(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
function worker() { | |
setInterval(function() { | |
postMessage({foo: "bar"}); | |
}, 1000); | |
} | |
var code = worker.toString(); | |
code = code.substring(code.indexOf("{")+1, code.lastIndexOf("}")); | |
var blob = new Blob([code], {type: "application/javascript"}); |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
#!/bin/bash | |
##################################################### | |
# Name: Bash CheatSheet for Mac OSX | |
# | |
# A little overlook of the Bash basics | |
# | |
# Usage: | |
# | |
# Author: J. Le Coupanec | |
# Date: 2014/11/04 |
var cameraZ = camera.position.z; | |
var planeZ = 5; | |
var distance = cameraZ - planeZ; | |
var aspect = viewWidth / viewHeight; | |
var vFov = camera.fov * Math.PI / 180; | |
var planeHeightAtDistance = 2 * Math.tan(vFov / 2) * distance; | |
var planeWidthAtDistance = planeHeightAtDistance * aspect; | |
// or |
The Gilbert–Johnson–Keerthi (GJK) distance algorithm is a method of determining the minimum distance between two convex sets. The algorithm's stability, speed which operates in near-constant time, and small storage footprint make it popular for realtime collision detection.
Unlike many other distance algorithms, it has no requirments on geometry data to be stored in any specific format, but instead relies solely on a support function to iteratively generate closer simplices to the correct answer using the Minkowski sum (CSO) of two convex shapes.
":" //#;exec /usr/bin/env node --input-type=module - $@<$0 | |
import process from 'process' | |
const { argv } = process | |
console.log(argv) |