viktorklang

#!/usr/bin/env zsh

# in fino veritas
 
# Borrowing shamelessly from these oh-my-zsh themes:
#   fino-time
#   pure
#   https://gist.github.com/smileart/3750104

# Set required options

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

ragingwind

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

igor-shevchenko

from itertools import combinations
from nltk.tokenize import sent_tokenize, RegexpTokenizer
from nltk.stem.snowball import RussianStemmer
import networkx as nx

def similarity(s1, s2):
    if not len(s1) or not len(s2):
        return 0.0
    return len(s1.intersection(s2))/(1.0 * (len(s1) + len(s2)))

domenic

This article has been given a more permanent home on my blog. Also, since it was first written, the development of the Promises/A+ specification has made the original emphasis on Promises/A seem somewhat outdated.

You're Missing the Point of Promises

Promises are a software abstraction that makes working with asynchronous operations much more pleasant. In the most basic definition, your code will move from continuation-passing style:

getTweetsFor("domenic", function (err, results) {
 // the rest of your code goes here.

seanparsons

object SKI_Applicative {
/*
First, let's talk about the SK combinator calculus and how it contributes to solving your problem.

The SK combinator calculus is made of two functions (aka combinators): S and K. It is sometimes called the SKI combinator calculus, 
however, the I combinator can be derived from S and K. The key observation of SK is that it is a turing-complete system and therefore, 
anything that can be expressed as SK is also turing-complete. Here is a demonstration that Scala's type system is turing-complete 
(and therefore, undecidable) for example[1].

The K combinator is the most trivial of the two. It is sometimes called "const" (as in Haskell). There is also some discussion about 

tonymorris

object SKI_Applicative {
/*
First, let's talk about the SK combinator calculus and how it contributes to solving your problem.

The SK combinator calculus is made of two functions (aka combinators): S and K. It is sometimes called the SKI combinator calculus, however, the I combinator can be derived from S and K. The key observation of SK is that it is a turing-complete system and therefore, anything that can be expressed as SK is also turing-complete. Here is a demonstration that Scala's type system is turing-complete (and therefore, undecidable) for example[1].

The K combinator is the most trivial of the two. It is sometimes called "const" (as in Haskell). There is also some discussion about its evaluation strategy in Scala and how to best express it[2]. The K function might be paraphrased as, "takes a value and returns a (constant) unary function that always returns that value."
*/
  def k[A, B]: A => B => A =
    a => _ => a

juanriaza

# brew install rtmpdump
print ' && '.join(['rtmpdump -r "rtmp://streamcloud.klewel.com/cfx/st/v0/djangocon-2012-flash-%d.flv" -o djangocon-2012-flash-%d.flv' % (i, i) for i in xrange(1, 45 + 1)])

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

osa1

(in-package :cl-user)

(defpackage :m-utils
  (:use :cl)
  (:export :from-list :print-ht))

(in-package m-utils)

(defmacro from-list (list &rest params)
  "A hash-table generator inspired by Haskell's fromList."