Journal tags: language

Picture someone tediously going through a spreadsheet that someone else has filled in by hand and finding yet another error.

“I wish to God these calculations had been executed by steam!” they cry.

The year was 1821 and technically the spreadsheet was a book of logarithmic tables. The frustrated cry came from Charles Babbage, who channeled his frustration into a scheme to create the world’s first computer.

His difference engine didn’t work out. Neither did his analytical engine. He’d spend his later years taking his frustrations out on street musicians, which—as a former busker myself—earns him a hairy eyeball from me.

But we’ve all been there, right? Some tedious task that feels soul-destroying in its monotony. Surely this is exactly what machines should be doing?

I have a hunch that this is where machine learning and large language models might turn out to be most useful. Not in creating breathtaking works of creativity, but in menial tasks that nobody enjoys.

Someone was telling me earlier today about how they took a bunch of haphazard notes in a client meeting. When the meeting was done, they needed to organise those notes into a coherent summary. Boring! But ChatGPT handled it just fine.

I don’t think that use-case is going to appear on the cover of Wired magazine anytime soon but it might be a truer glimpse of the future than any of the breathless claims being eagerly bandied about in Silicon Valley.

You know the way we no longer remember phone numbers, because, well, why would we now that we have machines to remember them for us? I’d be quite happy if machines did that for the annoying little repetitive tasks that nobody enjoys.

I’ll give you an example based on my own experience.

Regular expressions are my kryptonite. I’m rubbish at them. Any time I have to figure one out, the knowledge seeps out of my brain before long. I think that’s because I kind of resent having to internalise that knowledge. It doesn’t feel like something a human should have to know. “I wish to God these regular expressions had been calculated by steam!”

Now I can get a chatbot with a large language model to write the regular expression for me. I still need to describe what I want, so I need to write the instructions clearly. But all the gobbledygook that I’m writing for a machine now gets written by a machine. That seems fair.

Mind you, I wouldn’t blindly trust the output. I’d take that regular expression and run it through a chatbot, maybe a different chatbot running on a different large language model. “Explain what this regular expression does,” would be my prompt. If my input into the first chatbot matches the output of the second, I’d have some confidence in using the regular expression.

A friend of mine told me about using a large language model to help write SQL statements. He described his database structure to the chatbot, and then described what he wanted to select.

Again, I wouldn’t use that output without checking it first. But again, I might use another chatbot to do that checking. “Explain what this SQL statement does.”

Playing chatbots off against each other like this is kinda how machine learning works under the hood: generative adverserial networks.

Of course, the task of having to validate the output of a chatbot by checking it with another chatbot could get quite tedious. “I wish to God these large language model outputs had been validated by steam!”

Sounds like a job for machines.

You know how when you’re on hold to any customer service line you hear a message that thanks you for calling and claims your call is important to them. The message always includes a disclaimer about calls possibly being recorded “for training purposes.”

Nobody expects that any training is ever actually going to happen—surely we would see some improvement if that kind of iterative feedback loop were actually in place. But we most certainly want to know that a call might be recorded. Recording a call without disclosure would be unethical and illegal.

Consider chatbots.

If you’re having a text-based (or maybe even voice-based) interaction with a customer service representative that doesn’t disclose its output is the result of large language models, that too would be unethical. But, at the present moment in time, it would be perfectly legal.

That needs to change.

I suspect the necessary legislation will pass in Europe first. We’ll see if the USA follows.

In a way, this goes back to my obsession with seamful design. With something as inherently varied as the output of large language models, it’s vital that people have some way of evaluating what they’re told. I believe we should be able to see as much of the plumbing as possible.

The bare minimum amount of transparency is revealing that a machine is in the loop.

This shouldn’t be a controversial take. But I guarantee we’ll see resistance from tech companies trying to sell their “AI” tools as seamless, indistinguishable drop-in replacements for human workers.

We use metaphors all the time. To quote George Lakoff, we live by them.

We use analogies some of the time. They’re particularly useful when we’re wrapping our heads around something new. By comparing something novel to something familiar, we can make a shortcut to comprehension, or at least, categorisation.

But we need a certain amount of vigilance when it comes to analogies. Just because something is like something else doesn’t mean it’s the same.

With that in mind, here are some ways that people are describing generative machine learning tools. Large language models are like…

• …a blurry JPEG of the web,
• …fly-by-wire avionics,
• …drum machines,
• …an octopus.

I’ve mentioned before that I’m not a fan of initialisms and acronyms. They can be exclusionary.

It bothers me doubly when everyone is talking about AI.

First of all, the term is so vague as to be meaningless. Sometimes—though rarely—AI refers to general artificial intelligence. Sometimes AI refers to machine learning. Sometimes AI refers to large language models. Sometimes AI refers to a series of if/else statements. That’s quite a spectrum of meaning.

Secondly, there’s the assumption that everyone understands the abbreviation. I guess that’s generally a safe assumption, but sometimes AI could refer to something other than artificial intelligence.

In countries with plenty of pastoral agriculture, if someone works in AI, it usually means they’re going from farm to farm either extracting or injecting animal semen. AI stands for artificial insemination.

I think that abbreviation might work better for the kind of things currently described as using AI.

We were discussing this hot topic at work recently. Is AI coming for our jobs? The consensus was maybe, but only the parts of our jobs that we’re more than happy to have automated. Like summarising some some findings. Or perhaps as a kind of lorem ipsum generator. Or for just getting the ball rolling with a design direction. As Terence puts it:

Midjourney is great for a first draft. If, like me, you struggle to give shape to your ideas then it is nothing short of magic. It gets you through the first 90% of the hard work. It’s then up to you to refine things.

That’s pretty much the conclusion we came to in our discussion at Clearleft. There’s no way that we’d use this technology to generate outputs for clients, but we certainly might use it to generate inputs. It’s like how we’d do a quick round of sketching to get a bunch of different ideas out into the open. Terence is spot on when he says:

Midjourney lets me quickly be wrong in an interesting direction.

To put it another way, using a large language model could be a way of artificially injecting some seeds of ideas. Artificial insemination.

So now when I hear people talk about using AI to create images or articles, I don’t get frustrated. Instead I think, “Using artificial insemination to create images or articles? Yes, that sounds about right.”

I just finished watching The Billion Dollar Code, a German language miniseries on Netflix. It’s no Halt and Catch Fire, but it combines ’90s nostalgia, early web tech, and an opportunity for me to exercise my German comprehension.

It’s based on a true story, but with amalgamated characters. The plot, which centres around the preparation for a court case, inevitably invites comparison to The Social Network, although this time the viewpoint is from that of the underdogs trying to take on the incumbent. The incumbent is Google. The underdogs are ART+COM, artist-hackers who created the technology later used by Google Earth.

Early on, one of the characters says something about creating a one-to-one model of the whole world. That phrase struck me as familiar…

I remember being at the inaugural Future Of Web Apps conference in London back in 2006. Discussing the talks with friends afterwards, we all got a kick out of the speaker from Google, who happened to be German. His content and delivery was like a wonderfully Stranglovesque mad scientist. I’m sure I remember him saying something like “vee made a vun-to-vun model of the vurld.”

His name was Steffen Meschkat. I liveblogged the talk at the time. Turns out he was indeed on the team at ART+COM when they created Terravision, the technology later appropriated by Google (he ended up working at Google, which doesn’t make for as exciting a story as the TV show).

His 2006 talk was all about Ajax, something he was very familiar with, being on the Google Maps team. The Internet Archive even has a copy of the original audio!

It’s easy to forget now just how much hype there was around Ajax back then. It prompted me to write a book about combining Ajax and progressive enhancement.

These days, no one talks about Ajax. But that’s not because the technology went away. Quite the opposite. The technology became so ubiquituous that it no longer even needs a label.

A web developer today might ask “what’s Ajax?” in the same way that a fish might ask “what’s water?”

When I was braindumping my thoughts prompted by last week’s UX Fest conference, I wrote about dark patterns.

Well, actually I wrote about deceptive dark patterns. That was a deliberate choice.

The phrase “dark pattern” is …problematic. We really don’t need to be associating darkness with negativity any more than we already do in our language and culture.

This is something I discussed with Melissa Smith after her talk on this topic. The consensus in general seems to be that the terminology is far from ideal, but it’s a bit late to change it now (I’m sure if Harry were coining the term today, he would choose a different phrase).

The defining characteristic of a “dark” pattern is that intentionally deceptive. How about we shift the terminology to talk about deceptive patterns?

Now, I get that inertia is a powerful force and it would be confusing to try do to a find-and-replace on all the resources that already exist on documenting “dark” patterns. So here’s a compromise:

From here on out, let’s start using the adjective “deceptive” in addition to the existing adjective “dark.” That’s what I did in my blog post. I only used the phrase “deceptive dark patterns.”

If we do that consistently, then after a while we’ll be able to drop one of those adjectives—“dark”—and refer to “deceptive patterns.”

Personally I’d love it if we could change the terminology overnight—and I’m quite heartened by the speed at which we changed our Github branches from “master” to “main”—but being pragmatic, I think this approach stands a greater chance of success.

Who’s with me?

I work with words. Sometimes they’re my words. Sometimes they’re words that my colleagues have written:

One of my roles at Clearleft is “content buddy.” If anyone is writing a talk, or a blog post, or a proposal and they want an extra pair of eyes on it, I’m there to help.

I also work with web technologies, usually front-of-the-front-end stuff. HTML, CSS, and JavaScript. The technologies that users experience directly in web browsers.

I think a lot about design principles for the web. The two principles I keep coming back to are the robustness principle and the principle of least power.

When it comes to words, the guide that I return to again and again is George Orwell, specifically his short essay, Politics and the English Language.

Towards the end, he offers some rules for writing.

1. Never use a metaphor, simile, or other figure of speech which you are used to seeing in print.
2. Never use a long word where a short one will do.
3. If it is possible to cut a word out, always cut it out.
4. Never use the passive where you can use the active.
5. Never use a foreign phrase, a scientific word, or a jargon word if you can think of an everyday English equivalent.
6. Break any of these rules sooner than say anything outright barbarous.

These look a lot like design principles. Not only that, but some of them look like specific design principles. Take the robustness principle:

Be conservative in what you send, be liberal in what you accept.

That first part applies to Orwell’s third rule:

If it is possible to cut a word out, always cut it out.

Be conservative in what words you send.

Then there’s the principle of least power:

Choose the least powerful language suitable for a given purpose.

Compare that to Orwell’s second rule:

Never use a long word where a short one will do.

That could be rephrased as:

Choose the shortest word suitable for a given purpose.

Or, going in the other direction, the principle of least power could be rephrased in Orwell’s terms as:

Never use a powerful language where a simple language will do.

Oh, I like that! I like that a lot.

One of my roles at Clearleft is “content buddy.” If anyone is writing a talk, or a blog post, or a proposal and they want an extra pair of eyes on it, I’m there to help.

Sometimes a colleague will send a link to a Google Doc where they’ve written an article. I can then go through it and suggest changes. Using the “suggest” mode rather than the “edit” mode in Google Docs means that they can accept or reject each suggestion later.

But what works better—and is far more fun—is if we arrange to have a video call while we both have the Google Doc open in our browsers. That way, instead of just getting the suggestions, we can talk through the reasoning behind each one. It feels more like teaching them to fish instead of giving them a grammatically correct fish.

Some of the suggestions are very minor; punctuation, capitalisation, stuff like that. Where it gets really interesting is trying to figure out and explain why some sentence constructions feel better than others.

A fairly straightforward example is long sentences. Not all long sentences are bad, but the longer a sentence gets, the more it runs the risk of overwhelming the reader. So if there’s an opportunity to split one long sentence into two shorter sentences, I’ll usually recommend that.

Here’s an example from Chris’s post, Delivering training remotely – the same yet different. The original sentence read:

I recently had the privilege of running some training sessions on product design and research techniques with the design team at Duck Duck Go.

There’s nothing wrong with that. But maybe this is a little easier to digest:

I recently had the privilege of running some training sessions with the design team at Duck Duck Go. We covered product design and research techniques.

Perhaps this is kind of like the single responsibility principle in programming. Whereas the initial version was one sentence that conveyed two pieces of information (who the training was with and what the training covered), the final version has a separate sentence for each piece of information.

I wouldn’t take that idea too far though. Otherwise you’d end up with something quite stilted and robotic.

Speaking of sounding robotic, I’ve noticed that people sometimes avoid using contractions when they’re writing online: “there is” instead of “there’s” or “I am” instead of “I’m.” Avoiding contractions seems to be more professional, but actually it makes the writing a bit too formal. There’s a danger of sounding like a legal contract. Or a Vulcan.

Sometimes a long sentence can’t be broken down into shorter sentences. In that case, I watch out for how much cognitive load the sentence is doling out to the reader.

Here’s an example from Maite’s post, How to engage the right people when recruiting in house for research. One sentence initially read:

The relevance of the people you invite to participate in a study and the information they provide have a great impact on the quality of the insights that you get.

The verb comes quite late there. As a reader, until I get to “have a great impact”, I have to keep track of everything up to that point. Here’s a rephrased version:

The quality of the insights that you get depends on the relevance of the people you invite to participate in a study and the information they provide.

Okay, there are two changes there. First of all, the verb is now “depends on” instead of “have a great impact on.” I think that’s a bit clearer. Secondly, the verb comes sooner. Now I only have to keep track of the words up until “depends on”. After that, I can flush my memory buffer.

Here’s another changed sentence from the same article. The initial sentence read:

You will have to communicate at different times and for different reasons with your research participants.

I suggested changing that to:

You will have to communicate with your research participants at different times and for different reasons.

To be honest, I find it hard to explain why that second version flows better. I think it’s related to the idea of reducing dependencies. The subject “your research participants” is dependent on the verb “to communicate with.” So it makes more sense to keep them together instead of putting a subclause between them. The subclause can go afterwards instead: “at different times and for different reasons.”

Here’s one final example from Katie’s post, Service Designers don’t design services, we all do. One sentence initially read:

Understanding the relationships between these moments, digital and non-digital, and designing across and between these moments is key to creating a compelling user experience.

That sentence could be broken into shorter sentences, but it might lose some impact. Still, it can be rephrased so the reader doesn’t have to do as much work. As it stands, until the reader gets to “is key to creating”, they have to keep track of everything before that. It’s like the feeling of copying and pasting. If you copy something to the clipboard, you want to paste it as soon as possible. The longer you have to hold onto it, the more uncomfortable it feels.

So here’s the reworked version:

The key to creating a compelling user experience is understanding the relationships between these moments, digital and non-digital, and designing across and between these moments.

As a reader, I can digest and discard each of these pieces in turn:

1. The key to creating a compelling user experience is…
2. understanding the relationships between these moments…
3. digital and non-digital…
4. and…
5. designing across and between these moments.

Maybe I should’ve suggested “between these digital and non-digital moments” instead of “between these moments, digital and non-digital”. But then I worry that I’m intruding on the author’s style too much. With the finished sentence, it still feels like a rousing rallying cry in Katie’s voice, but slightly adjusted to flow a little easier.

I must say, I really, really enjoy being a content buddy. I know the word “editor” would be the usual descriptor, but I like how unintimidating “content buddy” sounds.

I am almost certainly a terrible content buddy to myself. Just as I ignore my own advice about preparing conference talks, I’m sure I go against my own editorial advice every time I blurt out a blog post here. But there’s one piece I’ve given to others that I try to stick to: write like you speak.

I think my co-workers are getting annoyed with me. Any time they use an acronym or initialism—either in a video call or Slack—I ask them what it stands for. I’m sure they think I’m being contrarian.

The truth is that most of the time I genuinely don’t know what the letters stand for. And I’ve got to that age where I don’t feel any inhibition about asking “stupid” questions.

But it’s also true that I really, really dislike acronyms, initialisms, and other kinds of jargon. They’re manifestations of gatekeeping. They demarcate in-groups from outsiders.

Of course if you’re in a conversation with an in-group that has the same background and context as you, then sure, you can use acronyms and initialisms with the confidence that there’s a shared understanding. But how often can you be that sure? The more likely situation—and this scales exponentially with group size—is that people have differing levels of inside knowledge and experience.

I feel sorry for anyone trying to get into the field of web performance. Not only are there complex browser behaviours to understand, there’s also a veritable alphabet soup of initialisms to memorise. Here’s a really good post on web performance by Harry, but notice how the initialisms multiply like tribbles as the post progresses until we’re talking about using CWV metrics like LCP, FID, and CLS—alongside TTFB and SI—to look at PLPs, PDPs, and SRPs. And fair play to Harry; he expands each initialism the first time he introduces it.

But are we really saving any time by saying FID instead of first input delay? I suspect that the only reason why the word “cumulative” precedes “layout shift” is just to make it into the three-letter initialism CLS.

Still, I get why initialisms run rampant in technical discussions. You can be sure that most discussions of particle physics would be incomprehensible to outsiders, not necessarily because of the concepts, but because of the terminology.

Again, if you’re certain that you’re speaking to peers, that’s fine. But if you’re trying to communicate even a little more widely, then initialisms and abbreviations are obstacles to overcome. And once you’re in the habit of using the short forms, it gets harder and harder to apply context-shifting in your language. So the safest habit to form is to generally avoid using acronyms and initialisms.

Unnecessary initialisms are exclusionary.

Think about on-boarding someone new to your organisation. They’ve already got a lot to wrap their heads around without making them figure out what a TAM is. That’s a real example from Clearleft. We have a regular Thursday afternoon meeting. I call it the Thursday afternoon meeting. Other people …don’t.

I’m trying—as gently as possible—to ensure we’re not being exclusionary in our language. My co-workers indulge me, even it’s just to shut me up.

But here’s the thing. I remember many years back when a job ad went out on the Clearleft website that included the phrase “culture fit”. I winced and explained why I thought that was a really bad phrase to use—one that is used as code for “more people like us”. At the time my concerns were met with eye-rolls and chuckles. Now, as knowledge about diversity and inclusion has become more widespread, everyone understands that using a phrase like “culture fit” can be exclusionary.

But when I ask people to expand their acronyms and initialisms today, I get the same kind of chuckles. My aversion to abbreviations is an eccentric foible to be tolerated.

But this isn’t about me.

There are three ways—that I know of—to associate styles with markup.

External CSS

This is probably the most common. Using a link element with a rel value of “stylesheet”, you point to a URL using the href attribute. That URL is a style sheet that is applied to the current document (“the relationship of the linked resource it is that is a ‘stylesheet’ for the current document”).

<link rel="stylesheet" href="/path/to/styles.css">

In theory you could associate a style sheet with a document using an HTTP header, but I don’t think many browsers support this in practice.

You can also pull in external style sheets using the @import declaration in CSS itself, as long as the @import rule is declared at the start, before any other styles.

@import url('/path/to/more-styles.css');

When you use link rel="stylesheet" to apply styles, it’s a blocking request: the browser will fetch the style sheet before rendering the HTML. It needs to know how the HTML elements will be painted to the screen so there’s no point rendering the HTML until the CSS is parsed.

Embedded CSS

You can also place CSS rules inside a style element directly in the document. This is usually in the head of the document.

<style>
element {
    property: value;
}
</style>

When you embed CSS in the head of a document like this, there is no network request like there would be with external style sheets so there’s no render-blocking behaviour.

You can put any CSS inside the style element, which means that you could use embedded CSS to load external CSS using an @import statement (as long as that @import statement appears right at the start).

<style>
@import url('/path/to/more-styles.css');
element {
    property: value;
}
</style>

But then you’re back to having a network request.

Inline CSS

Using the style attribute you can apply CSS rules directly to an element. This is a universal attribute. It can be used on any HTML element. That doesn’t necessarily mean that the styles will work, but your markup is never invalidated by the presence of the style attribute.

<element style="property: value">
</element>

Whereas external CSS and embedded CSS don’t have any effect on specificity, inline styles are positively radioactive with specificity. Any styles applied this way are almost certain to over-ride any external or embedded styles.

You can also apply styles using JavaScript and the Document Object Model.

element.style.property = 'value';

Using the DOM style object this way is equivalent to inline styles. The radioactive specificity applies here too.

Style declarations specified in external style sheets or embedded CSS follow the rules of the cascade. Values can be over-ridden depending on the order they appear in. Combined with the separate-but-related rules for specificity, this can be very powerful. But if you don’t understand how the cascade and specificity work then the results can be unexpected, leading to frustration. In that situation, inline styles look very appealing—there’s no cascade and everything has equal specificity. But using inline styles means foregoing a lot of power—you’d be ditching the C in CSS.

A common technique for web performance is to favour embedded CSS over external CSS in order to avoid the extra network request (at least for the first visit—there are clever techniques for caching an external style sheet once the HTML has already loaded). This is commonly referred to as inlining your CSS. But really it should be called embedding your CSS.

This language mix-up is not a hill I’m going to die on (that hill would be referring to blog posts as blogs) but I thought it was worth pointing out.

The latest episode of the Clearleft podcast is out. If you’re a subscriber, it will magically appear in your podcast software of choice using the power of RSS. If you’re not a subscriber, it isn’t too late to change that.

This week’s episode is all about design ops. I began contructing the episode by gathering raw material from talks at Leading Design. There’s good stuff from Kim Fellman, Jacqui Frey, Courtney Kaplan, and Meredith Black.

But as I was putting the snippets together, I felt like the episode was missing something. It needed a bit of oomph. So I harangued Andy for some of his time. I wasn’t just fishing for spicy hot takes—something that Andy is known for. Andy is also the right person to explain design ops. If you search for that term, one of the first results you’ll get is a post he wrote on the Clearleft blog a few years back called Design Ops — A New Discipline.

I remember helping Andy edit that post and I distinctly recall my feedback. The initial post didn’t have a definition of the term, and I felt that a definition was necessary (and Andy added one to the post).

My cluenessness about the meaning of terms like “design ops” or “service design” isn’t some schtick I’m putting on for the benefit of the podcast. I’m genuinely trying to understand these terms better. I don’t like the feeling of hearing a term being used a lot without a clear understanding of what that term means. All too often my understanding feels more like “I think I know it means, but I’m not sure I could describe it.” I’m not comfortable with that.

Making podcast episodes on these topics—which are outside my comfort zone—has been really helpful. I now feel like I have a much better understanding of service design, design ops and other topical terms. I hope that the podcast will be just as helpful for listeners who feel as bamboozled as I do.

Ben Holliday recently said:

The secret of design being useful in many places is not talking about design too much and just getting on with it. I sometimes think we create significant language barriers with job titles, design theory and making people learn a new language for the privilege of working with us.

I think there’s some truth to that. Andy disagrees. Strongly.

In our chat, Andy and I had what politicians would describe as “a robust discussion.” I certainly got some great material for the podcast (though some of the more contentious bits remain on the cutting room floor).

Calling on Andy for this episode was definitely the right call. I definitely got the added oomph I was looking for. In fact, this ended up being one of my favourite episodes.

There’s a lot of snappy editing, all in service of crafting a compelling narrative. First, there’s the origin story of design ops. Then there’s an explanation of what it entails. From around the 13 minute mark, there’s a pivot—via design systems—into asking whether introducing a new term is exclusionary. That’s when the sparks start to fly. Finally, I pull it back to talking about how Clearleft can help in providing design ops as a service.

The whole episode comes out at 21 minutes, which feels just right to me.

I’m really pleased with how this episode turned out, and I hope you’ll like it too. Have a listen and decide for yourself.

I compared design systems to dictionaries. My point was that design systems—like language—can be approached in a prescriptivist or descriptivist manner. And I favour descriptivism.

A prescriptive approach might give you a beautiful design system, but if it doesn’t reflect the actual product, it’s fiction. A descriptive approach might give a design system with imperfections and annoying flaws, but at least it will be accurate.

I think it’s more important for a design system to be accurate than beautiful.

Meanwhile, over on Frank’s website, he’s been documenting the process of its (re)design. He made an interesting comparison in his post Redesign: Gardening vs. Architecture. He talks about two styles of writing:

In interviews, Martin has compared himself to a gardener—forgoing detailed outlines and overly planned plot points to favor ideas and opportunities that spring up in the writing process. You see what grows as you write, then tend to it, nurture it. Each tendrilly digression may turn into the next big branch of your story. This feels right: good things grow, and an important quality of growth is that the significant moments are often unanticipated.

On the other side of writing is who I’ll call “the architect”—one who writes detailed outlines for plots and believes in the necessity of overt structure. It puts stock in planning and foresight. Architectural writing favors divisions and subdivisions, then subdivisions of the subdivisions. It depends on people’s ability to move forward by breaking big things down into smaller things with increasing detail.

It’s not just me, right? It all sounds very design systemsy, doesn’t it?

This is a false dichotomy, of course, but everyone favors one mode of working over the other. It’s a matter of personality, from what I can tell.

Replace “personality” with “company culture” and I think you’ve got an interesting analysis of the two different approaches to design systems. Descriptivist gardening and prescriptivist architecture.

Frank also says something that I think resonates with the evergreen debate about whether design systems stifle creativity:

It can be hard to stay interested if it feels like you’re painting by numbers, even if they are your own numbers.

I think Frank’s comparison—gardeners and architects—also speaks to something bigger than design systems…

I gave a talk last year called Building. You can watch it, listen to it, or read the transcript if you like. The talk is about language (sort of). There’s nothing about prescriptivism or descriptivism in there, but there’s lots about metaphors. I dive into the metaphors we use to describe our work and ourselves: builders, engineers, and architects.

It’s rare to find job titles like software gardener, or information librarian (even though they would be just as valid as other terms we’ve made up like software engineer or information architect). Outside of the context of open source projects, we don’t talk much about maintenance. We’re much more likely to talk about making.

Back in 2015, Debbie Chachra wrote a piece in the Atlantic Monthly called Why I Am Not a Maker:

When tech culture only celebrates creation, it risks ignoring those who teach, criticize, and take care of others.

Anyone who’s spent any time working on design systems can tell you there’s no shortage of enthusiasm for architecture and making—“let’s build a library of components!”

There’s less enthusiasm for gardening, care, communication and maintenance. But that’s where the really important work happens.

In her article, Debbie cites Ethan’s touchstone:

In her book The Real World of Technology, the metallurgist Ursula Franklin contrasts prescriptive technologies, where many individuals produce components of the whole (think about Adam Smith’s pin factory), with holistic technologies, where the creator controls and understands the process from start to finish.

(Emphasis mine.)

In that light, design systems take their place in a long history of dehumanising approaches to manufacturing like Taylorism. The priorities of “scientific management” are the same as those of design systems—increasing efficiency and enforcing consistency.

Humans aren’t always great at efficiency and consistency, but machines are. Automation increases efficiency and consistency, sacrificing messy humanity along the way:

Machine with the strength of a hundred men
Can’t feed and clothe my children.

Historically, we’ve seen automation in terms of physical labour—dock workers, factory workers, truck drivers. As far as I know, none of those workers participated in the creation of their mechanical successors. But when it comes to our work on the web, we’re positively eager to create the systems to make us redundant.

The usual response to this is the one given to other examples of automation: you’ll be free to spend your time in a more meaningful way. With a design system in place, you’ll be freed from the drudgery of manual labour. Instead, you can spend your time doing more important work …like maintaining the design system.

You’ve heard the joke about the factory of the future, right? The factory of the future will have just two living things in it: one worker and one dog. The worker is there to feed the dog. The dog is there to bite the worker if he touches anything.

Good joke.

Everybody laugh.

Roll on snare drum.

Curtains.

Years ago, the world of web standards was split. Two groups—the W3C and the WHATWG—were working on the next iteration of HTML. They had different ideas about the nature of standardisation.

Broadly speaking, the W3C followed a specification-first approach. Figure out what should be implemented first and foremost. From this perspective, specs can be seen as blueprints for browsers to work from.

The WHATWG, by contrast, were implementation led. The way they saw it, there was no point specifying something if browsers weren’t going to implement it. Instead, specs are there to document existing behaviour in browsers.

I’m over-generalising somewhat in my descriptions there, but the point is that there was an ideological difference of opinion around what standards bodies should do.

This always reminded me of a similar ideological conflict when it comes to language usage.

Language prescriptivists attempt to define rules about what’s right or right or wrong in a language. Rules like “never end a sentence with a preposition.” Prescriptivists are generally fighting a losing battle and spend most of their time bemoaning the decline of their language because people aren’t following the rules.

Language descriptivists work the exact opposite way. They see their job as documenting existing language usage instead of defining it. Lexicographers—like Merriam-Webster or the Oxford English Dictionary—receive complaints from angry prescriptivists when dictionaries document usage like “literally” meaning “figuratively”.

Dictionaries are descriptive, not prescriptive.

I’ve seen the prescriptive/descriptive divide somewhere else too. I’ve seen it in the world of design systems.

Jordan Moore talks about intentional and emergent design systems:

There appears to be two competing approaches in designing design systems.

An intentional design system. The flavour and framework may vary, but the approach generally consists of: design system first → design/build solutions.

An emergent design system. This approach is much closer to the user needs end of the scale by beginning with creative solutions before deriving patterns and systems (i.e the system emerges from real, coded scenarios).

An intentional design system is prescriptive. An emergent design system is descriptive.

I think we can learn from the worlds of web standards and dictionaries here. A prescriptive approach might give you a beautiful design system, but if it doesn’t reflect the actual product, it’s fiction. A descriptive approach might give a design system with imperfections and annoying flaws, but at least it will be accurate.

I think it’s more important for a design system to be accurate than beautiful.

As Matthew Ström says, you should start with the design system you already have:

Instead of drawing a whole new set of components, start with the components you already have in production. Document them meticulously. Create a single source of truth for design, warts and all.

The latest episode of Ariel’s excellent Offworld video series (and podcast) is all about Close Encounters Of The Third Kind.

I have such fondness for this film. It’s one of those films that I love to watch on a Sunday afternoon (though that’s true of so many Spielberg films—Jaws, Raiders Of The Lost Ark, E.T.). I remember seeing it in the cinema—this would’ve been the special edition re-release—and feeling the seat under me quake with the rumbling of the musical exchange during the film’s climax.

Ariel invited Rose Eveleth and Laura Welcher on to discuss the film. They spent a lot of time discussing the depiction of first contact communication—Arrival being the other landmark film on this topic.

This is a timely discussion. There’s a new book by Daniel Oberhaus published by MIT Press called Extraterrestrial Languages:

If we send a message into space, will extraterrestrial beings receive it? Will they understand?

You can a read an article by the author on The Guardian, where he mentions some of the wilder ideas about transmitting signals to aliens:

Minsky, widely regarded as the father of AI, suggested it would be best to send a cat as our extraterrestrial delegate.

Don’t worry. Marvin Minsky wasn’t talking about sending a real live cat. Rather, we transmit instructions for building a computer and then we can transmit information as software. Software about, say, cats.

It’s not that far removed from what happened with the Voyager golden record, although that relied on analogue technology—the phonograph—and sent the message pre-compiled on hardware; a much slower transmission rate than radio.

But it’s interesting to me that Minsky specifically mentioned cats. There’s another long-term communication puzzle that has a cat connection.

The Yukka Mountain nuclear waste repository is supposed to store nuclear waste for 10,000 years. How do we warn our descendants to stay away? We can’t use language. We probably can’t even use symbols; they’re too culturally specific. A think tank called the Human Interference Task Force was convened to agree on the message to be conveyed:

This place is a message… and part of a system of messages… pay attention to it! Sending this message was important to us. We considered ourselves to be a powerful culture.

This place is not a place of honor…no highly esteemed deed is commemorated here… nothing valued is here.

What is here is dangerous and repulsive to us. This message is a warning about danger.

A series of thorn-like threatening earthworks was deemed the most feasible solution. But there was another proposal that took a two pronged approach with genetics and folklore:

1. Breed cats that change colour in the presence of radioactive material.
2. Teach children nursery rhymes about staying away from cats that change colour.

This is the raycat solution.

Cheryl Platz is speaking at An Event Apart Chicago. Her inaugural An Event Apart presentation is all about voice interfaces, and I’m going to attempt to liveblog it…

Why make a voice interface?

Successful voice interfaces aren’t necessarily solving new problems. They’re used to solve problems that other devices have already solved. Think about kitchen timers. There are lots of ways to set a timer. Your oven might have one. Your phone has one. Why use a $200 device to solve this mundane problem? Same goes for listening to music, news, and weather.

People are using voice interfaces for solving ordinary problems. Why? Context matters. If you’re carrying a toddler, then setting a kitchen timer can be tricky so a voice-activated timer is quite appealing. But why is voice is happening now?

Humans have been developing the art of conversation for thousands of years. It’s one of the first skills we learn. It’s deeply instinctual. Most humans use speach instinctively every day. You can’t necessarily say that about using a keyboard or a mouse.

Voice-based user interfaces are not new. Not just the idea—which we’ve seen in Star Trek—but the actual implementation. Bell Labs had Audrey back in 1952. It recognised ten words—the digits zero through nine. Why did it take so long to get to Alexa?

In the late 70s, DARPA issued a challenge to create a voice-activated system. Carnagie Mellon came up with Harpy (with a thousand word grammar). But none of the solutions could respond in real time. In conversation, we expect a break of no more than 200 or 300 milliseconds.

In the 1980s, computing power couldn’t keep up with voice technology, so progress kind of stopped. Time passed. Things finally started to catch up in the 90s with things like Dragon Naturally Speaking. But that was still about vocabulary, not grammar. By the 2000s, small grammars were starting to show up—starting an X-Box or pausing Netflix. In 2008, Google Voice Search arrived on the iPhone and natural language interaction began to arrive.

What makes natural language interactions so special? It requires minimal training because it uses the conversational muscles we’ve been working for a lifetime. It unlocks the ability to have more forgiving, less robotic conversations with devices. There might be ten different ways to set a timer.

Natural language interactions can also free us from “screen magnetism”—that tendency to stay on a device even when our original task is complete. Voice also enables fast and forgiving searches of huge catalogues without time spent typing or browsing. You can pick a needle straight out of a haystack.

Natural language interactions are excellent for older customers. These interfaces don’t intimidate people without dexterity, vision, or digital experience. Voice input often leads to more inclusive experiences. Many customers with visual or physical disabilities can’t use traditional graphical interfaces. Voice experiences throw open the door of opportunity for some people. However, voice experience can exclude people with speech difficulties.

Making the case for voice interfaces

There’s a misconception that you need to work at Amazon, Google, or Apple to work on a voice interface, or at least that you need to have a big product team. But Cheryl was able to make her first Alexa “skill” in a week. If you’re a web developer, you’re good to go. Your voice “interaction model” is just JSON.

How do you get your product team on board? Find the customers (and situations) you might have excluded with traditional input. Tell the stories of people whose hands are full, or who are vision impaired. You can also point to the adoption rate numbers for smart speakers.

You’ll need to show your scenario in context. Otherwise people will ask, “why can’t we just build an app for this?” Conduct research to demonstrate the appeal of a voice interface. Storyboarding is very useful for visualising the context of use and highlighting existing pain points.

Getting started with voice interfaces

You’ve got to understand how the technology works in order to adapt to how it fails. Here are a few basic concepts.

Utterance. A word, phrase, or sentence spoken by a customer. This is the true form of what the customer provides.

Intent. This is the meaning behind a customer’s request. This is an important distinction because one intent could have thousands of different utterances.

Prompt. The text of a system response that will be provided to a customer. The audio version of a prompt, if needed, is generated separately using text to speech.

Grammar. A finite set of expected utterances. It’s a list. Usually, each entry in a grammar is paired with an intent. Many interfaces start out as being simple grammars before moving on to a machine-learning model later once the concept has been proven.

Here’s the general idea with “artificial intelligence”…

There’s a human with a core intent to do something in the real world, like knowing when the cookies in the oven are done. This is translated into an intent like, “set a 15 minute timer.” That’s the utterance that’s translated into a string. But it hasn’t yet been parsed as language. That string is passed into a natural language understanding system. What comes is a data structure that represents the customers goal e.g. intent=timer; duration=15 minutes. That’s sent to the business logic where a timer is actually step. For a good voice interface, you also want to send back a response e.g. “setting timer for 15 minutes starting now.”

That seems simple enough, right? What’s so hard about designing for voice?

Natural language interfaces are a form of artifical intelligence so it’s not deterministic. There’s a lot of ruling out false positives. Unlike graphical interfaces, voice interfaces are driven by probability.

How do you turn a sound wave into an understandable instruction? It’s a lot like teaching a child. You feed a lot of data into a statistical model. That’s how machine learning works. It’s a probability game. That’s where it gets interesting for design—given a bunch of possible options, we need to use context to zero in on the most correct choice. This is where confidence ratings come in: the system will return the probability that a response is correct. Effectively, the system is telling you how sure or not it is about possible results. If the customer makes a request in an unusual or unexpected way, our system is likely to guess incorrectly. That’s because the system is being given something new.

Designing a conversation is relatively straightforward. But 80% of your voice design time will be spent designing for what happens when things go wrong. In voice recognition, edge cases are front and centre.

Here’s another challenge. Interaction with most voice interfaces is part conversation, part performance. Most interactions are not private.

Humans don’t distinguish digital speech fom human speech. That means these devices are intrinsically social. Our brains our wired to try to extract social information, even form digital speech. See, for example, why it’s such a big question as to what gender a voice interface has.

Delivering a voice interface

Storyboards help depict the context of use. Sample dialogues are your new wireframes. These are little scripts that not only cover the happy path, but also your edge case. Then you reverse engineer from there.

Flow diagrams communicate customer states, but don’t use the actual text in them.

Prompt lists are your final deliverable.

Functional prototypes are really important for voice interfaces. You’ll learn the real way that customers will ask for things.

If you build a working prototype, you’ll be building two things: a natural language interaction model (often a JSON file) and custom business logic (in a programming language).

Eventually voice design will become a core competency, much like mobile, which was once separate.

Ask yourself what tasks your customers complete on your site that feel clunkly. Remember that voice desing is almost never about new scenarious. Start your journey into voice interfaces by tackling old problems in new, more inclusive ways.

May the voice be with you!

One evening last month, during An Event Apart Seattle, a bunch of the speakers were gathered in the bar in the hotel lobby, shooting the breeze and having a nightcap before the next day’s activities. In a quasi-philosophical mode, the topic of goals came up. Not the sporting variety, but life and career goals.

As I everyone related (confessed?) their goals, I had to really think hard. I don’t think I have any goals. I find it hard enough to think past the next few months, much less form ideas about what I might want to be doing in a decade. But then I remembered that I did once have a goal.

Back in the ’90s, when I was living in Germany and first starting to make websites, there was a website I would check every day for inspiration: Project Cool’s Cool Site Of The Day. I resolved that my life’s goal was to one day have a website I made be the cool site of the day.

About a year later, to my great shock and surprise, I achieved my goal. An early iteration of Jessica’s site—complete with whizzy DHTML animations—was the featured site of the day on Project Cool. I was overjoyed!

I never bothered to come up with a new goal to supercede that one. Maybe I should’ve just retired there and then—I had peaked.

Megan Sapnar Ankerson wrote an article a few years back about How coolness defined the World Wide Web of the 1990s:

The early web was simply teeming with declarations of cool: Cool Sites of the Day, the Night, the Week, the Year; Cool Surf Spots; Cool Picks; Way Cool Websites; Project Cool Sightings. Coolness awards once besieged the web’s virtual landscape like an overgrown trophy collection.

It’s a terrific piece that ponders the changing nature of the web, and the changing nature of that word: cool.

Perhaps the word will continue to fall out of favour. Tim Berners-Lee may have demonstrated excellent foresight when he added this footnote to his classic document, Cool URIs don’t change—still available at its original URL, of course:

Historical note: At the end of the 20th century when this was written, “cool” was an epithet of approval particularly among young, indicating trendiness, quality, or appropriateness.

There’s a worrying tendency for “real” programmers look down their noses at CSS. It’s just a declarative language, they point out, not a fully-featured programming language. Heck, it isn’t even a scripting language.

That may be true, but that doesn’t mean that CSS isn’t powerful. It’s just powerful in different ways to traditional languages.

Take CSS selectors, for example. At the most basic level, they work like conditional statments. Here’s a standard if statement:

if (condition) {
// code here
}

The condition needs to evaluate to true in order for the code in the curly braces to be executed. Sound familiar?

condition {
// styles here
}

That’s a very simple mapping, but what if the conditional statement is more complicated?

if (condition1 && condition2) {
// code here
}

Well, that’s what the decendant selector does:

condition1 condition2 {
// styles here
}

In fact, we can get even more specific than that by using the child combinator, the sibling combinator, and the adjacent sibling combinator:

• condition1 > condition2
• condition1 ~ condition2
• condition2 + condition2

AND is just one part of Boolean logic. There’s also OR:

if (condition1 || condition2) {
// code here
}

In CSS, we use commas:

condition1, condition2 {
// styles here
}

We’ve even got the :not() pseudo-class to complete the set of Boolean possibilities. Once you add quantity queries into the mix, made possible by :nth-child and its ilk, CSS starts to look Turing complete. I’ve seen people build state machines using the adjacent sibling combinator and the :checked pseudo-class.

Anyway, my point here is that CSS selectors are really powerful. And yet, quite often we deliberately choose not to use that power. The entire raison d’être for OOCSS, BEM, and Smacss is to deliberately limit the power of selectors, restricting them to class selectors only.

On the face of it, this might seem like an odd choice. After all, we wouldn’t deliberately limit ourselves to a subset of a programming language, would we?

We would and we do. That’s what templating languages are for. Whether it’s PHP’s Smarty or Twig, or JavaScript’s Mustache, Nunjucks, or Handlebars, they all work by providing a deliberately small subset of features. Some pride themselves on being logic-less. If you find yourself trying to do something that the templating language doesn’t provide, that’s a good sign that you shouldn’t be trying to do it in the template at all; it should be in the controller.

So templating languages exist to enforce simplicity and ensure that the complexity happens somewhere else. It’s a similar story with BEM et al. If you find you can’t select something in the CSS, that’s a sign that you probably need to add another class name to the HTML. The complexity is confined to the markup in order to keep the CSS more straightforward, modular, and maintainable.

But let’s not forget that that’s a choice. It’s not that CSS in inherently incapable of executing complex conditions. Quite the opposite. It’s precisely because CSS selectors (and the cascade) are so powerful that we choose to put guard rails in place.

I like the robustness that comes with declarative languages. I also like the power that comes with imperative languages. Best of all, I like having the choice.

Take the video and audio elements, for example. If you want, you can embed a video or audio file into a web page using a straightforward declaration in HTML.

<audio src="..." controls><!-- fallback goes here --></audio>

Straightaway, that covers 80%-90% of use cases. But if you need to do more—like, provide your own custom controls—there’s a corresponding API that’s exposed in JavaScript. Using that API, you can do everything that you can do with the HTML element, and a whole lot more besides.

It’s a similar story with animation. CSS provides plenty of animation power, but it’s limited in the events that can trigger the animations. That’s okay. There’s a corresponding JavaScript API that gives you more power. Again, the CSS declarations cover 80%-90% of use cases, but for anyone in that 10%-20%, the web animation API is there to help.

Client-side form validation is another good example. For most us, the HTML attributes—required, type, etc.—are probably enough most of the time.

<input type="email" required />

When we need more fine-grained control, there’s a validation API available in JavaScript (yes, yes, I know that the API itself is problematic, but you get the point).

I really like this design pattern. Cover 80% of the use cases with a declarative solution in HTML, but also provide an imperative alternative in JavaScript that gives more power. HTML5 has plenty of examples of this pattern. But I feel like the history of web standards has a few missed opportunities too.

Geolocation is a good example of an unbalanced feature. If you want to use it, you must use JavaScript. There is no declarative alternative. This doesn’t exist:

<input type="geolocation" />

That’s a shame. Anyone writing a form that asks for the user’s location—in order to submit that information to a server for processing—must write some JavaScript. That’s okay, I guess, but it’s always going to be that bit more fragile and error-prone compared to markup.

(And just in case you’re thinking of the fallback—which would be for the input element to be rendered as though its type value were text—and you think it’s ludicrous to expect users with non-supporting browsers to enter latitude and longitude coordinates by hand, I direct your attention to input type="color": in non-supporting browsers, it’s rendered as input type="text" and users are expected to enter colour values by hand.)

Geolocation is an interesting use case because it only works on HTTPS. There are quite a few JavaScript APIs that quite rightly require a secure context—like service workers—but I can’t think of a single HTML element or attribute that requires HTTPS (although that will soon change if we don’t act to stop plans to create a two-tier web). But that can’t have been the thinking behind geolocation being JavaScript only; when geolocation first shipped, it was available over HTTP connections too.

Anyway, that’s just one example. Like I said, it’s not that I’m in favour of declarative solutions instead of imperative ones; I strongly favour the choice offered by providing declarative solutions as well as imperative ones.

In recent years there’s been a push to expose low-level browser features to developers. They’re inevitably exposed as JavaScript APIs. In most cases, that makes total sense. I can’t really imagine a declarative way of accessing the fetch or cache APIs, for example. But I think we should be careful that it doesn’t become the only way of exposing new browser features. I think that, wherever possible, the design pattern of exposing new features declaratively and imperatively offers the best of the both worlds—ease of use for the simple use cases, and power for the more complex use cases.

Previously, it was up to browser makers to think about these things. But now, with the advent of web components, we developers are gaining that same level of power and responsibility. So if you’re making a web component that you’re hoping other people will also use, maybe it’s worth keeping this design pattern in mind: allow authors to configure the functionality of the component using HTML attributes and JavaScript methods.

I like words. I like the way they can be tethered together to produce a satisfying sentence.

Jessica likes words even more than I do (that’s why her website is called “wordridden”). She studied linguistics and she’s a translator by trade—German into English. Have a read of her post about translating Victor Klemperer to get an inkling of how much thought and care she puts into it.

Given the depth of enquiry required for a good translation, I was particularly pleased to read this remark by John Le Carré:

No wonder then that the most conscientious editors of my novels are not those for whom English is their first language, but the foreign translators who bring their relentless eye to the tautological phrase or factual inaccuracy – of which there are far too many. My German translator is particularly infuriating.

That’s from an article called Why we should learn German, but it’s really about why we should strive for clarity in our use of language:

Clear language — lucid, rational language — to a man at war with both truth and reason, is an existential threat. Clear language to such a man is a direct assault on his obfuscations, contradictions and lies. To him, it is the voice of the enemy. To him, it is fake news. Because he knows, if only intuitively, what we know to our cost: that without clear language, there is no standard of truth.

It reminds me of one of my favourite Orwell essays, Politics and the English Language:

Political language — and with variations this is true of all political parties, from Conservatives to Anarchists — is designed to make lies sound truthful and murder respectable, and to give an appearance of solidity to pure wind.

But however much I agree with Le Carré’s reprise of Orwell’s call for clarity, I was brought up short by this:

Every time I hear a British politician utter the fatal words, “Let me be very clear”, these days I reach for my revolver.

Le Carré’s text was part of a speech given in Berlin, where everyone would get the reference to the infamous Nazi quote—Wenn ich Kultur höre … entsichere ich meinen Browning—and I’m sure it was meant with a sly wink. But words matter.

Words are powerful. Words can be love and comfort — and words can be weapons.

Chris recently documented the results from his survey which asked:

Is it useful to distinguish between “web apps” and “web sites”?

His conclusion:

There is just nothing but questions, exemptions, and gray area.

This is something I wrote about a while back:

Like obscenity and brunch, web apps can be described but not defined.

The results of Chris’s poll are telling. The majority of people believe there is a difference between sites and apps …but nobody can agree on what it is. The comments make for interesting reading too. The more people chime in an attempt to define exactly what a “web app” is, the more it proves the point that the the term “web app” isn’t a useful word (in the sense that useful words should have an agreed-upon meaning).

Tyler Sticka makes a good point:

By this definition, web apps are just a subset of websites.

I like that. It avoids the false dichotomy that a product is either a site or an app.

But although it seems that the term “web app” can’t be defined, there are a lot of really smart people who still think it has some value.

Having spent years working on both apps & sites, I think there’s a significant difference. Hard to explain doesn’t mean non-existent.

— Cennydd Bowles (@Cennydd) December 6, 2013

I think Cennydd is right. I think the differences exist …but I also think we’re looking for those differences at the wrong scale. Rather than describing an entire product as either a website or an web app, I think it makes much more sense to distinguish between patterns.

Ok, I’ll try. An app’s primary material is behaviour. A website’s primary material is information.

— Cennydd Bowles (@Cennydd) December 6, 2013

Let’s take those two modifiers—behavioural and informational. But let’s apply them at the pattern level.

The “get stuff” sites that Jake describes will have a lot of informational patterns: how best to present a flow of text for reading, for example. Typography, contrast, whitespace; all of those attributes are important for an informational pattern.

The “do stuff” sites will probably have a lot of behavioural patterns: entering information or performing an action. Feedback, animation, speed; these are some of the possible attributes of a behavioural pattern.

But just about every product out there on the web contains a combination of both types of pattern. Like I said:

Is Wikipedia a website up until the point that I start editing an article? Are Twitter and Pinterest websites while I’m browsing through them but then flip into being web apps the moment that I post something?

Now you could make an arbitrary decision that any product with more than 50% informational patterns is a website, and any product with more than 50% behavioural patterns is a web app, but I don’t think that’s very useful.

Take a look at Brad’s collection of responsive patterns. Some of them are clearly informational (tables, images, etc.), while some of them are much more behavioural (carousels, notifications, etc.). But Brad doesn’t divide his collection into two, saying “Here are the patterns for websites” and “Here are the patterns for web apps.” That would be a dumb way to divide up his patterns, and I think it’s an equally dumb way to divide up the whole web.

What I’m getting at here is that, rather than trying to answer the question “what is a web app, anyway?”, I think it’s far more important to answer the other question I posed:

Why?

Why do you want to make that distinction? What benefit do you gain by arbitrarily dividing the entire web into two classes?

I think by making the distinction at the pattern level, that question starts to become a bit easier to answer. One possible answer is to do with the different skills involved.

For example, I know plenty of designers who are really, really good at informational patterns—they can lay out content in a beautiful, clear way. But they are less skilled when it comes to thinking through all the permutations involved in behavioural patterns—the “arrow of time” that’s part of so much interaction design. And vice-versa: a skilled interaction designer isn’t necessarily the best at old-skill knowledge of type, margins, and hierarchy. But both skillsets will be required on an almost every project on the web.

So I do believe there is value in distinguishing between behaviour and information …but I don’t believe there is value in trying to shoehorn entire products into just one of those categories. Making the distinction at the pattern level, though? That I can get behind.

Addendum

Incidentally, some of the respondents to Chris’s poll shared my feeling that the term “web app” was often used from a marketing perspective to make something sound more important and superior:

Perhaps it’s simply fashion. Perhaps “website” just sounds old-fashioned, and “web app” lends your product a more up-to-date, zingy feeling on par with the native apps available from the carefully-curated walled gardens of app stores.

Approaching things from the patterns perspective, I wonder if those same feelings of inferiority and superiority are driving the recent crop of behavioural patterns for informational content: parallaxy, snowfally, animation patterns are being applied on top of traditional informational patterns like hierarchy, measure, and art direction. I’m not sure that the juxtaposition is working that well. Taking the single interaction involved in long-form informational patterns (that interaction would be scrolling) and then using it as a trigger for all kinds of behavioural patterns feels …uncanny.