1987 model.
Swarm IntelligenceNeural Networks: From Perceptron to Deep Learning
An interactive walkthrough from a single AND-gate neuron, through the XOR hidden-layer problem, to a deep network and the path to LLMs - with three live simulations embedded in the article.
Machine LearningGenetic Algorithm
Watch a population of strings evolve toward a target phrase through selection, crossover, and mutation - the core operators of Darwinian evolution applied to computation.
Evolutionary ComputingCNN Forward Pass
Watch a Convolutional Neural Network process spatial data step by step - from raw pixel input through convolution, ReLU activation, max pooling, and a fully connected layer to a softmax classification.
Machine LearningAnt Colony Optimization
Artificial ants deposit pheromone on edges between cities, converging on short tours for the Travelling Salesman Problem through stigmergy - indirect communication via the environment.
Swarm IntelligenceConway's Game of Life
The classic cellular automaton - four simple rules produce gliders, oscillators, guns, and Turing-complete computation from nothing but a grid of cells.
Cellular AutomataHexagonal Tessellation
Watch a honeycomb grow outward cell by cell and compare the tiling efficiency of hexagons, squares, and triangles - visualising why nature favours six sides.
GeometryPhysarum Network Growth
Watch a slime-mold-inspired network self-organise between food sources - reinforcing high-flux tubes and pruning weak ones, echoing the experiment that recreated the Tokyo rail system. Based on the Tero et al. (2010) model.
Swarm Intelligence