Let’s rewind 25 years and remember what websites and web technology used to look like. Kevin Gut:

This website is a trip down memory lane. I’m not trying to tell you to stop modern web development. This website uses technologies not available at the time the content here is about.

Elizabeth Goodspeed contextualizes today’s growing design influencers against designers-cum-artists like April Greiman and Stefan Sagmeister. Along with Tibor Kalman, Jessica Walsh, and Wade and Leta, all of these designers put themselves into their work.

Other designers ran with similar instincts. 40 Days of Dating, a joint project by Jessica Walsh and Timothy Goodman created in 2013, was presented as a kind of art-directed relationship experiment: two friends, both single, agreed to date each other for 40 days (40 days being the purported time needed to build a habit). The project was presented through highly polished daily updates with lush photography, motion graphics, custom lettering, and a parade of commissioned work from other artists – all accompanied by alarming candid journal entries from both parties about the dates they were going on. It wasn’t exactly a design project in the traditional sense, but it was unmistakably design-led; the relationship itself was the content, but it was design that made it viral.

These self-directed, clientless projects remind me of MFA design theses where design is the medium for self-expression. Bringing it back to 2025, Godspeed writes:

Designers film themselves in their bedrooms and running errands, narrating design decisions and venting about clients along the way. Just as remote work expects us to perform constant busyness, design influencing demands a continuous performance of creative output. …Brands have jumped in on the trend, too. Where once a designer might have been hired to create packaging or campaigns behind the scenes, many are now brought forward as faces of collaborations – they’re photographed in their studios and interviewed about their process as part of launch. The designer’s body, personality, and public profile become a commercial asset.

And of course, like with all content creators, it becomes a job that just might require more work than it seems.

Influencing can seem like a good, low-lift side-hustle at first. Most designers already have tons of unused work and in-progress sketches to share. Why not just post it and see what happens? But anyone who’s ever had to write captions or cut reels knows that making content is, in fact, harder than it looks. The more energy that goes into showcasing work, the less time there is to actually make work, even if you want to. “Influencing” can quickly become a time suck.

It’s no secret that I am a big fan of Severance, the Apple TV+ show that has 21 Emmy nominations this year. I made a fan project earlier in the year that generates Outie facts for your Innie.

After launching a teaser campaign back in April, Atomic Keyboard is finally taking pre-orders for their Severance-inspired keyboard just for Macrodata Refinement department users. The show based the MDR terminals on the Data General Dasher D2 terminal from 1977. So this new keyboard includes three layouts:

1. “Innie” which is show-accurate, meaning no Escape, no Option, and no Control keys, and includes the trackball
2. “Outie,” a 60% layout that includes modern modifier keys and the trackball
3. “Dasher” which replicates the DG terminal layout

It’s not cheap. The final retail price will be $899, but they’re offering a pre-Kickstarter price of $599.

Stephanie Tyler, in a great essay about remembering what we do as designers:

In an age where AI can generate anything, the question is no longer ‘can it be made?’ but ‘is it worth making?’ The frontier isn’t volume—it’s discernment. And in that shift, taste has become a survival skill.

And this is my favorite passage, because this is how I think about this blog and my newsletter.

There will always be creators. But the ones who stand out in this era are also curators. People who filter their worldview so cleanly that you want to see through their eyes. People who make you feel sharper just by paying attention to what they pay attention to.

Curation is care. It says: I thought about this. I chose it. I didn’t just repost it. I didn’t just regurgitate the trending take. I took the time to decide what was worth passing on.

That’s rare now. And because it’s rare, it’s valuable.

We think of curation as a luxury. But it’s actually maintenance. It’s how you care for your mind. Your attention. Your boundaries.

This blog represents my current worldview, what I’m interested in and exploring. What I’m thinking about now.

This is a really well-written piece that pulls the AI + design concepts neatly together. Sharang Sharma, writing in UX Collective:

As AI reshapes how we work, I’ve been asking myself, it’s not just how to stay relevant, but how to keep growing and finding joy in my craft.

In my learning, the new shift requires leveraging three areas

1. AI tools: Assembling an evolving AI design stack to ship fast
2. AI fluency: Learning how to design for probabilistic systems
3. Human-advantage: Strengthening moats like craft, agency and judgment to stay ahead of automation

Together with strategic thinking and human-centric skills, these pillars shape our path toward becoming an AI-native designer.

Sharma connects all the crumbs I’ve been dropping this week:

• Designing for AI interfaces
• Designing for control
• Understanding orchestration and agents
• Prompt and context engineering
• Being AI native

From UX Magazine:

Copilots helped enterprises dip their toes into AI. But orchestration platforms and tools are where the real transformation begins — systems that can understand intent, break it down, distribute it, and deliver results with minimal hand-holding.

Think of orchestration as how “meta-agents” are conducting other agents.

The first iteration of AI in SaaS was copilots. They were like helpful interns eagerly awaiting your next command. Orchestration platforms are more like project managers. They break down big goals into smaller tasks, assign them to the right AI agents, and keep everything coordinated. This shift is changing how companies design software and user experiences, making things more seamless and less reliant on constant human input.

For designers and product teams, it means thinking about workflows that cross multiple tools, making sure users can trust and control what the AI is doing, and starting small with automation before scaling up.

Let’s stay on the train of designing AI interfaces for a bit. Here’s a piece by Rob Chappell in UX Collective where he breaks down how to give users control—something I’ve been advocating—when working with AI.

AI systems are transforming the structure of digital interaction. Where traditional software waited for user input, modern AI tools infer, suggest, and act. This creates a fundamental shift in how control moves through a experience or product — and challenges many of the assumptions embedded in contemporary UX methods.

The question is no longer: “What is the user trying to do?”

The more relevant question is: “Who is in control at this moment, and how does that shift?”

Designers need better ways to track how control is initiated, shared, and handed back — focusing not just on what users see or do, but on how agency is negotiated between human and system in real time.

Most design frameworks still assume the user is in the driver’s seat. But AI is changing the rules. The challenge isn’t just mapping user flows or intent—it’s mapping who holds the reins, and how that shifts, moment by moment. Designers need new tools to visualize and shape these handoffs, or risk building systems that feel unpredictable or untrustworthy. The future of UX is about negotiating agency, not just guiding tasks.

Vitaly Friedman writes a good primer on the design possibilities for users to interact with AI features. As AI capabilities become more and more embedded in the products designers make, we have to become facile in manipulating AI as material.

Many products are obsessed with being AI-first. But you might be way better off by being AI-second instead. The difference is that we focus on user needs and sprinkle a bit of AI across customer journeys where it actually adds value.

Speaking of prompt engineering, apparently, there’s a new kind in town called context engineering.

Developer Philipp Schmid writes:

What is context engineering? While “prompt engineering” focuses on crafting the perfect set of instructions in a single text string, context engineering is a far broader. Let’s put it simply: “Context Engineering is the discipline of designing and building dynamic systems that provides the right information and tools, in the right format, at the right time, to give a LLM everything it needs to accomplish a task.”

Since its debut at Config back in May, Figma has steadily added practical features to Figma Make for product teams. Supabase integration now allows for authentication, data storage, and file uploads. Designers can import design system libraries, which helps maintain visual consistency. Real-time collaboration has improved, giving teams the ability to edit code and prototypes together. The tool now supports backend connections for managing state and storing secrets. Prototypes can be published to custom domains. These changes move Figma Make closer to bridging the gap between design concepts and advanced prototypes.

In my opinion, there’s a stronger relationship between Sites and Make than there is Make and Design. The Make-generated code may be slightly better than when Sites debuted, but it is still not semantic.

Anyhow, I think Make is great for prototyping and it’s convenient to have it built right into Figma. Julius Patto, writing in UX Collective:

Prompting well in Figma Make isn’t about being clever, it’s about being clear, intentional, and iterative. Think of it as a new literacy in the design toolkit: the better you get at it, the more you unlock AI’s potential without losing your creative control.

In case you missed it, there’s been a major shift in the AI tool landscape.

On Friday, OpenAI’s $3 billion offer to acquire AI coding tool Windsurf expired. Windsurf is the Pepsi to Cursor’s Coke. They’re both IDEs, the programming desktop application that software developers use to code. Think of them as supercharged text editors but with AI built in.

On Friday evening, Google announced that it had hired Windsurf’s CEO Varun Mohan, co-founder Douglas Chen, and several key researchers for $2.4 billion.

On Monday, Cognition, the company behind Devin, the self-described “AI engineer” announced that it had acquired Windsurf for an undisclosed sum, but noting that its remaining 250 employees will “participate financially in this deal.”

Why does this matter to designers?

The AI tools market is changing very rapidly. With AI helping to write these applications, their numbers and features are always increasing—or in this case, maybe consolidating. Choose wisely before investing too deeply into one particular tool. The one piece of advice I would give here is to avoid lock-in. Don’t get tied to a vendor. Ensure that your tool of choice can export your work—the code.

Jason Lemkin has more on the business side of things and how it affects VC-backed startups.

Ted Goas, writing in UX Collective:

I predict the early parts of projects, getting from nothing to something, will become shared across roles. For designers looking to branch out, code is a natural next step. I see a future where we’re fixing small bugs ourselves instead of begging an engineer, implementing that animation that didn’t make the sprint but you know would absolutely slap, and even building simple features when engineering resources are tight.

Our new reality is that anyone can make a rough draft.

But that doesn’t mean those drafts are good. That’s where our training and taste come in.

I think Goas is right and it echoes the AI natives post by Elena Verna. I wrote a little more extensively in my newsletter over the weekend.

Miquad Jaffer, a product leader at OpenAI shares his 4D method on how to build AI products that users want. In summary, it’s…

• Discover: Find and prioritize real user pain points and friction in daily workflows.
• Design: Make AI features invisible and trustworthy, fitting naturally into users’ existing habits.
• Develop: Build AI systematically, with robust evaluation and clear plans for failures or edge cases.
• Deploy: Treat each first use like a product launch, ensuring instant value and building user trust quickly.

Geoffrey Litt, Josh Horowitz, Peter van Hardenberg, and Todd Matthews writing a paper for research lab Ink & Switch, offer a great, well-thought piece on what they call “malleable software.”

We envision a new kind of computing ecosystem that gives users agency as co-creators. … a software ecosystem where anyone can adapt their tools to their needs with minimal friction. … When we say ‘adapting tools’ we include a whole range of customizations, from making small tweaks to existing software, to deep renovations, to creating new tools that work well in coordination with existing ones. Adaptation doesn’t imply starting over from scratch.

In their paper, they use analogies like kitchen tools and tool arrangement in a workshop to explore their idea. With regard to the current crop of AI prompt-to-code tools…

We think these developments hold exciting potential, and represent a good reason to pursue malleable software at this moment. But at the same time, AI code generation alone does not address all the barriers to malleability. Even if we presume that every computer user could perfectly write and edit code, that still leaves open some big questions.

How can users tweak the existing tools they’ve installed, rather than just making new siloed applications? How can AI-generated tools compose with one another to build up larger workflows over shared data? And how can we let users take more direct, precise control over tweaking their software, without needing to resort to AI coding for even the tiniest change? None of these questions are addressed by products that generate a cloud-hosted application from a prompt.

Kind of a different take than the “personal software” we’ve seen written about before.

This post has been swimming in my head since I read it. Elena Verna, who joined Lovable just over a month ago to lead marketing and growth, writing in her newsletter, observes that everyone at the company is an AI-native employee. “An AI-native employee isn’t someone who ‘uses AI.’ It’s someone who defaults to AI,” she says.

On how they ship product:

Here, when someone wants to build something (anything) - from internal tools, to marketing pages, to writing production code - they turn to AI and… build it. That’s it.

No headcount asks. No project briefs. No handoffs. Just action.

At Lovable, we’re mostly building with… Lovable. Our Shipped site is built on Lovable. I’m wrapping hackathon sponsorship intake form in Lovable as we speak. Internal tools like credit giveaways and influencer management? Also Lovable (soon to be shared in our community projects so ya’ll can remix them too). On top of that, engineering is using AI extensively to ship code fast (we don’t even really have Product Managers, so our engineers act as them).

I’ve been hearing about more and more companies operating this way. Crazy time to be alive.

More on this topic in a future long-form post.

John Calhoun joined Apple 30 years ago as a programmer to work on the Color Picker.

Having never written anything in assembly, you can imagine how overjoyed I was. It’s not actually a very accurate analogy, but imagine someone handing you a book in Chinese and asking you to translate it into English (I’m assuming here that you don’t know Chinese of course). Okay, it wasn’t that hard, but maybe you get a sense that this was quite a hurdle that I would have to overcome.

Calhoun was given an old piece of code and tasked with updating it. Instead, he translated it into a programming language he knew—C—and then decided to add to the feature. He explains:

I disliked HSL as a color space, I preferred HSV (Hue, Saturation, Value) because when I did artwork I was more comfortable thinking about color in those terms. So writing an HSV color picker was on my short list.

When I had my own color picker working I think I found that it was kind of fun. Perhaps for that reason, I struck out again and wrote another color picker. The World Wide Web (www) was a rather new thing that seemed to be catching on, so I naturally thought that an HTML color picker made sense. So I tackled that one as well. It was more or less the RGB color picker but the values were in hexadecimal and a combined RGB string value like “#FFCC33” was made easy to copy for the web designer.

So an engineer decided, all on his own, that he’d add a couple extra features. Including the fun crayon picker:

On a roll, I decided to also knock out a “crayon picker”. At this point, to be clear, the color picker was working and I felt I understood it well enough. As I say, I was kind of just having some fun now.

And Calhoun makes this point:

It was frankly a thing I liked about working for Apple in those days. The engineers were the one’s driving the ship. As I said, I wrote an HSV picker because it was, I thought, a more intuitive color space for artists. I wrote the HTML color picker because of the advent of the web. And I wrote the crayon picker because it seemed to me to be the kind of thing Apple was all about: HSL, RGB — these were kind of nerdy color spaces — a box of crayons is how the rest of us picked colors.

Making software—especially web software—has matured since then, with product managers and designers now collaborating closely with engineers. But with AI coding assistants, the idea of an individual contributor making solo decisions and shipping code might become de rigueur again.

Let’s continue down Mac memory lane with this fun post from Basic Apple Guy:

With macOS 26, Apple has announced a dramatically new look to their UI: Liquid Glass. Solid material icon elements give way to softer, shinier, glassier icons. The rounded rectangle became slightly more rounded, and Apple eliminated the ability for icon elements to extend beyond the icon rectangle (as seen in the current icons for GarageBand, Photo Booth, Dictionary, etc.).

With this release being one of the most dramatic visual overhauls of macOS’s design, I wanted to begin a collection chronicling the evolution of the system icons over the years. I’ve been rolling these out on social media over the past week and will continue to add to and update this collection slowly over the summer. Enjoy!

This is an amazing article and website by Marcin Wichary, the man behind the excellent Shift Happens book.

…I had a realization that the totemic 1984 Mac control panel, designed by Susan Kare, is still to this day perhaps the only settings screen ever brought up in casual conversation.

I kept wondering about that screen, and about what happened since then. Turns out, the Mac settings have lived a far more fascinating life than I imagined, have been redesigned many times, and can tell us a lot about the early history and the troubled upbringing of this interesting machine.

Indeed, Wichary goes through multiple versions of Mac operating systems and performs digital paleontology, uncovering long lost Settings minutiae. It’s also a great lesson in UI along the way. Be sure to click in the Mac screens.

Paul Worthington writing about the recent Cannes Festival of Creativity:

…nostalgia is rapidly becoming a major idea d’jour among marketers targeting that oh-so-desirable “Gen Z” demographic.

As a result, it should come as no surprise that if you were to walk around Cannes over the past month or so, you’d be forgiven for thinking brands no longer had any interest in the future: Lisa Frank notebooks. Tamagotchi cameos. Taglines from 1999. Brand after brand strapping itself to the past, seeking refuge in comfort. Instacart. Mattel. Burger King. Skoda. All treating relevance as if it were a rerun.

But along with nostalgia, another theme was present at Cannes—differentiation:

Cannes was also a parade of brands betting on something riskier. Something sharper. Something new. Liquid Death. Stripe. Tesla. Anduril. Companies building out from belief systems focused resolutely on what makes them unique. Making things you couldn’t have predicted because they weren’t remixes of the past—they were statements of the future.

Worthington argues that these two themes are diametrically opposed. Nostalgia brands are “fundamentally risk-averse” and feel safe. While differentiated brands are “risk-embracing,” betting that consumers are desperate for “something weird, sharp, and built from scratch.”

Stephen Heller, writing for PRINT magazine, revisits a long out-of-print book called *Visual Persuasion *by Stephen Baker, a creative director from the Mad Men era of advertising.

Although published in 1961, Visual Persuasion has as much relevance, vitality, insight, vision and spunk as any recently published book (including those that I’ve authored). The truth is this: I wish I had written it. Even though it is nearly 65 years out of print (and contains its share of outdated mores and stereotypes), it easily could still serve (with a minute refresh) to provide ideas to ward off what designers fear is the inevitable AI apocalypse—an end to original thinking and making, visual or otherwise.

One maxim, Heller notes:

Eye movements are based on conditioned reflexes. “Left-to-right habit makes our eyes travel clockwise in exploring a [layout],” Baker notes. The optical center of a page is slightly to the left. The tendency is to focus attention on a person’s eyes more than on any other part of their face. This mirrors one’s emotions with fair accuracy.

Read past some of the hyperbole in this piece by Andy Budd. I do think the message is sound.

If you’re working at a fast-growth tech startup, you’re probably already feeling the pressure. Execs want more output with fewer people. Product and engineering are experimenting with AI tooling. And you’re being asked to move faster than ever — with less clarity on what the team should even own.

I will admit that I personally feel this pressure too. Albeit, not from my employer but from the chatter in our industry. I’m observing the younger companies experiment with the process, collapsing roles, and expanding responsilities.

As AI eats into the production layer, the traditional boundaries between design and engineering are starting to dissolve. Many of the tasks once owned by design will soon be handled by others — or by machines.

Time will tell when this becomes widespread. I think designers will be asked to ship more code. And PMs and engineers may ship small design tweaks.

The reality is, we’ll likely need fewer designers overall. But the ones we do need will be more specialised, more senior, and more strategically valuable than ever before.

You’ll want AI-literate, full-stack designers — people who are comfortable working across the entire product surface, from UX to code, and from interface to infrastructure. Designers who can navigate ambiguity, embrace new tooling, and confidently operate in the blurred space between design and engineering.

I don’t know if I agree with the fewer number of designers. At least not in the near-term. The more AI is embedded into app experiences, the trend—I predict—will go in the opposite direction. The term “AI as material” has been floating around for a few months, but I think its meaning will morph. AI will be the new UI, and thus we need designers to help define those experiences.

I love this from Marc Brooker:

Every organization and industry has watering holes where the whiners hang out. The cynical. The jaded. These spots feel attractive. Everybody has something they can complain about, and complaining is fun. These places are inviting and inclusive: as long as you’re whining, or complaining, or cynical, you’re in. If you’re positive, optimistic, or ambitious, you’re out.

Avoid these places.

I’ve seen this firsthand on Reddit. Seems like the r/graphic_design and r/UXDesign subreddits have been full of posts decrying the state of the job market and attacking AI. Any meaningful conversations about the work or debates about AI are too few and far between.

Brooker again:

My advice: find the yes, and communities, and spend time there. Find the people doing cool stuff you admire, and spend time with them. Find the people doing the work you want to do, or living the life you want to live, and find ways to learn from them.

Those are hard to find online. If you know of any, please let me know!

Tom Scott, giving advice to startups about how to hire designers:

The worst thing for a designer is join a company under the premise they are going to invest in craft and never get serious about it. This results in the designer getting stuck in an average company, making it harder for them to move into a top-tier design-led company afterwards.

The TL;DR is if you’re serious about hiring great talent, put your money where your mouth is, create the right environment and get serious about design like you do with product, eng, marketing etc.

While the post is aimed at startup employers, it’s good for designers to understand the advice they’re being given.

This piece from Mike Schindler is a good reminder that a lot of the content we see on LinkedIn is written for engagement. It’s a double-edged sword, isn’t it? We want our posts to be read, commented upon, and shared. We see the patterns that get a lot of reactions and we mimic them.

We’re losing ourselves to our worst instincts. Not because we’re doomed, but because we’re treating this moment like a game of hot takes and hustle. But right now is actually a rare and real opportunity for a smarter, more generous conversation — one that helps our design community navigate uncertainty with clarity, creativity, and a sense of shared agency.

But the point that Schindler is making is this: AI is a fundamental shift in the technology landscape that demands nuanced and thoughtful discourse. There’s a lot of hype. But as technologists, designers, and makers of products, we really need to lead rather than scare.

I’ve tried to do that in my writing (though I may not always be successful). I hope you do too.

He has this handy table too…

Designed by Mike Schindler (mschindler.com)

Jeff Beer, writing for Fast Company about a documentary on Ilon Specht, the copywriter who wrote the iconic line for L’Oreal, “Because I’m Worth It.”

In the film, she describes male colleagues who were always arguing with her and taking credit when something worked. She recalled how during pitch and idea meetings for L’Oreal Preference hair color, male colleagues had suggested an idea that cast the woman as an object, rather than the subject. “I was feeling angry. I’m not interested in writing anything about looking good for men. Fuck ‘em,” says an elderly, and terminally ill, Specht in the film, before looking straight down the camera to the male camera operator. “And fuck you, too.”

The film won the Grand Prix at the Cannes Lions a couple weeks ago as it was commissioned by L’Oreal.

The original ad for L’Oreal Preference hair color that first used the line, “Because I’m Worth It” is a single shot of a woman walking towards the camera, explaining why she likes it, and how it makes her feel.

In the doc, we find out that spot almost never happened. In fact, Specht went behind her bosses’ back to create the ad after her agency produced and the brand approved a spot with almost the exact same script, except it was a man speaking the words on behalf of his wife, walking silently beside him. It’s clear that 50 years later it still made Specht angry. Angry enough to not want to talk about advertising or that campaign ever again.