bobes

#!/usr/bin/env ruby

# 1. export your RIL bookmarks
# 2. save this file to the same directory where your ril_export.html is
# 3. change username and password in the script bellow
# 4. run 'ruby ril_to_instapaper.rb' in terminal

require "cgi"
require "net/http"
require "net/https"

isaacsanders

This is a post by Joel Spolsky. The original post is linked at the bottom.

This is such a common question here and elsewhere that I will attempt to write the world's most canonical answer to this question. Hopefully in the future when someone on answers.onstartups asks how to split up the ownership of their new company, you can simply point to this answer.

The most important principle: Fairness, and the perception of fairness, is much more valuable than owning a large stake. Almost everything that can go wrong in a startup will go wrong, and one of the biggest things that can go wrong is huge, angry, shouting matches between the founders as to who worked harder, who owns more, whose idea was it anyway, etc. That is why I would always rather split a new company 50-50 with a friend than insist on owning 60% because "it was my idea," or because "I was more experienced" or anything else. Why? Because if I split the company 60-40, the company is going to fail when we argue ourselves to death. And if you ju

welch

It is a rite of passage to post one's successful build instructions for OpenCV on a Mac
after you've tried all the other conflicting instructions out there and still failed. 
brew failed for me (was this because I could never get a happy brew doctor situation? 
I'll never know). macports? nope. build-from-source recipes? I didn't find one that 
worked for me.

Here's what did work to build OpenCV 2.4.5 from the distribution tarball using cmake,
on Mac OSX 10.8.4, linked to an anaconda installation rather than the system python. 
It is a mashup of various bits of advice out there. If you're already comfortable with 
build/install from source, all you need to read is the cmake invocation in step 3 and 

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

kyleve

/**
 Provides the ability to verify key paths at compile time.
 
 If "keyPath" does not exist, a compile-time error will be generated.
 
 Example:
    // Verifies "isFinished" exists on "operation".
    NSString *key = SQKeyPath(operation, isFinished);
 
    // Verifies "isFinished" exists on self.

stucchio

from matplotlib import use

from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

fprieur

#heroku config:set HEROKU=1

import os
import urlparse
import psycopg2
from flask import Flask
from flask_peewee.db import Database


if 'HEROKU' in os.environ:

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

mitmul

#! /bin/bash
sudo aptitude update
sudo aptitude full-upgrade -y
sudo reboot

wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_6.5-14_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_6.5-14_amd64.deb 
sudo aptitude update
sudo aptitude install -y linux-image-extra-virtual
sudo aptitude install -y cuda

arnov

#!/usr/bin/python
# -*- coding: utf-8 -*-

import math

def calc_ab(alpha_a, beta_a, alpha_b, beta_b):
    '''
    See http://www.evanmiller.org/bayesian-ab-testing.html

    αA is one plus the number of successes for A