#!/usr/bin/env python2
# Quick and dirty demonstration of CVE-2014-0160 by Jared Stafford (
# The author disclaims copyright to this source code.
import sys
import struct
import socket
import time
import select
View gist:9679049
require 'rubygems'
require 'keen'
require 'json'
require 'date'
require 'active_support/all' #for datetime calculation e.g. weeks.ago.at_beginning_of_week
require 'simple_xlsx' #for outputting excel files
require 'cgi' #for URL encoding
View Makefile.patch
diff --git a/Makefile b/Makefile
index 33bfb0a..af625c8 100644
--- a/Makefile
+++ b/Makefile
@@ -29,10 +29,10 @@ OBJS = \
# Cross-compiling (e.g., on Mac OS X)
-#TOOLPREFIX = i386-jos-elf-
+TOOLPREFIX = /opt/gnu/bin/i386-jos-elf-
View Main.scala
import mesosphere.mesos.util.FrameworkInfo
import org.apache.mesos.MesosSchedulerDriver
* @author Tobi Knaup
object Main extends App {
View DuplicateRequestFilter.scala
package com.tumblr.fibr.service.filter
import com.tumblr.fibr.config.FilterConfig
import com.tumblr.fibr.tsdb.{TsdbRequest, TsdbResponse}
import{Cache, CacheBuilder}
import com.twitter.finagle.Service
import com.twitter.util.Future
import java.util.concurrent.{Callable, TimeUnit}
View SCombinator.scala
* <b>Fixed Point Combinator is:</b>
* Y = λf.(λx.f (x x)) (λx.f (x x))
* <b>Proof of correctness:</b>
* Y g = (λf . (λx . f (x x)) (λx . f (x x))) g (by definition of Y)
* = (λx . g (x x)) (λx . g (x x)) (β-reduction of λf: applied main function to g)
* = (λy . g (y y)) (λx . g (x x)) (α-conversion: renamed bound variable)
* = g ((λx . g (x x)) (λx . g (x x))) (β-reduction of λy: applied left function to right function)
* = g (Y g) (by second equality) [1]
View twophase.scala
// Run this with scala <filename>
* A Two-phase commit Monad
trait Transaction[+T] {
def map[U](fn: T => U): Transaction[U] = flatMap { t => Constant(fn(t)) }
def flatMap[U](fn: T => Transaction[U]): Transaction[U] =
FlatMapped(this, fn)

CvRDTs are as general as they can be

What are you talking about, and why should I care?

Now that we live in the Big Data, Web 3.14159 era, lots of people want to build databases that are too big to fit on a single machine. But there's a problem in the form of the CAP theorem, which states that if your network ever partitions (a machine goes down, or part of the network loses its connection to the rest) then you can keep consistency (all machines return the same answer to