Linda Liukas, Hello Ruby and the magic of coding

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by an author

Linda Liukas’s best-selling Hello Ruby books teach children that computers are fun and coding can be a magical experience.

See the original article to watch a great video interview with Linda Liukas. Linda is the founder of Rails Girls and author of a number of books encouraging children to learn computer programming (which I’m hoping to show copies of to ours, when they’re a tiny bit older). I’ve mentioned before how important I feel an elementary understanding of programming concepts is to children.

The Ruby Story

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By 2005, Ruby had become more popular, but it was still not a mainstream programming language. That changed with the release of Ruby on Rails. Ruby on Rails was the “killer app” for Ruby, and it did more than any other project to popularize Ruby. After the release of Ruby on Rails, interest in Ruby shot up across the board, as measured by the TIOBE language index:

It’s sometimes joked that the only programs anybody writes in Ruby are Ruby-on-Rails web applications. That makes it sound as if Ruby on Rails completely took over the Ruby community, which is only partly true. While Ruby has certainly come to be known as that language people write Rails apps in, Rails owes as much to Ruby as Ruby owes to Rails.

As an early adopter of Ruby (and Rails, when it later came along) I’ve always found that it brings me a level of joy I’ve experienced in very few other languages (and never as much). Every time I write Ruby, it takes me back to being six years old and hacking BASIC on my family’s microcomputer. Ruby, more than any other language I’ve come across, achieves the combination of instant satisfaction, minimal surprises, and solid-but-flexible object orientation. There’s so much to love about Ruby from a technical perspective, but for me: my love of it is emotional.

Why it is just lazy to bad-mouth Ruby on Rails

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It’s inevitable these days: we will see an article proclaiming the demise of Ruby on Rails every once in a while. It’s the easiest click bait, like this one from TNW.Now, you may say “another Ruby fanboy.” That’s fair, but a terrible argument, as it’s a poor and common argumentum ad hominem. And on the subject of fallacies, the click-bait article above is wrong exactly because it falls for a blatantly Post hoc ergo propter hoc fallacy plus some more confirmation bias which we are all guilty of falling for all the time.

I’m not saying that the author wrote fallacies on purpose. Unfortunately, it’s just too easy to fall for fallacies. Especially when everybody has an intrinsic desire to confirm one’s biases. Even trying to be careful, I end up doing that as well…

Rails is f*cking boring! I love it.

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Together with a friend I recently built Dropshare Cloud. We offer online storage for the file and screenshot sharing app Dropshare for macOS/iOS. After trying out Django for getting started (we both had some experience using Django) I decided to rewrite the codebase in Rails. My past experience developing in Rails made the process quick — and boring…

What Does Jack FM Sound Like?

Those who know me well know that I’m a bit of a data nerd. Even when I don’t yet know what I’m going to do with some data yet, it feels sensible to start collecting it in a nice machine-readable format from the word go. Because you never know, right? That’s how I’m able to tell you how much gas and electricity our house used on average on any day in the last two and a half years (and how much off that was offset by our solar panels).

Daily energy usage at Dan's house for the last few years. Look at the gas peaks in the winters, when the central heating ramps up!
The red lumps are winters, when the central heating comes on and starts burning a stack of gas.

So it should perhaps come as no huge surprise that for the last six months I’ve been recording the identity of every piece of music played by my favourite local radio station, Jack FM (don’t worry: I didn’t do this by hand – I wrote a program to do it). At the time, I wasn’t sure whether there was any point to the exercise… in fact, I’m still not sure. But hey: I’ve got a log of the last 45,000 songs that the radio station played: I might as well do something with it. The Discogs API proved invaluable in automating the discovery of metadata relating to each song, such as the year of its release (I wasn’t going to do that by hand either!), and that gave me enough data to, for example, do this (click on any image to see a bigger version):

Jack FM: Decade Frequency by Hour
Decade frequency by hour: you’ve got a good chance of 80s music at any time, but lunchtime’s your best bet (or perhaps just after midnight). Note that times are in UTC+2 in this graph.

I almost expected a bigger variance by hour-of-day, but I guess that Jack isn’t in the habit of pandering to its demographics too heavily. I spotted the post-midnight point at which you get almost a plurality of music from 1990 or later, though: perhaps that’s when the young ‘uns who can still stay up that late are mostly listening to the radio? What about by day-of-week, then:

Jack FM: Decade Frequency by Day of Week
Even less in it by day of week… although 70s music fans should consider tuning in on Fridays, apparently, and 80s fans will be happiest on Sundays.

The chunks of “bonus 80s” shouldn’t be surprising, I suppose, given that the radio station advertises that that’s exactly what it does at those times. But still: it’s reassuring to know that when a radio station claims to play 80s music, you don’t just have to take their word for it (so long as their listeners include somebody as geeky as me).

It feels to me like every time I tune in they’re playing an INXS song. That can’t be a coincidence, right? Let’s find out:

Jack FM: Artist Frequency
One in every ten songs are by just ten artists (including INXS). One in every four are by just 34 artists.

Yup, there’s a heavy bias towards Guns ‘n’ Roses, Michael Jackson, Prince, Oasis, Bryan Adams, Madonna, INXS, Bon Jovi, Queen, and U2 (who collectively are responsible for over a tenth of all music played on Jack FM), and – to a lesser extent – towards Robert Palmer, Meatloaf, Blondie, Green Day, Texas, Whitesnake, the Pet Shop Boys, Billy Idol, Madness, Rainbow, Elton John, Bruce Springsteen, Aerosmith, Fleetwood Mac, Phil Collins, ZZ Top, AC/DC, Duran Duran, the Police, Simple Minds, Blur, David Bowie, Def Leppard, and REM: taken together, one in every four songs played on Jack FM is by one of these 34 artists.

Jack FM: Top 20
Amazingly, the most-played song on Jack FM (Alice Cooper’s “Poison”) is not by one of the most-played 34 artists.

I was interested to see that the “top 20 songs” played on Jack FM these last six months include several songs by artists who otherwise aren’t represented at all on the station. The most-played song is Alice Cooper’s Poison, but I’ve never recorded them playing any other Alice Cooper songs (boo!). The fifth-most-played song is Fight For Your Right, by the Beastie Boys, but that’s the only Beastie Boys song I’ve caught them playing. And the seventh-most-played – Roachford’s Cuddly Toy – is similarly the only Roachford song they ever put on.

Next I tried a Markov chain analysis. Markov chains are a mathematical tool that examines a sequence (in this case, a sequence of songs) and builds a map of “chains” of sequential songs, recording the frequency with which they follow one another – here’s a great explanation and playground. The same technique is used by “predictive text” features on your smartphone: it knows what word to suggest you type next based on the patterns of words you most-often type in sequence. And running some Markov chain analysis helped me find some really… interesting patterns in the playlists. For example, look at the similarities between what was played early in the afternoon of Wednesday 19 October and what was played 12 hours later, early in the morning of Thursday 20 October:

19 October 2016 20 October 2016
12:06:33 Kool & The Gang – Fresh Kool & The Gang – Fresh 00:13:56
12:10:35 Bruce Springsteen – Dancing In The Dark Bruce Springsteen – Dancing In The Dark 00:17:57
12:14:36 Maxi Priest – Close To You Maxi Priest – Close To You 00:21:59
12:22:38 Van Halen – Why Can’t This Be Love Van Halen – Why Can’t This Be Love 00:25:00
12:25:39 Beats International / Lindy – Dub Be Good To Me Beats International / Lindy – Dub Be Good To Me 00:29:01
12:29:40 Kasabian – Fire Kasabian – Fire 00:33:02
12:33:42 Talk Talk – It’s My Life Talk Talk – It’s My Life 00:38:04
12:41:44 Lenny Kravitz – Are You Gonna Go My Way Lenny Kravitz – Are You Gonna Go My Way 00:42:05
12:45:45 Shalamar – I Can Make You Feel Good Shalamar – I Can Make You Feel Good 00:45:06
12:49:47 4 Non Blondes – What’s Up 4 Non Blondes – What’s Up 00:50:07
12:55:49 Madness – Baggy Trousers Madness – Baggy Trousers 00:54:09
Eagle Eye Cherry – Save Tonight 00:56:09
Feeling – Love It When You Call 01:04:12
13:02:51 Fine Young Cannibals – Good Thing Fine Young Cannibals – Good Thing 01:10:14
13:06:54 Blur – There’s No Other Way Blur – There’s No Other Way 01:14:15
13:09:55 Pet Shop Boys – It’s A Sin Pet Shop Boys – It’s A Sin 01:17:16
13:14:56 Zutons – Valerie Zutons – Valerie 01:22:18
13:22:59 Cure – The Love Cats Cure – The Love Cats 01:26:19
13:27:01 Bryan Adams / Mel C – When You’re Gone Bryan Adams / Mel C – When You’re Gone 01:30:20
13:30:02 Depeche Mode – Personal Jesus Depeche Mode – Personal Jesus 01:33:21
13:34:03 Queen – Another One Bites The Dust Queen – Another One Bites The Dust 01:38:22
13:42:06 Shania Twain – That Don’t Impress Me Much Shania Twain – That Don’t Impress Me Much 01:42:23
13:45:07 ZZ Top – Gimme All Your Lovin’ ZZ Top – Gimme All Your Lovin’ 01:46:25
13:49:09 Abba – Mamma Mia Abba – Mamma Mia 01:50:26
13:53:10 Survivor – Eye Of The Tiger Survivor – Eye Of The Tiger 01:53:27
Scouting For Girls – Elvis Aint Dead 01:57:28
Verve – Lucky Man 02:00:29
Fleetwood Mac – Say You Love Me 02:05:30
14:03:13 Kiss – Crazy Crazy Nights Kiss – Crazy Crazy Nights 02:10:31
14:07:15 Lightning Seeds – Sense Lightning Seeds – Sense 02:14:33
14:11:16 Pretenders – Brass In Pocket Pretenders – Brass In Pocket 02:18:34
14:14:17 Elvis Presley / JXL – A Little Less Conversation Elvis Presley / JXL – A Little Less Conversation 02:21:35
14:22:19 U2 – Angel Of Harlem U2 – Angel Of Harlem 02:24:36
14:25:20 Trammps – Disco Inferno Trammps – Disco Inferno 02:28:37
14:29:22 Cast – Guiding Star Cast – Guiding Star 02:31:38
14:33:23 New Order – Blue Monday New Order – Blue Monday 02:36:39
14:41:26 Def Leppard – Let’s Get Rocked Def Leppard – Let’s Get Rocked 02:40:41
14:46:28 Phil Collins – Sussudio Phil Collins – Sussudio 02:45:42
14:50:30 Shawn Mullins – Lullaby Shawn Mullins – Lullaby 02:49:43
14:55:31 Stars On 45 – Stars On 45 Stars On 45 – Stars On 45 02:53:45
16:06:35 Dead Or Alive – You Spin Me Round Like A Record Dead Or Alive – You Spin Me Round Like A Record 03:00:47
16:09:36 Dire Straits – Walk Of Life Dire Straits – Walk Of Life 03:03:48
16:13:37 Keane – Everybody’s Changing Keane – Everybody’s Changing 03:07:49
16:17:39 Billy Idol – Rebel Yell Billy Idol – Rebel Yell 03:10:50
16:25:41 Stealers Wheel – Stuck In The Middle Stealers Wheel – Stuck In The Middle 03:14:51
16:28:42 Green Day – American Idiot Green Day – American Idiot 03:18:52
16:33:44 A-Ha – Take On Me A-Ha – Take On Me 03:21:53
16:36:45 Cranberries – Dreams Cranberries – Dreams 03:26:54
Elton John – Philadelphia Freedom 03:30:56
Inxs – Disappear 03:36:57
Kim Wilde – You Keep Me Hanging On 03:40:59
16:44:47 Living In A Box – Living In A Box
16:47:48 Status Quo – Rockin’ All Over The World Status Quo – Rockin’ All Over The World 03:45:00

The similarities between those playlists (which include a 20-songs-in-a-row streak!) surely can’t be coincidence… but they do go some way to explaining why listening to Jack FM sometimes gives me a feeling of déjà vu (along with, perhaps, the no-talk, all-jukebox format). Looking elsewhere in the data I found dozens of other similar occurances, though none that were both such long chains and in such close proximity to one another. What does it mean?

There are several possible explanations, including:

  • The exotic, e.g. they’re using Markov chains to control an auto-DJ, and so just sometimes it randomly chooses to follow a long chain that it “learned” from a real DJ.
  • The silly, e.g. Jack FM somehow knew that I was monitoring them in this way and are trying to troll me.
  • My favourite: these two are actually the same playlist, but with breaks interspersed differently. During the daytime, the breaks in the list are more-frequent and longer, which suggests: ad breaks! Advertisers are far more-likely to pay for spots during the mid-afternoon than they are in the middle of the night (the gap in the overnight playlist could well be a short ad or a jingle), which would explain why the two are different from one another!

But the question remains: why reuse playlists in close proximity at all? Even when the station operates autonomously, as it clearly does most of the time, it’d surely be easy enough to set up an auto-DJ using “smart random” (because truly random shuffles don’t sound random to humans) to get the same or a better effect.

Jack FM Style Guide
One of the things I love about Jack FM is how little they take seriously. Like their style guide.

Which leads to another interesting observation: Jack FM’s sister stations in Surrey and Hampshire also maintain a similar playlist most of the time… which means that they’re either synchronising their ad breaks (including their duration – I suspect this is the case) or else using filler jingles to line-up content with the beginnings and ends of songs. It’s a clever operation, clearly, but it’s not beyond black-box comprehension. More research is clearly needed. (And yes, I’m sure I could just call up and ask – they call me “Newcastle Dan” on the breakfast show – but that wouldn’t be even half as fun as the data mining is…)

Ruby is still great! · Hendrik Mans

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I realized something today:

Ruby is still great.

I’ve spent the last couple of weeks digging into some of the newer/fancier/shinier technologies that have been in the limelight of the development world lately – specifically Elixir, Phoenix and Elm – and while I’ve thoroughly enjoyed them all (and instantly had a bunch of fun ideas for things to build with them), I also realized once more how much I like Ruby, and what kind of project it’s still a great choice for…

Non-Rails Frameworks in Ruby: Cuba, Sinatra, Padrino, Lotus

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It’s common for a Ruby developer to describe themselves as a Rails developer. It’s also common for someone’s entire Ruby experience to be through Rails. Rails began in 2003 by David Heinemeier Hansson, quickly becoming the most popular web framework and also serving as an introduction to Ruby for many developers.

Rails is great. I use it every day for my job, it was my introduction to Ruby, and this is the most fun I’ve ever had as a developer. That being said, there are a number of great framework options out there that aren’t Rails.

This article intends to highlight the differences between Cuba, Sinatra, Padrino, Lotus, and how they compare to or differ from Rails. Let’s have a look at some Non-Rails Frameworks in Ruby.

The Hardest Hangman

What’s the hardest word to guess, when playing hangman? I’ll come back to that.

A game of hangman; SCAT_A_, wrong guesses are R, E, O, P, L, H.
Whatever could the missing letters be?

Last year, Nick Berry wrote a fantastic blog post about the optimal strategy for Hangman. He showed that the best guesses to make to get your first “hit” in a game of hangman are not the most-commonly occurring letters in written English, because these aren’t the most commonly-occurring letters in individual words. He also showed that the first guesses should be adjusted based on the length of the word (the most common letter in 5-letter words is ‘S’, but the most common letter in 6-letter words is ‘E’). In short: hangman’s a more-complex game than you probably thought it was! I’d like to take his work a step further, and work out which word is the hardest word: that is – assuming you’re playing an optimal strategy, what word takes the most-guesses?

A hanged man being quartered by his executioner.
The rules of hangman used to be a lot more brutal. Nowadays, very few people die as a result of the game.

First, though, we need to understand how hangman is perfectly played. Based on the assumption that the “executioner” player is choosing words randomly, and that no clue is given as to the nature of the word, we can determine the best possible move for all possible states of the game by using a data structure known as a tree. Suppose our opponent has chosen a three-letter word, and has drawn three dashes to indicate this. We know from Nick’s article that the best letter to guess is A. And then, if our guess is wrong, the next best letter to guess is E. But what if our first guess is right? Well, then we’ve got an “A” in one or more positions on the board, and we need to work out the next best move: it’s unlikely to be “E” – very few three-letter words have both an “A” and an “E” – and of course what letter we should guess next depends entirely on what positions the letters are in.

A tiny fragment of possible states for the tree of three-letter words, in hangman.
There are billions of possible states of game play, but you can narrow them down quickly with strategic guessing.

What we’re actually doing here is a filtering exercise: of all of the possible letters we could choose, we’re considering what possible results that could have. Then for each of those results, we’re considering what guesses we could make next, and so on. At each stage, we compare all of the possible moves to a dictionary of all possible words, and filter out all of the words it can’t be: after our first guess in the diagram above, if we guess “A” and the board now shows “_ A _”, then we know that of the 600+ three-letter words in the English language, we’re dealing with one of only about 134. We further refine our guess by playing the odds: of those words, more of them have a “C” in than any other letter, so that’s our second guess. If it has a C in, that limits the options further, and we can plan the next guess accordingly. If it doesn’t have a C in, that still provides us with valuable information: we’re now looking for a three-letter word with an A in the second position and no letter C: that cuts it down to 124 words (and our next guess should be ‘T’). This tree-based mechanism for working out the best moves is comparable to that used by other game-playing computers. Hangman is simple enough that it can be “solved” by contemporary computers (like draughts – solved in 2007 – but unlike chess: while modern chess-playing computers can beat humans, it’s still theoretically possible to build future computers that will beat today’s computers).

Pile of stones, and the text "the best word to choose in hangman is the word that your opponent will not guess"
Zen Hangman asks the really important questions. If a man has one guess left and refused to pick a letter, does he live forever, or not at all?

Now that we can simulate the way that a perfect player would play against a truly-random executioner, we can use this to simulate games of hangman for every possible word (I’m using version 0.7 of this British-English dictionary). In other words, we set up two computer players: the first chooses a word from the dictionary, the second plays “perfectly” to try to guess the word, and we record how many guesses it took. So that’s what I did. Here’s the Ruby code I used. It’s heavily-commented and probably pretty understandable/good learning material, if you’re into that kind of thing. Or if you fancy optimising it, there’s plenty of scope for that too (I knocked it out on a lunch break; don’t expect too much!). Or you could use it as the basis to make a playable hangman game. Go wild.

The hardest three-letter hangman words: xxv, xxx, wiz, oak, vex, vox, aux, fox, yuk, www...
The hardest three-letter hangman words. “Sly” is particularly… well, sly.

Running the program, we can see that the hardest three-letter word is “xxv”, which would take 22 guesses (20 of them wrong!) to get. But aside from the roman numeral for 25, I don’t think that “xxv” is actually a word. Perhaps my dictionary’s not very good. “Oak”, though, is definitely a word, and at 20 guesses (17 wrong), it’s easily enough to hang your opponent no matter how many strokes it takes to complete the gallows.

The hardest four-letter hangman words: xxxv, quiz, jazz, zinc, faux, foxy, hazy, jibe, quay, buzz, gibe, guan, huge...
Interestingly, “oaks” is an easier word than “oak” (although it’s still very difficult): the addition of an extra letter to a word does not make it harder, especially when that letter is common.

There are more tougher words in the four-letter set, like the devious “quiz”, “jazz”, “zinc”, and “faux”. Pick one of those and your opponent – unless they’ve seen this blog post! – is incredibly unlikely to guess it before they’re swinging from a rope.

The hardest five, six, and seven-letter hangman words, including jazzy, quaff, foxed, foxing, queued, favour, vaquero, jazzier, quizzed...
“Hazing foxes, fucking cockily” is not only the title of a highly-inappropriate animated film, but also a series of very challenging Hangman words.

As we get into the 5, 6, and 7-letter words you’ll begin to notice a pattern: that the hardest words with any given number of letters get easier the longer they are. That’s kind of what you’d expect, I suppose: if there were a hypothetical word that contained every letter in the alphabet, then nobody would ever fail to (eventually) get it.

The hardest eight, nine, and ten-letter hangman words, including quizzing, puppyish, picklock, jazziness, pollywogs, cufflinks, humbuggery, juxtaposed, bucketfuls...
Some of the longer words are wonderful, like: dysprosium, semivowel, harrumph, and googolplex.

When we make a graph of each word length, showing which proportion of the words require a given number of “wrong” guesses (by an optimised player), we discover a “sweet spot” window in which we’ll find all of the words that an optimised player will always fail to guess (assuming that we permit up to 10 incorrect guesses before they’re disqualified). The window seems small for the number of times I remember seeing people actually lose at hangman, which implies to me that human players consistently play sub-optimally, and do not adequately counteract that failing by applying an equal level of “smart”, intuitive play (knowing one’s opponent and their vocabulary, looking for hints in the way the game is presented, etc.).

Graph showing the proportion of each word of a given length that take a given number of "wrong" guesses to optimally solve.
The “sweet spot” in the bottom right is the set of words which you would expect a perfect player to fail to guess, assuming that they’re given a limit of 10 “wrong” guesses.

In case you’re interested, then, here are the theoretically-hardest words to throw at your hangman opponent. While many of the words there feel like they would quite-rightly be difficult, others feel like they’d be easier than their ranking would imply: this is probably because they contain unusual numbers of vowels or vowels in unusual-but-telling positions, which humans (with their habit, inefficient under normal circumstances, of guessing an extended series of vowels to begin with) might be faster to guess than a computer.

Word Guesses taken “Wrong” guesses needed
quiz 24 20
jazz 22 19
jazzy 22 18
quaff 22 18
zinc 21 17
oak 20 17
vex 20 17
vox 20 17
foxing 22 16
foxed 21 16
queued 20 16
fuzzy 20 16
quay 20 16
pinup 20 16
fox 19 16
yuk 19 16
vaquero 22 15
jazzier 21 15
quizzed 21 15
hazing 21 15
favour 21 15
yoking 21 15
quays 20 15
quark 20 15
joked 20 15
guyed 20 15
foyer 20 15
bumph 20 15
huge 19 15
quip 19 15
gibe 19 15
rump 19 15
guan 19 15
quizzed 19 15
oaks 19 15
murk 19 15
fezzes 19 15
yuck 19 15
keno 19 15
kazoo 19 15
Download a longer list
(there’s plenty more which you’d expect to “win” with)

If you use this to give you an edge in your next game, let me know how it works out for you!

Too Ruby

Ruby, a programming language of which I’m quite fond, is well-known for it’s readability and ease of comprehension, among about thirty-seven other wonderful features.

I rediscovered quite how readable the language is when I genuinely ended up writing the following method last week:

# On saving, updates the #Shift counters if the #ExperienceLevel of this
# #Volunteer has been changed
def update_counters_if_experience_level_changed
  update_counters if experience_level_changed?
end

For the benefit of those of you who aren’t programmers, I’ll point out that which is obvious to those of us who are: the body of the method (that’s the line that’s indented) is almost identical to the method name (the line that starts with “def”).

This is the equivalent of going to WikiHow and looking up the article on, say, How to Make a Tie Dyed Cake, only to discover that the text of the article simply says, “Choose what colours you want, and then make a cake in those colours”… and you understand perfectly and go and make the cake, because you’ve got that good an understanding. In this metaphor, you’re the Ruby interpreter, by the way. And the cake is delicious.

Okay, I cheated a little: the experience_level_changed? method was provided for me by the Rails framework. And I had to write the update_counters method myself (although it, too, contains only one line of code in its body). But the point is still the same: writing Ruby, and thinking in a Rubyish way, produces beautifully readable, logical code.

Kitty

~$ sudo gem install kitty
Successfully installed kitty-0.0.2
1 gem installed
Installing ri documentation for kitty-0.0.2...
Installing RDoc documentation for kitty-0.0.2...

~$ kitty
     ____
    (.   \
      \  |
       \ |___(\--/)
     __/    (  . . )
    "'._.    '-.O.'
         '-.  \ "|\
            '.,,/'.,,

~$ kitty
         /\_/\
    /\  / o o \
   //\\ \~(*)~/
   `  \/   ^ /
      | \|| ||
      \ '|| ||
       \)()-())

Kitty. That is all.

Hiding In Plain “Site”

I’ve written a program called PicInHTML, which makes web pages with concealed images which are shown when text on the page is selected. What’s clever about these page are how they work: they’re a single file, with no dependence on images nor Javascript, and they work by leveraging the little-used ::selected CSS selector. Each individual letter on the page is given a CSS class to associate it with the colour of a corresponding pixel in the source image, and selecting the text changes the background colour to that pixel colour.

That’s a wordy way of putting it. Let’s try an example:

An example of a special page - selecting the text in this page reveals the Reddit alien. Click on the image to see the discussion about this example on Reddit.

Give it a go on any of the following pages. You’ll need to not be using Microsoft Internet Explorer, I’m afraid, as it doesn’t support the ::selected CSS selector. All you have to do is select the text on the page to reveal the secret image!

If you’re interested in the mechanics of how it works, or you’d like to get a copy of the source code and have a play yourself, see my project page on PicInHTML. You could also try looking at the source code of any of the pages, above: they’re not too-hard to read, especially for machine-generated code.

!

An example of a special page - selecting the text in this page reveals the Reddit alien. Click on the image to see the discussion about this example on Reddit.

T

SSL Client Certificate Authentication In Ruby On Rails

I’ve been playing with using client-side SSL certificates (installed into your web browser) as a means to authenticate against a Ruby on Rails-powered application. This subject is geeky and of limited interest even to the people who read this blog (with the possible exception of Ruth, who may find herself doing exactly this as part of her Masters dissertation), so rather than write about it all here, I’ve written a howto/article: SSL Client Certificate Authentication In Ruby On Rails. If you’re at all interested in the topic, you’re welcome to have a read and give me any feedback.