FREE TECHNICAL NOTES

Robotics and ML, explained with code you can watch

Do not stop at the equation. Advance the algorithm one step at a time and connect each visual change to a small piece of code. Every implementation here is deliberately educational, not production-ready.

ROBOTICS · PATH PLANNING

A* Path Planning, Step by Step on an Obstacle Grid

How much search can a good estimate remove? Edit the walls, switch between A* and Dijkstra, and compare path length with the number of expanded cells.

Run the visualizer →

MACHINE LEARNING · CLUSTERING

Understand k-means by Watching the Centroids Move

Assign every point to its nearest centroid. Move each centroid to the group's mean. Watch those two operations turn scattered points into clusters.

Run the visualizer →

Start with what you want to see

Place obstacles and compare the search footprint of A* and Dijkstra
Add points and watch k-means move its centroids and boundaries
Copy the small Python example and change one input

← Back to the home page