phpdude

        location /resize {
                alias /tmp/nginx/resize;
                set $width 150;
                set $height 100;
                set $dimens "";

                if ($uri ~* "^/resize_(\d+)x(\d+)/(.*)" ) {
                        set $width  $1;
                        set $height $2;
                        set $image_path $3;

tsuna

// Copyright (C) 2012  Benoit Sigoure
// This program is free software: you can redistribute it and/or modify it
// under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or (at your
// option) any later version.  This program is distributed in the hope that it
// will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
// of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Lesser
// General Public License for more details.  You should have received a copy
// of the GNU Lesser General Public License along with this program.  If not,
// see <http://www.gnu.org/licenses/>.

misterbrownlee

I just had to set up Jenkins to use GitHub. My notes (to myself, mostly):

Detailed Instructions

For setting up Jenkins to build GitHub projects. This assumes some ability to manage Jenkins, use the command line, set up a utility LDAP account, etc. Please share or improve this Gist as needed.

Install Jenkins Plugins

• get both the git and github plugin
• http://wiki.jenkins-ci.org/display/JENKINS/Git+Plugin

azymnis

import com.twitter.scalding._
import com.twitter.algebird.{ MinHasher, MinHasher32, MinHashSignature }

/**
 * Computes similar items (with a string itemId), based on approximate
 * Jaccard similarity, using LSH.
 *
 * Assumes an input data TSV file of the following format:
 *
 *    itemId   userId

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

vvondra

import org.hibernate.Session;
import org.hibernate.SessionFactory;
import org.hibernate.Transaction;
import org.hibernate.context.internal.ManagedSessionContext;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.function.Function;

sjednac

public final class BusyApp {
  public static void main(String []args) throws Exception {
    final java.util.Random generator = new java.util.Random();
    while (true) {
      generator.ints(1000000, 0, 100).sorted();
      Thread.sleep(5000L);
    }
  }
}

acolyer

A collection of The Morning Paper posts relating to containers, operating systems etc.

In order of first appearance in The Morning Paper.

• On the duality of operating system structures
• Why threads are a bad idea
• Why events are a bad idea
• Unikernels: library operating systems for the cloud
• The scalable commutativity rule: designing scalable software for multicore processors
• Jitsu: just-in time summoning of unikernels

quentinms

{
    "apps": {
        "http": {
            "servers": {
                "localhost": {
                    "listen": [
                        ":9081"
                    ],
                    "automatic_https": {
                        "disable_redirects": true