max-mapper

#!/usr/bin/env bash
# chmod +x this and save in your PATH. Assumes `eslint` + `prettier` are in your `devDependencies`
BRANCH=$(git branch | grep \* | cut -d ' ' -f2)
BASE=$(git merge-base master $BRANCH) # change master to whatever your trunk branch is
COMMITED=$(git diff --name-only $BASE $BRANCH)
STAGED=$(git diff --staged --name-only)
FILES=$(printf "$COMMITED\n$STAGED" | sort | uniq)
LINT="npx eslint --ignore-path=.prettierignore $FILES"
PRETTIER="npx prettier --list-different $FILES"

mauricedb

import { useState } from 'react';

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

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

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

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"

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
    */

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);

bbrewer97202

var gm = require('gm');
var source = '<?xml version="1.0" encoding="utf-8"?><svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 2550 3300" enable-background="new 0 0 2550 3300" xml:space="preserve"><g><path fill="#53381A" stroke="#1C1308" d="M807.6,1092.5L527.3,1067c-636.9-25.5-509.5,0-458.6,509.5c51,203.8,0,331.2,254.8,331.2 L807.6,1092.5z"/><path fill="#8A5B28" stroke="#1C1308" d="M909.5,1143.5H629.2c-636.9-25.5-509.5,0-458.6,509.5c51,203.8,25.5,305.7,305.7,229.3 L909.5,1143.5z"/><path fill="#454F2E" stroke="#1C1308" d="M807.6,990.6c127.4-254.8,76.4-305.7,458.6-254.8c509.5,127.4,509.5,152.9,891.6,152.9 c254.8,25.5,509.5,101.9,254.8,407.6l-280.2,509.5H450.9L807.6,990.6z"/><path fill="#76894C" stroke="#1C1308" d="M323.5,1703.9v178.3l1146.4,458.6v-178.3L323.5,1703.9z M807.6,990.6 c127.4-254.8,76.4-305.7,382.1-203.8c509.5,152.9,254.8,152.9,789.7,152.9c254.8,25.5,458.6,101.9,254.8,407.6L1954,1856.8 L527.3,1627.5L807.6,990.6z"/

morgangiraud

# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Select one driver (the last one is a decent choice)
sudo apt install nvidia-370

metasyn

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