Testing

Testing...





1..2..3...

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Making Logic Gates in JavaScript

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Everyone knows that computer programs are made of bits - the ones and zeros that hold all the information that keeps our modern life running. But how do you get from numbers to programs - how simple ones and zeros do  anything? The answer is logic gates (as well as a whole bunch of stuff about how electronics are built - you need to actually make these logic gates on out of circuits in order for our logic structures to be enacted; but I'm a programmer, not an electrical engineer, so let's not go there).  A logic gate is what's known as a  Boolean function , a mathematical operation that takes in bits and spits out more bits. Each logic gate takes in two bits of information (as mentioned before, generally denoted as a 1 (also sometimes represented as true ) or a zero (also sometimes represented as false )), so there are four possible inputs to a logic gate: {0, 0}, {0, 1}, {1, 0}, and {1, 1}.  For each of these inputs, a logic gate can spit out either a 1 or a 0, so t...
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Bitwise Operations

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 When I was in 9th grade, I once spent the day going to school with my cousin.  At lunchtime, one of her friends was showing off a newfound skill: "I can count to ten in German! Eins, zwei, drei, vier, funf, sechs, sieben, acht, neun, zehn!" "That's nothing," I said, "I can count to ten in binary.  1, 10, 11, 100, 101, 110, 111, 1000, 1001, 1010!" No one got it.  And unfortunately, it seems like binary is also foreign to a lot of software developers as well. What is Binary? Binary is the two-digit radix (or base) system.  In the decimal system - what most people use on a daily basis - we use ten different digits (the Arabic numerals 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) to represent number values.  Numbers in the rightmost position before the decimal point represent that digit's value multiplied by ten to the zeroth power, and each position to the right increases the power of ten by one.  So 12 is actually another way of saying 1*10^1 + 2*10^0; 433 is equi...
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Here's a lesson about the difference between theory and practical applications. Everyone knows about  bubble Sort  and  merge Sort  - they're probably the first two sorting algorithms you learned.  Everyone also knows that (1) bubble sort sucks and (2) this is due to the fact that its average time complexity is O(n^2), aka the worst time complexity.  It even generally performs worse than things like insertion sort and  selection sort , the "a D- is still a passing grade" of sorting algorithms. While all these things are true, there's a more important truth: always test your code. While reading  The Imposter's Handbook by Rob Conery  the other day and flipping through the comparison sort algorithms he provides, I decided, Why not actually test out this code?   And, the  speed test demon  that I am, my test involved sorting out his [23,4,42,15,16,8] array one million times with bubble sort and with merge sort, and I got this res...
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