paultyng

#all local branches that are merged into HEAD

git branch -d `git branch --merged | grep -v '^*' | tr -d '\n'`


#Delete all remote branches that are merged into HEAD

#jump back to master
git co master
#get rid of all local branches not on origin

paultyng

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Irony.Parsing;

namespace IronyHours
{
    public class HoursGrammar : Grammar
    {

paultyng

Instructions taken from Amazon's docs and modified as necessary.

1. Install stunnel:

• Download from stunnel's download page
• Run installer w/ default options, create self signed cert by answering questions
• Open the c:\program files (x86)\stunnel\stunnel.conf file in notepad
• Clear all the server configs (under Example SSL server mode services section, won't have a client = yes line)
• Create a new client config:

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<
%3C
&lt
&lt;
&LT
&LT;
&#60
&#060
&#0060
&#00060

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# This method finds related articles using Jaccard index (optimized for PostgreSQL).
# More info: http://en.wikipedia.org/wiki/Jaccard_index

class Article < ActiveRecord::Base

  def related(limit=10)
    Article.find_by_sql(%Q{
      SELECT
      	a.*,
      	( SELECT array_agg(t.name) FROM taggings tg, tags t

paultyng

#!/usr/bin/env ruby

# Please read http://otobrglez.opalab.com for more information about this code.

class Book < Struct.new(:title)

  def words
    @words ||= self.title.gsub(/[a-zA-Z]{3,}/).map(&:downcase).uniq.sort
  end

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# see http://stackoverflow.com/questions/5880962/how-to-destroy-jobs-enqueued-by-resque-workers - old version
# see https://github.com/defunkt/resque/issues/49
# see http://redis.io/commands - new commands

namespace :resque do
  desc "Clear pending tasks"
  task :clear => :environment do
    queues = Resque.queues
    queues.each do |queue_name|
      puts "Clearing #{queue_name}..."

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require 'redis'

RedisMessage = Struct.new(:body)

class RedisQueue
  def initialize(redis)
    @redis = redis
  end

  def poll(&block)

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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/* bling.js */

window.$ = document.querySelectorAll.bind(document)

Node.prototype.on = window.on = function (name, fn) {
  this.addEventListener(name, fn)
}

NodeList.prototype.__proto__ = Array.prototype