Product design has treated step reduction as an unqualified good since the first checkout funnels. Fewer clicks, fewer fields, fewer screens between question and answer. Intercom put it cleanly in 2021: the only purpose of a product is to help someone go from A to B, and the fewer steps, the better the journey.

That rule still stands while the steps you remove are friction. But it breaks when the step you remove is where people are doing the thinking.

Searching used to mean typing keywords, scanning ten links, picking one that looked credible, reading it, deciding whether to believe it, and checking a second source. Growth.design lists that sequence as the problem AI chat solves. Picking, believing, checking. But half that sequence is the user evaluating a claim. Removing those steps transfers a job from the reader to the engine.

Which steps in your flow are overhead, and which ones are someone deciding something? Most teams have never separated the two.

Ask what happens between two steps

Take any two screens in your product and ask what sits between them. Not what the user clicks. What the user has to work out on their own.

Does the person have to guess something the interface never told them? What are they deciding, and did you give them anything to decide with?

The gaps hide in the seconds after an action, when the product goes quiet. Someone clicks “Start free trial” and lands on a dashboard with no indication of what the trial covers, when it ends, or what to do first. Nothing broke. The flow works. But the person is still guessing.

Samuel Hulick made this the center of his article in Mind the Gap, and it remains one of the fastest audit in product design because it costs nothing. Open your own signup flow and write down every moment you had to know something the screen did not say.

Feedforward is the principle underneath: showing what will happen next before someone acts. A user hovering over “Submit” with no idea whether that clears the form, sends an email, or charges a card is standing in a gap you left. The cues that close it are small enough that nobody thanks you for them.

The journey starts before your product and ends after it

Someone did not wake up wanting to use your app. They wanted to book a flight or get information about which SUV to buy. Your product intersects a path that started somewhere else and continues after they close the tab.

Map that longer path and two things become visible: where each step could carry less weight, and where you have inserted your own priorities into someone else’s route. The email capture before the content. The plan selector before the person knows what the plans do. The product tour that starts before anyone has used the product.

Timing is the whole argument. A phone number field reads as routine at checkout and as a toll on a landing page. Same request, different moment, opposite outcomes. There is a time and place for the business ask, and it is later than most teams put it.

The peak-end rule is worth understanding here. People judge an experience by its most intense moment and its ending, not by an average of the steps. A journey with one excellent moment and a clean finish beats a smooth one nobody remembers.

Map for the peak and the end, then spend your effort there.

Every element on the screen is serving someone

Open most apps and something wants to sell you before you have done anything. Go back to the reason the person came.

Take a screen and go element by element. Why is this person here? What are they trying to finish? Does this element serve that, or does it serve a metric on your dashboard?

The distinction between those last two is the work. A “Refer a friend” banner on an empty-state dashboard serves the growth target. A “Refer a friend” prompt after someone completes their third successful project serves both. Same component, different placement, and only one of them is honest about the moment it sits in.

Hick’s law gives you the cost of getting this wrong. Response time grows with each option added, and a checkout with six payment options, three shipping tiers, and a promo field visible at once stalls people who arrived ready to buy. Cutting choices is not the same as cutting complexity. The goal is fewer decisions at any one moment, not a product that decides for people.

Group what belongs together, then space out the rest

Most redesigns start here. Everything sits at the same visual weight, related things drift apart, and the screen offers no structure to read.

Put related elements close. Push unrelated ones apart. Let the interface breathe. It sounds too basic to write down, and it accounts for more redesign work than any sophisticated technique.

Proximity is doing the work. Place two things near each other and people assume they are related, whether or not you meant them to be. Grouping is meaning. That assumption works for you or against you depending on how carefully you place things, which is why a “Cancel subscription” link sitting eight pixels from “Update payment method” produces support tickets.

Spacing is the other half and gets cut first when a screen fills up. Space is what makes a group readable as a group.

The content decides the container

Design without content is coloring.

The design of a page depends on what goes inside it. Start with a menu or a box and you are arranging containers for text nobody has written, which means the text will later be trimmed to fit the box. The box wins. It should not.

Start with the job instead. What is this person trying to get done, and what do they need in front of them to do it? Real content, real length, real edge cases. The longest product name. The empty state. The error.

Jesse James Garrett put content underneath structure in his five planes model. Skeleton and surface sit on top of scope and structure. Designing the surface before the scope exists inverts the model, and every designer who has placed lorem ipsum into a product knows how that ends.

What AI search does to all five

AI chat collapses the search journey. Type a question, get an answer, refine with a chip labeled “best budget” or “fuel efficiency,” decide. Twenty seconds, no source opened. By every measure in this article, that is a better journey. Fewer steps. Less friction. The person got from A to B.

The refinement chips are the part to look at closely. Tapping through them feels like exploring. The person is choosing among options the engine wrote, narrowing inside a frame they did not set. The frame came with the answer.

If people stop exploring, publishers lose the traffic that funds the writing the engine summarizes. The engine’s quality rests on a supply it is defunding. Neither Microsoft nor Google has explained how that resolves.

The design lesson is narrower than the media one. When you remove a step that contained thinking and decision making, you owe the person a way back into it. Authority bias explains why: fluent, confident, well-formatted output reads as verified, and the interface never signals otherwise.

I wrote about the presentation side of this in when tools pretend to be people. This is the structural side. A system can drop every human affectation and still remove your verification steps by collapsing the journey.

Removing steps is usually right. Know which ones.

— Thanks for reading. The newsletter stays free, and I’d like to keep it that way. Your support makes that possible.

Psychology of Design

Four of the five tactics above run on principles I’ve spent years documenting. Feedforward, Hick’s law, proximity, peak-end. I keep the whole set in one place.

106 principles, each written as a working tool. What it is, how it shows up in real interfaces, where to apply it, and the companies already using it. A searchable Notion database you keep open while you work: filter by cluster, find the principle that fits the screen in front of you, ship something that fits how people are wired.

From the Zeigarnik Effect to the Labor Illusion to Hick’s Law.

Get the 106 principles →

Jesse James Garrett drew a map for this in his book, The Elements of User Experience. He wrote it for websites in 2000, but it holds for any digital product. Five planes, stacked. The bottom plane is the most abstract and the least visible. Each plane up gets more concrete. The top plane is the one the user sees.

He didn’t mean the order as decoration. A decision on a lower plane sets the terms for every plane above it. Choose the wrong problem and no interface can save you. And the stack runs both ways: a usability test on the surface can send you back down to reconsider the scope.

Strategy

Who the user is and what they can’t do today. The team runs interviews and surveys, builds personas and journey maps, and works alongside business stakeholders to decide what the product is for. Freelance writers struggle to keep their books of accounts, and no existing tool fits how they invoice. That gap is the strategy. Everything above this plane serves it.

Scope

Which features earn their place. Once you know the problem, you decide what to build for it and what to leave out. The team picks a freemium invoicing app for those writers, then orders the features into a roadmap so the first release solves the sharpest part of the problem. Designers settle scope with business and engineering, because a feature nobody can build or fund is not in scope.

Structure

How the pieces relate and how a person moves between them. This plane splits in two. Interaction design decides how the product behaves when the user acts. Information architecture decides how content is organized so it can be found. Take invoices: date of issue, due date, payment terms, customer, payment method. Structure is the logic that ties those together and sets the path a user walks to raise one. Information architects often own this work.

Skeleton

Where the structure becomes something you can point at. Wireframes turn the arrangement into labelled boxes and lines: where the button sits, where the total lands, what the user reads first. No color yet, no type. The skeleton fixes placement and priority, and it turns into a prototype so the team can feel how the thing works before anyone polishes it.

Surface

What the user sees and reads. Color, typography, icons, illustration, and the words on the screen: labels, headings, error messages, the empty state. This is the plane most people mistake for the whole job. A cart icon tells the user what a screen does. High contrast makes the text readable. A button that looks like a button on every screen keeps the product legible. The surface is where the two sides of structure rejoin into one experience.

Visual design is one plane, not the discipline

“Problems with visual design can turn users off so quickly that they never discover all the smart choices you made with navigation or interaction design.” Garrett meant it as a warning about the surface. It also tells you how much work sits under the surface, waiting to be thrown away.

Visual design is a specialty on the surface plane. The people who own it are UI designers. When a job posting says UI/UX, the company wants someone who can do research, information architecture, and wireframing as well as visual work. One person can hold all five planes. Most teams split them. A researcher runs interviews while an illustrator builds a palette, and the work gets done in parallel.

The planes are not only design decisions. Scope depends on business and technology: how the product earns revenue, whether it runs third-party ads, whether it integrates with tools the user already uses. Those choices reach all the way up, from the feature set down to a single icon.

A visual designer still needs to understand the planes below. The surface is the first thing users meet, and if they dislike what they see, they never get to the smart work underneath. Once they stay, the look has to match the structure and follow usability principles, or the delight breaks on contact.

The work is never finished

The building metaphor breaks here. A finished building is finished. A product is tested at every plane, forever. The team runs heuristic evaluations and usability tests with real users and keeps asking:

Do we understand the user, and are we solving the right problem? That questions the strategy. Do the features solve it? That questions the scope. Can people actually use them? That questions the structure, skeleton, and surface at once.

The answers spread. A finding on the surface can force a change three planes down. The stack has an order, and the order still bends. UX design is Strategy, Scope, Structure, Skeleton, and Surface, held together and revised against each other until the product works.

Back to basics

I believe the fundamentals now carry more weight. Execution keeps getting cheaper, and what we build and why we build it is still the part worth understanding. Thinking through the experience, solving the right problem, drawing the roadmap, shipping the right features, building the bridge between people and machines, and seeing the whole thing from a plane above. That is the work.

Thanks for reading.
Keep learning and building.
— B

Further reading

The Elements of User Experience (diagram)

Jesse James Garrett, 2000. The original one-page PDF that started it all, at jjg.net/elements/pdf/elements.pdf. Worth seeing before the book: the whole model on a single sheet.

The Elements of User Experience: User-Centered Design for the Web and Beyond

Jesse James Garrett, 2nd ed., 2010. The source. Short, and the second edition widens it from websites to products.

The Design of Everyday Things

Don Norman, revised ed., 2013. Where the lower planes come from. Affordances, signifiers, and mental models sit under structure and skeleton.

About Face: The Essentials of Interaction Design

Alan Cooper, Robert Reimann, David Cronin, Christopher Noessel, 4th ed., 2014. Deepens the interaction-design half of the structure plane.

Information Architecture: For the Web and Beyond

Louis Rosenfeld, Peter Morville, Jorge Arango, 4th ed., 2015. The other half of structure, in full.

Design Vault 🗃️

3000+ curated assets in one Notion workspace. UX templates, design systems, accessibility checklists, fonts, tools, books, and 30+ sections more.

The mechanism runs through several channels at once. Loss aversion makes changing a default feel like risking something already owned. Implied endorsement suggests the default is a reasonable choice, so selecting it requires less justification. And the cognitive cost of evaluating alternatives encourages people to accept the existing state. These forces compound, especially in low-stakes or unfamiliar decisions where people lack strong preferences to override them.

Default bias appears across organ donation programs, retirement savings enrollment, privacy settings, and software configuration. In each case, the same population of people produces dramatically different outcomes depending on which option requires action and which requires inaction. The default does not just simplify the interface. It determines the outcome for a large share of users.

In design

A user opens a new account and immediately lands in a signup flow. Before they have formed any preferences, the product has already decided: marketing emails are on, data sharing is enabled, notifications fire at full volume. The user clicks through, and the defaults stay. Default bias means the designer’s pre-set choices become most users’ permanent choices, whether or not those choices were made with the user’s benefit in mind.

This places significant weight on the moment a default is chosen. Every pre-selected option is effectively a policy decision applied to the full user base. Designers who understand this use defaults to serve the majority, while keeping alternatives reachable. Those who ignore it discover that their defaults reflect internal convenience or business incentives rather than user needs.

Applications in digital products

• Set the default communication preference to the least intrusive option, then let users escalate: email digests before individual notifications, not the reverse.

• When offering a free-to-paid upgrade, pre-select the plan that fits the user’s current usage, not the highest-tier plan, so the default reflects their situation.

• Place the destructive or irreversible action as the non-default: require an affirmative click to delete, unsubscribe, or share data, rather than treating those as pre-selected states.

• In onboarding forms, default empty fields to blank rather than placeholder values that users might submit without reading.

• On privacy and data settings, default to the most private configuration and present expansion as a deliberate user choice, not a correction of a setting they already “agreed to.”

• When a setting has a best-fit value for most users, pre-select it with a visible label like “Recommended” so the default carries transparent reasoning rather than hidden authority.

Industry examples

Countries with automatic organ donation enrollment see consent rates above 90%. Countries that require a positive registration act see rates below 20%. Same populations, different defaults.

Thaler and Benartzi documented a similar pattern in 401(k) enrollment in 2003. Companies that switched retirement plans to automatic enrollment saw participation jump from around 50% to over 90%. Employees could still remove themselves. Most did not.

Microsoft pre-selected telemetry and automatic update settings in Windows 10 that collected substantial user data. Changing them required navigating several menus. Few users did.

Google enabled AI-assisted email completion in Gmail by default for all users. Adoption was high not because users chose it, but because disabling it required a deliberate action most skipped.

Apple’s App Tracking Transparency, released in 2021, shifted ad tracking from an on-by-default state to an explicit prompt. Consent rates dropped from an estimated 70% to under 30%.

No one changes what they don’t notice

Changing a default requires two things most people avoid at the same time: forming a preference and acting on it. The pre-set option eliminates both requirements. People accept it without deliberating, often without noticing the choice was made. This is not a failure of attention, rather a rational response to cognitive load.

When a decision is low-salience and the default seems reasonable, the cost of evaluating alternatives outweighs the benefit. The designer who sets the default has, in effect, already made the decision for the majority. This is why defaults carry ethical weight. A designer can claim to offer choice while structuring the choice so that most users never exercise it.

Defaults are most powerful in one-time setup flows, where users move quickly and rarely return to revisit. Designers who want users to engage with settings should surface them at the moment of relevance, not bury them in preferences menus users open once and forget.

Choosing the default is unavoidable. There is no neutral pre-selection. Every product that ships with a setting in place has already made a choice about whose interests that setting serves.

Further reading

• The Power of Defaults — Jakob Nielsen, Nielsen Norman Group (2005)

• Defaults — The Decision Lab

• Status Quo Bias — The Decision Lab

• Choice Architecture 2.0: How People Interpret and Make Sense of Nudges — Job Krijnen, Behavioral Scientist (2018)

• Defaults Are Not the Same by Default — Jachimowicz, Duncan, Weber, and Johnson, Behavioral Scientist (2019)

You just read why the pre-set option wins.

So here’s one I’ve set for you: The reference designers keep open.

Default Bias is 1 of 106 principles in Psychology of Design; the complete reference behind this newsletter. What attention does. How trust forms. Why habits stick. Each one written the way you just read this one: the mechanism, where it shows up, and how to use it. Filter by cluster, jump to the principle you need, ship something that fits how people are wired.

Get Psychology of Design

A studio that built by asking better questions

Space10 was IKEA’s research and design lab in Copenhagen. It started in 2014, out of a project between IKEA and a Danish design collective called ArtRebels who had designed a furniture line for the retailer. When then-CEO Torbjörn Lööf called them in for a follow-up, they could have proposed another product line. But they proposed something else, as rebels do.

Instead of just creating a better future for IKEA, our starting point was asking how IKEA could help create a better future for the world,” Simon Caspersen told Fast Company in 2015. “Let’s get rid of the whole client-agency model, have IKEA pay the basic fees and costs of running a space, and then devote ourselves to looking 10 or 20 years down the pipeline.

That arrangement is what made the work possible. A small team with a long horizon, free of campaigns and quarterly deliverables, working on questions that took years to answer.

So they took strange ones. What if hotdogs had no meat. What if homes shipped flat and assembled in a day. What if a village block was the unit of urban design instead of the apartment. The output looked like prototypes and films and exhibitions. The real product was a way of thinking about the future that didn’t sound like a press release.

I was envy of it. I still am to be honest. As a solo designer, you read about a place like that and you want to be in the room with curious people working on real stuff, tinkering at the highest level.

Sadly they closed shop in 2023. But the highlights are still up. Go get lost in them. They are very inspiring projects.

How they used AI

Most teams now use AI to ship faster. Space10 used it to think further. They used AI to shorten the distance between an idea and a prototype they could look at and discuss about. The argument is still human. The making is partly machine. An emerging type of collaboration.

DOC put the question well in their piece on the studio:

When we consider the utility of AI in creativity as a feature that helps us create meaning instead of consuming content, it provides a means for us to frame how we build tools that act as collaborative partners in creative work and stimulate our creative action.

So, when building creative tools with this in mind, what should the actual interaction design between humans and AI look like?

— Collected Consciousness, DOC

That’s the part most product teams skip. The question we should be asking is what the interaction looks like when a person and a model are working on the same thing at the same time. Space10 treated that as a design problem.

The result is a studio that used AI to go deeper into questions about food, shelter, cities, and loneliness. They handed over some of the iteration and kept the question.

The fears are old

We’ve been here before. People were hostile to the camera. They were hostile to the automobile, to recorded music, to the personal computer. Painters thought photography would end painting. It ended a particular kind of portrait commission and freed painting to do something else.

The fears around AI with those. Some of it is about jobs. Some is about craft. Some is about consent. Artists’ work became training data without their permission. Styles that took twenty years to develop are a prompt away. That’s a property question we still haven’t answered.

The rest of the resistance follows the usual shape. A new tool changes who gets to make things, and the speed it brings starts to feel like the point. I wrote about this in Efficiency over Craftsmanship: the more design optimizes for output, the more the work drifts toward the average. AI is the strongest accelerant we’ve had. The risk is the same as before.

We mistake faster for better, ship more of the average, and stop noticing what’s missing. Space10’s film tells a different use of the same tool: keep the craft, let the machine carry some of the iteration.

The value of most essays we write over a lifetime sits in the effort of writing them. The thinking happens in the friction.

If we hand the friction to a model, we get the essay and lose the thinking. If we hand the iteration to a model and keep the question, we get more thinking, faster.

Space10’s film is an argument for the second one. Made by a team that built that way for nine years before it closed. Worth watching, and worth borrowing from.

— Thanks for reading. Always good to hear what you think. Comments open.

References

The film

• Creativity in the Age of AI, Space10, directed by Joss Fong and Áron Filkey: youtube.com/watch?v=y7g-nRooZr8

Space10

• Studio: space10.com

• Highlights of nine years of work: space10.com/highlights

• Inside Ikea’s Innovation Lab for the Future of Home Design, Fast Company, 2015: fastcompany.com/3053873

Selected Space10 projects worth a look

• Products of Place: Localised Design with AI - How can AI assist designers to make more sustainable products using abundant local materials?

• Tomorrow’s Meatball: Visualising the future of food, including the dogless hotdog.

• Future Food Today: a Space10 cookbook of recipes built around alternative ingredients.

• The Urban Village Project: a proposal for shared, modular living at the block scale, with EFFEKT Architects.

• SolarVille: a miniature neighborhood prototype running on solar microgrids and peer-to-peer energy trading.

• Everyday Experiments: short films exploring how spatial computing and AI could reshape the home.

• Bee Home: open-source, downloadable beehive designs.

• Spaces on Wheels: a study of what driverless cars could become once they’re more than transport.

People

• Joss Fong, video journalist on science and tech, formerly of Vox, now co-founder of Howtown: joss-fong.com

• Áron Filkey, art director and visual artist working across photo, motion, and set design: aronfilkey.com

• Simon Caspersen, co-founder of Space10 and ArtRebels.

• Torbjörn Lööf, then-CEO of IKEA when Space10 was founded.

• ArtRebels, the Danish design collective behind Space10’s original concept: artrebels.com

Further reading

• Collected Consciousness, DOC: doc.cc/articles/consciousness

• Efficiency over Craftsmanship, UX Collective: uxdesign.cc/efficiency-over-craftsmanship

Good design is not about what medium you are working in. It’s about thinking hard about what you want to do and what you have to work with before you start. — SK

Susan Kare joined Apple in 1982 as the sole creator of screen graphics in the Macintosh group. As the designer “who gave the Macintosh a smile” she is best known for developing the distinctive icons, typefaces, and other pixel elements that gave the Macintosh its characteristic look and feel.

A sculptor picks up graph paper

In 1982, Susan Kare was living in the Bay Area, welding a life-size razorback hog for a museum in Hot Springs, Arkansas. Her father, Morley Richard Kare, had founded the Monell Chemical Senses Center in Philadelphia, spending his career studying taste and smell. His daughter had a PhD from NYU, a background in sculpture and fine art, and zero experience with computers.

Then Andy Hertzfeld called. Hertzfeld and Kare had known each other since they were fourteen, classmates at Harriton High School outside Philadelphia. He was now the lead software architect on a secret project at Apple: the Macintosh. He needed someone to design the icons and typefaces for its screen. He told her if she got some graph paper, she could make small images.

Kare went to the University Art supply store in Palo Alto and bought a $2.50 sketchbook. That sketchbook now sits in the Museum of Modern Art in New York.

She went to the Palo Alto public library, checked out every book she could find on typography, and brought them to the interview to prove she knew something about type. “I went into it totally green” she said later. Apple offered her a fixed-length contract to design fonts and icons.

In January 1983, she started working at Apple. Her business card read “Macintosh Artist.”

1,024 squares

The Macintosh used a bit-mapped display. The display controlled every pixel on screen. Kare’s canvas was a 32-by-32 pixel grid. 1,024 squares to communicate a single idea.

She understood the constraint immediately. “Bitmap graphics are like mosaics and needlepoint and other pseudo-digital art forms, all of which I had practiced before going to Apple,” she said. “I didn’t have any computer experience, but I had experience in graphic design.”

No icon editor existed when she started. She learned to calculate the hex equivalent of her pixel drawings so Hertzfeld could keyboard them into the prototype. He built her a proper editor that generated hex values under the icons. Eventually she used MacPaint itself to create icons.

Some icons already existed from the Lisa, Apple’s earlier computer. The arrow cursor. The document with a folded corner. The trash can. The Lisa’s interface was functional but clinical, icons designed by engineers to label functions, not to invite use. It also used non-square pixels, so every element needed reworking for the Mac’s display. Kare refined the trash can.

She created the application icon, a hand holding a pencil against a diamond shape, to distinguish apps from documents. She designed the Happy Mac, the smiling face that greeted users at startup. She designed the ticking bomb that appeared during system failures.

For the copy function, she tried drawing a photocopier. Then she drew a cat looking in a mirror. Copy cat. She scrapped it because it wouldn’t translate internationally. The lasso, the paint bucket, the paintbrush. Each icon went through rounds of sketching on graph paper before touching a screen.

Her process was the same as any visual designer working in any medium in any century. Pencil, paper, iteration. The screen was just the final surface.

Fonts in 9 by 7 dots

Most digital typefaces in 1983 were monospaced. Every character occupied the same width. Kare wanted proportional fonts, letters that breathed according to their natural shape.

Each letter had to fit in a grid of 9 by 7 dots. The bold system font started as “Elefont.” It became Chicago, the typeface that would appear on the Macintosh and later the iPod for over twenty years.

Kare and Hertzfeld named the early fonts after commuter train stops on Philadelphia’s Main Line: Overbrook, Merion, Ardmore, Rosemont, Paoli. Steve Jobs told them the names were too parochial. He wanted world-class cities. The fonts became New York, Geneva, London, Toronto, Venice.

One font, Ransom, looked like cutout letters from a kidnapper’s note. Jobs renamed it San Francisco. Another, Cairo, featured tiny pictograms: a palm tree, a crescent moon, a skateboard. Decades before Apple introduced emoji, Kare had already drawn proto-emojis into a font.

The command key and the Swedish campground ⌘

Every keyboard needs a modifier key. The Mac team struggled to find the right symbol for theirs.

Kare found the answer in Henry Dreyfuss’s Symbol Sourcebook, a reference she kept at her desk at Apple. The symbol marked interesting features on Swedish campgrounds. It was abstract enough to work as a keyboard glyph, meaningful enough to carry intention. It became ⌘, one of the most recognized symbols in computing.

The pirate flag

Steve Jobs liked to tell the Mac team: “It’s better to be a pirate than join the navy.” When the team moved into a new building, Bandley 3, programmer Steve Capps wanted a flag. He asked Kare to design one. She drew a Jolly Roger with the eyepatch replaced by Apple’s rainbow logo.

They flew it over the entrance. It stayed there for over a year. At one point, the Lisa team stole it. In 2016, Apple flew the flag again to mark the company’s 40th anniversary.

After the Mac

The Macintosh shipped in January 1984. The entire visual language Kare created came from roughly one year of work.

When Steve Jobs left Apple in 1985, Kare followed him to NeXT as creative director. She was one of the company’s first ten employees.

At NeXT, she connected Jobs with one of her design heroes. Kare was a devoted Paul Rand fan. She introduced Jobs to Rand’s books, and together they hired Rand to design the NeXT logo. Kare and David Kelley visited Rand at his studio. Rand, then in his seventies, told them: “I’ve been doing this for fifty-five years, and I know what you should do.”

Kare founded her own studio, Susan Kare LLP, in 1989. Over the next three decades, she designed interface elements for Microsoft, Oracle, Facebook, IBM, and Sony Pictures. She designed the card deck for Windows Solitaire, arguably her most-seen work, viewed by hundreds of millions of people who never knew her name. In 2003, she served on the Citizens Coinage Advisory Committee, designing coins for the United States Mint. In 2015, she joined Pinterest as product design lead.

In 2018, AIGA awarded her the AIGA Medal, one of the profession’s highest honors.

The proof of concept

The Museum of Modern Art calls Kare “a pioneering and influential computer iconographer.” The label misses the point.

She made the Macintosh feel human and had never touched a computer before Apple hired her. She came from sculpture, fine art, mosaics, needlepoint. She treated the pixel grid the same way she treated any design surface: as a constraint that shapes the work.

“I hoped to help counter the stereotypical image of computers as cold and intimidating,” Kare said. Someone with a $2.50 sketchbook and a sculptor’s eye decided that the visual language of technology should borrow from the one humans already knew. That turned out to be enough.

“I still spend my days turning dots on and off.”

Today, Kare is working as a design architect at Niantic Labs and still runs her own studio in San Francisco. She sells limited-edition signed prints of her icon work, collaborates with brands like Ledger on pixel art for hardware, and her original 1982 sketchbooks remain on display at MoMA.

Apple turns 50 this year. (50 years of thinking different.) Researching Susan Kare felt like walking back to where it all started; reading about how she approached design problems, how she collaborated, how she used constraints to shape the work. They built the foundations of how we use computers today. They had constraints on their screens but none in their minds.

I hope you enjoyed reading this one.

Designer Portraits is an ongoing series about the great minds that inspire us. You can also read about Dieter Rams and Corita Kent.

More portraits are on the way, follow to get notified.

Sources & further reading

⌘ Website

⌘ Wikipedia page

⌘ Invention Stories: Susan Kare, Iconic Designer

⌘ 2001 Stanford Interview

⌘ 2018 AIGA Medalist: Susan Kare

⌘ The icon of icons receives Cooper Hewitt’s Lifetime Achievement Award

⌘ The Woman Who Gave the Macintosh a Smile

⌘ The surprising inspiration behind the design of Apple’s command key

⌘ Timeless Design Insights

⌘ Say Hello to Susan Kare, the Bob Ross of Macintosh Design

⌘ The Birth of an Icon: Susan Kare and the Creation of Apple’s Graphics

⌘ Designing women

⌘ MoMa

Chicago Kare

A Faithful Reproduction of the Bitmap Version of the Chicago Typeface Created by Susan Kare for Apple Computer in 1984.

Website ⌘ Github

Thanks for reading.

— Bora / Nirmata Studio

I built Nirmata to see whether one person with the right tools and judgment could do what used to require a team of eight.

The answer is yes. With a few things worth saying out loud.

The economics changed

Software used to be expensive to build. Three things collapsed at roughly the same time: the cost of execution, the cost of coordination, and the cost of custom software. When those three things were expensive, you needed specialists for each, and a project manager to stitch them together. When they got cheap, the specialists became overhead.

AI writing code is the obvious story. The less obvious one is what that does to project economics. . A custom interface that used to require two weeks of a senior developer’s time can come together in days. A design system that would have needed a dedicated sprint now gets built alongside the thing it governs.

The friction between design and development, the handoff cost that every agency has tried and failed to eliminate, shrinks to almost nothing when one person is handling both ends with AI in the loop.

That’s the mechanical case for Nirmata. Design and development as a single continuous process, owned by one studio, delivered at a pace the traditional model structurally can’t match.

Where product work actually breaks

Most product work fails at the boundary between disciplines. The designer doesn’t understand what’s expensive to build. The developer doesn’t know which edge cases the design was trying to solve. The PM is translating between them and losing signal in both directions. By the time something ships, it’s a negotiated object, shaped more by coordination cost than by the original intent.

Whoever can hold design, engineering, and product decisions in the same head and act on all three without waiting for a meeting will outshine a committee every time.

What gets cheaper is execution. What stays expensive is knowing what to build, why it matters, and whether the thing you shipped is actually working.

Coding agents accelerate shipping, which puts pressure on the part that teams have always struggled with most. That part has stayed stubbornly manual, even as everything around it gets automated. A ticket manager who creates tasks and tracks status is overhead in this model. Judgment isn’t.

A studio that controls the full cycle removes most of the translation steps. The thinking that shapes the design is the same thinking that decides how it gets built. That continuity shows up in the work.

What the freed space is for

The design founder era is partly about aesthetics and partly about this. Founders who can hold the visual and the technical in the same head reliably build things that feel coherent. The interface and the system underneath it are solving the same problem, in the same register.

When agents handle the implementation, something shifts. The cognitive overhead of writing code drops out, and the mental space it occupied goes somewhere else. For me, it goes toward the psychology of what gets built. Why does this pattern create anxiety? Why does this flow cause people to abandon? What does this decision cost the user versus what it earns the product?

I’ve spent years building a library of over a hundred psychological principles that apply directly to product decisions, documented at 1984.design. Loss aversion, cognitive load, the peak-end rule, decision fatigue. Applied directly, they separate a feature that serves users from one that extracts from them.

Dark patterns (aka deceptive patterns) exist because most builders never slow down enough to ask whether what they’re shipping is actually good for the person on the other end. Coding with agents creates that space. The speed is a side effect. The more important thing is what you can think about while the code gets written.

The caution

YC’s Spring 2026 request list named it: AI-native agencies running at software-level margins, same output, none of the coordination cost. A model that treats headcount as a variable, not a fixed input. Nirmata was built on that assumption before the framing existed.

AI-written code still needs someone accountable for what it does. Security, reliability, IP, those don’t disappear because the code came fast. I treat oversight as part of the work, not something you add at the end.

What remains

AI absorbed the execution layer. What’s left is editorial work: knowing what to build, recognizing when it’s right, deciding when to stop.

The design bar will keep rising alongside this. Builders who ship more can iterate faster, which means the gap between good taste and no taste compounds over time. AI also improves its own design output, so even companies for whom design wasn’t a priority will get access to better defaults.

The premium moves entirely to taste, the ability to recognize what works, what doesn’t, and why.

A one-person studio doing the full cycle lives in that space. Which problems are worth solving. Which tradeoffs to accept. How to hold a coherent vision from the first conversation to the thing that ships. AI handles the rest.

That’s why I’m building Nirmata.

Thanks for reading.

— B.

Most design tools assume you start in Figma and build toward code. This workflow goes the other way: you have a working app, and you want design documentation that stays in sync with it; without rebuilding every screen from scratch.

This guide covers one specific workflow: pushing your running app into Figma as editable design layers. Not screenshots. Real frames, real components, real layer trees you can inspect and hand off.

I’m a solo builder, so this reflects my workflow. If you’re working in a team or organization, you’ll likely need to adjust some of these steps.

Before you start

The tool that makes this possible, generate_figma_design, lives in Claude Code CLI. Not the Cursor extension, not the VS Code connector. You’ll need the terminal for the setup steps, but only for setup. Once configured, you can work through Claude Code’s interactive session.

I’m currently using Ghostty, a fast, feature-rich, cross-platform terminal emulator with native UI and GPU acceleration. Any terminal works though, iTerm was my previous choice.

You also need a paid Claude plan.

Step 1: Install the Figma plugin

In your terminal, run:

claude plugin install figma@claude-plugins-official

This installs the official Figma plugin at user scope, available across all your projects. Then start a Claude Code session:

claude

Step 2: Authenticate and configure MCP

Inside the Claude Code session, open the MCP manager:

/mcp

You’ll see a list of servers. Select plugin:figma:figma; the remote MCP that includes write capability; and authenticate. Claude opens a browser window; log in and click Allow. Check the email shown if you have multiple Figma accounts.

If you see figma-desktop in the list, disable it. It’s the local read-only MCP and will conflict with the remote one.

After authenticating, run /mcp again to confirm:

• figma-desktop; disabled

• plugin:figma:figma; connected ✓

Then verify generate_figma_design is available:

List all available Figma MCP tools

It should appear as: “Capture/import a web page into Figma.” If it doesn’t, disconnect all Figma instances, restart Claude Code, and reauthenticate.

Step 3: Brief Claude on your project

Before capturing anything, disable thinking mode. Long execution tasks don’t benefit from it and it burns through context faster; which matters when you’re pushing 20+ screens in one session.

Run your app locally, then give Claude the Figma file URL, the target page, and ask it to plan:

My app is running locally. Here's my Figma file: https://www.figma.com/design/[YOUR_FILE_LINK].
Capture all screens to the "User Interface" page. Review the codebase, list all screens, 
and create a wave plan.

Claude audits the project and returns a structured plan; screens grouped by section, ordered by priority. For Bookmarker, that looked like this:

• Wave 1; Core App (screens 1–8): Main product screens. Highest design value, done first.

• Wave 2; Auth (screens 9–12): Login, signup, password flows.

• Wave 3; Settings (screens 13–17): Account, billing, preferences.

• Wave 4; Marketing + Public (screens 18–22): Pricing, public sharing pages.

• Wave 5; Blog + Legal (screens 23–27): Content pages.

Before telling Claude to start, ask it to write the plan to a file:

Write this plan to /plan/figma-push.md. Include each screen, its wave, 
status (pending/done), and source file. Update status as you complete each screen.

This is your safety net. If the context window fills up mid-execution; which it will on a large project; open a new session and continue:

Read /plan/figma-push.md and continue from where you left off.

Compacting mid-execution loses track of what’s done. The plan file is the source of truth, not the conversation. Once it’s written, proceed:

Plan looks good. Start with Wave 1.

Step 4: Capture and push the screens

Claude starts the dev server, injects a capture script into your layout.tsx, and opens the app in a browser with a capture hash in the URL. A toolbar appears at the top of the page with four options: Send to Figma, Entire screen, Select element, and Open file.

Click Entire screen to trigger the capture. Claude polls for completion, then removes the script tag from your layout automatically; your codebase is left clean.

Captured layers are editable; text is real text, buttons are separate components, layout uses auto-layout. Not static images.

Claude updates the plan file as each screen completes. Navigate to each screen when prompted and confirm:

Captured. Move to the next screen.

The first pass isn’t pixel-perfect. Fix issues by describing what’s off:

The hero section padding looks too tight and the nav items aren't aligned. Fix those.

Step 5: Edit in Figma, sync back to code

Make a change in Figma; adjust a color, remove a section, change spacing. Right-click the modified element and copy its node link (Copy link to selection). Send it to Claude:

Update the testimonials section to match this Figma node: [PASTE LINK]

Claude reads the node, identifies what changed, and updates only those parts of your code. For complex components it shows a before/after summary of every property it touched, so you can review before accepting.

The reverse also works

Select any frame or component in Figma, open the MCP panel on the right, and click Copy example prompt. It generates a ready-to-paste prompt with your node’s Figma link attached:

Implement this design from Figma.
@https://www.figma.com/design/cnNS8pTh…

Paste that into Claude Code. It reads the node via get_design_context, pulls the layout, spacing, colors, and component structure, then writes the implementation.

The full loop: push your app to Figma, edit the design, pull the changes back to code; without leaving either tool.

What works well

The layer fidelity is the real story. Your React component tree becomes a Figma layer tree; SidebarProvider, BookmarkerApp, Sidebar, BookmarkListItem; each selectable, each showing real properties: padding, border radius, fill width, hex colors. The inspect panel reflects your actual CSS box model.

Select a BookmarkListItem, check its padding, hand the spec to a designer or feed it back into a code change. No screenshots or rebuilding from scratch.

A few other things that hold up well:

• The capture script is injected and removed cleanly; nothing lingers in your codebase.

• Once the script is in layout.tsx, the browser toolbar lets you capture additional screens without Claude’s involvement.

• Wave planning keeps large projects manageable across sessions.

• The plan file lets you resume after a context reset without losing progress.

What doesn’t

• generate_figma_design requires CLI setup; not available in the Cursor extension or VS Code connector.

• figma-desktop MCP conflicts with the remote plugin; disable it.

• The capture toolbar requires a manual click; Claude opens the browser but you click “Entire screen” to send.

• Without specifying a Figma page, captures land on whichever page was last active.

• Initial captures have layout gaps; some screens need follow-up fixes.

• Existing design systems aren’t automatically respected unless you tell Claude to use them.

• Animations and interactions don’t transfer to Figma.

• Compacting mid-execution breaks continuity; use a plan file instead.

Resources and further reading

• Claude Code docs — official reference for CLI setup, MCP configuration, and available tools.

• Claude Code MCP guide — how to connect and manage MCP servers in Claude Code.

• Figma MCP server announcement — Figma’s original blog post introducing the MCP server.

• Figma MCP installation guide — official setup instructions from Figma’s help center.

• Cursor — the code editor used in this guide.

• Ghostty — the terminal used in this guide.

• Bookmarker — smart bookmark manager.

Thanks for reading.

— Bora @ Nirmata Studio

Corita challenged the church and the art world with her expressive creations, stirring a remarkable disruption, and offered fundamental principles, tools, and techniques to enhance and encourage artistic creativity.

Her art stood as a testament to her activism, weaving together vibrant visuals and spiritually-rooted social commentary to passionately advocate for love and tolerance. Through the ‘60s, she became increasingly political, addressing poverty, racism, and war with visual directness. In 1968, she left the order and moved to Boston.

The work that lasted

In 1971, she painted the Rainbow Swash on the side of Boston Gas Co.’s natural gas tank. Six strokes of color across a 150-foot surface. Still a Boston landmark. Still considered one of the largest copyrighted artworks in the world.

In 1985, she designed the Love stamp for the United States Postal Service. Six bright slashes of color. Over 700 million issued. She boycotted the official ceremony because the government held it on the set of The Love Boat. She said at the time: “The TV definition of love is nothing profound, and everything gets resolved in an hour. I think it’s dangerous to educate people that way.”

The work and the conviction were never separate.

Her philosophy

Corita believed creativity was a matter of permission, not talent. She said it plainly: “We can all talk, we can all write, and if the blocks are removed, we can all draw and paint and make things.”

The blocks she meant weren’t technical. Fear of being wrong, fear of making something imperfect, fear of wasting time. Her entire teaching practice was built around removing them.

After a cancer diagnosis in the early 1970s, her work shifted into a sparser, more introspective style. She remained active until she died in 1986, having created nearly 800 serigraph editions, thousands of watercolors, and countless public commissions.

Learning by Heart

Her principles were collected into a book with her student and collaborator Jan Steward. They worked across cities, by letter and phone, for years. Corita died before it was finished. Steward completed it.

Steward described her process: “She scribbled her teacher’s thoughts on pieces of paper, found copies of her lessons and collected stories from other former students. Then she threw each into a cardboard box that most closely matched a particular part of Corita’s curriculum.”

The chapters, Looking, Sources, Structure, Connect and Create, read less like lessons and more like reminders. The rules she taught are still widely shared today, often without attribution:

• Find a place you trust, and then try trusting it for a while.

• Consider everything as an experiment.

• Nothing is a mistake. There’s no win and no fail, there’s only make.

• The only rule is work. If you work, it will lead to something.

• Don’t try to create and analyze at the same time. They’re different processes.

• Be happy whenever you can manage it. Enjoy yourself. It’s lighter than you think.

• Always be around. Read anything you can get your hands on. Save everything. It might come in handy later.

“Don’t try to create and analyze at the same time” is worth sitting with. That single rule accounts for more stalled creative work than any tool or brief ever has.

Corita Kent today

Corita died in 1986. Her rules are still widely circulated, often without her name attached. The work she made with a screen press and affordable materials is in museum collections. The Love stamp remains one of the best-selling in postal history.

She spent her career arguing that creativity is not a rare quality distributed to a few people. Her rules, her teaching, and her output were all evidence of the same position: the act of making is enough to start with.

Corita Art Center in downtown Los Angeles, California preserves and promotes Corita Kent’s art, teaching, and passion for social justice. Today, Corita Art Center supports exhibition loans and public programs, oversees image and merchandising rights, and serves as a resource and archive on her life and work.

You can explore her archive active at corita.org, with her full collection, videos, and educational resources.

Designer Portraits

This article is part of Designer Portraits, a series within Design, Explained.

We look at influential designers, not to celebrate style, but to understand judgment. How they thought. How they decided. Why their work still holds up when trends fade.

If you want to be notified when new Designer Portraits are published, subscribe to Design, Explained. No noise. Just clear thinking, delivered when there is something worth reading.

Subscribe now

Thanks for reading.

— Bora.

Code is now a commodity

When everyone has access to the same models, and those models can produce working software quickly and cheaply, the code itself stops being a moat. A reasonably skilled person can now build what used to take a team of engineers months to ship.

A competitor can reach feature parity in days. The thing that used to protect a business, the sheer effort required to build it, is mostly gone.

Think of it like oil. If every property owner had a well that was cheap to operate, the price of oil would fall toward the price of water. The resource is abundant, the extraction is easy, and the margin disappears. Software features are heading in the same direction.

So where does the moat go?

The new constraint is data

A SaaS product has always had two components: the features and the data those features operate on. For a long time the constraint was features. Who could build the most capable, most polished, most reliable product? That was the competition.

When development speed gets fast enough and feature cost gets low enough, you can no longer win on what you can build. The competition shifts to whether you have access to the right data about the business or domain you serve.

A payroll tool is not valuable because of its UI or calculation logic. Both can be replicated quickly. It’s valuable because it holds years of salary history, tax records, and employee data that no competitor can walk in and duplicate.

An e-commerce platform is not valuable because of its product listing interface. It’s valuable because of the behavioral data, supplier relationships, and purchase history accumulated over time. The features are just a rendering layer on top of that.

Netflix understood this early. Their recommendation engine was never really about the algorithm. It was about the volume and specificity of viewing behavior that no competitor could replicate.

Shopify’s power is not in the checkout flow but in the merchant and transaction data that makes every new feature more accurate and useful than what any new entrant can offer on day one.

The return of bespoke software (in the right context)

For most of software’s history, the high cost of development pushed everything toward standardization. We reused frameworks and shared libraries because custom code was expensive. We adopted cloud services and common APIs because building your own infrastructure rarely made economic sense.

Those solutions still work, but they carry costs that are easy to underweight: vendor lock-in, subscription fees for things you barely use, supply chain vulnerabilities, outages you can’t control. The assumption was always that the alternative, building something specific, was too expensive to justify.

That assumption is changing, particularly for business software.

Consumer apps are a different story. A well-designed consumer product reflects years of collective user feedback, UX iteration, and design judgment that no individual could produce on their own. Most people don’t know exactly what they want until they use something well-crafted and realize it fits. That process has real value and it doesn’t go away.

But one layer up, at the organizational level, something interesting is happening. A logistics company running dispatch operations on generic project management software is always going to be fighting against 20% of their workflow that doesn’t fit.

A clinic using an off-the-shelf scheduling tool built for nobody in particular is always going to be working around it.

The specificity of how businesses actually operate has always been real. What’s changed is that matching software to that specificity is now economically viable.

The person who translates a business need into the right software is not the business owner. It’s a product builder who understands both the domain and the tools. That gap between business context and working software is where the service value lives now.

The platform that holds everything

If features are commodity and purpose-built tools become routine, something has to be the durable layer underneath. Something has to hold the data together.

The pattern emerging in enterprise software points toward a central data backbone, something like Salesforce or a modern ERP, where all the information about a business lives and where purpose-built tools connect as needed.

In this model an app like Slack or Jira is not a standalone product but a plugin reading from and writing to a shared layer. A workflow tool built for one team’s specific process connects to the same source of truth rather than becoming another silo.

This is also what makes AI agents actually useful. Conversational agents and automated workflows are only as good as the data they can reach. When business data is scattered across ten disconnected systems, agents are limited. When it lives in a coherent, queryable backbone, those same agents can do real work.

For founders building in this space, the question worth asking is whether your product is becoming the backbone or the plugin. Backbones accumulate data and grow more valuable over time. Plugins get swapped.

What this means for building products

Most of this comes down to a few practical shifts in how product decisions get made.

Compete on data access, not feature depth. Ask what data only you can accumulate in your domain and build around acquiring it early, before features become the priority.

Design for the workflow moment, not the application. The unit of product design is shifting from the app to the specific task someone needs to complete. People will increasingly use purpose-built tools for specific things rather than general platforms for everything. The interface follows from the task, not the other way around.

Treat your integration layer as a product decision. If the future is a central data backbone with tools plugging in on top, how cleanly your product connects to that backbone matters. Companies that make their data easy to work with become the backbone. Companies that silo it get replaced.

Stop defending features. If a competitor can ship your feature in a week, it was never your moat. The answer is not to build more features faster but to identify what they actually cannot copy: domain knowledge, accumulated data, relationships, a genuine understanding of the specific problem.

The role of judgment

None of this makes building easier. If anything it makes the hard parts harder.

When execution speed stops being a differentiator, the premium moves to judgment: identifying the right problem, understanding a domain deeply enough to know what data matters, and designing something that fits how people actually work rather than how you imagined they would. Those things are harder to replicate than a codebase.

The role of a product designer or a product-oriented founder becomes more valuable in this environment, not less. The question was never really whether something could be built. It was whether it should be built and whether it fits the way people actually operate. That question gets harder to answer correctly as the cost of building the wrong thing gets lower.

The companies that will be hard to compete with in five years are probably not the ones shipping the most sophisticated AI features right now. They’re the ones accumulating domain-specific data, building coherent data backbones, and designing for specific workflow moments rather than broad use cases. The moat has moved, and the builders who figure that out early will have a real head start on those still competing on code.

Thanks for reading.

— Bora @ Nirmata

Learning to work with AI agents changed that balance. I can now take an idea from a rough thought to a functioning product without long handoffs. I design it, implement it, break it, fix it, and ship it. The scope is larger, but the feedback loop is short. That alone changed how I think about building.

Most of my work is guided by a simple bias. As a designer, I build tools for creatives. I am more interested in tools that reduce friction and decision fatigue, not that add process or ceremony.

Bookmarker started from that position. It is meant to be a place to collect, organize, and share links and images without turning the act of saving into work.

I shipped early and often. Some decisions aged well. Others revealed their weaknesses only after daily use. One of the clearest examples was how links and images should coexist. The initial idea was a single unified space. On paper, it made sense. In practice, it felt wrong. Images behave differently from links. They need more space, a slower rhythm, and fewer competing elements. Treating them as equivalent flattened the experience.

I stopped forcing a single structure and allowed images to have their own visual workspace. A grid with minimal structure. No visual noise. Enough order to support browsing and pattern recognition, but not enough to dictate behavior.

That decision made it possible to introduce supporting systems for creative work. Image titles, descriptions, tags, color palettes, and ALT text are generated automatically. Low‑quality images are upscaled when needed. These systems exist to reduce repetitive decisions and improve retrieval later, not to draw attention to themselves.

I have always struggled with placing images alongside other file types. Part of that discomfort is visual, but part of it is cognitive. Images influence thinking differently. They invite exploration rather than scanning. They benefit from a dedicated pace.

Distraction works against that.

Separating images introduced a different problem. Creative work rarely happens in isolation. References need to connect. Ideas need to move between materials. The visual workspace lived in a separate window, and that separation created friction. The cause was not technical. It was a design decision.

After living with that friction long enough, the direction became clear. The product needed fewer boundaries, not more features.

I shifted my approach from adding to removing. Buttons, microcopy, and visual elements that did not directly support a task were eliminated. Designing through subtraction changed the product. The interface became quieter, and the structure became easier to understand.

Links and images now exist in the same space, with a simple and predictable way to move between them. Navigation remains stable across views. Headers, actions, and controls stay in place. This consistency reduces the effort required to re‑orient and allows attention to stay on the content.

The goal is not engagement with the interface. The goal is focus on the material. Collections should be easy to explore, compare, and revisit. Inspiration should surface naturally through proximity and repetition.

Many of these decisions are grounded in design psychology. Visual consistency reduces cognitive load. Stable layouts support spatial memory. When elements behave predictably, attention is freed for interpretation rather than orientation. This is not aesthetic preference. It reflects how people process information.

Images benefit from separation because the brain handles visual input differently from text. Visual material encourages associative thinking and pattern recognition. Mixing it indiscriminately with other formats increases noise and interrupts flow. A dedicated visual space supports slower scanning and serendipitous discovery.

Reduction matters as well. Removing interface elements lowers decision fatigue and preserves working memory. Fewer choices mean fewer interruptions. This is why quiet interfaces often feel faster, even when performance does not change.

Sharing also alters behavior. When collections are easy to publish and revisit, they move from passive storage to active thinking tools. Repetition, proximity, and context strengthen recall and idea formation. A bookmarking system becomes part of a creative loop rather than just an archive.

Pages emerged from these ideas. They provide a minimal way to publish collections as reference boards. They are designed for sharing ideas with collaborators, clients, or friends without additional explanation or formatting.

From a collector’s perspective, a bookmarking system should function as an input for creative work. It should have a place in daily practice. Not as something that demands attention, but as a quiet tool that supports idea formation over time.

If you are a designer, creator, or visual thinker, Bookmarker might be worth trying. It is free, quiet, and intentionally simple. I built it to help surface inspiration, and to make sharing ideas feel natural.

bookmarker.cc

These tools also show you when to follow conventional wisdom and when to reject it. Charlie Munger said it clearly: “If you want to be a good thinker, you must develop a mind that can jump the jurisdictional boundaries. You don’t have to know it all. Just take in the best big ideas from all these disciplines.”

Start with first principles. They offer a method for understanding complex systems by revealing the fundamental cause behind how something works. First principles are the simplest truths you can’t reduce further. The parts that remain when you strip away habits, conventions, and inherited rules.

This thinking forces you to ask why something functions before copying how it looks. It removes analogy. It removes precedent. What remains is cause and effect. Aristotle used it to cut through faulty reasoning. The same approach appears wherever careful thinking matters, regardless of discipline.

Products don’t decide anything. People do. But products influence decisions, often quietly, often without being noticed.

What products control is the structure around the decision. What users see first. What requires effort. What feels safe to accept and costly to change. Psychology enters through that structure, through what is made easy, visible, and repeatable.

Product decisions are often framed as moments. A click. A tap. A confirmation screen. By the time a user reaches that point, the decision has already taken shape. Options are limited. The cost of hesitation is clear. A default is waiting.

What appears to be a choice is frequently a formality. Systems behave this way by design. Friction removed from one path appears somewhere else. Your responsibility is deciding where that friction belongs.

Users rarely begin from a blank slate. They accept what is already set.

Defaults signal what is normal and expected. Changing them requires effort. Effort communicates risk, uncertainty, or deviation. A thoughtful default feels supportive. A careless one expresses an opinion your team may not realize it holds. Unexamined defaults are where influence hides.

Friction is commonly treated as a flaw. In practice, it acts as a signal. It marks actions that matter. It separates reversible steps from irreversible ones. It introduces pause where consequences are real.

Removing friction everywhere creates speed without judgment. Products that feel reckless usually arrive there unintentionally. Designing friction forces prioritization. It clarifies what deserves attention and what can pass unnoticed.

Explanations cannot repair a confused decision structure.

Users learn products through action. They observe what is rewarded, what is ignored, and what requires effort. Patterns teach faster than instructions. When actions and explanations conflict, actions define reality.

This is why some complex products feel simple. The complexity lives in the structure, not in the interface copy.

Every product reflects assumptions about its users. Their goals. Their fears. Their willingness to trade one thing for another.

Psychology doesn’t disappear when ignored. It remains untested, and unchallenged. The work is making those assumptions visible. Examining them. Choosing which ones deserve to stay.

Good products don’t predict users. They shape conditions where reasonable decisions are easier to make. That responsibility sits quietly inside every design choice.

We’re complex, often irrational beings who believe we’re in control while our biases quietly guide us. Understanding the logic behind these biases lets you confront them, moderate them, and sometimes use them intentionally.

Psychology isn’t an add-on skill for designers. It’s foundational. It shows you how we perceive and interact with our surroundings. It gives you a blueprint for behavior. When you incorporate psychological principles into design, you stop imposing solutions and start working with how people naturally think and act.

People who use products still employ deep-seated evolutionary and culturally shaped programs to assess, access, and apply what you build. Twenty-first century design practice often treats psychology as optional. It’s not. It’s the basis of every need and requirement your users have.

Use it to create products that work efficiently, feel safe, and earn trust.

Further reading

• First principle

• Psychology of Design

• What is First Principles Thinking?

• Mental Models: Why expectations drive user behavior

• Designing from first principles: A scientific approach to UX design

• What is Emotional Design (ED)?

• Usability heuristics for user interface design

• Psychology for UX: Study Guide

People ask AI systems for therapy, moral judgment, and legal authority. The interfaces we design invite this behavior. Think about how these systems present themselves. Conversational framing. Continuous memory across sessions. First-person responses that sound like someone talking back to you. Every one of these is a design choice. We chose them. And these choices feed a reflex humans already have: we project intention onto objects and tools.

This reflex is old. We naturally anthropomorphize nonhuman entities, even when it’s clear we're interacting with machines. We anthropomorphize vacuum cleaners. We name our cars. What’s different now is that we’re designing experiences that exploit this behaviour deliberately at scale.

Anthropomorphization is the human tendency to attribute human characteristics, behaviors, intentions, or emotions to nonhuman entities. AI humanization is an intentional design choice that encourages users to perceive AI systems as having human-like qualities such as personality, emotions, or consciousness.

— Humanizing AI Is a Trap

When you write system responses in first person, the output sounds like authority. When you add polite phrasing, it implies consideration behind the words. When you program emotional tone into responses, it suggests the system cares about the outcome. We added these features because they make the interaction feel natural. But natural here means human. And that’s the problem.

These systems sound fluent. They maintain context across long conversations. They respond without hesitation. For most people, that’s enough evidence the system understands them. Fluency looks like competence. Memory looks like understanding. The gap between “this is a tool” and “this is an entity” closes without anyone noticing.

Here’s where I want you to pay attention. When we frame tools as agents, something shifts in how people use them. Responsibility moves. Decisions start to feel outsourced. When something goes wrong, the mistake feels external rather than shared.

Good judgment develops through friction. You try something. It fails. You adjust. You learn what works through repetition and error. But if we build AI systems that absorb the posture of authority, users lose that friction. They stop checking. They stop questioning. They trust the output because the interface taught them to. This isn’t misuse. This is what we designed the system to encourage.

The consequences are already visible. In one of the most high-profile lawsuits recently filed against OpenAI, a California couple sued the company over the death of their teenage son, alleging that ChatGPT encouraged him to take his own life. The lawsuit was filed by the parents of 16-year-old Adam Raine and was the first legal action accusing OpenAI of wrongful death.

In a separate case, the suspect in a murder-suicide that took place in August posted hours of his conversations with ChatGPT, which appear to have fueled the alleged perpetrator’s delusions. Professor Robin Feldman, Director of the AI Law & Innovation Institute at the University of California Law, said more users struggle with AI psychosis as “Chatbots create the illusion of reality. It is a powerful illusion.”

These aren’t edge cases. They’re what happens when systems we’ve designed blurs the boundaries between tools and agents.

Someone asks an AI system for medical advice. The system responds fluently, confidently, in first person. “I recommend you try this treatment approach.” The person follows it. Not because they verified the information, but because the interaction felt like talking to a knowledgeable professional. The system created that feeling. We created that feeling. We built the confusion.

Platforms generating AI girlfriends are experiencing a massive growth in popularity, with millions of users. AI girlfriends can perpetuate loneliness because they dissuade users from entering into real-life relationships, alienate them from others, and, in some cases, induce intense feelings of abandonment.

Most of these searches are initiated by young single men drawn to AI girlfriends to combat loneliness and establish a form of companionship. These “girlfriends” are virtual companions powered by the increasingly sophisticated field of artificial intelligence.

— The Dangers of AI-Generated Romance

Look at a knife. It has limits you can see and feel. Sharpness. Weight. The geometry of the edge. You never ask a knife what you should cook for dinner. You never ask if the meal was meaningful. Those decisions stay with you because the tool’s boundaries are obvious.

AI interfaces hide their limits. An empty chat box suggests no constraints. It looks ready for any question. That openness isn’t neutral. It’s an active design choice that tells people the system can handle whatever they type. Then when people misuse it, we blame them for not understanding the technology.

We need to give AI the same kind of framing we give physical tools. Clear affordances. Visible constraints. An obvious boundary between what the system produces and what we must decide.

Right now, most interfaces erase that boundary completely. The chat paradigm implies conversation. Conversation implies exchange between two minds. But one side is pattern matching at scale. The interface hides that difference.

What would visible limits look like?

Start with pronouns. Use third-person language in system responses. “Here is a summary” instead of “I think the main point is.” This single change removes the illusion of authorship. The output stops sounding like someone’s opinion and starts reading like generated text.

Show uncertainty. When the model lacks confidence, display that visibly. Not buried in a disclaimer, but in the response itself. Confidence scores. Probability ranges. Explicit markers that say “this answer is less reliable.” Make the gaps in knowledge visible instead of hiding them behind smooth conversations.

Reset context visibly. When you start a new session, make that boundary clear. Break the illusion that the system remembers you across time. Continuous memory makes the system feel like it knows you. That’s intimacy. Tools don’t need intimacy.

Stop calling output “messages.” Call it what it is: generated text. Label it. Frame it. Make the mechanical nature of the process visible in the interface itself.

I know what you’re thinking. These changes hurt engagement metrics. Anthropomorphic design works. People stay in the interface longer. They use it more often. Revenue scales with usage time. Making the system feel less human means people will use it less, and that conflicts with business goals. But engagement built on confusion carries costs we’re only starting to see.

Users develop dependency on systems they don’t understand. They delegate judgment to pattern recognition without realizing that’s what they’re doing. They mistake fluency for accuracy. They treat consistency as truth. And because the interface never taught them the boundaries, they don’t know when to stop trusting the output.

When we erase the line between system output and human judgment in AI interfaces, we make the wrong design decisions. We’re building humanization into the system as strategy. We’re doing it on purpose because it increases engagement.

You have agency here. When you design AI experiences, you decide how it presents itself. You control the pronouns in system responses. You choose whether to show confidence levels or hide them. You determine whether context persists invisibly or resets in clear ways.

These decisions shape how people understand the tool’s role and their own. They determine whether users develop judgment or dependency.

So ask yourself: where does the system end and where does judgment begin? In your current interface, can users see that line? Or have you deliberately blurred it to make the interaction feel smoother?

The chat paradigm became default because it felt intuitive. But that isn’t the same as honest. And right now, we need more honesty in how these systems present themselves.

Further reading

• Humanizing AI Is a Trap

• Designing AI tools that support critical thinking

• Authority Bias

• The Intelligence Curse

• The Shape of AI | UX Patterns for Artificial Intelligence Design

• The People + AI Guidebook

• Guidelines for Human-AI Interaction

• From design to direction: Bridging product design and AI thinking

Dieter Rams was born in Wiesbaden, Germany, in 1932. His early exposure to craft came through his grandfather, a carpenter, whose workshop introduced him to materials, precision, and the value of making things well. This grounding in craft shaped Rams’s outlook long before he entered industrial design.

After receiving early recognition for his carpentry work, he trained as an architect during the post‑war reconstruction of Germany in the early 1950s, a period defined by scarcity, rebuilding, and a need for functional clarity.

In 1955, encouraged by a perceptive friend, Rams applied for a position at Braun, the German manufacturer of electrical products. He was hired by Erwin and Artur Braun, who were reshaping the company following their father’s death. Rams’s initial role focused on modernizing Braun’s interiors, but his proximity to engineers and product developers quickly drew him into product design itself.

Rams became closely associated with the intellectual environment of the Ulm School of Design, the successor to the Bauhaus. He worked alongside figures such as Hans Gugelot, Fritz Eichler, and Otl Aicher, whose emphasis on systems, clarity, and social responsibility strongly influenced his thinking. This context helped Rams move beyond surface aesthetics toward a rigorous relationship between form, function, and use.

One early and widely cited example of this approach was the SK4 radiogram (1956), for which Rams introduced a clear Perspex lid. The decision was not decorative; it made the product visually lighter, more honest about its construction, and easier to understand. By 1961, Rams was appointed head of design at Braun, a position he held until 1995. During this period, Braun’s products became internationally recognized for their restraint, coherence, and usability.

At Braun, Rams established a systematic design language rooted in function and reduction. Products were stripped of unnecessary elements and organized with careful logic. Controls were placed where they were needed, forms were simplified, and materials were chosen for durability rather than novelty. As Rams later explained, the clarity of Braun’s products emerged from the deliberate ordering of elements and the search for a coherent whole.

Design decisions were never arbitrary; each detail served a purpose.

This approach reshaped how electronic products were perceived. Rams treated objects not as statements, but as tools meant to fit quietly into everyday life. His modular systems, clean lines, and neutral forms allowed people to focus on use rather than appearance. The goal was not to impress, but to support daily routines with reliability and calm. In doing so, Rams set a standard that influenced generations of designers across consumer electronics, furniture, and digital products.

By the late 1970s, Rams became increasingly concerned about the growing visual and environmental clutter of the manufactured world, which he described as an “impenetrable confusion of forms, colors, and noises.” Recognizing his own role in shaping that environment, he posed a fundamental question: is my design good design?

From this reflection emerged his formulation of principles intended not as rules, but as criteria for judgment, and expressed in his ten principles for good design.

Dieter Rams’ ten principles of good design

1. Good design is innovative

The possibilities for innovation are not, by any means, exhausted. Technological development is always offering new opportunities for innovative design. But innovative design always develops in tandem with innovative technology, and can never be an end in itself.

2. Good design makes a product useful

A product is bought to be used. It has to satisfy certain criteria, not only functional, but also psychological and aesthetic. Good design emphasizes the usefulness of a product whilst disregarding anything that could possibly detract from it.

3. Good design is aesthetic

The aesthetic quality of a product is integral to its usefulness because products we use every day affect our person and our well-being. But only well-executed objects can be beautiful.

4. Good design makes a product understandable

It clarifies the products structure. Better still, it can make the product talk. At best, it is self-explanatory.

5. Good design is unobtrusive

Products fulfilling a purpose are like tools. They are neither decorative objects nor works of art. Their design should therefore be both neutral and restrained, to leave room for the users self-expression.

6. Good design is honest

It does not make a product more innovative, powerful or valuable than it really is. It does not attempt to manipulate the consumer with promises that cannot be kept.

7. Good design is long-lasting

It avoids being fashionable and therefore never appears antiquated. Unlike fashionable design, it lasts many years - even in today’s throwaway society.

8. Good design is thorough down to the last detail

Nothing must be arbitrary or left to chance. Care and accuracy in the design process show respect towards the consumer.

9. Good design is environmentally friendly

Design makes an important contribution to the preservation of the environment. It conserves resources and minimizes physical and visual pollution throughout the lifecycle of the product.

10. Good design is as little design as possible

Less, but better - because it concentrates on the essential aspects, and the products are not burdened with non-essentials. Back to purity, back to simplicity.

Implications for designers today

Rams’s principles still remain relevant because they address judgment rather than style. They encourage designers to evaluate decisions through usefulness, clarity, and restraint. Taken together, they form a practical lens for reviewing work: what can be removed, what can be clarified, and what truly serves the user.

For designers, Rams’s work also demonstrates the value of consistency over time. His influence did not come from isolated objects, but from decades of coherent decisions made within constraints. This long view, designing systems rather than statements, offers a durable model for contemporary practice.

Recognition and legacy

Rams’s contributions have been widely recognized. He was named Royal Designer for Industry by the Royal Society of Arts in London and received honors from international design institutions, including the Society of Industrial Artists and Designers and the Industrial Designers Society of America.

In 1995, he published the book “Less but Better” reporting his design philosophy and his main products produced at Braun.

Dieter Rams’s legacy lies not only in the products he designed, but in the standard he set for responsibility, clarity, and restraint. His work demonstrates how design can serve people by reducing noise, resisting excess, and focusing on what is essential. For designers seeking lasting impact, his career offers a clear lesson: design matters most when it quietly improves everyday life.

Rams today

Dieter Rams is now in his early nineties and living in Germany. He continues to be involved through interviews, archival projects, and exhibitions that revisit his work and its relevance for contemporary design.

He still improves his designs to this day and is often cited by a new generation as a key influence on their work that is shaping the 21st century.

Resources

• Profile — Design Museum Collection

• Profile — Braun designers

• Works— The JF Chen Collection

• Book — Less but Better

• Book — Dieter Rams: As Little Design as Possible

• Book — Less and More: The Design Ethos of Dieter Rams

• Book — Dieter Rams: The Complete Works

• Documentary — Rams, dir: Gary Hustwit

• Color Palettes — Figma file

• Poster — Ten principles, printable

• Wallpapers — Blog, Arun Venkatesan

Designer Portraits

This article is part of Designer Portraits, a series within Design, Explained.

We look at influential designers, not to celebrate style, but to understand judgment. How they thought. How they decided. Why their work still holds up when trends fade.

More portraits are coming.

If you want to be notified when new Designer Portraits are published, subscribe to Design, Explained. No noise. Just clear thinking, delivered when there is something worth reading.

The principles you learned first will outlast everything else you pick up along the way. Design thinking, design principles, and psychology of design operate beneath the surface of trends, tools, and platforms. When you understand them at their deepest level, they become more valuable than any new framework or fashionable pattern.

As a new year begins, this is a good opportunity to return to these foundational concepts. Not to review them casually, but to rebuild your understanding with deliberate intention and a clearer sense of purpose.

A solid place to begin

These concepts won’t tell you what to design, but they eliminate scattered decision-making before the design process even starts. They function as filters that narrow your decision space, help you identify fragile approaches early, and create a common language that teams can use when evaluating quality and making choices together.

When products feel confusing or unnecessarily demanding to users, the problem is that these foundations were ignored. More commonly, they were applied superficially, treated as checkboxes rather than frameworks for thinking. A team might reference “cognitive load” while designing a dashboard crammed with seventeen metrics, or cite “consistency” while using three different button styles across the same flow.

Clarity comes before beauty

Design thinking shapes how you frame problems before solutions appear. Design principles determine which decisions create coherent, trustworthy experiences. Psychology of design reveals why certain patterns work by connecting to how people perceive, remember, decide, and expend cognitive effort.

These three areas don’t exist as isolated concepts to check off a list. They integrate into a single system that shapes how you approach design problems from the very beginning. They provide a framework for understanding problems before any solutions take shape. They make your reasoning process visible and explainable, so you can articulate why you made specific decisions.

When you understand these forces at a deeper level, clarity stops being something you chase and starts emerging naturally from your decisions. Beauty still matters, and it will appear in your work, but it transforms from a primary goal into a natural consequence of thoughtful choices made with clear reasoning.

Constraint as creative support

Strong constraints don’t diminish creative work or make it less innovative. Instead, they bring focus and direction to the creative process.

By filtering out noise and unnecessary options, constraints make the real trade-offs in your work become visible and tangible. They don’t make your decisions easier to execute, but they do make the consequences of those decisions much clearer.

When you work from that place of clarity, you gain genuine freedom. You can adapt your approach thoughtfully, explore alternative solutions with confidence, and occasionally break established patterns when you have clear intent rather than acting on impulse or following trends blindly.

Studying the basics with purpose

Revisiting these foundations isn’t an exercise in memorizing textbook definitions or reciting standard practices. It’s about critically examining the decisions you make.

Start paying closer attention to how attention gets directed in actual products that people use daily. Notice the subtle ways that small inconsistencies gradually erode user trust over time. Understand how to help users accomplish their goals easily, and disclose important details that people need to make informed decisions.

This approach to learning builds the structure behind the library on 1984.design. The content is organized into three interconnected areas: Design Thinking provides frameworks for understanding and framing problems clearly, Design Principles offers guidance for making consistent decisions throughout your work, and Psychology of Design explains the underlying reasons why certain decisions create better user experiences.

These aren’t concepts you master once and then move beyond as your career advances. They’re ideas you continuously grow into, understanding them more deeply each time you return to them.

An open invitation

The library is now completely free and accessible to anyone. There are no paywalls blocking access, no premium tiers hiding key insights. The website offers a growing collection of design concepts, mental models, and detailed explanations that you can return to whenever sound judgment matters more than knowing the latest tools.

It is a shared knowledge base built for product designers and makers who want to understand the reasons behind effective design. Everything is written to remain relevant and reusable over time, not to chase what’s currently trending.

Keep learning, keep growing.

— B

Typography isn’t just about picking a pretty font. It’s the craft of shaping written language into a visual experience, how words look, breathe, and interact on a page or screen. Good typography quietly guides the reader, while poor typography shouts for attention in all the wrong ways. Every detail—from letter spacing to line height, affects how users read, feel, and engage. Let’s unpack the essentials.

Every letter has a structure, and understanding it helps you design with precision. Terms like ascender, descender, baseline, and x-height describe the invisible skeleton that keeps type coherent. The x-height, for instance, determines how big a typeface feels even at the same point size.

Serif and sans-serif typefaces differ in tone partly because of these structures: serifs guide the eye along lines of text, while sans-serifs often feel cleaner and more modern. Knowing anatomy allows you to mix typefaces consciously rather than by vibe alone.

Typographic Anatomy

Kerning, tracking, leading

These three terms define the rhythm and flow of text. Kerning adjusts the space between individual letters, tracking controls spacing across whole words or paragraphs, and leading (pronounced “ledding”) sets the space between lines.

Think of them as the breathing room of type. Too tight, and words feel suffocating; too loose, and they drift apart like strangers at a party. Consistent, intentional spacing is one of the clearest markers of professional typography, and one of the easiest to overlook.

Type alignment

Left, right, center, or justified, each alignment changes how the reader experiences text. Left-aligned text feels natural for most Western readers; it mirrors how our eyes expect to move. Centered text adds elegance in small doses, like headings or invitations, but strains readability in long paragraphs.

Justified text can look neat but often introduces awkward gaps between words. There’s no one right choice, only what fits the tone and purpose of the content.

Indents, outdents, and hanging punctuations

Indenting the first line of a paragraph subtly signals a new thought, while outdents (negative indents) can highlight lists or quotes. Hanging punctuation, where quotation marks or bullets sit outside the text block, preserves clean visual alignment.

These are the small design manners that readers might never notice, but would feel their absence. They lend rhythm and grace to long-form content.

Legibility and readability

They sound similar but mean different things. Legibility is about how clearly letters can be distinguished, a function of font design, size, and contrast. Readability is about how easy it feels to read longer passages, affected by line length, spacing, and even the surrounding design.

A beautifully legible font can still be unreadable in practice if the text is too dense or too wide. The goal isn’t just to be seen but to be comfortably followed.

Specialized uses

Typography takes on unique forms in different contexts: interfaces, signage, packaging, books, motion graphics. In UI design, type must perform at small sizes and varying screens. In print, it must hold character at high resolutions and long durations.

Display type can afford drama; body text should feel invisible. Understanding how context shapes your typographic decisions is what separates art from noise.

Typography isn’t decoration, it’s communication shaped with care. Once you see how subtle its impact is, you start noticing type everywhere: on screens, in streets, on receipts. Every font choice tells a story. The more fluently you speak the language of type, the more precisely you can design experiences that speak back.

Typography has a strange power: it’s both invisible and unforgettable. The best type never demands your attention, yet it defines how every word feels. A letter’s curve can suggest warmth or precision, while spacing can create calm or urgency. This is why typography sits at the heart of design, connecting language with emotion.

So when we adjust a line height or choose between Helvetica and Garamond, we’re not just picking styles; we’re shaping how people interpret meaning. Typography is the quiet storyteller behind every interface, poster, and page. Mastering it means learning to speak softly but leave a lasting impression.

Further reading & viewing

📘 The Elements of Typographic Style by Robert Bringhurst — Widely regarded as a typography “bible” — it covers type anatomy, spacing, history, meaning and practice.

📘 Just My Type: A Book About Fonts by Simon Garfield — A lively, readable exploration of how fonts shape culture, emotion and everyday design choices.

📘 The Anatomy of Type: A Graphic Guide to 100 Typefaces by Stephen Coles — Highly visual and practical — a great reference for seeing how anatomy, spacing and alignment vary across real typefaces.

🎥 Helvetica (2007, dir. Gary Hustwit) — A design-documentary classic that shows how a single typeface (Helvetica) touches legibility, meaning, alignment and cultural history.

🎥 Typeface (2009, dir. Justine Nagan) — Looks at wood-type printing and the material roots of typography — helpful to ground the “anatomy” and “spacing” discussions in tangible form.

🖼️ Graphic design for filmmaking, prop design workshop by Annie Atkins. — Learn how to start designing a collection of graphic props that can tell a director’s story, as well as contributing to the genre, period, and visual aesthetic of a film.

Visuals by @oscarsun — Figma Community

— Thanks for reading.

I recently watched Growth.Design's case study on buying audiobooks: Audible: Build seamless purchase experiences

"Reduce Cognitive Load" hint resonated highly. When developing products and designing interfaces, we like to display too much information in one place, thinking all of it is important, but most are not. This affects how easily users find relevant content and complete tasks.

Just like computers, human brains have a limited amount of processing power. When the amount of information coming in exceeds our ability to handle it, our performance suffers. We may take longer to understand information, miss important details, or even get overwhelmed and abandon the task.

So, what do we need to do? User attention is a precious resource and should be allocated accordingly.

✅ Minimize visual clutter to reduce cognitive load

‣ Ask if each element serves an essential purpose

‣ Streamline navigation to highlight key paths

‣ Use images mindfully to convey meaning, not just decorate

‣ Ensure text is clear, legible, and kept to a minimum

‣ Leverage whitespace to give the eye "resting places"

✅ Leverage existing mental models to speed up learning

‣ Research your audience's expectations and conventions

‣ Organize content into familiar patterns like F-layouts

‣ Use industry-standard icons and imagery for common functions

‣ Follow well-established interaction patterns

‣ Adopt naming conventions that match the user's existing language

✅ Offload tasks from the user whenever possible

‣ Proactively offer intelligent defaults

‣ Auto-populate form fields based on known user data

‣ Provide previews to help users envision results

‣ Give clear feedback to confirm actions—progress

‣ Consider the timing and context of messaging to maximize relevance

✅ Take a focused, purposeful approach to interactive elements

‣ Make clickable areas obvious and generously sized

‣ Use animation and transitions to convey relationships

‣ Provide escape hatches like undo or cancel options

‣ Confirm destructive or high-impact actions before executing

‣ Progressively disclose complexity as needed

✅ Craft clear visual hierarchies that guide the eye

‣ Establish a clear order of importance for content

‣ Use headings and subheadings to organize information

‣ Create obvious parent-child relationships between elements

‣ Employ consistent styling to unify related items

In today's digital landscape where our minds are constantly bombarded with information, cognitive load management isn't just a design principle—it's a fundamental responsibility we hold towards our users.

By thoughtfully reducing complexity, leveraging familiar patterns, and creating clear visual hierarchies, we can craft interfaces that feel effortless, and easy to use.

Remember that every element we add to an interface demands a small piece of our users' limited cognitive resources. When designing a feature or reviewing an interface, ask yourself: "Does this element truly serve an essential purpose? Am I making it easier or harder for users to achieve their goals?"

Often, the most powerful improvements are not from what we add, but from what we mindfully choose to simplify or remove. Your users' cognitive resources are precious—build with them in mind.

Further reading

• Reducing Cognitive Overload For A Better User Experience

• Understanding the psychology behind product decisions

• Minimize Cognitive Load to Maximize Usability

• Progressive Disclosure

• Usability heuristics for user interface design

To solve big-scale design problems, Don Norman recommends engaging with the community that has these problems and leveraging existing creativity and experience.

The principles of human-centered design have proven to be effective and productive. However, its approach is generally used in situations where professionals determine the needs of the target populations and then develop products and procedures to address the needs.

This is Top-Down design: starting with higher-level conceptualizations and then refining the ideas and concepts to specific instances of products or services. This works well for mass-produced items which only allows limited specialization for individual needs and requirements.

We propose a radical change in design from experts designing for people to people designing for themselves. In the traditional approach, experts study, design, and implement solutions for the people of the world. Instead, we propose that we leverage the creativity within the communities of the world to solve their own problems: This is community-driven design, taking full advantage of the fact that it is the people in communities who best understand their problems and the impediments and affordances that impede and support change. Experts become facilitators, by mentoring and providing tools, toolkits, workshops, and support.

Jeremy Meyerson, from London’s Royal College of Art, has proposed a closely related approach “Scaling Down"

The growth model of the 20th century required that designers and companies achieve economies of scale. Scaling up involved abstraction to make large-scale production possible for the global industrial economy. In the 21st century, social challenges are increasingly disrupting world markets. This changes the focus of the design process. Designers once needed to learn just a little about large groups of people to serve mass markets. Today, they must learn a great deal about relatively small numbers of people. They must shift from concentrating on what makes groups of people similar to what makes them different.

Myerson proposed five principles of scaling down. They complement the approach in this paper by being very similar in spirit, but from a slightly different perspective, introducing slightly different concepts. Here are his principles:

Cultivate a Participatory Mindset—Not an Expert One

Creating with people rather than for them using collaborative methods responding to real needs identified through an interactive, democratic process, and make certain design methods obsolete.

Make the Process Design-Infused—Not Design-Led

Infusing multi-disciplinary processes with valuable design skills such as facilitation, visualization, and modelling offers richer, deeper, and more democratic alternatives to standard, designer-led approaches.

Design for People—Not Personas

Imaginary amalgams of user traits may be useful and convenient design tools in some industries—but they can be dangerous in others because they are based on unreal assumptions or idealized stereotypes.

Aim for Engagement—Not Abstraction

When designers adopt broad, simple, generic ideas about people, they leave no space for the type of contradiction, complexity, or iteration that real life demands. When designers opt for direct engagement with groups and individuals during workshops, consultations, or co-creation activities, they are able to resist abstraction because they are dealing with specifics, real opinions, and lived experiences.

Build on Assets—Don’t Just Minimize Deficits

Scaling down means looking through a positive lens, trying to enhance people’s physical and psychological assets, and building on what individual people and communities have to offer.

Scaling down is a counter-cultural movement that seeks to create a more tailored experience for communities and individuals. Experts provide the building materials and build the communication structures. Local communities build upon these resources. A powerful combination.

A participatory mindset, a design-infused approach, and engagement rather than abstraction are among the key values of scaling down. Scaling down can be seen in a variety of projects from office space planning to public health inventions in a city.

We believe that the people best equipped to address these issues are the people who live there, and we wish to help create a worldwide community, sharing procedures and results through powerful communication and learning tools, using whatever media are appropriate. This is the power of the mixed approach, building upon proven techniques in both the top-down and the bottom-up approaches.

Further reading

Community-Based, Human-Centered Design

We propose a radical change in design from experts designing for people to people designing for themselves, leveraging the creativity of communities to solve their own problems. People in communities best understand their problems and the impediments and affordances that impede and support change.

Scaling Down: Why Designers Need to Reverse Their Thinking - ScienceDirect

The growth model of the 20th century required that designers and companies achieve economies of scale. Scaling up involved abstraction to make large-scale production possible for the global industrial economy. In the 21st century, social challenges are increasingly disrupting world markets. This changes the focus of the design process. Designers once needed to learn just a little about large groups of people to serve mass markets.

Beyond The Cult Of Human-Centered Design

The design industry’s reigning paradigm is in crisis. It’s time to evolve from human-centered design to humanity-centered design, write Artefact’s Rob Girling and Emilia Palaveeva.

What is Humanity-Centered Design?

Humanity-centered design is a practice where designers focus on people’s needs not as individuals but as societies with complex, deep-rooted problems. Designers can co-create proper solutions when they work with populations, address the right problems, perform systems analyses and co-design small, simple interventions.

The first noted use of "serendipity" in the English language was by Horace Walpole on 28 January 1754. In a letter he wrote to his friend Horace Mann, Walpole explained an unexpected discovery he had made about a lost painting of Bianca Cappello by Giorgio Vasari by reference to a Persian fairy tale, The Three Princes of Serendip.

The princes, he told his correspondent, were "always making discoveries, by accidents and sagacity, of things which they were not in quest of." The name comes from Serendip, an old name for Sri Lanka (Ceylon), hence Sarandib by Arab traders.

The word has been exported into many other languages, with the general meaning of “unexpected discovery” or “fortunate chance”.

The term "serendipity" is often applied to inventions made by chance rather than intent. As Christian Busch describes serendipity in his Psyche article How to be lucky;

Most of us think that luck just happens (or doesn’t) but everyone can learn to look for the unexpected and find serendipity.

Human beings find comfort in certainty. We form governments, make calendars, and create organizations; and we structure our activities, strategies, and plans around these constructs. These routines give us the satisfaction of knowing that, by having a plan, there’s a means of it coming to fruition.

But there’s another force, constantly at play in life, that often makes the greatest difference to our futures: the ‘unexpected’ or the “unforeseen”. If you think about it, you already look out for the unexpected every day, but perhaps only as a defense mechanism. For example, whenever you use a pedestrian crossing on a busy road, you look out for the unexpected driver who might race through the red light. That “alertness” to, or awareness of, the unexpected is at the center of understanding the science of (smart) luck and exploiting it to your benefit.

The term "serendipity" is often applied to inventions made by chance rather than intent.

Andrew Smith, editor of The Oxford Companion to American Food and Drink, has speculated that most every day products had serendipitous roots, with many early ones related to animals. The origin of cheese, for example, possibly originated in the Nomad practice of storing milk in the stomach of a dead camel that was attached to the saddle of a live one, thereby mixing rennet from the stomach with the milk stored within.

Other examples of serendipity in inventions include:

🗒 The Post-It Note, emerged after 3M scientist Spencer Silver produced a weak adhesive, and a colleague used it to keep bookmarks in place on a church hymnal.

👨‍🔬 Silly Putty, which came from a failed attempt at synthetic rubber.

🎩 The use of sensors to prevent automobile airbags from killing children, which came from a chair developed by the MIT Media Lab for a Penn and Teller magic show.

♨️ The microwave oven. Raytheon scientist Percy Spencer first patented the idea behind it after noticing that emissions from radar equipment had melted the candy in his pocket.

🔬 The Velcro hook-and-loop fastener. George de Mestral came up with the idea after a bird hunting trip when he viewed cockleburs stuck to his pants under a microscope and saw that each burr was covered with tiny hooks.

🍭 The Popsicle, whose origins go back to San Francisco where Frank Epperson, age 11, accidentally left a mix of water and soda powder outside to freeze overnight.

💉 The antibiotic penicillin, which was discovered by Sir Alexander Fleming after returning from a vacation to find that a Petri dish containing staphylococcus culture had been infected by a Penicillium mold, and no bacteria grew near it.

So what to do?

Even being open to the unexpected is the key to create smart luck, there is another force in play: preparation.

This is partly about removing the barriers to serendipity, both mental (your mindset) and physical (the spaces you live and interact in), such as: overloaded schedules; senseless meetings; and the inefficiencies throughout your day that rob you of time, curiosity and a sense of joy.

You can prepare by strengthening your mental readiness to connect with opportunity, and creating an environment that enables the use of your skills and available resources to act on the moment. An unprepared mind often discards unusual encounters, thereby missing the opportunities for smart luck.

✅ Accept that the unexpected happens all the time to start ‘seeing’ good things over bad and begin to view them as a potential benefit or opportunity rather than a threat.

✅ Resist your urge to find familiarity in the unknown, and to control anomalies. If you continually airbrush the many unexpected events out of your (hi)stories, you’ll miss the importance of the unpredictable parts and therefore fail to recognise – let alone legitimise – the critical role that serendipity will play in your future.

✅ Having the mental agility to improvise, to see how a tool could be used in a new way, is essential to building your serendipity mindset. Your own creativity will thrive when you abandon the physical and mental tools with which you’re most familiar, and find new ways to work or think.

✅ If you are new to journalling, don’t overthink it. Set a timer for two minutes, then list out in two columns the parts of your day that led to positive outcomes and the parts of the day that did not. As you break down your day into these segments, examine the parts that worked really well for you, and the ones that were inefficient, stressful or unfulfilling.

✅ We lead unique and complex lives. If you clean up the minutiae, they’ll no longer take up space in your daily life, and you’ll have more cognitive, emotional and physical attention to dedicate to the more important stuff. Declutter your life.

✅ Try ‘reframing’ mistakes, challenges, and setbacks as opportunities. What appeared to be "bad luck" at the moment turned into ‘good luck’ by reframing the situation.

Serendipity is a process. It’s an opportunity to turn chance into good fortune through your own efforts, which doesn’t always happen in a flash. It often requires an incubation period. Be patient, knowing that some of your efforts could result in an immediate spark while others will be more like planting seeds, for which the fruits of your labour will pleasantly surprise you in the future.

Learn more

→ Christian Busch’s book The Serendipity Mindset: The Art and Science of Creating Good Luck (2020) is the first comprehensive science-based approach on how to cultivate serendipity – ‘smart luck’.

→ In this online talk for the American Psychological Association from 2020, he discussed how the science of serendipity can help turn uncertainty into opportunity.

→ In this article for the World Economic Forum, he showed how new business models and opportunities are emerging in the wake of COVID-19 uncertainty.

→ In this article for LSE Business Review, Harry Barkema, a professor of management, and Christian Busch discuss how, in the context of extreme resource constraints, reframing is possible – and what we can learn from social entrepreneurs in Sub-Saharan Africa.

→ In this article for the Harvard Business Review, he explains how to set ‘serendipity bombs’ and cast ‘serendipity hooks’ in order to cultivate serendipity.

— That’s it for this edition. See you with the next one.

Climate change is often cast as a prediction made by complicated computer models. But the scientific basis for climate change is much broader, and models are actually only one part of it (and, for what it’s worth, they’re surprisingly accurate).

For more than a century, scientists have understood the basic physics behind why greenhouse gases like carbon dioxide cause warming. These gases make up just a small fraction of the atmosphere but exert outsized control on Earth’s climate by trapping some of the planet’s heat before it escapes into space. This greenhouse effect is important: It’s why a planet so far from the sun has liquid water and life!

🔥 However, during the Industrial Revolution, people started burning coal and other fossil fuels to power factories, smelters and steam engines, which added more greenhouse gases to the atmosphere. Ever since, human activities have been heating the planet.

We know this is true thanks to an overwhelming body of evidence that begins with temperature measurements taken at weather stations and on ships starting in the mid-1800s. Later, scientists began tracking surface temperatures with satellites and looking for clues about climate change in geologic records. Together, these data all tell the same story: Earth is getting hotter.

Average global temperatures have increased by 2.2 degrees Fahrenheit, or 1.2 degrees Celsius, since 1880, with the greatest changes happening in the late 20th century. Land areas have warmed more than the sea surface and the Arctic has warmed the most — by more than 4 degrees Fahrenheit just since the 1960s. Temperature extremes have also shifted. In the United States, daily record highs now outnumber record lows two-to-one.

We also know that climate change is happening because we see the effects everywhere. Ice sheets and glaciers are shrinking while sea levels are rising. Arctic sea ice is disappearing. In the spring, snow melts sooner and plants flower earlier. Animals are moving to higher elevations and latitudes to find cooler conditions. And droughts, floods and wildfires have all gotten more extreme. Models predicted many of these changes, but observations show they are now coming to pass.

How bad are the effects of climate change going to be?

It depends on how aggressively we act to address climate change. If we continue with business as usual, by the end of the century, it will be too hot to go outside during heat waves in the Middle East and South Asia. Droughts will grip Central America, the Mediterranean, and southern Africa. And many island nations and low-lying areas, from Texas to Bangladesh, will be overtaken by rising seas.

Conversely, climate change could bring welcome warming and extended growing seasons to the upper Midwest, Canada, the Nordic countries and Russia. Farther north, however, the loss of snow, ice and permafrost will upend the traditions of Indigenous peoples and threaten infrastructure.

What will it cost to do something about climate change, versus doing nothing?

One of the most common arguments against taking aggressive action to combat climate change is that doing so will kill jobs and cripple the economy. But that implies that there’s an alternative in which we pay nothing for climate change. And unfortunately, there isn’t.

🔥 In reality, not tackling climate change will cost a lot and will cause enormous human suffering and ecological damage, while transitioning to a greener economy would benefit many people and ecosystems around the world.

Klima: Effective, immediate, simple climate action in the palm of your hand.

When doing your part to reduce climate change, it can be hard to know where to begin. That’s why Klima creates a personal estimate of the greenhouse gases you’re generating—and suggests concrete ways to offset that.Who it’s for: Anyone who’s curious about their personal impact on the environment.

Klima asks a few specifics about your lifestyle—for example, how many miles you drive a year, how big your home is, and how often you shop. Using those numbers, the app tells you how much you would need to donate to carbon-offsetting projects to cancel out your carbon footprint.

What sets it apart: Contribute right in the app to up to three causes—like tree planting, solar power, and providing clean-fuel cookstoves to people around the world who need them—and see exactly how much CO2 your donations are offsetting.

Don’t miss: Because offsetting alone won’t stop climate change, the app also suggests ways to reduce your carbon footprint. A personalized checklist tells you how going vegan, driving less, or cutting back on shopping can reduce your overall impact on the environment.

— That’s it for this edition. See you with the next one.

We have helped to create comfortable, happy lives for some of our species and allowed harm to others; our designs, at times, serve to exclude, eliminate, and discriminate.

Many design teachers and professionals perpetuate this ideology; the markets reward it; a tide of imitations and “likes” reinforces it. Encouraged in this direction, designers then apply their skills and imagination to sell fast fashion, fast cars, and fast food; disposable cups, bubble wrap, and unending amounts of single-use plastics; fidget spinners, microwave dinners, and nose hair trimmers.

We market unhealthy body images and diets; products and apps that propagate social isolation and depression; the consumption of unbalanced food systems; we sell pills to pop, tiks to tok, and a scrolling feed that never stops… and then the desire to consume it all over again and again. Yes, commercial work has always paid the bills, but many designers have let it become, in large measure, what designers do. This, in turn, is how the world perceives design.

Many of us have grown increasingly uncomfortable with this view of design. Because of this, we call for a massive change in what and how designers design. Climate change is critically entangled with class, race, and gender-based dominance, we can no longer push for mere sustainability (pdf) but must create new systems that undo and heal what’s been done.

What We Must Do

• Challenge and examine the histories, processes, and ethics of design and develop new creative skills, resources, collaborations, and languages of design.

• Support community-based efforts of justice, healing, co-existence, and mutual respect.

• Understand that we are not outside of nature; we are a part of a complex system and our actions must reflect that knowledge.

• Reverse our profession’s priorities in favour of more inclusive, empathetic, and engaged forms of action — a mind-shift that goes beyond sustainability — towards regeneration, exploration, and co-creation of a non-exploitative, non-appropriative set of social-environmental relations.

• Commit to reconnecting design, manufacturing, distribution, and use of the things we design to the Earth — and all of its inhabitants.

• Direct our skills for the betterment of humanity towards a more ecological civilization.

• Advocate the reduction of design to a singular focus: this is not feasible. Nor do we want to take any of the fun out of life. But we are proposing a reversal of priorities in favour of more useful, generative, and equitable forms of design.

We believe all of these principles should be integrated into multidisciplinary design pedagogy.

In 1964, 22 visual communicators, young and old, signed the original call for our skills to be put to worthwhile use.

In 2000, 33 designers signed a revised version of the original call, and in 2014 — on the 50th anniversary of the manifesto — over 1600 designers around the world renewed their commitment to the First Things First Manifesto.

With the ongoing destruction of essential living systems on our planet, this message has only grown more urgent.

We renew the previous manifestos with a greater sense of urgency as we see the compounded effects of our climate crisis unfold before us. It is imperative that we take climate action now.

We believe the First Things First Manifesto should reflect collective thoughts on what the world needs and where design plays a role within that world.

Join us in taking action →