justecorruptio

import os,tty;tty.setcbreak(0);M=['']*16
def G(v):
 p=['']*4;u=list(filter(str,v));i=3
 while u:z=u.pop();p[i]=u and z==u[-1]and 2*u.pop()or z;i-=1
 return p
def Y(M,k):i=1;M=zip(*[iter(M)]*4);exec'M=map([list,G][i*k==k*k],zip(*M))[::-1];i+=1;'*4;return sum(M,[])
while 1:
 r=id(M)%71+17
 while M[r%16]*r:r-=1
 if r:M[r%16]=r%7%2*2+2

dmolesUC

import javafx.application.Application;
import javafx.beans.property.ReadOnlyObjectWrapper;
import javafx.collections.ListChangeListener;
import javafx.collections.ObservableList;
import javafx.scene.Group;
import javafx.scene.Scene;
import javafx.scene.control.TableColumn;
import javafx.scene.control.TableView;
import javafx.scene.control.skin.TableHeaderRow;
import javafx.stage.Stage;

jklingsporn

JMX_IP=$(curl http://169.254.169.254/latest/meta-data/public-ipv4)
exec java $JAVA_OPTS -Djava.rmi.server.hostname=$JMX_IP -jar app.jar $JAVA_ARGS

bekce

package utils;

import java.security.GeneralSecurityException;
import java.security.SecureRandom;
import java.security.cert.X509Certificate;
import javax.net.ssl.HostnameVerifier;
import javax.net.ssl.HttpsURLConnection;
import javax.net.ssl.SSLContext;
import javax.net.ssl.TrustManager;
import javax.net.ssl.X509TrustManager;

joshenders

#!/bin/bash
# https://gist.github.com/joshenders/52cb78bedaea6787faac

# This script uses the BSD variants of commands and is intended to
# be run on an unmodified installation of OSX.

pmset noidle &

now=$(date +%s)
duration=$((86400*3)) # 3days

bom-d-van

Using nc and protoc as protobuf client and server

Inspiration: Linux and Unix nc command

Example protobuf definition:

message Person {
  required string name = 1;
  required int32 id = 2;

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

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