hhromic

Slimming Down Raspbian Stretch Lite

Notes for slimming down a fresh installation of Raspbian Stretch Lite. This guide does not strip Raspbian of basic functionality such as Bluetooth and mDNS.

Instructions

Install a fresh Raspbian Stretch Lite image into the SD card ([source][1]).

$ unzip -p 2018-04-18-raspbian-stretch-lite.zip | dd bs=4M of=/dev/sdX conv=fsync

eduardocardoso

#!/bin/bash

set -o errexit

echo "Removing exited docker containers..."
docker ps -a -f status=exited -q | xargs -r docker rm -v

echo "Removing dangling images..."
docker images --no-trunc -q -f dangling=true | xargs -r docker rmi

julian-boolean

$(window).on('resize', function(){
  graph.configure({
    width: window.innerWidth - 20,
    height: window.innerHeight - 20
  });
  graph.render();
});

samklr

1. General Background and Overview
  a) Probabilistic Data Structures for Web Analytics and Data Mining : On Highly Scalable Blog (http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/) : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  b) Models and Issues in Data Stream Systems : (http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.106.9846)
  c) Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani (http://www.vldb.org/conf/2002/S10P03.pdf) : One of the early papers on the subject.
  d) Methods for Finding Frequent Items in Data Streams by Graham Cormode & Marios Hadjieleftheriou (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&type=pdf)
  e) The space complexity of approximating the frequency moments by Noga Alon, Yossi Matias, Mario Szegedy : one of the most influential papers introducing succinctness in computing fre

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

pjsvis

// Requires jQuery from http://jquery.com/
// and jQuerySparklines from http://omnipotent.net/jquery.sparkline

// AngularJS directives for jquery sparkline
angular.module('sparkline', []);
 
angular.module('sparkline')
    .directive('jqSparkline', [function () {
        'use strict';
        return {

slant

require 'action_dispatch/middleware/session/redis_store'

ActionDispatch::Session::RedisStore.class_eval do
  def set_cookie(env, session_id, cookie)
    request = ActionDispatch::Request.new(env)
    request.cookie_jar[key] = cookie
  end
end

radu-gheorghe

#cleanup first
curl -XDELETE localhost:9200/test/
echo

#create index
curl -XPUT localhost:9200/test
echo

#create parent type
curl -XPUT localhost:9200/test/test_parent

jch

#!/bin/sh

# Start fresh
curl -q -XDELETE 'localhost:19200/emails?pretty' 2>1 1>/dev/null

# Create index 'emails' with a mapping for type 'user', and settings for
# custom email analyzers
echo "Create Index"
curl -XPUT 'localhost:19200/emails?pretty' -d '{
  "settings": {

gist:2523336

#!/bin/sh

git filter-branch --env-filter '

an="$GIT_AUTHOR_NAME"
am="$GIT_AUTHOR_EMAIL"
cn="$GIT_COMMITTER_NAME"
cm="$GIT_COMMITTER_EMAIL"

if [ "$GIT_COMMITTER_EMAIL" = "your@email.to.match" ]