mrflip

votes CF 

"back in black" => { 201005211200 => '1', 201005201159 => '1', 201005201157 => '1',  201005011900 => '1', 201004190600 => '1' },
"black album"   => { 201005021800 => '1', 201005010600 => '1' },
"black star"    => { 201005011000 => '1' }

cached_counts CF

"back in black" => { 'cached_count' => 2, 'counted_until' => 201005011931 },
"black album"   => { 'cached_count' => 1, 'counted_until' => 201005010600 }

hramos

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>items</key>
	<array>
		<dict>
			<key>assets</key>
			<array>
				<dict>

conorbuck

var p1 = {
	x: 20,
	y: 20
};

var p2 = {
	x: 40,
	y: 40
};

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

sekati

# xcode-build-bump.sh
# @desc Auto-increment the build number every time the project is run. 
# @usage
# 1. Select: your Target in Xcode
# 2. Select: Build Phases Tab
# 3. Select: Add Build Phase -> Add Run Script
# 4. Paste code below in to new "Run Script" section
# 5. Drag the "Run Script" below "Link Binaries With Libraries"
# 6. Insure that your starting build number is set to a whole integer and not a float (e.g. 1, not 1.0)

rantav

A few show tricks to find slow queries in mongodb

Enable profiling

First, you have to enable profiling

> db.setProfilingLevel(1)

Now let it run for a while. It collects the slow queries ( > 100ms) into a capped collections, so queries go in and if it's full, old queries go out, so don't be surprised that it's a moving target...

willurd

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

ashrithr

Introduction to Kafka

Kafka acts as a kind of write-ahead log (WAL) that records messages to a persistent store (disk) and allows subscribers to read and apply these changes to their own stores in a system appropriate time-frame.

Terminology:

• Producers send messages to brokers
• Consumers read messages from brokers
• Messages are sent to a topic

risacher

var util = require('util');
require('NodObjC/global');
framework('ScriptingBridge');

var iTunes = SBApplication('applicationWithBundleIdentifier', 
                           NSString('stringWithUTF8String', 'com.apple.iTunes'));

if (!iTunes('isRunning')) {
    console.log('iTunes is not running');
    iTunes('run')

debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t