paultyng

ssh-keygen -t rsa -b 4096 -f jwtRS256.key
# Don't add passphrase
openssl rsa -in jwtRS256.key -pubout -outform PEM -out jwtRS256.key.pub
cat jwtRS256.key
cat jwtRS256.key.pub

paultyng

package auth

import (
	"encoding/json"
	"io/ioutil"
	"net/http"
	"net/url"
	"os"
	"regexp"
	"time"

paultyng

function dedent(callSite, ...args) {

    function format(str) {

        let size = -1;

        return str.replace(/\n(\s+)/g, (m, m1) => {

            if (size < 0)
                size = m1.replace(/\t/g, "    ").length;

paultyng

Better SSH Authorized Keys Management

A seemingly common problem that people encounter is how to handle all of your users authorized_keys file.

People struggle over management, ensuring that users only have specific keys in the authorized_keys file or even a method for expiring keys. A centralized key management system could help provide all of this functionality with a little scripting.

One piece of functionality overlooked in OpenSSH is the AuthorizedKeysCommand configuration keyword. This configuration allows you to specify a command that will run during login to retrieve a users public key file from a remote source and perform validation just as if the authorized_keys file was local.

Here is an example directory structure for a set of users with SSH public keys that can be shared out via a web server:

paultyng

/* bling.js */

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

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

NodeList.prototype.__proto__ = Array.prototype

paultyng

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

paultyng

# 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}..."

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

paultyng

# 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

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