jdcrensh

@interface UIColor (Hex)

+ (UIColor *)colorWithHex:(uint)rgbValue;

@end

@implementation UIColor (Hex)

+ (UIColor *)colorWithHex:(uint)rgbValue {
	float redValue = ((rgbValue & 0xFF0000) >> 16) / 255.0;

jdcrensh

int main (int argc, const char *argv[]) {
	NSAutoreleasePool *pool = [[NSAutoreleasePool alloc] init];
	NSUserDefaults *args = [NSUserDefaults standardUserDefaults];	

	NSString *hexValue = [args stringForKey:@"hex"];
	if (hexValue && [hexValue length] == 6) {
		NSString *hexPart;
		float red, green, blue;

		for (int i=0; i<[hexValue length]; i++) {

jdcrensh

#!/usr/bin/python

def collatz(n,i):
	while n > 1:
		n = n % 2 == 0 and n / 2 or n * 3 + 1
		i += 1
	return i

def main():
	n = 1         # starting number

jdcrensh

#!/usr/bin/python

import os, sys, time, uuid

# get self code
self_content = file(sys.argv[0]).read()

while True:
    # wait 10 seconds
    time.sleep(10)

jdcrensh

find src -type f \( -name '*.java' -o -name '*.jspx' \) \
-exec wc -l {} \; | awk '{sum+=$1} END {print sum}'

jdcrensh

public class Base62 {

	public static final String ALPHABET = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789";

	public static final int BASE = ALPHABET.length();

	private Base62() {}

	public static String fromBase10(int i) {
		StringBuilder sb = new StringBuilder("");

jdcrensh

var sweetAlert = require('sweetalert2');
sweetAlert('Hello World!', 'It works!', 'success');

jdcrensh

/**
 * Compares String concatenation performance vs StringBuilder.
 * At around 21000 iterations, actual time spent is nearly identical. At higher iterations,
 * StringBuilder comes out ahead in less actual time spent and with lower CPU usage. However in
 * lower iterations, concatenation wins both measurements.
 *
 * Another consideration is that the StringBuilder pattern does not seem to affect the heap
 * size limit at all, which could be a bug with Apex profiling.
 */

jdcrensh

const streamBuffers = require('stream-buffers');
const archiver = require('archiver');

const zipDir = (dir) => {
  const output = new streamBuffers.WritableStreamBuffer({
    initialSize: (100 * 1024),    // start at 100 kilobytes.
    incrementAmount: (10 * 1024), // grow by 10 kilobytes each time buffer overflows.);
  });
  const archive = archiver('zip');
  archive.on('end', () => {

jdcrensh

@RestResource(urlMapping = '/LimitProfiling/*')
global with sharing class LimitProfilingCallout {

	@HttpPost
	global static void callMeMaybe() {
		RestRequest req = RestContext.request;
		RestResponse res = RestContext.response;
		
		Map<String, Object> data = (Map<String, Object>) JSON.deserializeUntyped(req.requestBody.toString());