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.

When I was younger, I had a sheet of US Bicentennial stamps and I always loved the red, white, and blue star. Little did I know then that I would become a graphic designer.

The symbol, designed by Bruce Blackburn at Chermayeff & Geismar is a multilayered stylized five-pointed star. It folds like bunting. Its rounded corners evoke both a flower and a pinwheel at the same time. And finally, the negative space reveals a classic, pointed star.

I was in London last week with my family and spotted this ad in a Tube car. With the headline “Humans Were the Beta Test,” this is for Artisan, a San Francisco-based startup peddling AI-powered “digital workers.” Specifically an AI agent that will perform sales outreach to prospects, etc.

I’ve long left the Bay Area, but I know that the 101 highway is littered with cryptic billboards from tech companies, where the copy only makes sense to people in the tech industry, which to be fair, is a large part of the Bay Area economy. Artisan is infamous for its “Stop Hiring Humans” campaign which went up late last year. Being based in San Diego, much further south in California, I had no idea. Artisan wasn’t even on my radar.

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…

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.

Before there was Jessica Hische, there was Jim Parkinson. You might not know his name, but you’ve seen his work. Most famously, he was known for the mastheads for Rolling Stone magazine and the LA Times. Stephen Coles has this remembrance.

Here we go. Figma has just dropped their S-1, or their registration for an initial public offering (IPO).

While a lot of the risk factors are boilerplate—legalese to cover their bases—the one about AI is particularly interesting, “Competitive developments in AI and our inability to effectively respond to such developments could adversely affect our business, operating results, and financial condition.”

Developments in AI are already impacting the software industry significantly, and we expect this impact to be even greater in the future. AI has become more prevalent in the markets in which we operate and may result in significant changes in the demand for our platform, including, but not limited to, reducing the difficulty and cost for competitors to build and launch competitive products, altering how consumers and businesses interact with websites and apps and consume content in ways that may result in a reduction in the overall value of interface design, or by otherwise making aspects of our platform obsolete or decreasing the number of designers, developers, and other collaborators that utilize our platform. Any of these changes could, in turn, lead to a loss of revenue and adversely impact our business, operating results, and financial condition.

There’s a lot of uncertainty they’re highlighting:

• Could competitors use AI to build competing products?
• Could AI reduce the need for websites and apps which decreases the need for interfaces?
• Could companies reduce workforces, thus reducing the number of seats they buy?

These are all questions the greater tech industry is asking.

In a dual profile, Ben Blumenrose spotlights Phil Vander Broek—whose startup Dopt was acquired last year by Airtable—and Filip Skrzesinski—who is currently working on Subframe—in the Designer Founders newsletter.

One of the lessons Vander Broek learned was to not interview customers just to validate an idea. Interview them to get the idea first. In other words, discover the pain points:

They ran 60+ interviews in three waves. The first 20 conversations with product and growth leaders surfaced a shared pain point: driving user adoption was painfully hard, and existing tools felt bolted on. The next 20 calls helped shape a potential solution through mockups and prototypes—one engineer was so interested he volunteered for weekly co-design sessions. A final batch of 20 calls confirmed their ideal customer was engineers, not PMs.

As for Skrzesinski, he’s learning that being a startup founder isn’t about building the product—it’s about building a business:

But here’s Filip’s counterintuitive advice: “Don’t start a company because you love designing products. Do it in spite of that.”

“You won't be designing in the traditional sense—you’ll be designing the company’s DNA,” he explains. “It’s the invisible work: how you organize, how you think, how you make decisions. How it feels to work there, to use what you're making, to believe in it.”

Darragh Burke and Alex Kern, software engineers at Figma, writing on the Figma blog:

Building code layers in Figma required us to reconcile two different models of thinking about software: design and code. Today, Figma's visual canvas is an open-ended, flexible environment that enables users to rapidly iterate on designs. Code unlocks further capabilities, but it’s more structured—it requires hierarchical organization and precise syntax. To reconcile these two models, we needed to create a hybrid approach that honored the rapid, exploratory nature of design while unlocking the full capabilities of code.

The solution turned out to be code layers, actual canvas primitives that can be manipulated just like a rectangle, and respects auto layout properties, opacity, border radius, etc.

The solution we arrived at was to implement code layers as a new canvas primitive. Code layers behave like any other layer, with complete spatial flexibility (including moving, resizing, and reparenting) and seamless layout integration (like placement in autolayout stacks). Most crucially, they can be duplicated and iterated on easily, mimicking the freeform and experimental nature of the visual canvas. This enables the creation and comparison of different versions of code side by side. Typically, making two copies of code for comparison requires creating separate git branches, but with code layers, it’s as easy as pressing ⌥ and dragging. This automatically creates a fork of the source code for rapid riffing.

In my experience, it works as advertised, though the code layer element will take a second to render when its spatial properties are edited. Makes sense though, since it's rendering code.

Christoph Niemann, in a visual essay about generative AI and art:

…the biggest challenge is that writing an A.I. prompt requires the artist to know what he wants. If only it were that simple.

Creating art is a nonlinear process. I start with a rough goal. But then I head into dead ends and get lost or stuck.

The secret to my process is to be on high alert in this deep jungle for unexpected twists and turns, because this is where a new idea is born.

It’s a fun meditation on the meaning of AI-assisted and AI-generated artwork.

My family and I are going on vacation, and I thought it might be great to share this design summer reading list from Design Better. For me, I'll be picking up Nonlinear by Kevin Bethune. It looks great.

If you want an introduction on how to use Cursor as a designer, here’s a must-watch video. It's just over half-an-hour long and Elizabeth Lin goes through several demos in Cursor.

Cursor is much more advanced than the AI prompt-to-code tools I've covered here before. But with it, you'll get much more control because you're building with actual code. (Of course, sigh, you won't have sliders and inputs for controlling design.)

David Singleton, writing in his blog:

Somewhere in the last few months, something fundamental shifted for me with autonomous AI coding agents. They’ve gone from a “hey this is pretty neat” curiosity to something I genuinely can’t imagine working without. Not in a hand-wavy, hype-cycle way, but in a very concrete “this is changing how I ship software” way.

I have to agree. My recent tinkering projects with Cursor using Claude 4 Sonnet (and set to Cursor’s MAX mode) have been much smoother and much more autonomous.

And Singleton has found that Claude Code and OpenAI Codex are good for different things:

For personal tools, I’ve completely shifted my approach. I don’t even look at the code anymore - I describe what I want to Claude Code, test the result, make some minor tweaks with the AI and if it’s not good enough, I start over with a slightly different initial prompt. The iteration cycle is so fast that it’s often quicker to start over than trying to debug or modify the generated code myself. This has unlocked a level of creative freedom where I can build small utilities and experiments without the usual friction of implementation details.

And the larger point Singleton makes is that if you direct the right context to the reasoning model, it can help you solve your problem more effectively:

This points to something bigger: there’s an emerging art to getting the right state into the context window. It’s sometimes not enough to just dump code at these models and ask “what’s wrong?” (though that works surprisingly often). When stuck, you need to help them build the same mental framework you’d give to a human colleague. The sequence diagram was essentially me teaching Claude how to think about our OAuth flow. In another recent session, I was trying to fix a frontend problem (some content wouldn’t scroll) and couldn’t figure out where I was missing the correct CSS incantation. Cursor’s Agent mode couldn’t spot it either. I used Chrome dev tools to copy the entire rendered HTML DOM out of the browser, put that in the chat with Claude, and it immediately pinpointed exactly where I was missing an overflow: scroll.

For my designer audience out there—likely 99% of you—I think this post is informative as to how to work with reasoning models like Claude 4 or o4. This can totally apply to prompt-to-code tools like Lovable and v0. And these ideas can likely apply to Figma Make and Subframe.

Brian Balfour, writing for the Reforge blog:

Speed isn't just about shipping faster, it's about accelerating your entire learning metabolism. The critical metric isn't feature velocity but rather your speed through the complete Insight → Act → Learn loop. This distinction separates products that compound advantages from those that compound technical debt.

The point being that now with AI, product teams are shipping faster. And those who aren’t might get lapped (to use an F1 phrase).