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THREE.js: minecraft in a weekend

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I have been enjoying exploring the ideas behind procedural worlds and in a previous post even showed a simple voxel renderer based in THREE.js which would load chunks when the camera got close enough to the edge. That was based on the great tutorial here which explains how to create a voxel-based mesh in THREE.js such that it doesn’t use all of the machine’s resources for the smallest of worlds. I found that to be an invaluable source of information and continually referenced back to that throughout the weekend. Looking back at my git logs for this project to be able to type this up, I have no intention of this post being a tutorial in itself, my progress was as follows: Walking around rather than observer view Sideways collision detection - not walking through the map Changing to a system where there was a sensible grass - sand - water height system Adding trees Another optimisation for terrain generation in addition to the afor...
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Exploring models calculating the likelihood of the 100th digit of pi being a 7

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In creating a post explaining the relevance and prospect of using prediction markets in the study of logical uncertainty I produced a graph showing an example of how the answer to the question of if the 100th digit of pi is a 7, might be evaluated if each digit of pi needed to be expanded one after the other. This is an accompanying post, something of an appendix to the future post, so might not make much sense on its own. A possible graph of the stock price over time, assuming that to get to the 100th digit you need to expand each digit. I’ve included a few different models: You can see that here for all models the price starts at 50¢ falls to 10¢ and from that point, the models diverge somewhat in their price prediction.  Explaining the models There are two underlying models which lead to the production of all the lines in these graphs. The first is the model that as there are 10 possible values a digit of pi could take, that the probability of the 100th digit being 7 is 10%. The...
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Picking apart Perlin noise from first Principles

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Although there are a lot of tutorials explaining the what and even the how of Perlin noise and the general class of algorithms; gradient noise, I have found none explaining the why behind the algorithm. Why does this algorithm work? In this post I aim to build up perlin noise from first principles, explaining why we add each element to the algorithm as we do. We will start by trying to make a surface, perhaps for a computer game world map. We are aiming for something that looks like a mountainous region. We don’t want to hand craft this landscape as we want it to go on forever, how can we procedurally generate this world? Given we want the surface to be random, we could start with that. However as you can see that doesn’t lead to the most pleasing of surfaces, in fact it wouldn’t be traversable in the game. Our problem is that there is no smoothness from one place to another. The lowest of points could be adjacent to the highest of points. What we want is for it to take som...
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