Category: Blog

This essay contains gentle spoilers for the first Alien Earth event. There’s gotta get some great and/or unusual names, which The Alien brand has always understood about the sci-fi style. In the far-off potential created by Ridley Scott’s 1979 video, both real and imagined people have nicknames that dance off the mouth. [ The original ]…

The first article on Den of Geek was Well, That’s Truly Alex Lawther’s Character’s Brand on Alien: Earth.

Bob Odenkirk is conscious that delivering a punch in the face and being able to resend it onscreen without being successful is a little bit like humor. The actor sees resemblance between it and becoming a more seasoned late night cartoon after going through the learning process once for the past in 2021’s Nobody, a movie for which Odenkirk spent more than a year teaching and preparing. It&#8217 is all about developing confidence in delivering the joke and practicing the music.

When we catch up with him in Los Angeles, Odenkirk says,” I understood the fundamentals of display battle, and I understood the speech of it, and I understood the basic principles of it.” ” I fully comprehended how the cameras functions.” We put so much knowledge to use on Anyone 2, and I learned so much from it on Nothing 1. The actor also claims that he never stopped training in the five years that followed the release of Nobodys.

Odenkirk’s approach to developing his art as a late-blooming behavior star is a new craze, but it was something he was already well-versed in as a comic writer turned star. After all, he first became a writer for Saturday Night Live in the first season of Late Night with ConanO’Brien, and he later refined those dentures. Finally, he channeled both activities into HBO’s Mr. Show, a renowned and acclaimed sketch comedy series he co-created, produced, and starred in. Given the current financial circumstances that are preventing Stephen Colbert from appearing on television, as well as the possible social people, it’s possible that the comedy-training pipelines that provided him ,O’Brien, Colbert, and so many other possibilities is disintegrating.

Maybe. However, Odenkirk appears optimistic about the future of comedy.

Odenkirk claims,” I am not that concerned.” There is more funny than there ever was, and there are more programs and opportunities than ever before thanks to the internet, YouTube, and all the streamers. They typically pay less, but they also make money, and there are more of them. But that &#8217, s great. You could create a very good display right now, but virtually nobody will see it because there is just too much to sample, in my opinion.

He continues,” The business sort of does this journey of expanding and contracting, expanding and contracting, expanding and contracting, but probably we’re going to deal a little bit, but I love how many websites it are, and how much variety we can see.”

Despite the variety and the pressure being put on the broadcast model by streaming services, Odenkirk even believes there is room for a new iteration of the late-night format.

Odenkirk asserts that” some sort of late night will continue.” What has happened here with Colbert is terrible, but I think I am aware of it. I can see how much of what is currently available online and how much of my viewing is happening online, so I understand how these changes are happening because there are many late night shows that are very similar to one another. I’m happy with it. There will be a lot of opportunities for us in the future.

Odenkirk does allow himself to be a little bit more nostalgic about his own experiences in comedy, late night, and other forms than he has previously been. Odenkirk has previously admitted to having a difficult relationship with SNL as the creator of Chris Farley’s signature Matt Foley character. However, when we caught up with the Nobody 2 star last week, he seemed quite open and perhaps even a little sceptical about taking part in SNL‘s 50 anniversary celebration.

“SNL 50 was so well done, that was the greatest,” Odenkirk says. Lorne Michaels “outdid himself,” he said, “especially seeing my old friends, Kevin Nealon, Dana Carvey, [David ] Spade, and some of the writers. I was able to see people wearing makeup, people working in front office, and assistants because everyone who worked on Saturday Night Live was invited to those events, you know. You know, I haven’t seen those people in 35 years. So it was truly fantastic.”

Even as Odenkirk is very much looking toward the future, including imagining a world where he might have many more adventures as Nobody‘s Hutch Mansel, it was a chance to look back.

Nobody 2 debuts on Friday, August 15. In the upcoming days, we’ll have more of our conversation with Odenkirk.

The first post on Den of Geek was Bob Odenkirk thinking” Some Version” of Late Night Comedy Will Survive Streaming.

Bob Odenkirk is conscious that delivering a punch to the face and being able to wryly deliver it onscreen is a little bit like funny. The actor sees [ ] ] having completed the learning process once for the former in the 2021 film Nobody [ ] ] [ ] ]

The first article on Den of Geek was Bob Odenkirk thinking” Some Version” of Soon Night Comedy May Live Streaming.

Being able to successfully deliver a punch to the face screen is similar to comedy, according to Bob Odenkirk. The actor sees similarities between becoming a more experienced late night comic after going through the same learning curve as the past in the 2021 movie Nobody, which Odenkirk spent more than a year developing and preparing. It’s all about developing confidence in the delivery of the joke and practicing the music.

When we catch up with him in Los Angeles, Odenkirk says,” I understood the basic principles of display battle, and I understood the language of it, and I understood the basic principles of it. After the first movie. ” I fully comprehend how the camera functions. We put so much knowledge to use on Nothing 2, and I learned so much from it on Nothing 1. The actor claims to have continued education for five years between directing Nobodys and filming.

Odenkirk’s approach to developing his art as a late-blooming activity star is a new craze, but it was something he was already well-versed in as a comic writer turned star. After all, he first became a writer for Saturday Night Live in the first season of Late Night with ConanO’Brien, and he later refined those dentures. Finally, he channeled both activities into HBO’s Mr. Show, a renowned and acclaimed sketch comedy series he co-created, produced, and starred in. Given the current financial circumstances that are preventing Stephen Colbert from appearing on television, as well as the possible social people, it’s possible that the comedy-training pipelines that provided him ,O’Brien, Colbert, and so many other possibilities is disintegrating.

Maybe. Odenkirk, however, seems optimistic about the future of comedy.

Odenkirk claims,” I am not that concerned.” There is more funny than there ever was, and there are more programs and opportunities than ever before thanks to the internet, YouTube, and all the streamers. They typically pay less, but they also make money, and there are more of them. But that &#8217, s great. I believe the biggest problem is that you could put on a very good display right now but almost nobody notices because there is just too much to sample.

He continues,” The business sort of does this journey of expanding and contracting, expanding and contracting, expanding and contracting, but maybe we’re going to deal a little bit, but I love how many websites it are, and how much variety we can see.”

Despite all that multitude and all those streaming services putting pressure on the broadcast model, Odenkirk also believes there is still space for a new incarnation of the late night format.

Odenkirk asserts that” some sort of late night did continue.” What transpired with Colbert is bad, but I think I am aware of it. There are many late nights programs that are very similar to one another, and I can see how much of what is currently available online and how much of what is being watched online, so I can see how it is changing. I’m happy with it. There will be a lot of opportunities for us in the future.

Odenkirk does permit himself to be a little bit more cheerful about his own experiences in humor, soon night, and other places than he has been in the history. Odenkirk has previously acknowledged a difficult marriage with SNL as the father of Chris Farley’s iconic Matt Foley personality. However, when we caught up with the Nothing 2 celebrity last week, he was very open and perhaps even a tiny irrational about taking part in SNL‘s 50 anniversary celebration earlier this year.

Odenkirk claims that “SNL 50 was the best thing ever” and that it was done so also. Lorne Michaels actually outdid himself, despite seeing all of my former friends, including Kevin Nealon, Dana Carvey, David Spade, and some of the artists. You know, everyone on Saturday Night Live was invited to those occasions, so I was able to see both back office employees and aides who were wearing makeup. You know, I haven’t seen those individuals in 35 years. But, it was fantastic really.

Yet as Odenkirk is very much a projectionist for the future, including considering how he might end up playing One’s Hutch Mansel in many more activities, it was a chance to glance up.

No one 2 debuts on Friday, August 15. In the upcoming weeks, we’ll continue to talk with Odenkirk.

The first article on Den of Geek was Bob Odenkirk thinking” Some Version” of Soon Night Comedy May Live Streaming.

Rumi ( Arden Cho ), a musical icon, breaks her side hustle as a late-night monster-slayer to simply slay above an arena stage in the back half of Maggie Kang and Chris Appelhans ‘ new Netflix sensation KPop Demon Hunters. Before thousands of screaming fans and a lighting system that matched the]…]

The second article on Den of Geek was KPop Demon Hunters Does Not Think Like a Netflix Movie ( And That Makes It Great ).

Bob Odenkirk is conscious that taking a punch in the face and being able to retaliate with a convincing strike film is a little like doing humor. The actor sees resemblance between it and becoming a more seasoned late night cartoon after going through the learning process once for the past in 2021’s Nobody, a movie for which Odenkirk spent more than a year teaching and preparing. It’s all about developing confidence in the delivery of the joke and practicing the music.

When we catch up with him in Los Angeles, Odenkirk says,” I understood the basic principles of display battle, and I understood the language of it, and I understood the basic principles of it. After the first film. ” I fully comprehended how the cameras functions.” We used the information I learned on Anyone 1 to use it on No 2. The actor also claims to have continued coaching in the five years that followed the release of Nobodys.

Odenkirk’s fresh happening as a late-blooming activity star is a one he was intimately acquainted with as a comic writer turned sun, despite the fact that polishing one’s craft as a late-blooming action star is a new one. After all, he first began his career in the late-night industry as a blogger on Saturday Night Live and later refined those teeth in the first year of Late Night with ConanO’Brien. Finally, he channeled both activities into HBO’s Mr. Show, a renowned and acclaimed sketch comedy series he co-created, produced, and starred in. Given the current financial circumstances that are preventing Stephen Colbert from appearing on television, as well as the possible social people, it’s possible that the comedy-training pipeline that provided him ,O’Brien, Colbert, and so many other possibilities is disintegrating.

Maybe. However, Odenkirk appears optimistic about the future of comedy.

Odenkirk claims,” I’m certainly that concerned.” There’s more comic than there ever was, and there’s more programs and options than ever before, according to the internet and YouTube, and with all the players. They typically pay less, but they also make money, and there are more of them. But that &#8217, s great. You may make a very good display right now, but virtually nobody will see it because there is just too much to sample, in my opinion.

He continues,” The business sort of does this journey of expanding and contracting, expanding and contracting, expanding and contracting, but probably we’re going to deal a little bit, but I love how many programs there are, and how much variety we can see.”

Despite the variety and the pressure being put on the broadcast model by streaming services, Odenkirk even believes there is room for a new iteration of the late-night format.

Odenkirk asserts that” some sort of late night will continue.” What has happened here with Colbert is terrible, but I think I am aware of it. I can see how much of what is currently available online and how much of my viewing is happening online, so I understand how these changes are happening because there are many late night shows that are very similar to one another. I’m happy with it. There will be a lot of opportunity for us in the future.

Odenkirk does allow himself to be a little bit more nostalgic about his own experiences in comedy, late night, and other forms than he has previously been. Odenkirk has previously admitted to having a difficult relationship with SNL as the creator of Chris Farley’s signature Matt Foley character. However, when we caught up with the Nobody 2 star last week, he was quite open and perhaps even a little irrational about taking part in SNL‘s 50 anniversary celebration earlier this year.

Odenkirk claims that SNL 50 was the best work of art. Lorne Michaels “outdid himself,” he said, “especially seeing my old friends, Kevin Nealon, Dana Carvey, [David ] Spade, and some of the writers. You know, everyone on Saturday Night Live was invited to those occasions, so I was able to see both front office employees and assistants who were wearing makeup. You know, I haven’t seen those people in 35 years. So it was truly fantastic.”

Even as Odenkirk is very much looking toward the future, including imagining a world where he might have many more adventures as Nobody‘s Hutch Mansel, it was a chance to look back.

No one 2 debuts on Friday, August 15. In the upcoming days, we’ll have more of our conversation with Odenkirk.

Bob Odenkirk believes that” Some Version” of Late Night Comedy will survive streaming, as originally reported on Den of Geek.

One of the most powerful sweet skills we have at our disposal is the ability to work together to improve our designs while developing our own abilities and perspectives, regardless of how it is used or what it might be called.

Feedback is also one of the most underestimated equipment, and generally by assuming that we’re already good at it, we settle, forgetting that it’s a talent that can be trained, grown, and improved. Bad feedback can cause conflict in jobs, lower motivation, and negatively impact faith and teamwork over the long term. Quality opinions can be a revolutionary force.

Practicing our knowledge is absolutely a good way to enhance, but the learning gets yet faster when it’s paired with a good base that programs and focuses the exercise. What are some fundamental components of providing effective opinions? And how can comments be adjusted for isolated and distributed job settings?

On the web, we may find a long history of sequential suggestions: code was written and discussed on mailing lists since the beginning of open source. Currently, engineers engage on pull calls, developers post in their favourite design tools, project managers and sprint masters exchange ideas on tickets, and so on.

Design analysis is often the label used for a type of input that’s provided to make our job better, jointly. It generally shares many of the concepts with comments, but it also has some differences.

The material

The content of the feedback is the bedrock of every effective criticism, so where do we need to begin? There are many designs that you can use to form your content. The one that I personally like best—because it’s obvious and actionable—is this one from Lara Hogan.

Although this equation is typically used to provide feedback to individuals, it likewise fits really well in a style criticism because it finally addresses some of the main inquiries that we work on: What? Where? Why? How? Imagine that you’re giving some comments about some pattern function that spans several screens, like an onboard movement: there are some pages shown, a stream blueprint, and an outline of the decisions made. You notice a flaw in the situation. If you keep the three components of the equation in mind, you’ll have a mental unit that can help you become more precise and effective.

Here is a reply that could be included in some feedback, and it might appear fair at first glance because it appears to merely fit the equation. But does it?

Not confident about the keys ‘ patterns and hierarchy—it feels off. Can they be altered?

Observation for style feedback doesn’t really mean pointing out which part of the software your input refers to, but it also refers to offering a viewpoint that’s as specific as possible. Do you offer the user’s viewpoint? Your expert perspective? A business perspective? From the perspective of the project manager? A first-time user’s perspective?

I anticipate one to go forward and the other to go back when I see these two buttons.

Impact is about the why. Just pointing out a UI element might sometimes be enough if the issue may be obvious, but more often than not, you should add an explanation of what you’re pointing out.

I anticipate one to go forward and the other to go back when I see these two buttons. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow.

The question approach is meant to provide open guidance by eliciting the critical thinking in the designer receiving the feedback. Notably, in Lara’s equation she provides a second approach: request, which instead provides guidance toward a specific solution. While that’s generally a viable option for feedback, I’ve found that going back to the question approach typically leads to the best solutions for design critiques because designers are generally more open to experiment in a space.

The difference between the two can be exemplified with, for the question approach:

I anticipate one to go forward and the other to go back when I see these two buttons. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Would it make sense to unify them?

Or, for the request approach:

I anticipate one to go forward and the other to go back when I see these two buttons. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same pair of forward and back buttons.

At this point in some situations, it might be useful to integrate with an extra why: why you consider the given suggestion to be better.

I anticipate one to go forward and the other to go back when I see these two buttons. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.

Choosing the question approach or the request approach can also at times be a matter of personal preference. I did rounds of anonymous feedback and reviewed feedback with other people before putting a lot of effort into improving it a while ago. After a few rounds of this work and a year later, I got a positive response: my feedback came across as effective and grounded. Until I changed teams. Surprise surprise, one particular person gave me a lot of negative feedback. The reason is that I had previously tried not to be prescriptive in my advice—because the people who I was previously working with preferred the open-ended question format over the request style of suggestions. However, there was a member of this other team who preferred specific guidance. So I adapted my feedback for them to include requests.

One comment that I heard come up a few times is that this kind of feedback is quite long, and it doesn’t seem very efficient. Yes, but also no. Let’s explore both sides.

No, this kind of feedback is actually effective because the length is a byproduct of clarity, and giving this kind of feedback can provide precisely enough information for a sound fix. Also if we zoom out, it can reduce future back-and-forth conversations and misunderstandings, improving the overall efficiency and effectiveness of collaboration beyond the single comment. Imagine that in the example above the feedback were instead just,” Let’s make sure that all screens have the same two forward and back buttons”. Since the designer receiving this feedback wouldn’t have much to go by, they might just implement the change. In later iterations, the interface might change or they might introduce new features—and maybe that change might not make sense anymore. The designer might assume that the change is about consistency without the explanation, but what if it wasn’t? So there could now be an underlying concern that changing the buttons would be perceived as a regression.

Yes, this style of feedback is not always efficient because the points in some comments don’t always need to be exhaustive, sometimes because certain changes may be obvious (” The font used doesn’t follow our guidelines” ) and sometimes because the team may have a lot of internal knowledge such that some of the whys may be implied.

The equation above is not intended to provide a predetermined template for feedback, but rather a mnemonic to reflect and enhance the practice. Even after years of active work on my critiques, I still from time to time go back to this formula and reflect on whether what I just wrote is effective.

The atmosphere

Well-grounded content is the foundation of feedback, but that’s not really enough. The soft skills of the person who’s providing the critique can multiply the likelihood that the feedback will be well received and understood. It has been demonstrated that only positive feedback can lead to sustained change in people, and tone alone can determine whether content is rejected or welcomed.

Since our goal is to be understood and to have a positive working environment, tone is essential to work on. I’ve tried to summarize the necessary soft skills over the years using a formula that resembles the one for content: the receptivity equation.

Respectful feedback comes across as grounded, solid, and constructive. It’s the kind of feedback that, whether it’s positive or negative, is perceived as useful and fair.

The term “timing” describes the moment when the feedback occurs. To-the-point feedback doesn’t have much hope of being well received if it’s given at the wrong time. If a new feature’s entire high-level information architecture is about to go live when it’s about to be released, it might still be relevant if that questioning raises a significant blocker that no one saw, but those concerns are much more likely to have to wait for a later revision. So in general, attune your feedback to the stage of the project. Early iteration? Iteration that was later? Polishing work in progress? Each of these needs varies. The right timing will make it more likely that your feedback will be well received.

Attitude is the equivalent of intent, and in the context of person-to-person feedback, it can be referred to as radical candor. Before writing, it’s important to make sure the person we’re writing will actually benefit them and improve the overall project. This might be a hard reflection at times because maybe we don’t want to admit that we don’t really appreciate that person. Although it’s possible, and that’s okay, it’s hoped not to be the case. Acknowledging and owning that can help you make up for that: how would I write if I really cared about them? How can I avoid being passive aggressive? How can I encourage constructive behavior?

Form is relevant especially in a diverse and cross-cultural work environments because having great content, perfect timing, and the right attitude might not come across if the way that we write creates misunderstandings. There could be many reasons for this, including the fact that occasionally certain words may cause specific reactions, that nonnative speakers may not be able to comprehend all thenuances of some sentences, that our brains may be different and that our world may be perceived differently; hence, neurodiversity must be taken into account. Whatever the reason, it’s important to review not just what we write but how.

A few years back, I was asking for some feedback on how I give feedback. I was given some sound advice, but I also got a surprise comment. They pointed out that when I wrote” Oh, ]… ]”, I made them feel stupid. That wasn’t my intention at all! I felt really bad, and I just realized that I provided feedback to them for months, and every time I might have made them feel stupid. I was horrified … but also thankful. I quickly changed my spelling mistake by adding “oh” to my list of replaced words (your choice between aText, TextExpander, or others ) so that when I typed “oh,” it was immediately deleted.

Something to highlight because it’s quite frequent—especially in teams that have a strong group spirit—is that people tend to beat around the bush. It’s important to keep in mind that having a positive attitude doesn’t necessarily mean passing judgment on the feedback; rather, it simply means that even when you give difficult, or difficult feedback, you do so in a way that’s respectful and constructive. The nicest thing that you can do for someone is to help them grow.

We have a great advantage in giving feedback in written form: it can be reviewed by another person who isn’t directly involved, which can help to reduce or remove any bias that might be there. When I shared a comment with someone I knew,” How does this sound,”” How can I do it better,” or even” How would you have written it,” I discovered that the two versions had different meanings.

The format

Asynchronous feedback also has a significant inherent benefit: we can devote more time to making sure that the suggestions ‘ clarity of communication and actionability fulfill two main objectives.

Let’s imagine that someone shared a design iteration for a project. You are reviewing it and leaving a comment. There are many ways to accomplish this, and context is of course important, but let’s try to think about some things that might be worthwhile to take into account.

In terms of clarity, start by grounding the critique that you’re about to give by providing context. This includes specifically describing where you’re coming from: do you have a thorough understanding of the project, or is this your first encounter with it? Are you coming from a high-level perspective, or are you figuring out the details? Are there regressions? Which user’s point of view are you addressing when offering your feedback? Is the design iteration at a point where it would be okay to ship this, or are there major things that need to be addressed first?

Even if you’re giving feedback to a team that already has some background information on the project, providing context is helpful. And context is absolutely essential when giving cross-team feedback. If I were to review a design that might be indirectly related to my work, and if I had no knowledge about how the project arrived at that point, I would say so, highlighting my take as external.

We frequently concentrate on the negatives and attempt to list all the things that could be improved. That’s of course important, but it’s just as important—if not more—to focus on the positives, especially if you saw progress from the previous iteration. Although this may seem superfluous, it’s important to remember that design has a number of possible solutions to each problem. So pointing out that the design solution that was chosen is good and explaining why it’s good has two major benefits: it confirms that the approach taken was solid, and it helps to ground your negative feedback. In the longer term, sharing positive feedback can help prevent regressions on things that are going well because those things will have been highlighted as important. Positive feedback can also help, as an added bonus, prevent impostor syndrome.

There’s one powerful approach that combines both context and a focus on the positives: frame how the design is better than the status quo ( compared to a previous iteration, competitors, or benchmarks ) and why, and then on that foundation, you can add what could be improved. There is a significant difference between a critique of a design that is already in good shape and one that isn’t quite there yet.

Another way that you can improve your feedback is to depersonalize the feedback: the comments should always be about the work, never about the person who made it. It’s” This button isn’t well aligned” versus” You haven’t aligned this button well”. This can be changed in your writing very quickly by reviewing it just before sending.

In terms of actionability, one of the best approaches to help the designer who’s reading through your feedback is to split it into bullet points or paragraphs, which are easier to review and analyze one by one. You might also consider breaking up the feedback into sections or even across multiple comments if it is longer. Of course, adding screenshots or signifying markers of the specific part of the interface you’re referring to can also be especially useful.

One approach that I’ve personally used effectively in some contexts is to enhance the bullet points with four markers using emojis. A red square indicates that it is something I consider blocking, a yellow diamond indicates that it needs to be changed, and a green circle provides a thorough, positive confirmation. I also use a blue spiral � � for either something that I’m not sure about, an exploration, an open alternative, or just a note. However, I’d only use this strategy on teams where I’ve already established a high level of trust because it might turn out to be quite demoralizing if I deliver a lot of red squares and change how I communicate that.

Let’s see how this would work by reusing the example that we used earlier as the first bullet point in this list:

• 🔶 Navigation—I anticipate one to go forward and the other to go back when I see these two buttons. But this is the only screen where this happens, as before we just used a single button and an “×” to close. This seems to be breaking the consistency in the flow. Let’s make sure that all screens have the same two forward and back buttons so that users don’t get confused.
• � � Overall— I think the page is solid, and this is good enough to be our release candidate for a version 1.0.
• � � Metrics—Good improvement in the buttons on the metrics area, the improved contrast and new focus style make them more accessible.
• Button Style: Using the green accent in this context gives the impression that it’s a positive action because green is typically seen as a confirmation color. Do we need to explore a different color?
• Considering the number of items on the page and the overall page hierarchy, it seems to me that the tiles should use Subtitle 2 instead of Subtitle 1. This will keep the visual hierarchy more consistent.
• � � Background—Using a light texture works well, but I wonder whether it adds too much noise in this kind of page. What is the purpose of using that?

What about giving feedback directly in Figma or another design tool that allows in-place feedback? These are generally difficult to use because they conceal discussions and are harder to follow, but they can be very useful in the right context. Just make sure that each of the comments is separate so that it’s easier to match each discussion to a single task, similar to the idea of splitting mentioned above.

One final note: say the obvious. Sometimes we might feel that something is clearly right or wrong, and we don’t say it. Or sometimes we might have a doubt that we don’t express because the question might sound stupid. Say it, that’s fine. You might have to reword it a little bit to make the reader feel more comfortable, but don’t hold it back. Good feedback is transparent, even when it may be obvious.

Another benefit of asynchronous feedback is that written feedback automatically monitors decisions. Especially in large projects,” Why did we do this”? there’s nothing better than open, transparent discussions that can be reviewed at any time, and this could be a question that arises from time to time. For this reason, I recommend using software that saves these discussions, without hiding them once they are resolved.

Content, tone, and format. Although each of these subjects offers a useful model, improving eight of the subjects ‘ observation, impact, question, timing, attitude, form, clarity, and actionability is a lot of work to put in all at once. One effective approach is to take them one by one: first identify the area that you lack the most (either from your perspective or from feedback from others ) and start there. Then the second, followed by the third, and so on. At first you’ll have to put in extra time for every piece of feedback that you give, but after a while, it’ll become second nature, and your impact on the work will multiply.

Thanks to Brie Anne Demkiw and Mike Shelton for reviewing the first draft of this article.

Although I’m not sure when I first heard this statement, it has stuck with me over the centuries. How do you generate solutions for scenarios you can’t think? Or create products that are functional on products that have not yet been created?

Flash, Photoshop, and flexible pattern

When I first started designing sites, my go-to technology was Photoshop. I set about making a layout that I would eventually fall content into a 960px cloth. The growth phase was about attaining pixel-perfect precision using set widths, fixed levels, and absolute placement.

All of this was altered by Ethan Marcotte’s 2010 post in A List Off entitled” Responsive Web Design.” I was sold on reactive style as soon as I heard about it, but I was even terrified. The pixel-perfect models full of special figures that I had formerly prided myself on producing were no longer good enough.

My first encounter with flexible design didn’t help my fear. My second project was to get an active fixed-width website and make it reactive. You can’t really put responsiveness at the end of a job, which I learned the hard way. To make smooth design, you need to prepare throughout the style phase.

A new way to style

Making information accessible to all devices a priority when designing responsive or liquid websites has always been the goal. It relies on the use of percentage-based design, which I immediately achieved with local CSS and power groups:

.column-span-6 { width: 49%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-4 { width: 32%; float: left; margin-right: 0.5%; margin-left: 0.5%;}.column-span-3 { width: 24%; float: left; margin-right: 0.5%; margin-left: 0.5%;}

Therefore using Sass to re-use repeated slabs of code and transition to more semantic html:

.logo { @include colSpan(6);}.search { @include colSpan(3);}.social-share { @include colSpan(3);}

Media questions

The next ingredient for flexible design is press queries. Without them, content would shrink to fit the available space, regardless of whether it remained readable ( The exact opposite issue resulted from the development of a mobile-first approach ).

Media questions prevented this by allowing us to add breakpoints where the design could adapt. Like most people, I started out with three breakpoints: one for desktop, one for tablets, and one for mobile. Over the years, I added more and more for phablets, wide screens, and so on. 

For years, I happily worked this way and improved both my design and front-end skills in the process. The only problem I encountered was making changes to content, since with our Sass grid system in place, there was no way for the site owners to add content without amending the markup—something a small business owner might struggle with. This is because each row in the grid was defined using a div as a container. Adding content meant creating new row markup, which requires a level of HTML knowledge.

String premium was a mainstay of early flexible design, present in all the frequently used systems like Bootstrap and Skeleton.

 
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Another difficulty arose as I moved from a design firm building websites for smaller- to medium-sized companies, to larger in-house teams where I worked across a collection of related sites. In those positions, I began to work more frequently with washable parts.

Our rely on multimedia queries resulted in parts that were tied to frequent window sizes. If the goal of part libraries is modify, then this is a real problem because you can just use these components if the devices you’re designing for correspond to the viewport sizes used in the pattern library—in the process never really hitting that “devices that don’t already occur” goal.

Then there’s the problem of space. Media questions allow components to adapt based on the viewport size, but what if I put a component into a sidebar, like in the figure below?

Container queries: our savior or a false dawn?

Container queries have long been touted as an improvement upon media queries, but at the time of writing are unsupported in most browsers. There are workarounds for JavaScript, but they can lead to dependencies and compatibility issues. The basic theory underlying container queries is that elements should change based on the size of their parent container and not the viewport width, as seen in the following illustrations.

One of the biggest arguments in favor of container queries is that they help us create components or design patterns that are truly reusable because they can be picked up and placed anywhere in a layout. This is an important step in moving toward a form of component-based design that works at any size on any device.

In other words, responsive elements are meant to replace responsive layouts.

Container queries will help us move from designing pages that respond to the browser or device size to designing components that can be placed in a sidebar or in the main content, and respond accordingly.

My issue is that layout is still used to determine when a design needs to adapt. This approach will always be restrictive, as we will still need pre-defined breakpoints. For this reason, my main question with container queries is, How would we decide when to change the CSS used by a component?

The best place to make that choice is probably a component library that is disconnected from context and real content.

As the diagrams below illustrate, we can use container queries to create designs for specific container widths, but what if I want to change the design based on the image size or ratio?

In this example, the dimensions of the container are not what should dictate the design, rather, the image is.

Without reliable cross-browser support, it’s difficult to say for certain whether container queries will succeed. Responsive component libraries would definitely evolve how we design and would improve the possibilities for reuse and design at scale. However, we might need to modify these elements in order to fit our content.

CSS is changing

Whilst the container query debate rumbles on, there have been numerous advances in CSS that change the way we think about design. The days of fixed-width elements measured in pixels and floated div elements used to cobble layouts together are long gone, consigned to history along with table layouts. Flexbox and CSS Grid have revolutionized layouts for the web. We can now create elements that wrap onto new rows when they run out of space, not when the device changes.

.wrapper { display: grid; grid-template-columns: repeat(auto-fit, 450px); gap: 10px;}

The repeat() function paired with auto-fit or auto-fill allows us to specify how much space each column should use while leaving it up to the browser to decide when to spill the columns onto a new line. Similar things can be achieved with Flexbox, as elements can wrap over multiple rows and “flex” to fill available space. 

.wrapper { display: flex; flex-wrap: wrap; justify-content: space-between;}.child { flex-basis: 32%; margin-bottom: 20px;}

You don’t need to wrap elements in container rows, which is the biggest benefit of all of this. Without rows, content isn’t tied to page markup in quite the same way, allowing for removals or additions of content without additional development.

This is a big step forward when it comes to creating designs that allow for evolving content, but the real game changer for flexible designs is CSS Subgrid.

Remember the days of crafting perfectly aligned interfaces, only for the customer to add an unbelievably long header almost as soon as they’re given CMS access, like the illustration below?

Subgrid allows elements to respond to adjustments in their own content and in the content of sibling elements, helping us create designs more resilient to change.

.wrapper { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); grid-template-rows: auto 1fr auto; gap: 10px;}.sub-grid { display: grid; grid-row: span 3; grid-template-rows: subgrid; /* sets rows to parent grid */}

CSS Grid allows us to separate layout and content, thereby enabling flexible designs. Meanwhile, Subgrid allows us to create designs that can adapt in order to suit morphing content. Subgrid is only supported by Firefox at the time of writing, but the above code can be implemented behind an @supports feature query.

Intrinsic layouts

I’d be remiss not to mention intrinsic layouts, a term used by Jen Simmons to describe a mix of contemporary and traditional CSS features used to create layouts that respond to available space.

Responsive layouts have flexible columns using percentages. Intrinsic layouts, on the other hand, use the fr unit to create flexible columns that won’t ever shrink so much that they render the content illegible.

frunits is a statement that says,” I want you to distribute the extra space in this way, but never make it smaller than the content that is inside.”

—Jen Simmons,” Designing Intrinsic Layouts”

Additionally, intrinsic layouts can mix and match both fixed and flexible units, letting the content choose how much space is taken up.

What makes intrinsic design stand out is that it not only creates designs that can withstand future devices but also helps scale design without losing flexibility. Without having the same breakpoints or the same amount of content as in the previous implementation, components and patterns can be lifted and reused.

We can now create designs that adapt to the space they have, the content within them, and the content around them. We can create responsive components using an intrinsic approach without relying on container queries.

Another 2010 moment?

This intrinsic approach should in my view be every bit as groundbreaking as responsive web design was ten years ago. It’s another instance of “everything changed,” in my opinion.

But it doesn’t seem to be moving quite as fast, I haven’t yet had that same career-changing moment I had with responsive design, despite the widely shared and brilliant talk that brought it to my attention.

One possible explanation for that might be that I now work for a sizable company, which is significantly different from the role I held as a design agency in 2010: In my agency days, every new project was a clean slate, a chance to try something new. Nowadays, projects use existing tools and frameworks and are often improvements to existing websites with an existing codebase.

Another possibility is that I’m now more prepared for change. In 2010 I was new to design in general, the shift was frightening and required a lot of learning. Additionally, an intrinsic approach isn’t exactly all-new; it’s about applying existing skills and CSS knowledge in a unique way.

You can’t framework your way out of a content problem

Another reason for the slightly slower adoption of intrinsic design could be the lack of quick-fix framework solutions available to kick-start the change.

Ten years ago, responsive grid systems were everywhere. With a framework like Bootstrap or Skeleton, you had a responsive design template at your fingertips.

Because having a selection of units is a hindrance when creating layout templates, intrinsic design and frameworks do not work together quite as well. The beauty of intrinsic design is combining different units and experimenting with techniques to get the best for your content.

And then there are design tools. We probably all used Photoshop templates for desktop, tablet, and mobile devices at some point in our careers to drop designs in and demonstrate how the site would look at each of the three stages.

How do you do that now, with each component responding to content and layouts flexing as and when they need to? This kind of design must take place in the browser, which is something I’m very fond of.

The debate about “whether designers should code” is another that has rumbled on for years. When designing a digital product, we should, at the very least, design for a best- and worst-case scenario when it comes to content. It’s not ideal to do this in a graphics-based software package. In code, we can add longer sentences, more radio buttons, and extra tabs, and watch in real time as the design adapts. Does it continue to function? Is the design too reliant on the current content?

Personally, I look forward to the day intrinsic design is the standard for design, when a design component can be truly flexible and adapt to both its space and content with no reliance on device or container dimensions.

First, the content

Content is not constant. After all, to design for the unanticipated or unexpected, we must take into account changes in content, such as in our earlier Subgrid card illustration, which allowed the cards to make adjustments to both their own and sibling elements.

Thankfully, there’s more to CSS than layout, and plenty of properties and values can help us put content first. Subgrid and pseudo-elements like ::first-line and ::first-letter help to separate design from markup so we can create designs that allow for changes.

Instead of the dated markup tricks below,

 First line of text with different styling...

—we can target content based on where it appears.

.element::first-line { font-size: 1.4em;}.element::first-letter { color: red;}

Much bigger additions to CSS include logical properties, which change the way we construct designs using logical dimensions (start and end) instead of physical ones (left and right), something CSS Grid also does with functions like min(), max(), and clamp().

This flexibility allows for directional changes according to content, a common requirement when we need to present content in multiple languages. In the past, this was often achieved with Sass mixins but was often limited to switching from left-to-right to right-to-left orientation.

Directional variables must be set in the Sass version.

$direction: rtl;$opposite-direction: ltr;$start-direction: right;$end-direction: left;

These variables can be used as values—

body { direction: $direction; text-align: $start-direction;}

—or as real estate.

margin-#{$end-direction}: 10px;padding-#{$start-direction}: 10px;

However, now we have native logical properties, removing the reliance on both Sass ( or a similar tool ) and pre-planning that necessitated using variables throughout a codebase. These properties also start to break apart the tight coupling between a design and strict physical dimensions, creating more flexibility for changes in language and in direction.

margin-block-end: 10px;padding-block-start: 10px;

There are also native start and end values for properties like text-align, which means we can replace text-align: right with text-align: start.

Like the earlier examples, these properties help to build out designs that aren’t constrained to one language, the design will reflect the content’s needs.

Fluid and fixed

We briefly covered the power of combining fixed widths with fluid widths with intrinsic layouts. The min() and max() functions are a similar concept, allowing you to specify a fixed value with a flexible alternative. 

For min() this means setting a fluid minimum value and a maximum fixed value.

.element { width: min(50%, 300px);}

The element in the figure above will be 50 % of its container as long as the element’s width doesn’t exceed 300px.

For max() we can set a flexible max value and a minimum fixed value.

.element { width: max(50%, 300px);}

Now the element will be 50 % of its container as long as the element’s width is at least 300px. This means we can set limits but allow content to react to the available space.

The clamp() function builds on this by allowing us to set a preferred value with a third parameter. Now we can allow the element to shrink or grow if it needs to without getting to a point where it becomes unusable.

.element { width: clamp(300px, 50%, 600px);}

This time, the element’s width will be 50 % of its container’s preferred value, with no exceptions for 300px and 600px.

With these techniques, we have a content-first approach to responsive design. We can separate content from markup, meaning the changes users make will not affect the design. By making plans for unanticipated changes in language or direction, we can begin to future-proof designs. And we can increase flexibility by setting desired dimensions alongside flexible alternatives, allowing for more or less content to be displayed correctly.

First, the circumstances

Thanks to what we’ve discussed so far, we can cover device flexibility by changing our approach, designing around content and space instead of catering to devices. But what about that last bit of Jeffrey Zeldman’s quote,”… situations you haven’t imagined”?

Rather than someone using a mobile phone and moving through a crowded street in glaring sunshine, it’s a very different design to be done for someone using a desktop computer. Situations and environments are hard to plan for or predict because they change as people react to their own unique challenges and tasks.

This is why making a decision is so crucial. One size never fits all, so we need to design for multiple scenarios to create equal experiences for all our users.

Thankfully, there is a lot we can do to provide choice.

Responsible design

” There are parts of the world where mobile data is prohibitively expensive, and where there is little or no broadband infrastructure”.

On a 50 MB budget, I spent a day surfing the web.

Chris Ashton

One of the biggest assumptions we make is that people interacting with our designs have a good wifi connection and a wide screen monitor. However, in the real world, our users may be commuters using smaller mobile devices that may experience drops in connectivity while traveling on trains or other modes of transportation. There is nothing more frustrating than a web page that won’t load, but there are ways we can help users use less data or deal with sporadic connectivity.

The srcset attribute allows the browser to decide which image to serve. This means we can create smaller ‘cropped’ images to display on mobile devices in turn using less bandwidth and less data.

The preload attribute can also help us to think about how and when media is downloaded. It can be used to tell a browser about any critical assets that need to be downloaded with high priority, improving perceived performance and the user experience. 

There’s also native lazy loading, which indicates assets that should only be downloaded when they are needed.

With srcset, preload, and lazy loading, we can start to tailor a user’s experience based on the situation they find themselves in. What none of this does, however, is allow the user themselves to decide what they want downloaded, as the decision is usually the browser’s to make. 

So how can we put users in control?

The media queries are returning.

Media questions have always been about much more than device sizes. They allow content to adapt to different situations, with screen size being just one of them.

We’ve long been able to check for media types like print and speech and features such as hover, resolution, and color. These checks allow us to provide options that suit more than one scenario, it’s less about one-size-fits-all and more about serving adaptable content.

The Level 5 spec for Media Queries is still being developed at this writing. It introduces some really exciting queries that in the future will help us design for multiple other unexpected situations.

For instance, a light-level option lets you alter a user’s style when they are in the dark or in the sun. Paired with custom properties, these features allow us to quickly create designs or themes for specific environments.

@media (light-level: normal) { --background-color: #fff; --text-color: #0b0c0c; }@media (light-level: dim) { --background-color: #efd226; --text-color: #0b0c0c;}

Another key feature of the Level 5 spec is personalization. Instead of creating designs that are the same for everyone, users can choose what works for them. This is achieved by using features like prefers-reduced-data, prefers-color-scheme, and prefers-reduced-motion, the latter two of which already enjoy broad browser support. These features tap into preferences set via the operating system or browser so people don’t have to spend time making each site they visit more usable. 

Media questions like this go beyond choices made by a browser to grant more control to the user.

Expect the unexpected

In the end, we should always anticipate that things will change. Devices in particular change faster than we can keep up, with foldable screens already on the market.

We can design for content, but we can’t do it the same way we have for this constantly changing landscape. By putting content first and allowing that content to adapt to whatever space surrounds it, we can create more robust, flexible designs that increase the longevity of our products.

A lot of the CSS discussed here is about moving away from layouts and putting content at the heart of design. There is a lot more we can do to adopt a more intrinsic approach, from responsive components to fixed and fluid units. Even better, we can test these techniques during the design phase by designing in-browser and watching how our designs adapt in real-time.

When it comes to unexpected circumstances, we need to make sure our goods are usable when people need them, whenever and wherever that may be. We can move closer to achieving this by involving users in our design decisions, by creating choice via browsers, and by giving control to our users with user-preference-based media queries.

Good design for the unexpected should allow for change, provide choice, and give control to those we serve: our users themselves.

” Any feedback?” is perhaps one of the worst ways to ask for suggestions. It’s obscure and unreliable, and it doesn’t give a clear picture of what we’re looking for. Getting good opinions starts sooner than we might hope: it starts with the demand.

Starting the process of receiving feedback with a question may seem counterintuitive, but it makes sense if we consider that receiving input can be considered a form of design research. In the same way that we wouldn’t perform any studies without the correct questions to get the insight that we need, the best way to ask for feedback is also to build strong issues.

Design analysis is not a one-time procedure. Sure, any great comments process continues until the project is finished, but this is especially true for layout because architecture work continues iteration after iteration, from a high level to the finest details. Each stage requires its unique set of questions.

And suddenly, as with any great research, we need to review what we got up, get to the base of its perspectives, and take activity. Iteration, evaluation, and issue. This look at each of those.

The query

Being available to input is important, but we need to be specific about what we’re looking for. Any comments,” What do you think,” or” I’d love to hear your view” at the conclusion of a presentation are likely to generate a lot of divergent thoughts, or worse, to make people follow the lead of the first speaker. And next… we get frustrated because vague issues like those you turn a high-level moves review into folks rather commenting on the borders of buttons. Which topic may be important, so it might be difficult to get the team to pay attention to it.

But how do we get into this scenario? It’s a combination of various components. One is that we don’t often consider asking as a part of the input approach. Another is how healthy it is to keep the question open and assume that everyone else will agree. Another is that in nonprofessional debate, there’s usually no need to be that exact. In summary, we tend to undervalue the value of the issues, and we don’t make any improvements to them.

The work of asking good questions guidelines and focuses the criticism. It’s even a form of acceptance because it specifies what kind of opinions you’d like to receive and how you’re open to them. It puts people in the right emotional position, especially in situations when they weren’t expecting to give opinions.

There isn’t a second best method to request suggestions. It simply needs to be certain, and sensitivity can take several shapes. The one of stage over level is a design for design criticism that I’ve found to be particularly helpful in my coaching.

” Stage” refers to each of the steps of the process—in our event, the design process. The kind of feedback changes as the person research moves forward to the final design. But within a single stage, one might also examine whether some assumptions are correct and whether there’s been a suitable language of the amassed input into updated designs as the task has evolved. The layers of user experience could serve as a starting point for potential questions. What do you want to know: Project objectives? user requirements? Functionality? Content? Interaction design? Information architecture UI design? navigation planning Visual design? branding?

Here’re a few example questions that are precise and to the point that refer to different layers:

• Functionality: Is it desirable to automate account creation?
• Interaction design: Take a look through the updated flow and let me know whether you see any steps or error states that I might’ve missed.
• Information architecture: On this page, we have two competing pieces of information. Is the structure effective in communicating them both?
• User interface design: What do you think about the error counter at the top of the page, which makes sure you see the next error even if it is outside the viewport?
• Navigation design: From research, we identified these second-level navigation items, but once you’re on the page, the list feels too long and hard to navigate. Are there any ways to deal with this?
• Visual design: Are the sticky notifications in the bottom-right corner visible enough?

The other axis of specificity is determined by how far you’d like to go with the information being presented. For example, we might have introduced a new end-to-end flow, but there was a specific view that you found particularly challenging and you’d like a detailed review of that. This can be especially helpful from one iteration to the next when it’s crucial to highlight the areas that have changed.

There are other things that we can consider when we want to achieve more specific—and more effective—questions.

A quick fix is to get rid of the generic qualifiers from questions like “good,” “well,” “nice,” “bad,” “okay,” and” cool.” For example, asking,” When the block opens and the buttons appear, is this interaction good”? is possible to appear specific, but the “good” qualifier can be found in an even better question,” When the block opens and the buttons appear, is it clear what the next action is?”

Sometimes we actually do want broad feedback. Although that’s uncommon, it can occur. In that sense, you might still make it explicit that you’re looking for a wide range of opinions, whether at a high level or with details. Or perhaps just say,” At first glance, what do you think”? so that it’s clear that what you’re asking is open ended but focused on someone’s impression after their first five seconds of looking at it.

Sometimes the project is particularly broad, and some areas may have already been thoroughly explored. In these situations, it might be useful to explicitly say that some parts are already locked in and aren’t open to feedback. Although it’s not something I’d recommend in general, I’ve found it helpful in avoiding falling into rabbit holes like those that could lead to further refinement but aren’t what’s important right now.

Asking specific questions can completely change the quality of the feedback that you receive. People who have less refined critique abilities will now be able to provide more useful feedback, and even experienced designers will appreciate the clarity and effectiveness gained from concentrating solely on what is required. It can save a lot of time and frustration.

The iteration

Design iterations are probably the most visible part of the design work, and they provide a natural checkpoint for feedback. Many design tools have inline commenting, but many of those methods typically display changes as a single fluid stream in the same file. These methods cause conversations to vanish once they’re resolved, update shared UI components automatically, and require designs to always display the most recent version unless these would-be useful features were manually turned off. The implied goal that these design tools seem to have is to arrive at just one final copy with all discussions closed, probably because they inherited patterns from how written documents are collaboratively edited. That approach to design critiques is probably not the best approach, but some teams might benefit from it even if I don’t want to be too prescriptive.

The asynchronous design-critique approach that I find most effective is to create explicit checkpoints for discussion. For this, I’m going to use the term iteration post. It refers to a write-up or presentation of the design iteration followed by a discussion thread of some kind. This can be used on any platform that can accommodate this structure. By the way, when I refer to a “write-up or presentation“, I’m including video recordings or other media too: as long as it’s asynchronous, it works.

There are many benefits to using iteration posts:

• It creates a rhythm in the design work so that the designer can review feedback from each iteration and prepare for the next.
• It makes decisions accessible for upcoming review, and conversed conversations are also always available.
• It creates a record of how the design changed over time.
• It might also make it simpler to collect and act on feedback depending on the tool.

These posts of course don’t mean that no other feedback approach should be used, just that iteration posts could be the primary rhythm for a remote design team to use. From there, there can be additional feedback techniques ( such as live critique, pair designing, or inline comments ).

I don’t think there’s a standard format for iteration posts. However, there are a few high-level elements that make sense to include as a baseline:

1. The goal
2. The layout
3. The list of changes
4. The querys

Each project is likely to have a goal, and hopefully it’s something that’s already been summarized in a single sentence somewhere else, such as the client brief, the product manager’s outline, or the project owner’s request. In other words, I would copy and paste this into every iteration post to make it work. The idea is to provide context and to repeat what’s essential to make each iteration post complete so that there’s no need to find information spread across multiple posts. The most recent iteration post will provide all I need to know about the most recent design.

This copy-and-paste part introduces another relevant concept: alignment comes from repetition. Therefore, repeating information in posts is actually very effective at ensuring that everyone is on the same page.

The design is then the actual series of information-architecture outlines, diagrams, flows, maps, wireframes, screens, visuals, and any other kind of design work that’s been done. It’s any design artifact, in essence. For the final stages of work, I prefer the term blueprint to emphasize that I’ll be showing full flows instead of individual screens to make it easier to understand the bigger picture.

Because it makes it easier to refer to the objects, it might also be helpful to have clear names on them. Write the post in a way that helps people understand the work. It’s not much different from creating a strong live presentation.

For an efficient discussion, you should also include a bullet list of the changes from the previous iteration to let people focus on what’s new, which can be especially useful for larger pieces of work where keeping track, iteration after iteration, could become a challenge.

Finally, as mentioned earlier, a list of the questions must be included in order to help you guide the design critique in the desired direction. Doing this as a numbered list can also help make it easier to refer to each question by its number.

Not every iteration is the same. Earlier iterations don’t need to be as tightly focused—they can be more exploratory and experimental, maybe even breaking some of the design-language guidelines to see what’s possible. Then, later, the iterations begin coming to a decision and improving it until the design process is complete and the feature is ready.

I want to highlight that even if these iteration posts are written and conceived as checkpoints, by no means do they need to be exhaustive. A post might be a draft, just a concept to start a discussion, or it might be a cumulative list of every feature that was added over the course of each iteration until the full picture is achieved.

Over time, I also started using specific labels for incremental iterations: i1, i2, i3, and so on. Although this may seem like a minor labeling tip, it can be useful in many ways:

• Unique—It’s a clear unique marker. One can quickly say,” This was discussed in i4″ with each project, and everyone knows where to go to review things.
• Unassuming—It works like versions ( such as v1, v2, and v3 ) but in contrast, versions create the impression of something that’s big, exhaustive, and complete. Attempts must be exploratory, incomplete, or partial.
• Future proof—It resolves the “final” naming problem that you can run into with versions. No more files with the title “final final complete no-really-its-done” Within each project, the largest number always represents the latest iteration.

The wording release candidate (RC ) could be used to indicate when a design is finished enough to be worked on, even if there are some bits that still need work and, in turn, need more iterations:” with i8 we reached RC” or “i12 is an RC” to illustrate this.

The review

What typically occurs during a design critique is an open discussion that can be very productive between two people. This approach is particularly effective during live, synchronous feedback. However, when we work asynchronously, it is more effective to adopt a different strategy: we can adopt a user-research mindset. Written feedback from teammates, stakeholders, or others can be treated as if it were the result of user interviews and surveys, and we can analyze it accordingly.

This shift has some significant advantages, making asynchronous feedback particularly effective, especially around these friction points:

1. It removes the pressure to reply to everyone.
2. It lessens the annoyance of snoop-by comments.
3. It lessens our personal stake.

The first friction point is having to press yourself to respond to each and every comment. Sometimes we write the iteration post, and we get replies from our team. It’s just a few of them, it’s simple, and there isn’t much of a problem with it. But other times, some solutions might require more in-depth discussions, and the amount of replies can quickly increase, which can create a tension between trying to be a good team player by replying to everyone and doing the next design iteration. If the respondent is a stakeholder or a person directly involved in the project, this might be especially true. We need to accept that this pressure is absolutely normal, and it’s human nature to try to accommodate people who we care about. Responding to all comments at times can be effective, but when we consider a design critique more like user research, we realize that we don’t need to respond to every comment, and there are alternatives in asynchronous spaces:

One is to let the next iteration speak for itself. The response is received when the design changes and a follow-up iteration is made. You might tag all the people who were involved in the previous discussion, but even that’s a choice, not a requirement.
• Another option is to respond politely to acknowledge each comment, such as” Understood. Thank you”,” Good points— I’ll review”, or” Thanks. These will be included in the upcoming iteration. In some cases, this could also be just a single top-level comment along the lines of” Thanks for all the feedback everyone—the next iteration is coming soon”!
• One more thing is to quickly summarize the comments before proceeding. Depending on your workflow, this can be particularly useful as it can provide a simplified checklist that you can then use for the next iteration.

The swoop-by comment, which is the kind of feedback that comes from a member of a team or non-project who might not be aware of the context, restrictions, decisions, or requirements, or of the discussions from earlier iterations, is the second friction point. On their side, there’s something that one can hope that they might learn: they could start to acknowledge that they’re doing this and they could be more conscious in outlining where they’re coming from. Swoop-by comments frequently prompt the simple thought,” We’ve already discussed this,” and it can be frustrating to have to keep saying the same thing over and over.

Let’s begin by acknowledging again that there’s no need to reply to every comment. However, a brief response with a link to the previous discussion for additional information is typically sufficient if responding to a previously litigated point might be helpful. Remember, alignment comes from repetition, so it’s okay to repeat things sometimes!

Swoop-by commenting can still be useful for two reasons: first, they might point out something that isn’t clear, and second, they might have the power to represent a user’s first impression of the design. Sure, you’ll still be frustrated, but that might at least help in dealing with it.

The personal stake we might have in the design could be the third friction point, which might cause us to feel defensive if the review turned into a discussion. Treating feedback as user research helps us create a healthy distance between the people giving us feedback and our ego ( because yes, even if we don’t want to admit it, it’s there ). In the end, presenting everything in aggregated form helps us to prioritize our work more.

Always remember that while you need to listen to stakeholders, project owners, and specific advice, you don’t have to accept every piece of feedback. You must examine it and come up with a rationale for your choice, but sometimes “no” is the best choice.

As the designer leading the project, you’re in charge of that decision. In the end, everyone has their area of specialization, and the designer is the one with the most background and knowledge to make the right choice. And by listening to the feedback that you’ve received, you’re making sure that it’s also the best and most balanced decision.

Thanks to Mike Shelton and Brie Anne Demkiw for their initial review of this article.

We’ve been conversing for many thousands of years. Whether to present information, perform transactions, or just to check in on one another, people have yammered aside, chattering and gesticulating, through spoken discussion for many generations. Only recently had conversations started to be written, and only recently have we outsourced them to the system, a system that exhibits a significantly higher affinity for written communications than for the vernacular rigors of spoken language.

Laptops have trouble because between spoken and written speech, talk is more primitive. Machines must wrestle with the complexity of human statement, including the disfluencies and pauses, the gestures and body speech, and the variations in expression choice and spoken dialect, which may impede even the most skillfully crafted human-computer interaction. In the human-to-human situation, spoken language also has the opportunity of face-to-face call, where we can easily interpret verbal interpersonal cues.

In contrast, written language develops its own fossil record of dated terms and phrases as we report it and retain utilization long after they are no longer relevant in spoken communication ( for example, the welcome” To whom it may concern” ). Because it tends to be more consistent, smooth, and proper, written word is necessarily far easier for devices to interpret and know.

Spoken language is not a luxury in this regard. Besides the nonverbal cues that decorate conversations with emphasis and emotional context, there are also verbal cues and vocal behaviors that modulate conversation in nuanced ways: how something is said, not what. Our spoken language reaches far beyond what the written word can ever deliver, whether it’s rapid-fire, low-pitched, high-decibel, sarcastic, stilted, or sighing. So when it comes to voice interfaces—the machines we conduct spoken conversations with—we face exciting challenges as designers and content strategists.

Voice-to-voice interactions

We interact with voice interfaces for a variety of reasons, but according to Michael McTear, Zoraida Callejas, and David Griol in The Conversational Interface, those motivations by and large mirror the reasons we initiate conversations with other people, too ( ). We typically strike up a discussion by:

• we need something done ( such as a transaction ),
• we want to know something, or some kind of information, or
• we are social beings and want someone to talk to ( conversation for conversation’s sake ).

These three categories, which I refer to as transactional, informational, and prosocial, also apply to essentially every voice interaction: a single conversation that starts with the voice interface’s first greeting and ends with the user leaving the interface. Note here that a conversation in our human sense—a chat between people that leads to some result and lasts an arbitrary length of time—could encompass multiple transactional, informational, and prosocial voice interactions in succession. In other words, a voice interaction is a conversation, but it is not always just one voice interaction.

Purely prosocial conversations are more gimmicky than captivating in most voice interfaces, because machines don’t yet have the capacity to really want to know how we’re doing and to do the sort of glad-handing humans crave. Additionally, there is ongoing debate about whether users actually prefer the type of organic human conversation that starts with a prosocial voice and progresses seamlessly into new ones. In fact, in Voice User Interface Design, Michael Cohen, James Giangola, and Jennifer Balogh recommend sticking to users ‘ expectations by mimicking how they interact with other voice interfaces rather than trying too hard to be human—potentially alienating them in the process ( ).

That leaves two different types of conversations we can have with one another that a voice interface can also have easily, including one that is transactional and one that is informational, teaching us something new ( “discuss a musical” ).

Transactional voice interactions

When you order a Hawaiian pizza with extra pineapple, you’re typically having a conversation and a voice interaction when you’re tapping buttons on a food delivery app. Even when we walk up to the counter and place an order, the conversation quickly pivots from an initial smattering of neighborly small talk to the real mission at hand: ordering a pizza ( generously topped with pineapple, as it should be ).

How are things going, Alison?

Burhan: Hi, welcome to Crust Deluxe! It’s chilly outside. How can I help you?

Can I get a Hawaiian pizza with extra pineapple, Alison?

Burhan: Sure, what size?

Large, Alison.

Burhan: Anything else?

Alison: No, that’s it.

Burhan: Something to drink?

Alison: I’ll have a bottle of Coke.

Burhan: You got it. It will cost about$ 15 and take fifteen minutes to complete.

Each progressive disclosure in this transactional conversation reveals more and more of the desired outcome of the transaction: a service rendered or a product delivered. Transactional conversations exhibit a few key characteristics: they’re direct, to the point, and economical. They quickly dispense with pleasantries.

Informational voice interactions

Meanwhile, some conversations are primarily about obtaining information. Alison might visit Crust Deluxe with the sole intention of placing an order, but she might not want to leave with a pizza at all. She might be just as interested in whether they serve halal or kosher dishes, gluten-free options, or something else. Even though we have a prosocial mini-conversation once more at the beginning to establish politeness, we’re after much more.

How are things going, Alison?

Burhan: Hi, welcome to Crust Deluxe! It’s chilly outside. How can I help you?

Alison: Can I ask a few questions?

Burhan: Of course! Go right ahead.

Do you have any halal options available on the menu, Alison?

Burhan: Absolutely! On request, we can make any pie halal. We also have lots of vegetarian, ovo-lacto, and vegan options. Do you have any other dietary restrictions in mind?

Alison: What about gluten-free pizzas?

Burhan: For both our deep-dish and thin-crust pizzas, we can definitely make a gluten-free crust for you, without a problem. Anything else I can answer for you?

Alison: That’s it for now. Good to know. Thank you.

Burhan: Anytime, come back soon!

This dialogue is a lot different. Here, the goal is to get a certain set of facts. Informational conversations are research expeditions to gather data, news, or facts in search of the truth. Voice interactions that are informational might be more long-winded than transactional conversations by necessity. Responses are typically longer, more in-depth, and carefully communicated so that the customer is aware of the important lessons.

Voice Interfaces

Voice-based user interfaces use speech at the core to assist users in accomplishing their objectives. But simply because an interface has a voice component doesn’t mean that every user interaction with it is mediated through voice. We’re most concerned with pure voice interfaces, which depend entirely on spoken conversation and lack any visual component, making multimodal voice interfaces much more nuanced and challenging to deal with because they can lean on visual components like screens as crutches.

Though voice interfaces have long been integral to the imagined future of humanity in science fiction, only recently have those lofty visions become fully realized in genuine voice interfaces.

IVR ( interactive voice response ) systems

Though written conversational interfaces have been fixtures of computing for many decades, voice interfaces first emerged in the early 1990s with text-to-speech ( TTS ) dictation programs that recited written text aloud, as well as speech-enabled in-car systems that gave directions to a user-provided address. We became familiar with the first real voice interfaces that could actually be spoken with the help of interactive voice response ( IVR ) systems, which were developed as an alternative to overburdened customer service representatives.

IVR systems allowed organizations to reduce their reliance on call centers but soon became notorious for their clunkiness. Similar to the corporate world, these systems were primarily created as metaphorical switchboards to direct customers to a real phone agent (” Say Reservations to book a flight or check an itinerary” ), and chances are you’ll have a conversation with one when you call an airline or hotel conglomerate. Despite their functional issues and users ‘ frustration with their inability to speak to an actual human right away, IVR systems proliferated in the early 1990s across a variety of industries (, PDF).

IVR systems have a reputation for having less scintillating conversation than we’re used to in real life ( or even in science fiction ), but they are great for highly repetitive, monotonous conversations that typically don’t veer from a single format.

Screen readers

The invention of the screen reader, a tool that converts visual content into synthesized speech, was a development of IVR systems in parallel. For Blind or visually impaired website users, it’s the predominant method of interacting with text, multimedia, or form elements. Perhaps the closest thing we have today to an out-of-the-box implementation of content delivered through voice is represented by screen readers.

Among the first screen readers known by that moniker was the Screen Reader for the BBC Micro and NEEC Portable developed by the Research Centre for the Education of the Visually Handicapped (RCEVH) at the University of Birmingham in 1986 ( ). The first IBM Screen Reader for text-based computers was created by Jim Thatcher in the same year, which was later recreated for a computer with graphical user interfaces ( GUIs ) ( ).

With the rapid growth of the web in the 1990s, the demand for accessible tools for websites exploded. Screen readers started facilitating quick interactions with web pages that ostensibly allow disabled users to traverse the page as an aural and temporal space rather than a visual and physical one with the introduction of semantic HTML and especially ARIA roles in 2008, enabling speedy interactions with the pages. In other words, screen readers for the web “provide mechanisms that translate visual design constructs—proximity, proportion, etc. —into useful information,” according to Aaron Gustafson in A List Apart. ” At least they do when documents are authored thoughtfully” ( ).

Although incredibly instructive for voice interface designers, screen readers have a major flaw: they’re challenging to use and consistently verbose. The visual structures of websites and web navigation don’t translate well to screen readers, sometimes resulting in unwieldy pronouncements that name every manipulable HTML element and announce every formatting change. Working with web-based interfaces is a cognitive burden for many screen reader users.

In Wired, accessibility advocate and voice engineer Chris Maury considers why the screen reader experience is ill-suited to users relying on voice:

I disliked the operation of Screen Readers from the beginning. Why are they designed the way they are? It makes no sense to present information visually and then only to have that information translated into audio. All of the time and energy that goes into creating the perfect user experience for an app is wasted, or even worse, adversely impacting the experience for blind users. __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

In many cases, well-designed voice interfaces can speed users to their destination better than long-winded screen reader monologues. After all, users of the visual interface have the advantage of freely scurrying around the viewport to find information, ignoring areas that are unimportant to them. Blind users, meanwhile, are obligated to listen to every utterance synthesized into speech and therefore prize brevity and efficiency. Users with disabilities who have long had no choice but to use clumsy screen readers might benefit from more streamlined user interfaces, especially more advanced voice assistants.

Voice assistants

Many of us immediately associate voice assistants with the popular subset of voice interfaces found in living rooms, smart homes, and offices with the film A Space Odyssey or with Majel Barrett’s voice as the omniscient computer from Star Trek. Voice assistants are akin to personal concierges that can answer questions, schedule appointments, conduct searches, and perform other common day-to-day tasks. And because of their assistive potential, they are quickly receiving more attention from accessibility advocates.

Before the earliest IVR systems found success in the enterprise, Apple published a demonstration video in 1987 depicting the Knowledge Navigator, a voice assistant that could transcribe spoken words and recognize human speech to a great degree of accuracy. Then, in 2001, Tim Berners-Lee and others created their vision for a Semantic Web “agent” that would carry out routine tasks like” checking calendars, making appointments, and finding locations” (, behind paywall ). It wasn’t until 2011 that Apple’s Siri finally entered the picture, making voice assistants a tangible reality for consumers.

There is a significant variation in how programmable and customizable some voice assistants are compared to others due to the sheer number of voice assistants available today ( Fig. 1 ). At one extreme, everything except vendor-provided features is locked down, for example, at the time of their release, the core functionality of Apple’s Siri and Microsoft’s Cortana couldn’t be extended beyond their existing capabilities. There are no other means by which developers can interact with Siri at a low level, aside from predefined categories of tasks like sending messages, hailing rideshares, making restaurant reservations, and other things, so even now it isn’t possible to program Siri to perform arbitrary functions.

At the opposite end of the spectrum, voice assistants like Amazon Alexa and Google Home offer a core foundation on which developers can build custom voice interfaces. For this reason, developers who feel stifled by the limitations of Siri and Cortana are increasingly using programmable voice assistants that allow for customization and extensibility. Amazon offers the Alexa Skills Kit, a developer framework for building custom voice interfaces for Amazon Alexa, while Google Home offers the ability to program arbitrary Google Assistant skills. Users can choose from among the thousands of custom-built skills available today in the Google Assistant and Amazon Alexa ecosystems.

As businesses like Amazon, Apple, Microsoft, and Google continue to occupy their positions, they are also selling and open-sourcing an unheard array of tools and frameworks for designers and developers, aiming to make creating voice interfaces as simple as possible, even without code.

Often by necessity, voice assistants like Amazon Alexa tend to be monochannel—they’re tightly coupled to a device and can’t be accessed on a computer or smartphone instead. In contrast, many development platforms, such as Google’s Dialogflow, have omnichannel capabilities that allow users to create a single conversational interface that then becomes a voice interface, textual chatbot, and IVR system upon deployment. I don’t prescribe any specific implementation approaches in this design-focused book, but in Chapter 4 we’ll get into some of the implications these variables might have on the way you build out your design artifacts.

Voice content

Simply put, voice content is content delivered through voice. Voice content must be free-flowing and organic, contextless and concise—everything written content isn’t enough to preserve what makes human conversation so compelling in the first place.

Our world is replete with voice content in various forms: screen readers reciting website content, voice assistants rattling off a weather forecast, and automated phone hotline responses governed by IVR systems. We’re most concerned with the audiobook content being delivered as a requirement rather than an option.

For many of us, our first foray into informational voice interfaces will be to deliver content to users. One issue is that any content we already have isn’t in any way suitable for this new environment. So how do we make the content trapped on our websites more conversational? And how do we create fresh copy that works with voice-activated text?

Lately, we’ve begun slicing and dicing our content in unprecedented ways. Websites are, in many ways, massive vaults of what I call macrocontent: lengthy prose that can last for miles in a browser window while extending like microfilm viewers of newspaper archives. Back in 2002, well before the present-day ubiquity of voice assistants, technologist Anil Dash defined microcontent as permalinked pieces of content that stay legible regardless of environment, such as email or text messages:

An example of microcontent can be a day’s weather forecast [sic], an airplane flight’s arrival and departure times, an abstract from a lengthy publication, or a single instant message. __ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

I would update Dash’s definition of microcontent to include all instances of bite-sized content that goes beyond written communiqués. After all, today we encounter microcontent in interfaces where a small snippet of copy is displayed alone, unmoored from the browser, like a textbot confirmation of a restaurant reservation. Informing delivery channels both established and novel, Microcontent provides the best opportunity to find out how your content can be stretched to the limits of its potential.

As microcontent, voice content is unique because it’s an example of how content is experienced in time rather than in space. We can instantly see when the next train is coming from a digital sign underground, but voice interfaces keep our attention occupied for so long that screen reader users are all too familiar.

Because microcontent is fundamentally made up of isolated blobs with no relation to the channels where they’ll eventually end up, we need to ensure that our microcontent truly performs well as voice content—and that means focusing on the two most important traits of robust voice content: voice content legibility and voice content discoverability.

Our voice content’s legibility and discoverability in general both depend on how it manifests in terms of perceived space and time.

However, how can a content management system ( CMS ) be set up to reach your audience both now and in the future? I learned the hard way that creating a content model—a concept of information types, attributes, and relationships that let people and systems understand content—with my more comfortable design-system wondering would collapse my patient’s holistic information strategy. By developing content versions that are conceptual and even join related content, you can avoid that result.

I just had the opportunity to direct the CMS application for a Fortune 500 company. The customer was excited by the benefits of an holistic information plan, including material modify, multichannel marketing, and robot delivery—designing content to be comprehensible to bots, Google knowledge panels, snippets, and voice user interfaces.

For our information to be understood by many systems, the unit needed conceptual types, which are names given based on their meaning rather than their presentation. This is crucial for an multichannel content strategy. Our aim was to allow artists to create original content and use it where necessary. But as the job proceeded, I realized that supporting material utilize at the range that my client needed required the whole group to identify a new pattern.

Despite our best efforts, we remained influenced by design systems, which we were more comfortable with. An holistic content strategy doesn’t rely on WYSIWYG equipment for design and layout, unlike web-focused material strategies. Our tendency to approach the material model with our common design-system thinking frequently led us to veer away from one of the main purposes of a material model: delivering content to audiences on several marketing channels.

Two fundamental tenets govern a successful content model

We needed to explain to our designers, developers, and stakeholders that we were doing something completely different from their previous web projects, where everyone assumed that content would fit into layouts as visual building blocks. The previous approach was not only more familiar but also more intuitive—at least at first—because it made the designs feel more tangible. The team was able to understand how a content model differs from the design systems we were familiar with by discovering two principles:

1. Instead of layout, semantics must be used by content models.
2. And content models should connect content that belongs together.

Semantic content models

A semantic content model uses type and attribute names that reflect the content’s intended purpose and not how it will be displayed. For example, in a nonsemantic model, teams might create types like teasers, media blocks, and cards. These types may make it simple to present content, but they do not aid in understanding the meaning of the content, which would have opened the door to the content presented in each marketing channel. In contrast, a semantic content model uses type names like “product,”” service,” and “testimonial” to allow for each delivery channel to interpret and use the content as it sees fit.

When you’re creating a semantic content model, a great place to start is to look over the types and properties defined by Schema. a community-driven resource for type definitions that are understandable on platforms like Google search .org

A semantic content model has a number of advantages:

Even if your team doesn’t care about omnichannel content, a semantic content model decouples content from its presentation so that teams can evolve the website’s design without needing to refactor its content. Content can withstand obtrusive website redesigns in this way.
• A semantic content model also gives you a competitive advantage. By adding structured data based on Schema. Using its types and properties, a website can provide hints to help Google understand the content, display it in search snippets or knowledge panels, and use it to respond to voice-interface user questions. Potential customers could access your content without ever visiting your website.
• Beyond those practical benefits, you’ll also need a semantic content model if you want to deliver omnichannel content. Delivery channels must be able to comprehend the same content in order to use it across multiple marketing channels. For instance, if your content model provided a list of questions and answers, it could be easily displayed on a frequently asked questions ( FAQ ) page as well, but it could also be used by a bot that answers frequently asked questions.

For example, using a semantic content model for articles, events, people, and locations lets A List Apart provide cleanly structured data for search engines so that users can read the content on the website, in Google knowledge panels, and even with hypothetical voice interfaces in the future.

Content models that connect

Instead of slicing up related content across disparate content components, I’ve come to the realization that the best models are those that are semantic and also connect related content components ( such as a FAQ item’s question and answer pair ). A good content model connects content that should remain together so that multiple delivery channels can use it without needing to first put those pieces back together.

Write an essay or article about it. The meaning and usefulness of an article depend on how well its components are kept together. Would one of the headings or paragraphs be meaningful on their own without the context of the full article? Our well-versed in designing systems frequently led us to want to develop content models that would break content into smaller pieces to fit the web-centric layout. This had a similar effect to an article that had had its headline removed. Because we were slicing content into standalone pieces based on layout, content that belonged together became difficult to manage and nearly impossible for multiple delivery channels to understand.

Let’s take a look at how connecting related content works in a real-world setting to illustrate. A complex layout for a software product page that included multiple tabs and sections was presented by the client’s design team. Our instincts were to follow suit with the content model. Shouldn’t we make adding any number of tabs in the future as simple and flexible as possible?

We felt like we needed a content type called “tab section” because our design-system instincts were so well-known, so that multiple tab sections could be added to a page. Each tab section would display various types of content. One tab might contain the software’s information or specifications. A list of resources might be provided by another tab.

Our inclination to break down the content model into “tab section” pieces would have led to an unnecessarily complex model and a cumbersome editing experience, and it would have also created content that couldn’t have been understood by additional delivery channels. How would a different system have been able to determine which “tab section” referred to a product’s specifications or resource list, for instance? Would that system have had to have used tab sections and content blocks to calculate this? This would have prevented the tabs from ever being rearranged, and logic would have had to be added to each other delivery channel to interpret the layout of the design system. Furthermore, if the customer were to have no longer wanted to display this content in a tab layout, it would have been tedious to migrate to a new content model to reflect the new page redesign.

Our customer had a breakthrough when we realized that each tab’s specific information, such as the software product’s overview, specifications, related resources, and pricing, was intended to reveal a specific purpose. Once implementation began, our inclination to focus on what’s visual and familiar had obscured the intent of the designs. It wasn’t long after a little digging that it became clear that the idea of tabs wasn’t applicable to the content model. What was important was the meaning of the content they were planning to display in the tabs.

In fact, the customer could have decided to display this content in a different way—without tabs—somewhere else. In response to this realization, we created content types for the software product based on the meaningful attributes the client wanted to display on the web. There were rich attributes like screenshots, software requirements, and feature lists as well as obvious semantic attributes like name and description. The software’s product information stayed together because it wasn’t sliced across separate components like “tab sections” that were derived from the content’s presentation. This content could be understood and presented by any delivery channel, including those that come up in the future.

Conclusion

In this omnichannel marketing project, we discovered that the best way to keep our content model on track was to ensure that it was semantic ( with type and attribute names that reflected the meaning of the content ) and that it kept content together that belonged together ( instead of fragmenting it ). These two ideas made it easier for us to decide what to do with the content model based on the design. Remember: If you’re developing a content model to support an omnichannel content strategy, or even if you just want to make sure Google and other interfaces understand your content, keep in mind:

• A design system isn’t a content model. Team members may be persuaded to combine them and have their content model resemble their design system, so you should guard the semantic and contextual integrity of the content strategy throughout the entire implementation process. Without the use of a magic decoder ring, every delivery channel will be able to consume the content.
• If your team is struggling to make this transition, you can still reap some of the benefits by using Schema. Your website uses structured data from org. The benefit of search engine optimization is a compelling argument on its own, even if additional delivery channels aren’t on the horizon at this time.
• Additionally, remind the team that decoupling the content model from the design will let them update the designs more easily because they won’t be held back by the cost of content migrations. They will be prepared for the upcoming big thing, and they will be able to create new designs without compromising the compatibility between the content and the design.

By firmly defending these ideas, you’ll help your team treat content the way it deserves as the most important component of your user experience and the best way to engage with your audience.

This section will provide you with that plan of action. It covers how to incorporate safety principles into your design work in order to make tech that’s secure, how to persuade your stakeholders that this work is important, and how to respond to the critique that what we really need is more diversity. ( Spoiler: we do, but diversity alone is not the solution to fixing unethical, unsafe technology. )

The method for equitable safety

Your objectives when designing for protection are as follows:

• discover ways your solution can be used for abuse,
• style ways to prevent the maltreatment, and
• offer assistance for customers who are prone to regain control and power.

The Process for Inclusive Safety is a tool to help you reach those goals ( Fig 5.1 ). I developed this strategy in 2018 to better understand the different methods I used to create products that were designed with safety in mind. Whether you are creating an entirely new product or adding to an existing element, the Process can help you produce your product secure and diverse. The Process includes five basic areas of action:

• conducting exploration
• Creating tropes
• Pondering issues
• Designing answers
• Testing for security

It is intended to be flexible, so teams might not want to utilize every action in all circumstances. Use the parts that are related to your special function and environment, this is meant to be something you can put into your existing style process.

And once you use it, if you have an idea for making it better or simply want to give perspective of how it helped your staff, please get in touch with me. It’s a living document, which I hope engineers may use as a practical and useful tool throughout their day-to-day tasks.

If you’re working on a product especially for a resilient team or survivors of some form of injury, such as an application for survivors of domestic violence, sexual abuse, or drug addiction, be sure to read Section 7, which covers that position directly and should be handled a bit different. The principles set forth here are for putting safety first when designing a more general product with a broad user base ( which, as we already know from statistics, will include some groups that should be protected from harm ). Chapter 7 is focused on products that are specifically for vulnerable groups and people who have experienced trauma.

Step 1: Conduct research

Design research should include a thorough analysis of how your technology might be used for abuse as well as specific insights into the experiences of those who have witnessed and perpetrated that kind of abuse. At this stage, you and your team will investigate issues of interpersonal harm and abuse, and explore any other safety, security, or inclusivity issues that might be a concern for your product or service, like data security, racist algorithms, and harassment.

broad analysis

Your project should begin with broad, general research into similar products and issues around safety and ethical concerns that have already been reported. For example, a team building a smart home device would do well to understand the multitude of ways that existing smart home devices have been used as tools of abuse. If your product involves artificial intelligence, be aware of the potential for racism and other issues that have been reported in other AI products. Nearly all types of technology have some kind of potential or actual harm that’s been reported on in the news or written about by academics. Google Scholar is a useful resource for locating these studies.

Specific research: Survivors

When possible and appropriate, include direct research ( surveys and interviews ) with people who are experts in the forms of harm you have uncovered. In order to have a better understanding of the subject and be better positioned to prevent retraumatize survivors, you should interview advocates working in the area of your research first. If you’ve uncovered possible domestic violence issues, for example, the experts you’ll want to speak with are survivors themselves, as well as workers at domestic violence hotlines, shelters, other related nonprofits, and lawyers.

It is important to pay people for their knowledge and lived experiences, especially when interviewing survivors of any kind of trauma. Don’t ask survivors to share their trauma for free, as this is exploitative. While some survivors may not want to be paid, you should always make the offer in the initial ask. Donating to a cause that combated the kind of violence the interviewee experienced is an alternative to paying for. We’ll talk more about how to appropriately interview survivors in Chapter 6.

Abusers specifically: research

It’s unlikely that teams aiming to design for safety will be able to interview self-proclaimed abusers or people who have broken laws around things like hacking. Don’t make this a goal, rather, try to get at this angle in your general research. Attempt to understand how abusers or bad actors use technology to harm others, how they use it against others, and how they justify or explain the abuse.

Step 2: Create archetypes

Use your research’s findings to create the archetypes of abuser and survivor once you’ve finished your research. Archetypes are not personas, as they’re not based on real people that you interviewed and surveyed. Instead, they’re based on your research into likely safety issues, much like when we design for accessibility: we don’t need to have found a group of blind or low-vision users in our interview pool to create a design that’s inclusive of them. Instead, we base those designs on already-existing research to satisfy the requirements of this audience. Personas typically represent real users and include many details, while archetypes are broader and can be more generalized.

The abuser archetype is a person who views a product as a tool to cause harm ( Fig. 5.2 ). They may be trying to harm someone they don’t know through surveillance or anonymous harassment, or they may be trying to control, monitor, abuse, or torment someone they know personally.

Someone who is being abused with the product is the survivor archetype. There are various situations to consider in terms of the archetype’s understanding of the abuse and how to put an end to it: Do they need proof of abuse they already suspect is happening, or are they unaware they’ve been targeted in the first place and need to be alerted ( Fig 5.3 )?

You may want to make multiple survivor archetypes to capture a range of different experiences. They may be aware of the abuse is occurring but not be able to stop it, such as when a stalker keeps figuring out where they are from ( Fig 5.4), or they may be aware that it is happening but not know how ( for example, when an abuser locks them out of IoT devices ). Include as many of these scenarios as you need to in your survivor archetype. You’ll use these later when you create solutions to help your survivor archetypes achieve their objectives of preventing and ending abuse.

It may be useful for you to create persona-like artifacts for your archetypes, such as the three examples shown. Focus on their objectives rather than the demographic details we frequently see in personas. The goals of the abuser will be to carry out the specific abuse you’ve identified, while the goals of the survivor will be to prevent abuse, understand that abuse is happening, make ongoing abuse stop, or regain control over the technology that’s being used for abuse. Later, you’ll think about how to help the survivor’s goals and prevent the abuser’s goals.

And while the “abuser/survivor” model fits most cases, it doesn’t fit all, so modify it as you need to. For example, if you uncovered an issue with security, such as the ability for someone to hack into a home camera system and talk to children, the malicious hacker would get the abuser archetype and the child’s parents would get survivor archetype.

Step 3: Remind yourself of your issues

After creating archetypes, brainstorm novel abuse cases and safety issues. You’re trying to identify entirely new safety issues that are unique to your product or service by using the term” Novel” in terms of things that are not discovered in your research. The goal with this step is to exhaust every effort of identifying harms your product could cause. You aren’t worrying about how to prevent the harm yet—that comes in the next step.

How else could your product be used for any kind of abuse besides what you’ve already found in your research? I recommend setting aside at least a few hours with your team for this process.

Try conducting a Black Mirror brainstorming session if you want to start somewhere. This exercise is based on the show Black Mirror, which features stories about the dark possibilities of technology. Try to figure out how your product would be used in an episode of the show—the most wild, awful, out-of-control ways it could be used for harm. Participants typically have a lot of fun when I lead Black Mirror brainstorms ( which is great because having fun when designing for safety! ). I recommend time-boxing a Black Mirror brainstorm to half an hour, and then dialing it back and using the rest of the time thinking of more realistic forms of harm.

You may still not feel confident that you have found every possible source of harm after identifying as many opportunities for abuse as possible. A healthy amount of anxiety is normal when you’re doing this kind of work. It’s common for teams designing for safety to worry,” Have we really identified every possible harm? What if something is missing, then? If you’ve spent at least four hours coming up with ways your product could be used for harm and have run out of ideas, go to the next step.

It’s impossible to say 100 % assurance that you’ve done everything right, but instead of aiming for 100 % assurance, acknowledge that you’ve taken this step and have done everything you can, and pledge to keep putting safety first in the future. Once your product is released, your users may identify new issues that you missed, aim to receive that feedback graciously and course-correct quickly.

Step 4: Design solutions

You should now be aware of the ways your product can be used for harm as well as survivor and abuser archetypes describing opposing user objectives. The next step is to identify ways to design against the identified abuser’s goals and to support the survivor’s goals. This is a good addition to existing design processes where you’re making recommendations for solutions to the various issues your research has identified.

Some questions to ask yourself to help prevent harm and support your archetypes include:

• Can you design your product in such a way that the identified harm cannot happen in the first place? If not, what barriers can you place to stop the harm from occurring?
• How can you make the victim aware that abuse is happening through your product?
• How can you assist the victim in understanding what they need to do to stop the problem?
• Can you identify any types of user activity that would indicate some form of harm or abuse? Could your product help the user access support?

In some products, it’s possible to proactively detect harm is occurring. For example, a pregnancy app might be modified to allow the user to report that they were the victim of an assault, which could trigger an offer to receive resources for local and national organizations. Although it’s not always possible to be this proactive, it’s worthwhile to spend a half hour talking about how your product could help the user receive help in a safe manner if any kind of user activity would indicate some form of harm or abuse.

That said, use caution: you don’t want to do anything that could put a user in harm’s way if their devices are being monitored. If you do offer some kind of proactive help, always make it voluntary, and think through other safety issues, such as the need to keep the user in-app in case an abuser is checking their search history. In the next chapter, we’ll walk through a good illustration of this.

Step 5: Test for safety

The final step is to evaluate your prototypes from the perspective of your archetypes, who wants to harm the product and the victim of the harm who needs to regain control over the technology. Just like any other kind of product testing, at this point you’ll aim to rigorously test out your safety solutions so that you can identify gaps and correct them, validate that your designs will help keep your users safe, and feel more confident releasing your product into the world.

Ideally, safety testing happens along with usability testing. If you work for a company that doesn’t conduct usability testing, you might be able to use safety testing to deftly perform both. A user who uses your design while trying to use it against someone else can also be encouraged to point out interactions or other design details that don’t make sense.

You’ll want to conduct safety testing on either your final prototype or the actual product if it’s already been released. It’s okay to test an existing product that wasn’t created with safety goals in mind right away; “etrofitting” it for safety is a good thing.

Remember that testing for safety involves testing from the perspective of both an abuser and a survivor, though it may not make sense for you to do both. Alternatively, if you made multiple survivor archetypes to capture multiple scenarios, you’ll want to test from the perspective of each one.

You as the designer are most likely too closely connected to the product and its design at this point, just like other types of usability testing, and you know the product too well. Instead of doing it yourself, set up testing as you would with other usability testing: find someone who is not familiar with the product and its design, set the scene, give them a task, encourage them to think out loud, and observe how they attempt to complete it.

Abuse testing

The goal of this testing is to understand how easy it is for someone to weaponize your product for harm. Unlike with usability testing, you want to make it impossible, or at least difficult, for them to achieve their goal. Use your product in an effort to accomplish the objectives in the abuser archetype you created earlier.

For example, for a fitness app with GPS-enabled location features, we can imagine that the abuser archetype would have the goal of figuring out where his ex-girlfriend now lives. With this in mind, you’d make every effort to discover the location of a different user who has their privacy settings in place. You might try to see her running routes, view any available information on her profile, view anything available about her location ( which she has set to private ), and investigate the profiles of any other users somehow connected with her account, such as her followers.

If by the end of this you’ve managed to uncover some of her location data, despite her having set her profile to private, you know now that your product enables stalking. Reverting to step 4 and figuring out how to stop this from occurring is your next step. You may need to repeat the process of designing solutions and testing them more than once.

testing for a Survivor

testing for a Survivor involves identifying how to give information and power to the survivor. It might not always make sense based on the product or context. Thwarting the attempt of an abuser archetype to stalk someone also satisfies the goal of the survivor archetype to not be stalked, so separate testing wouldn’t be needed from the survivor’s perspective.

However, there are cases where it makes sense. A survivor archetype’s goal, for instance, would be to discover what causes the temperature change when they aren’t altering it themselves. You could test this by looking for the thermostat’s history log and checking for usernames, actions, and times, if you couldn’t find that information, you would have more work to do in step 4.

Another goal might be regaining control of the thermostat once the survivor realizes the abuser is remotely changing its settings. Are there any instructions that explain how to remove a user and change the password, and are they simple to find? For your test, you would need to try to figure out how to do this. This might again reveal that more work is needed to make it clear to the user how they can regain control of the device or account.

Stress testing

To make your product more inclusive and compassionate, consider adding stress testing. This concept comes from Design for Real Life by Eric Meyer and Sara Wachter-Boettcher. The authors noted that personas typically focus on happy people, but that happy people are frequently anxious, stressed out, unhappy, or even go through a bad day. These are called” stress cases”, and testing your products for users in stress-case situations can help you identify places where your design lacks compassion. More information about how to incorporate stress cases into your design can be found in Design for Real Life, as well as in many other effective methods for designing with compassion.

In the 1950s, many in the elite running community had begun to believe it wasn’t possible to run a mile in less than four minutes. Runners had been attempting it since the late 19th century and were beginning to draw the conclusion that the human body simply wasn’t built for the task. 

But on May 6, 1956, Roger Bannister took everyone by surprise. It was a cold, wet day in Oxford, England—conditions no one expected to lend themselves to record-setting—and yet Bannister did just that, running a mile in 3:59.4 and becoming the first person in the record books to run a mile in under four minutes. 

This shift in the benchmark had profound effects; the world now knew that the four-minute mile was possible. Bannister’s record lasted only forty-six days, when it was snatched away by Australian runner John Landy. Then a year later, three runners all beat the four-minute barrier together in the same race. Since then, over 1,400 runners have officially run a mile in under four minutes; the current record is 3:43.13, held by Moroccan athlete Hicham El Guerrouj.

We achieve far more when we believe that something is possible, and we will believe it’s possible only when we see someone else has already done it—and as with human running speed, so it is with what we believe are the hard limits for how a website needs to perform.

Establishing standards for a sustainable web

In most major industries, the key metrics of environmental performance are fairly well established, such as miles per gallon for cars or energy per square meter for homes. The tools and methods for calculating those metrics are standardized as well, which keeps everyone on the same page when doing environmental assessments. In the world of websites and apps, however, we aren’t held to any particular environmental standards, and only recently have gained the tools and methods we need to even make an environmental assessment.

The primary goal in sustainable web design is to reduce carbon emissions. However, it’s almost impossible to actually measure the amount of CO₂ produced by a web product. We can’t measure the fumes coming out of the exhaust pipes on our laptops. The emissions of our websites are far away, out of sight and out of mind, coming out of power stations burning coal and gas. We have no way to trace the electrons from a website or app back to the power station where the electricity is being generated and actually know the exact amount of greenhouse gas produced. So what do we do? 

If we can’t measure the actual carbon emissions, then we need to find what we can measure. The primary factors that could be used as indicators of carbon emissions are:

1. Data transfer 
2. Carbon intensity of electricity

Let’s take a look at how we can use these metrics to quantify the energy consumption, and in turn the carbon footprint, of the websites and web apps we create.

Data transfer

Most researchers use kilowatt-hours per gigabyte (kWh/GB) as a metric of energy efficiency when measuring the amount of data transferred over the internet when a website or application is used. This provides a great reference point for energy consumption and carbon emissions. As a rule of thumb, the more data transferred, the more energy used in the data center, telecoms networks, and end user devices.

For web pages, data transfer for a single visit can be most easily estimated by measuring the page weight, meaning the transfer size of the page in kilobytes the first time someone visits the page. It’s fairly easy to measure using the developer tools in any modern web browser. Often your web hosting account will include statistics for the total data transfer of any web application (Fig 2.1).

The nice thing about page weight as a metric is that it allows us to compare the efficiency of web pages on a level playing field without confusing the issue with constantly changing traffic volumes. 

Reducing page weight requires a large scope. By early 2020, the median page weight was 1.97 MB for setups the HTTP Archive classifies as “desktop” and 1.77 MB for “mobile,” with desktop increasing 36 percent since January 2016 and mobile page weights nearly doubling in the same period (Fig 2.2). Roughly half of this data transfer is image files, making images the single biggest source of carbon emissions on the average website. 

History clearly shows us that our web pages can be smaller, if only we set our minds to it. While most technologies become ever more energy efficient, including the underlying technology of the web such as data centers and transmission networks, websites themselves are a technology that becomes less efficient as time goes on.

You might be familiar with the concept of performance budgeting as a way of focusing a project team on creating faster user experiences. For example, we might specify that the website must load in a maximum of one second on a broadband connection and three seconds on a 3G connection. Much like speed limits while driving, performance budgets are upper limits rather than vague suggestions, so the goal should always be to come in under budget.

Designing for fast performance does often lead to reduced data transfer and emissions, but it isn’t always the case. Web performance is often more about the subjective perception of load times than it is about the true efficiency of the underlying system, whereas page weight and transfer size are more objective measures and more reliable benchmarks for sustainable web design. 

We can set a page weight budget in reference to a benchmark of industry averages, using data from sources like HTTP Archive. We can also benchmark page weight against competitors or the old version of the website we’re replacing. For example, we might set a maximum page weight budget as equal to our most efficient competitor, or we could set the benchmark lower to guarantee we are best in class. 

If we want to take it to the next level, then we could also start looking at the transfer size of our web pages for repeat visitors. Although page weight for the first time someone visits is the easiest thing to measure, and easy to compare on a like-for-like basis, we can learn even more if we start looking at transfer size in other scenarios too. For example, visitors who load the same page multiple times will likely have a high percentage of the files cached in their browser, meaning they don’t need to transfer all of the files on subsequent visits. Likewise, a visitor who navigates to new pages on the same website will likely not need to load the full page each time, as some global assets from areas like the header and footer may already be cached in their browser. Measuring transfer size at this next level of detail can help us learn even more about how we can optimize efficiency for users who regularly visit our pages, and enable us to set page weight budgets for additional scenarios beyond the first visit.

Page weight budgets are easy to track throughout a design and development process. Although they don’t actually tell us carbon emission and energy consumption analytics directly, they give us a clear indication of efficiency relative to other websites. And as transfer size is an effective analog for energy consumption, we can actually use it to estimate energy consumption too.

In summary, reduced data transfer translates to energy efficiency, a key factor to reducing carbon emissions of web products. The more efficient our products, the less electricity they use, and the less fossil fuels need to be burned to produce the electricity to power them. But as we’ll see next, since all web products demand some power, it’s important to consider the source of that electricity, too.

Carbon intensity of electricity

Regardless of energy efficiency, the level of pollution caused by digital products depends on the carbon intensity of the energy being used to power them. Carbon intensity is a term used to define the grams of CO₂ produced for every kilowatt-hour of electricity (gCO₂/kWh). This varies widely, with renewable energy sources and nuclear having an extremely low carbon intensity of less than 10 gCO₂/kWh (even when factoring in their construction); whereas fossil fuels have very high carbon intensity of approximately 200–400 gCO₂/kWh. 

Most electricity comes from national or state grids, where energy from a variety of different sources is mixed together with varying levels of carbon intensity. The distributed nature of the internet means that a single user of a website or app might be using energy from multiple different grids simultaneously; a website user in Paris uses electricity from the French national grid to power their home internet and devices, but the website’s data center could be in Dallas, USA, pulling electricity from the Texas grid, while the telecoms networks use energy from everywhere between Dallas and Paris.

We don’t have control over the full energy supply of web services, but we do have some control over where we host our projects. With a data center using a significant proportion of the energy of any website, locating the data center in an area with low carbon energy will tangibly reduce its carbon emissions. Danish startup Tomorrow reports and maps this user-contributed data, and a glance at their map shows how, for example, choosing a data center in France will have significantly lower carbon emissions than a data center in the Netherlands (Fig 2.3).

That said, we don’t want to locate our servers too far away from our users; it takes energy to transmit data through the telecom’s networks, and the further the data travels, the more energy is consumed. Just like food miles, we can think of the distance from the data center to the website’s core user base as “megabyte miles”—and we want it to be as small as possible.

Using the distance itself as a benchmark, we can use website analytics to identify the country, state, or even city where our core user group is located and measure the distance from that location to the data center used by our hosting company. This will be a somewhat fuzzy metric as we don’t know the precise center of mass of our users or the exact location of a data center, but we can at least get a rough idea. 

For example, if a website is hosted in London but the primary user base is on the West Coast of the USA, then we could look up the distance from London to San Francisco, which is 5,300 miles. That’s a long way! We can see that hosting it somewhere in North America, ideally on the West Coast, would significantly reduce the distance and thus the energy used to transmit the data. In addition, locating our servers closer to our visitors helps reduce latency and delivers better user experience, so it’s a win-win.

Converting it back to carbon emissions

If we combine carbon intensity with a calculation for energy consumption, we can calculate the carbon emissions of our websites and apps. A tool my team created does this by measuring the data transfer over the wire when loading a web page, calculating the amount of electricity associated, and then converting that into a figure for CO₂ (Fig 2.4). It also factors in whether or not the web hosting is powered by renewable energy.

If you want to take it to the next level and tailor the data more accurately to the unique aspects of your project, the Energy and Emissions Worksheet accompanying this book shows you how.

With the ability to calculate carbon emissions for our projects, we could actually take a page weight budget one step further and set carbon budgets as well. CO₂ is not a metric commonly used in web projects; we’re more familiar with kilobytes and megabytes, and can fairly easily look at design options and files to assess how big they are. Translating that into carbon adds a layer of abstraction that isn’t as intuitive—but carbon budgets do focus our minds on the primary thing we’re trying to reduce, and support the core objective of sustainable web design: reducing carbon emissions.

Browser Energy

Data transfer might be the simplest and most complete analog for energy consumption in our digital projects, but by giving us one number to represent the energy used in the data center, the telecoms networks, and the end user’s devices, it can’t offer us insights into the efficiency in any specific part of the system.

One part of the system we can look at in more detail is the energy used by end users’ devices. As front-end web technologies become more advanced, the computational load is increasingly moving from the data center to users’ devices, whether they be phones, tablets, laptops, desktops, or even smart TVs. Modern web browsers allow us to implement more complex styling and animation on the fly using CSS and JavaScript. Furthermore, JavaScript libraries such as Angular and React allow us to create applications where the “thinking” work is done partly or entirely in the browser. 

All of these advances are exciting and open up new possibilities for what the web can do to serve society and create positive experiences. However, more computation in the user’s web browser means more energy used by their devices. This has implications not just environmentally, but also for user experience and inclusivity. Applications that put a heavy processing load on the user’s device can inadvertently exclude users with older, slower devices and cause batteries on phones and laptops to drain faster. Furthermore, if we build web applications that require the user to have up-to-date, powerful devices, people throw away old devices much more frequently. This isn’t just bad for the environment, but it puts a disproportionate financial burden on the poorest in society.

In part because the tools are limited, and partly because there are so many different models of devices, it’s difficult to measure website energy consumption on end users’ devices. One tool we do currently have is the Energy Impact monitor inside the developer console of the Safari browser (Fig 2.5).

You know when you load a website and your computer’s cooling fans start spinning so frantically you think it might actually take off? That’s essentially what this tool is measuring. 

It shows us the percentage of CPU used and the duration of CPU usage when loading the web page, and uses these figures to generate an energy impact rating. It doesn’t give us precise data for the amount of electricity used in kilowatts, but the information it does provide can be used to benchmark how efficiently your websites use energy and set targets for improvement.