NoahCardoza

#!/bin/bash

# args: browser
# example: ./getOpenTabs.sh "Brave Browser"
# credits:
#  https://gist.github.com/samyk/65c12468686707b388ec43710430a421

# TODO:
#  validate args
#  don't open app if not already open

loganvolkers

Custom format for displaying bytes as kb, mb, gb or tb.

Response to a few places on the internet: https://productforums.google.com/forum/#!topic/docs/x_T_N-yRUYg And here: https://stackoverflow.com/questions/1533811/how-can-i-format-bytes-a-cell-in-excel-as-kb-mb-gb-etc

Here is one that I have been using:

[<1000000]0.00," KB";[<1000000000]0.00,," MB";0.00,,," GB"

manofearth

#!/usr/bin/env bash
while getopts "d" opt; do
  case $opt in
    d) dryRunOpt="--dry-run";;
  esac
done

# prune local "cache" of remote branches first:
git fetch --prune origin
# delete merged to master branches:

justinjc

#!/bin/bash

set -euo pipefail

usage() { 
    echo "usage: $0 <repo> <name>"
    echo
    echo "<repo>"
    echo "    The repo link, e.g. https://github.com/m3db/m3coordinator.git"
    echo

prateek

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# golang_import_order_cleaner.py: cleans up import orders in golang files per the m3db convention
#
# Warning: This is very much alpha, take a backup of your files (e.g. commit to git) before running this.
#
# example usage:
# (1) Update all *.go files in the local directory (except vendor/), in-place
# $ golang_import_order_cleaner.py -o inplace

iamralch

package main

import (
	"fmt"
	"io"
	"io/ioutil"
	"net"
	"os"
	"strings"

johnynek

package mapreduce

/**
 * This is an attempt to find a minimal set of type classes that describe the map-reduce programming model
 * (the underlying model of Google map/reduce, Hadoop, Spark and others)
 * The idea is to have:
 * 1) lawful types that fully constrain correctness
 * 2) a minimal set of laws (i.e. we can't remove any laws,
 * 3) able to fully express existing map/reduce in terms of these types
 * 

ibuenros

//==================================================================
// SPARK INSTRUMENTATION
//==================================================================

import com.codahale.metrics.{MetricRegistry, Meter, Gauge}
import org.apache.spark.{SparkEnv, Accumulator}
import org.apache.spark.metrics.source.Source
import org.joda.time.DateTime
import scala.collection.mutable

laserson

Create a FlameGraph to visualize where your code is spending its time.

Requires plop and FlameGraph.

gwenshap

#!/usr/bin/python
import time
import datetime
import random
timestr = time.strftime("%Y%m%d-%H%M%S")

f = open('access_log_'+timestr+'.log','w')

ips=["123.221.14.56","16.180.70.237","10.182.189.79","218.193.16.244","198.122.118.164","114.214.178.92","233.192.62.103","244.157.45.12","81.73.150.239","237.43.24.118"]
referers=["-","http://www.casualcyclist.com","http://bestcyclingreviews.com/top_online_shops","http://bleater.com","http://searchengine.com"]