Simon Rentzke

Blue Blistering Barnacles

Removing sensitive data from GitHub wiki revision history

If you, like me, by mistake added a password to a Github Wiki, and would like to remove it from history. It's actually quite easy.

The whole wiki, is actually a git repo, which you can checkout locally like this:

git clone https://github.com/<user>/<repo>.wiki.git

You can then do a rebase, to amend the the wiki changes (commits). Remove the sensitive commits, and then do a force push.

 git rebase --interactive HEAD~2
 git push -f

Please note: that even though you won't be able to see it on the UI, the sensitive data will still exist in the git history, if you would like to be sure that nothing exists, you can destroy the whole wiki's history, by following this gist.

Playing with Googles Cloud Vision API

The age of intelligent api's is here.

If you want to get a taste of what Google's new "Cloud Vision API" can do. Sign up here https://cloud.google.com/vision/.

Once you're accepted, get an api key from the Google console

Then at your fingertips you have:

  • Optical Character Recognition
  • Face detection, and sentiment analysis i.e Happy, Sad
  • Landmark detection

I wrote a tiny little ruby script if you want to give a whirl. This specifically uses the landmark detection api.

  require 'base64'
  require 'json'
  require 'faraday'

  filepath = '/path/to/my/image.jpg'
  content = Base64.encode64(File.binread(filepath))
  conn = Faraday.new(:url => 'https://vision.googleapis.com') 
  data = {requests: [{'image' => {'content' => content}, 'features' => [{'type' => 'LANDMARK_DETECTION', 'maxResults' => 10}]}]};
  response = conn.post do |req|
    req.url '/v1alpha1/images:annotate?key=<your api key>'
    req.headers['Content-Type'] = 'application/json'
    req.body = data.to_json
  end

  puts response

So here is a picture I took in 2006, without any geolocation info attached in exif data.

tahoe

What does the api return?

It gives a confidence score, a description of the landmark and its rough latitude and longitude, and a bunch of other cool stuff I'm yet to understand.

[
    [0] {
        "landmarkAnnotations" => [
            [0] {
                         "mid" => "/m/01k_5m",
                 "description" => "Lake Tahoe",
                       "score" => 0.46974313,
                "boundingPoly" => {
                    "vertices" => [
                        [0] {
                            "x" => 1092,
                            "y" => 555
                        },
                        [1] {
                            "x" => 1273,
                            "y" => 555
                        },
                        [2] {
                            "x" => 1273,
                            "y" => 944
                        },
                        [3] {
                            "x" => 1092,
                            "y" => 944
                        }
                    ]
                },
                   "locations" => [
                    [0] {
                        "latLng" => {
                             "latitude" => 38.940395,
                            "longitude" => -119.91884
                        }
                    }
                ]
            },
            [1] {
                       "score" => 0.29812887,
                "boundingPoly" => {
                    "vertices" => [
                        [0] {
                            "x" => 960,
                            "y" => 902
                        },
                        [1] {
                            "x" => 1558,
                            "y" => 902
                        },
                        [2] {
                            "x" => 1558,
                            "y" => 1073
                        },
                        [3] {
                            "x" => 960,
                            "y" => 1073
                        }
                    ]
                },
                   "locations" => [
                    [0] {
                        "latLng" => {
                             "latitude" => 38.943923,
                            "longitude" => -119.928989
                        }
                    }
                ]
            }
        ]
    }
]

Thabo Mbeki

My new Hobby

I used to think I had a few hobbies, mountain biking, rugby, vege gardening, coding, etc, etc, but then my wife and I bought a Queenslander workers cottage that needed a bit of attention.

queenslander renovation

I had zero interest in renovation. But this beast has taken over, it consumes every minute of my spare time, and has for the past 2 years.

We're nearing the end of the whole experience, and although feeling exhausted, we're really stoked that a lot of things have come together.

If anyone is interested here is our progress in pictures (apologies for the blogger blog :) Egbert Blog

Internet Speed in Australia

Internet in Australia

Quickly setup a ruby on rails edge app

Setup your app directory

mkdir rails-edge-test-app
cd rails-ege-test-app

Specify your Ruby version

touch .ruby-version
echo 'ruby-2.3.1' > .ruby-version

Setup Gemfile with edge rails, rack and arel

touch Gemfile
printf "source 'https://rubygems.org'\nruby '2.3.1'\n\n\ngem 'rails', github: 'rails/rails'" >> Gemfile
bundle

Use rails generators to create new app, and run server

bundle exec rails new . --dev --force
bundle exec rails s

Machines are learning from us, and that's not always a good thing

I was recently playing with Google's new Photo application. An incredibly powerful little application, yet has an incredibly simple UI.

Google has gone through my entire library of 30k photos and classified them against their boat loads of data.

It's always nice to get a visual sense of what Machine Learning is doing, and after playing with the photo app it's quite clear how it classifies photos.

For example, if I search for 'festival', it finds photo's of all sorts of festivals, from Glastonbury:

Glastonbury

To the Soccer World Cup:

South Africa world cup

To the Notting Hill Carnival:

Notting Hill Carnival

To a few incorrect ones like these:

Vietnam

You can understand why machine learning might classify these, and it's not really a problem. When we have more data, those results will continue to get better, they will almost definitely result in more accuracy going into the future.

So, what's the problem?

The problem is here; do a search for something like 'desert', I get this result:

namibia desert

Do a search for 'dessert' and I get this result:

namibia desert

So, through our own stupidity, we have trained the machines to think 'dessert' is the same as 'desert'.

A trivial example, but in a time when we are ramping up our dependence on machine learning, we need to remember that the machine is not always correct and it is for the same reason as the crowd is not always correct.

Safety nets

I've had a few close calls when switching between the Dev/Staging/Prod environments. These will save you from any nasty suprises!

iTerm Colour

Change the colour of the terminal window when you ssh into your staging or production environments. I use a simple bash script that switches when I ssh into certain hosts.

Banner/Ribbon on Staging/Development

I just used a fork of the Github ribbon

Show App Name and Environment when in console

We used this handy gem Marco-Polo

That looks like this in the console:

~/Sites/myapp$ rails c
myapp(dev)>

Ruby "&& vs and" , "or vs ||"

tl;dr && and || have higher precendence

Difference between 'and' and '&&'

cat = 'meow'
dog = false


answer = cat && dog
=> false

answer
=>  false

answer = cat and dog
=> false

answer
=> 'meow'

Similarly with 'or' and '||'

cat = false
dog = 'bark'

answer = cat || dog
 => 'bark'

answer
=>  'bark'

answer = cat or dog
=> 'bark'

answer
=> false

I generally follow this style guide

Beats to think to

Like to concentrate whilst listening to hypnotic deep house tracks?

I've found the playlist for you.

I use it to focus whilst programming, check it out