When Figma acquired Weavy last month, I wrote a little bit about node-based UIs and ComfyUI. Looks like Adobe has been exploring this user interface paradigm as well.

Daniel John writes in Creative Bloq:

Project Graph is capable of turning complex workflows into user-friendly UIs (or ‘capsules’), and can access tools from across the Creative Cloud suite, including Photoshop, Illustrator and Premiere Pro – making it a potentially game-changing tool for creative pros.

But it isn’t just Adobe’s own tools that Project Graph is able to tap into. It also has access to the multitude of third party AI models Adobe recently announced partnerships with, including those made by Google, OpenAI and many more.

These tools can be used to build a node-based workflow, which can then be packaged into a streamlined tool with a deceptively simple interface.

And from Adobe’s blog post about Project Graph:

Project Graph is a new creative system that gives artists and designers real control and customization over their workflows at scale. It blends the best AI models with the capabilities of Adobe’s creative tools, such as Photoshop, inside a visual, node-based editor so you can design, explore, and refine ideas in a way that feels tactile and expressive, while still supporting the precision and reliability creative pros expect.

I’ve been playing around with ComfyUI a lot recently (more about this in a future post), so I’m very excited to see how this kind of UI can fit into Adobe’s products.

Critiques are the lifeblood of design. Anyone who went to design school has participated in and has been the focus of a crit. It’s “the intentional application of adversarial thought to something that isn’t finished yet,” as Fabricio Teixeira and Caio Braga, the editors of DOC put it.

A lot of solo designers—whether they’re a design team of one or if they’re a freelancer—don’t have the luxury of critiques. In my view, they’re handicapped. There are workarounds, of course. Such as critiques with cross-functional peers, but it’s not the same. I had one designer on my team—who used to be a design team of one in her previous company—come up to me and say she’s learned more in a month than a year at her former job.

Further down, Teixeira and Braga say:

In the age of AI, the human critique session becomes even more important. LLMs can generate ideas in 5 seconds, but stress-testing them with contextual knowledge, taste, and vision, is something that you should be better at. As AI accelerates the production of “technically correct” and “aesthetically optimized” work, relying on just AI creates the risks of mediocrity. AI is trained to be predictable; crits are all about friction: political, organizational, or strategic.

David Kelley is an icon in design. A restless tinkerer turned educator, he co-founded the reowned industrial design firm IDEO, helped shape human-centered design at Stanford’s d.school, and collaborated with Apple on seminal projects like the early mouse.

Here’s his take on creativity in a brief segment for PBS News Hour:

And as I started teaching, I realized that my purpose in life was figuring out how to help people gain confidence in their creative ability. Many people assume they’re not creative. Time and time again, they say, a teacher told me I wasn’t creative or that’s not a very good drawing of a horse or whatever it is. We don’t have to teach creativity. Once we remove the blocks, they can then feel themselves as being a creative person. Witnessing somebody realizing they’re creative for the first time is just a complete joy. You can just see them come out of the shop and beaming that I can weld. Like, what’s next?

I’ve been playing with my systems in the past month—switching browsers, notetaking apps, and RSS feed readers. If I’m being honest, it’s causing me anxiety because I feel unmoored. My systems aren’t familiar enough to let me be efficient.

One thing that has stayed relatively stable is my LLM app—well, two of them. ChatGPT for everyday and Claude for coding and writing.

Christina Wodtke, writing on her blog:

The most useful model might not win.

What wins is the model that people don’t want to leave. The one that feels like home. The one where switching would mean losing something—not just access to features, but fluency, comfort, all those intangible things that make a tool feel like yours.

Amazon figured this out with Prime. Apple figured it out with the ecosystem. Salesforce figured it out by making itself so embedded in enterprise workflows that ripping it out would require an act of God.

AI companies are still acting like this is a pure technology competition. It’s not. It’s a competition to become essential—and staying power comes from experience, not raw capability.

Your moat isn’t your model. Your moat is whether users feel at home.

Like it or not, as a designer, you have to be able to present your work proficiently. I remember I had always hated presenting. I was nervous and would get tongue-tied. Eventually, the more I did it, the more I got used to it. …But that’s public speaking, just half of what a presentation is. The other half is how to structure and tell your story. What story? The story of your design.

There’s a lot to be learned from master storytellers like Pixar. Laia Tremosa writing for the Interaction Design Foundation walks us through some storytelling techniques that we can pick up from Pixar.

Most professionals stay unseen not because their work lacks value, but because their message lacks resonance. They talk in facts when their audience needs meaning.

Storytelling is how you change that. It turns explanation into connection, and connection into influence. When you frame your ideas through story, people don’t just understand your work, they believe in it.

And out of the five that she mentions, the second one is my favorite, “Know Where You’re Going: Start with the End.”

Pixar designs for the final feeling. You should too. As a presenter, your version of that is a clear takeaway, a shift in perspective, or a call to action. You’re guiding your audience to a moment of clarity.

Maybe it’s a relief that a problem can be solved. Maybe it’s excitement about a new idea. Maybe it’s conviction that your proposal matters. Whatever the feeling, it’s your north star.

Don’t just prepare what to say; decide where you want to land. Start with the end, and build every word, visual, and story toward that moment of understanding and meaning.

There are dark patterns in UX, and there are also dark patterns specific to games. Dark Pattern Games is a website that catalogs such patterns and the offending mobile games.

The site’s definition of a dark pattern is:

A gaming dark pattern is something that is deliberately added to a game to cause an unwanted negative experience for the player with a positive outcome for the game developer.

The “Social Pyramid Scheme” is one of my most loathed:

Some games will give you a bonus when you invite your friends to play and link to them to your account. This bonus may be a one-time benefit, or it may be an ongoing benefit that improves the gaming experience for each friend that you add. This gives players a strong incentive to convince their friends to play. Those friends then have to sign up more friends and so on, leading to a pyramid scheme and viral growth for the game.

Geoffrey Litt is a design engineer at Notion. He is one of the authors at Ink & Switch of “Malleable software,” which I linked to back in July. I think it’s pretty fitting that he popped up at Notion, with the CEO Ivan Zhao likening the app to LEGO bricks.

In a recent interview with Rid on Dive Club, Litt explains the concept further:

So, when I say malleable software, I do not mean only disposable software. The main thing I think about with malleable software is actually much closer to … designing my interior space in my house. Let’s say when I come home I don’t want everything to be rearranged, right? I want it to be the way it was. And if I want to move the furniture or put things on the wall, I want to have the right to do that. And so I think of it much more as kind of crafting an environment over time that’s actually more stable and predictable, not only for myself, but also for my team. Having shared environments that we all work in together that are predictable is also really important, right? Ironically, actually, in some ways, I think sometimes malleable software results in more stable software because I have more control.

For building with AI, Litt advocates “coding like a surgeon”: stay in the loop and use agents for prep and grunt work.

How do we think of AI as a way to leverage our time better? [So we can] stay connected to the work and [do] it ourselves by having prep work done for us. Having tools in the moment helping us do it so that we can really focus on the stuff we love to do, and do less of everything else. And that’s how I’m trying to use coding agents for my core work that I care about today. Which is when I show up, sit down at my desk in the morning and work on a feature, I want to be prepped with a brief on all the code I’m going to be touching today, how it works, what the traps are. Maybe I’ll see a draft that the AI did for me overnight, sketching out how the coding could go. Maybe some ideas for me.

In other words, like an assistant who works overnight. And yeah, this could apply to design as well.

Design Thinking has gotten a bad rap in recent years. It was supposed to change everything in the corporate world but ended up changing very little. While Design Thinking may not be the darling anymore, designers still need time to think, which is, for the sake of argument, time away from Figma and pushing pixels.

Chris Becker argues in UX Collective:

However, the canary in the coalmine is that Designers are not being used for their “thinking” but rather their “repetition”. Much of the consternation we feel in the UX industry is catapulted on us from this point of friction.

He says that agile software development and time for designers to think aren’t incompatible:

But allowing Designers to implement their thinking into the process is about trust. When good software teams collaborate effectively, there are high levels of trust and autonomy (a key requirement of agile teams). Designers must earn that trust, of course, and when we demonstrate that we have “done the thinking,” it builds confidence and garners more thinking time. Thinking begets thinking. So, Designers, let’s continue to work to maximise our “thinking” faculties.

Game design is fascinating to me. As designers, “gamification” was all the rage a few years back, inspired by apps like Duolingo that made it fun to progress in a product. Raph Koster outlines a twelve-step, systems-first framework for game design, complete with illustrations. Notice how he’ll use UX terms like “affordance” because ultimately, game design is UX.

In step five, “Feedback,” Koster provides an example:

[The player] can’t learn and get better unless [they] get a whole host of information.

• You need to know what actions – we usually call them verbs — are even available to you. There’s a gas pedal.
• You need to be able to tell you used a verb. You hear the engine growl as you press the pedal.
• You need to see that the use of the verb affected the state of the problem, and how it changed. The spedometer moved!
• You need to be told if the state of the problem is better for your goal, or worse. Did you mean to go this fast?

Sound familiar? It’s Jakob Nielsen’s “Visibility of System Status.”

I think the headline is a hard stance, but I appreciate the sentiment. All the best designers and creatives—including developers—I’ve ever worked with do things on the side. Or in Rohit Prakash’s words, they tinker. They’re always making something, learning along the way.

Prakash, writing in his blog:

Acquiring good taste comes through using various things, discarding the ones you don’t like and keeping the ones you do. if you never try various things, you will not acquire good taste.

It’s important for designers to see other designs and use other products—if you’re a software designer. It’s equally important to look up from Dribbble, Behance, Instagram, and even this blog and go experience something unrelated to design. Art, concerts, cooking. All of it gets synthesized through your POV and becomes your taste.

Apologies for sharing back-to-back articles from NN/g, but this is a good comprehensive index of all the AI-related guides the firm has published. Start here if you’re just getting into it.

Highlights from my POV:

• Your AI UX Intern: Meet Ari. AI tools in UX act like junior interns whose output serves as a starting draft needing review, specific instructions, and added context. Their work should be checked and not used for final products or decisions without supervision.
• The Future-Proof Designer. AI speeds up product development and automates design tasks, but creates risks like design marginalization and information overload. Designers must focus on strategic thinking, outcomes, and critical judgment to ensure decisions benefit users and business value.
• Design Taste vs. Technical Skills in the Era of AI. Generative AI has equalized access to design output, but quality depends on creative discernment and taste, which remain essential for impactful results.

I’ve been a big fan of node-based UIs since I first experimented with Shake in the early 2000s. It’s kind of weird to wrap your head around, especially if you’re used to layers in Photoshop or Figma. The easiest way to think about nodes is to rotate the layer stack 90-degrees. Each node has inputs on the left, a distinct process that it does to the input, and outputs stuff on the right. You connect up multiple nodes to process assets to form your final composition. Popular apps with node-based workflows today include Unreal Engine (Blueprints), DaVinci Resolve (Fusion and Color), and n8n.

ComfyUI is another open source tool that uses the same node graph architecture. Made in 2023 to add some UI to the visual generative AI models like Stable Diffusion appearing around that time, it’s become popular among artists to wield the plethora of image and video gen AI models.

Fast-forward to last week, when Figma announced they had acquired Weavy, a much friendlier and cloud-based version of ComfyUI.

Weavy brings the world’s leading AI models together with professional editing tools on a single, browser-based canvas. With Weavy, you can choose the model you want for a task (e.g. Seedance, Sora, and Veo for cinematic video; Flux and Ideogram for realism; and Nano-Banana or Seedream for precision) and compose powerful primitives using generative AI outputs and hands-on edits (e.g. adjusting lighting, masking an object, color grading a shot). The end result is an inspiring environment for creative exploration and a flexible media pipeline where every output feeds the next.

This node-based approach brings a new level of craft and control to AI generation. Outputs can be branched, remixed, and refined, combining creative exploration with precision and craft. The Weavy team has inspired us with the balance they’ve struck between simplicity, approachability, and power. They’ve also created a tool that’s just a joy to use.

I must admit I had not heard about Weavy before the announcement. I had high hopes for Visual Electric, but it never quite lived up to its ambitions. I proceeded to watch all the official tutorial videos on YouTube and love it. Seems so much easier to use than ComfyUI. Let’s see what Figma does with the product.

In thinking about the three current AI-native web browsers, Fanny on Medium sees what lessons product designers can take from their different approaches.

On Perplexity Comet:

Design Insight: Comet succeeds by making AI feel like a natural extension of browsing, not an interruption. The sidecar model is brilliant because it respects the user’s primary task (reading, researching, shopping) while offering help exactly when context is fresh. But there’s a trade-off — Comet’s background assistant, which can handle multiple tasks simultaneously while you work, requires extensive permissions and introduces real security concerns.

On ChatGPT Atlas:

Design Insight: Atlas is making a larger philosophical statement — that the future of computing isn’t about better search, it’s about conversation as an interface. The key product decision here is making ChatGPT’s memory and context awareness central. Atlas remembers what sites you’ve visited, what you were working on, and uses that history to personalize responses. Ask “What was that doc I had my presentation plan in?” and it finds it.

On The Browser Company Dia:

Design Insight: Dia is asking the most interesting question — what happens when AI isn’t a sidebar or a search replacement, but a fundamental rethinking of input methods? The insertion cursor, the mouse, the address bar — these are the primitives of computing. Dia is making them intelligent.

She concludes that they “can’t all be right. But they’re probably all pointing at pieces of what comes next.”

I do think it’s a combo and Atlas is likely headed in the right direction. For AI to be truly assistive, it has to have relevant context. Since a lot of our lives are increasingly on the internet via web apps—and nearly everything is a web app these days—ChatGPT’s profile of you will have the most context, including your chats with the chatbot.

I began using Perplexity because I appreciated its accuracy compared with ChatGPT; this was pre-web search. But even with web search built into ChatGPT 5, I still find Perplexity’s (and therefore Comet’s) approach to be more trustworthy.

My conclusion stands though: I’m still waiting on the Arc-Dia-Comet browser smoothie.

Speaking of trusting AI, in a recent episode of Design Observer’s Design As, Lee Moreau speaks with four industry leaders about trust and doubt in the age of AI.

We’ve linked to a story about Waymo before, so here’s Ryan Powell, head of UX at Waymo:

Safety is at the heart of everything that we do. We’ve been at this for a long time, over a decade, and we’ve taken a very cautious approach to how we scale up our technology. As designers, what we have really focused on is that idea that more people will use us as a serious transportation option if they trust us. We peel that back a little bit. Okay, well, How do we design for trust? What does it actually mean?

Ellie Kemery, principal research lead, advancing responsible AI at SAP, on maintaining critical thinking and transparency in AI-driven products:

We need to think about ethics as a part of this because the unintended consequences, especially at the scale that we operate, are just too big, right?

…

So we focus a lot of our energy on value, delivering the right value, but we also focus a lot of our energy on making sure that people are aware of how the technology came to that output,…making sure that people are in control of what’s happening at all times, because at the end of the day, they need to be the ones making the call.

…

Everybody’s aware that without trust, there is no adoption. But there is something that people aren’t talking about as much, which is that people should also not blindly trust a system, right? And there’s a huge risk there because, humans we tend to, you know, we’ll try something a couple of times and if it works it works. And then we lose that critical thinking. We stop checking those things and we simply aren’t in a space where we can do that yet. And so making sure that we’re focusing on the calibration of trust, like what is the right amount of trust that people should have to be able to benefit from the technology while at the same time making sure that they’re aware of the limitations.

In this era of AI, we’ve been taught that LLMs are probabilistic, not deterministic, and that they will sometimes hallucinate. There’s a saying in AI circles that humans are right about 80% of the time, and so are AIs. Except when less than 100% accuracy is unacceptable. Accountants need to be 100% accurate, lest they lose track of money for their clients or businesses.

And that’s the problem Intuit had to solve to roll out their AI agent. Sean Michael Kerner, writing in VentureBeat:

Even when its accounting agent improved transaction categorization accuracy by 20 percentage points on average, they still received complaints about errors.

“The use cases that we’re trying to solve for customers include tax and finance; if you make a mistake in this world, you lose trust with customers in buckets and we only get it back in spoonfuls,” Joe Preston, Intuit’s VP of product and design, told VentureBeat.

So they built an agent that queries data from a multitude of sources and returns those exact results. But do users trust those results? It comes down to a design decision on being transparent:

Intuit has made explainability a core user experience across its AI agents. This goes beyond simply providing correct answers: It means showing users the reasoning behind automated decisions.

When Intuit’s accounting agent categorizes a transaction, it doesn’t just display the result; it shows the reasoning. This isn’t marketing copy about explainable AI, it’s actual UI displaying data points and logic.

“It’s about closing that trust loop and making sure customers understand the why,” Alastair Simpson, Intuit’s VP of design, told VentureBeat.

Circling back to Monday’s item on how caring is good design, Felix Haas has a subtly different take: build kindness into your products.

Kindness in design isn’t about adding smiley faces or writing cheerful copy. It’s deeper than tone. It’s about intent embedded in every interaction.

…

Kindness shows up in the patience of an empty state that doesn’t rush you. In the warmth of micro-interactions that acknowledge your actions without demanding attention. In error messages that guide rather than scold. In defaults that assume good intent rather than user incompetence.

These moments seem subtle, even trivial, in isolation. But they accumulate. They shape how we feel about a product over weeks and months. They turn interfaces into relationships. They build trust.

As a follow-up to our previous item on Claude Code, here’s an article by Nick Babich who gives us three ways product designers can use Claude to code.

Remember that Anthropic’s Claude has been the leading LLM for coding for a while now.

Building on Matthew Ström-Awn’s argument that true quality emerges from decentralized, ground-level ownership, Sean Goedecke writes an essay exploring how software companies navigate the tension between formalized control and the informal, often invisible work that actually drives product excellence.

But first, what does legibility even mean?

What does legibility mean to a tech company, in practice? It means:

• The head of a department knows, to the engineer, all the projects the department is currently working on
• That head also knows (or can request) a comprehensive list of all the projects the department has shipped in the last quarter
• That head has the ability to plan work at least one quarter ahead (ideally longer)
• That head can, in an emergency, direct the entire resources of the department at immediate work

Note that “shipping high quality software” or “making customers happy” or even “making money” is not on this list. Those are all things tech companies want to do, but they’re not legibility.

Goedecke argues that while leaders prize formal processes and legibility to facilitate predictability and coordination, these systems often overlook the messier, less measurable activities that drive true product quality and user satisfaction.

All organizations - tech companies, social clubs, governments - have both a legible and an illegible side. The legible side is important, past a certain size. It lets the organization do things that would otherwise be impossible: long-term planning, coordination with other very large organizations, and so on. But the illegible side is just as important. It allows for high-efficiency work, offers a release valve for processes that don’t fit the current circumstances, and fills the natural human desire for gossip and soft consensus.

Designer Ben Holliday writes a wonderful deep dive into how caring is good design. In it, he references the conversation that Jony Ive had with Patrick Collison a few months ago. (It’s worth watching in its entirety if you haven’t already.)

Watching the interview back, I was struck by how he spoke about applying care to design, describing how:

“…everyone has the ability to sense the care in designed things because we can all recognise carelessness.”

Talking about the history of industrial design at Apple, Ive speaks about the care that went into the design of every product. That included the care that went into packaging – specifically things that might seem as inconsequential as how a cable was wrapped and then unpackaged. In reality, the type of small interactions that millions of people experienced when unboxing the latest iPhone. These are details that people wouldn’t see as such, but Ive and team believed that they would sense care when they had been carefully considered and designed.

This approach has always been a part of Jony Ive’s design philosophy, or the principles applied by his creative teams at Apple. I looked back and found an earlier 2015 interview and notes I’d made where he says how he believes that the majority of our manufactured environment is characterised by carelessness. But then, how, at Apple, they wanted people to sense care in their products.

The attention to detail and the focus and attention we can all bring to design is care. It’s important.

Holliday’s career has been focused in government, public sector, and non-profit environments. In other words, he thinks a lot about how design can impact people’s lives at massive scale.

In the past few months, I’ve been drawn to the word ‘careless’ when thinking about the challenges faced by our public services and society. This is especially the case with the framing around the impact of technology in our lives, and increasingly the big bets being made around AI to drive efficiency and productivity.

The word careless can be defined as the failure to give sufficient attention to avoiding harm or errors. Put simply, carelessness can be described as ‘negligence’.

Later, he cites Facebook/Meta’s carelessness when they “used data to target young people when at their most vulnerable,” specifically, body confidence.

Writing for UX Collective, Filipe Nzongo argues that designers should embrace behavior as a fundamental design material—not just to drive metrics or addiction, but to intentionally create products that empower people and foster meaningful, lasting change in their lives.

Behavior should be treated as a design material, just as technology once became our material. If we use behavior thoughtfully, we can create better products. More than that, I believe there is a broader and more meaningful opportunity before us: to design for behavior. Not to make people addicted to products, but to help them grow as human beings, better parents, citizens, students, and professionals. Because if behavior is our medium, then design is our tool for empowerment.

A former colleague of mine, designer Evan Sornstein wrote a wonderful piece on LinkedIn applying Buddhist principles to design.

Buddhism begins with the recognition that life is marked by impermanence, suffering, and non-self. These aren’t abstract doctrines — they are observations about how the world actually works. Over centuries, these ideas contributed to Japanese aesthetics: wabi-sabi (imperfection), ma (meaningful emptiness), yo no bi (beauty in usefulness), the humility of the shokunin, and the care of omotenashi. What emerges is not a set of rules, but an extraordinary perspective: beauty is inseparable from impermanence; usefulness is inseparable from dignity; care is inseparable from design. In an age when our digital products too often prioritize stickiness and metrics over humanity, these ideas offer a different path. They remind us that design is not about control or cleverness — it’s about connection, trust, and care.

The following eight principles aren’t new “methods” or “laws,” but reflections of this lineage, reframed for product design — though they apply to nearly any creative practice. They are invitations to design with the same attention, humility, and compassion that Buddhism and Japanese aesthetics have carried for centuries.

There’s a famous quote that Henry Ford allegedly said:

If I had asked people what they wanted, they would have said faster horses.

Anton Sten argues that a lot of people use this quote to justify not doing any user (or market) research:

This quote gets thrown around constantly—usually by someone who wants to justify ignoring user research entirely. The logic goes: users don’t know what they want, so why bother asking them?

I think he’s right. The question to ask users isn’t “What should we build?” but “What are your biggest pain points?”

Good research uncovers problems. It reveals pain points. It helps you understand what people are actually struggling with in their daily lives. What they’re working around. What they’ve given up on entirely.

Users aren’t supposed to design your product. That’s your job. But they’re the only ones who can tell you what’s actually broken in their world.

When you focus on understanding problems instead of collecting feature requests, you stop getting “faster horses” and start hearing real needs.