yurifrl

import { concat, zipObj, keys, values, map, isEmpty, curry } from 'ramda'
const log = curry(console.log)
const snakeCaseObj = [{
  a_b: "asdasdasd",
  c_a: "2018-02-20T18:43:17.104Z",
  t_c: {
    s_d: "2018-02-20",
    e_d: "2018-02-20",
    b_t: {
      c_a: "2018-02-20T18:43:17.104Z"

kelvinn

# post_loc.txt contains the json you want to post
# -p means to POST it
# -H adds an Auth header (could be Basic or Token)
# -T sets the Content-Type
# -c is concurrent clients
# -n is the number of requests to run in the test

ab -p post_loc.txt -T application/json -H 'Authorization: Token abcd1234' -c 10 -n 2000 http://example.com/api/v1/locations/

nateware

# Step 1: Set priveleges

$ sudo /System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -configure -allowAccessFor -allUsers -privs -all

Starting...
Setting allow all users to YES.
Setting all users privileges to 1073742079.
Done.

# Step 2: Allow VNC clients

jdrew1303

Introduction

The computer driven markets for instruments like stocks and exchange traded stock options, have transformed finance and the flow of capital. These markets are enabled by order matching engines (and the infrastructure that supports this software). Before computer trading networks and matching engines, stocks where traded on cavernous exchange floors and transaction costs where high. When electronic trading fully matured, floor traders were a fading anachronism and transaction costs had been reduced to pennies a share in many cases. Electronic trading could not exist without advanced network infrastructure, but without the software matching engines no shares would change hands. The computer trading networks, the matching engine software has also created a concentrated nexus of potential failure. Failures in these systems have increased as the frequency and volume on the electronic networks has increased. The position of order matching engines in the trading infrastructure makes these systems o

starfys

//Based on the fast inverse square root function
// https://en.wikipedia.org/wiki/Fast_inverse_square_root
// Some original comments preserved for humor value
// Designed to try to mimic the original as closely as possible
function Q_rsqrt(number)
{ 
    var i;
    var x2, y;
    const threehalfs = 1.5;
  

mauricedb

import { useState } from 'react';

export function useCounter(initial = 0) {
  const [count, setCount] = useState(initial);

  return [count, () => setCount(count + 1)];
}

arundasan91

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

mmalecki

for (var i = 0; i < 1024 * 1024; i++) {
  process.nextTick(function () { Math.sqrt(i) } )
}

qwtel

// Node 8+
// --------------------------------------------------------------
// No external dependencies

const { promisify } = require('util');
const { resolve } = require('path');
const fs = require('fs');
const readdir = promisify(fs.readdir);
const stat = promisify(fs.stat);

kaytwo

'use strict';

const puppeteer = require('puppeteer');

(async () => {
    /* PRECONDITION:
    0. download ublock, I used https://github.com/gorhill/uBlock/releases/download/1.14.19b5/uBlock0.chromium.zip
    1. run $PATH_TO_CHROME --user-data-dir=/some/empty/directory --load-extension=/location/of/ublock
    2. enable block lists you want to use
    */