If you are reading this, I’m not around anymore. Take the phone and speed dial 1. Tell the woman who answers that you are Eric Sanderson. The woman is Dr Randle. She’ll understand what has happened and you will be able to see her straight away. Take the car keys and drive the yellow jeep to Dr Randle’s house. If you haven’t found it yet, there’s a map in the envelope – it isn’t too far and it’s not hard to find.
Dr Randle will be able to answer all your questions. It’s very important that you go straight away. Do not pass go. Do not explore. Do not collect two hundred pounds.
The house keys are hanging from a nail on the banister at the bottom of the stairs, don’t forget them.
With regret and also hope,
The First Eric Sanderson
I read the book pretty quickly, and loved it. I thought my brother James and my friend Dave would love it too. So I did what any sane person would do under the circumstances – I created a new Gmail address for The First Eric Sanderson, and sent them both emails, making sure I copied in both their personal and work email addresses. Neither of them drove at the time, and they lived in Barcelona at the time, so I had to tweak the blurb somewhat:
First things first, stay calm.
If you are reading this, I’m not around anymore. Take the phone and call Fnac. Tell the person who answers that you are looking for a book called ‘The Raw Shark Texts’, by Steven Hall. They’ll understand what you need and will be able to help you straight away. Take the train into the centre. If you haven’t found it yet, there’s a link at the end of this e-mail.
Fnac will be able to answer all your questions. It’s very important that you go straight away. Do not pass go. Do not explore. Do not collect two hundred pounds.
The train ticket is in your wallet, don’t forget it.
And then… I left them to it. I called Dave for a chat a week later. About 15 minutes into the call I said “So anything interesting happening? Have you received any weird emails?”. There was a long pause on the other end, and then: “You! It was you, you bastard!”. As it turned out the email had been quite effective – he hadn’t written it off as spam, and he’d been thinking about it. My brother, it turned out, had even gone to Fnac and asked about the book, though unfortunately they hadn’t received it yet (I’d bought the book when it was first published in hardback in the UK, and I guess it hadn’t made it to Spain yet). He did later get hold of it, and read and loved it.
With most my reading done on a Kindle these days, I expect the next time I do something like this it will have to involve some sort of digital crumb trail.
In this series of blog posts I will be exploring what I feel are some of the important aspects of software development management, particularly in relation to web application development.
A cross-functional team is generally defined as a group of people with different expertise working towards a single goal or objective. Within software development, this means a team of people who will collectively hold all the skills required to develop, build, and deliver the product. For example, when developing a web application, you would want the development team to have expertise not just in coding in the programming language of choice, but also in databases, systems, usability, UI/UX/front-end design, front-end development, testing, technical writing, and so forth.
The common single goal and nature of cross-functional teams make the greatest advantages to the approach seem fairly self-explanatory, but difficult to describe. Perhaps Mary and Tom Poppendieck summarised it best in their book Lean Software Development. They described seven types of waste in software development, and cross-functional teams can help in all of them:
Partially done work, or work which has not yet been delivered. For example, work which is yet to be tested. Cross-functional teams minimise the effect of this by ensuring no external dependency – continuous testing can take place, and in an Agile environment delivery is made at the end of each iteration ensuring partial work is not left waiting.
Extra features, or providing more features than have been requested. This is particularly pertinent when comparing Waterfall and Agile methodologies – in Waterfall this will not be apparent until delivery, whereas with Agile methodologies there will be more opportunity to act. A cross-functional team can help by providing domain experts who can assess when to stop working on a feature.
Relearning, or reinventing the wheel. A cross-functional team can share knowledge and prevent the team from working on a problem which has already been solved.
Hand-offs. Handing off work to a third party will suffer from two problems: the inevitable documentation, and a potential degradation of knowledge. Within a cross-functional team, less documentation will be required as there will already be prior shared knowledge, and there will similarly be less room for misunderstanding. All this will lead to fewer delays.
Delays. As alluded to above, cross-functional teams will reduce delays in communication and external parties, but also those due to, for example, differing priorities.
Task switching. Interruptions can be minimised when work can be coordinated within a single team, without external dependencies.
Defects. Not just bugs, but misunderstanding the functionality and delivering the wrong thing. A cross-functional team is better suited to defining the functionality, and involvement from all team members means better test coverage.
There is a certain amount of disagreement on whether Scrum should include specialists in defining a cross-functional team, or whether cross-functionality should be achieved by having team members work in areas outside their area of expertise. In practical terms, I’ve found it to be a little bit of both, but either way I think it’s clear that cross-functional teams are important.
Back in June 2012, I was watching Esc and Ctrl – a series of videos from Jon Ronson for The Guardian, in which he investigates attempts to control the Internet in some way, shape, or form. Some of the videos dealt with Twitterbots, and they piqued an interest on how the bots could generate responses from real Twitter users. As I had free time at lunch I thought I’d investigate how difficult writing a Twitterbot could be.
As it turns out, writing a Twitterbot is remarkably simple. This is probably testament to Twitter’s API – as far as I could tell, pretty much everything I would want to do as a user was available also via the API, and the rich data provided by Twitter was well suited to an application which would effectively parse it in order to generate a response. Additionally, there were plenty of libraries readily available for use in a variety of programming languages, so the barrier to entry was very low. I decided to write one, but I needed an idea for a bot.
Some 5 years earlier, I had read The Raw Shark Texts by Steven Hall. For those who haven’t read it, a brief synopsis: a man named Eric Sanderson is chased by a conceptual shark called a Ludovician, which feeds on words, language, human memory, and the intrinsic sense of self. Eric tries to throw the shark off his scent by travelling through unspace – forgotten tunnels, and abandoned buildings – and the use of other people’s information, writing, and dictaphone recordings. Eric wraps himself in a chaos of others. (As an aside, if you’re interested in the book, I recommend a physical copy, as it is beautifully styled with typography pictures which might lose something in an ebook format).
This felt like a perfect fit for a Twitterbot – what if the Ludovician was searching for Eric on Twitter? A drop of information in an ocean of data would attract it, and there was scope for creating a bit of a mashup. I set off to write it, using PHP for quick development. First, I created an outline of what the script would do:
Search Twitter for references to “Eric Sanderson”, “ludovician”, “luxophage”, or “cognicharius” – these would be rare enough to be people talking about the book, or an exact name match which might entice people to investigate further
For each of the resulting matches, follow any users which weren’t already being followed, store the geo location (if available), and issue a reply to their tweet
Every couple dozen tweets, average out the the locations collected to provide a position for the shark, and provide the likely location of Eric based on the number of matched tweets per location
The responses that the bot would issue would start off as relatively descriptive text, and I was hoping to provide increasing measures of stylised text, much like the book offers, with some specific quotes. This is difficult on Twitter, as ASCII art is limited by the formatting, and there is little control of this on the Twitter site or clients. For this reason I decided to use a technique which was used to great effect on YouTube – superscript and subscript characters. The full list of responses I prepared was as follows:
Pattern recognised, following
Reincident pattern, targeting
Reincident pattern, circling
Reincident pattern, increasing priority
Pattern memorised, attempting to locate
Location compromised, reverting to data collection
The final response was based on a separate “fragment” of the novel, which fans of the novel were more likely to have read, and made reference to the Ludovician and Eric becoming tangled.
I had initially considered the idea of taking the geo coordinates and doing a reverse lookup with Google Maps in order to get a near exact address to use in the response, but given the responses planned, I felt it might come across as a little too creepy/menacing.
The only thing left was to create a Twitter account for the bot. @Ludovician was unfortunately already taken, so I decided on @Cognicharius – the name of the family the Ludovician purportedly belongs to. I used an ASCII art creator to generate an image from a shark photo as its avatar. I set it loose on June 14th 2012, just four lunchtimes after starting.
The bot lived for approximately one year – until the 1.0 Twitter API was retired in June 2013 in favour of the 1.1 API. I didn’t upgrade the bot as I felt the experiment had run its course. In its year of life, @Cognicharius had tweeted 738 times, followed 310 Twitter users, and garnered 91 followers (far more than I can claim). It earned several retweets (including some from Steven Hall himself), generated some amusing responses from people as you can see below, and even got someone to create a Google Map tracking the location of the shark.
Maybe the next bot will just have to message all those users about this blog post.