April 7, 2010 Poisson Disk Sampling Example Code I decided to put the Poisson disk sampling code here for download since the site that hosted it is down. The code accompanies the…

October 25, 2009 Tips for Designing and Implementing a Stimulus Response Agent (Original Image by everyone’s idle.) This post was a originally published on Luma Labs, now dead. As old as stimulus-response techniques are, they still…

October 24, 2009 Guerrilla Tool Development Tools for editing game levels and AI for your own games are nice to have, but it is not always practical to implement these…

October 8, 2009 15 Steps to Implement a Neural Net (Original image by Hljod.Huskona / CC BY-SA 2.0). I used to hate neural nets. Mostly, I realise now, because I struggled to implement them correctly. Texts…

May 28, 2009 Getting More out of Seamless Tiles I wrote an article for Dev.Mag covering some techniques for working with seamless tile sets such as making blend tiles, getting more variety with procedural colour manipulation,…

May 27, 2009 How to Turn XSI Mod Tool into a Level Editor for your XNA Games: Example Updated for XNA 3.0. The example for the tutorial How to Turn XSI Mod Tool into a Level Editor for your XNA Games: Updated for XNA 3.0 have also been…

May 27, 2009 Update: Reference for Functional Equations In this new version of Reference for Functional Equations I added several more z-transform pairs. I also started to add binomial transform pairs. The definition for…

April 28, 2009 Generating Random Integers With Arbitrary Probabilities I finally laid my hands on Donald Knuth’s The Art of Computer Programming (what a wonderful set of books!), and found a neat algorithm…

April 15, 2009 Estimating a Continuous Distribution from a Sample Set It is sometimes necessary to find the distribution given a sample set from that distribution. If we do not know anything about the distribution,…

April 15, 2009 Generating Random Points from Arbitrary Distributions for 2D and Up I have already covered how to generate random numbers from arbitrary distributions in the one-dimensional case. Here we look at a generalisation of that…