Chatboxes have become the uber box for all things AI. The criticism of this blank box has been the cold start issue. New users don’t know what to type. Designers shipping these product mostly got around this problem by offering suggested prompts to teach users about the possibilities.

The issue on the other end is that expert users end up creating their own library of prompts to copy and paste into the chatbox for repetitive tasks.

Sharang Sharma writing in UX Collective illustrates how these UIs can be smarter by being predictive of intent:

Contrary, Predictive UX points to an alternate approach. Instead of waiting for users to articulate every step, systems can anticipate intent based on behavior or common patterns as the user types. Apple Reminders suggests likely tasks as you type. Grammarly predicts errors and offers corrections inline. Gmail’s Smart Compose even predicts full phrases, reducing the friction of drafting entirely.

Sharma says that the goal of predictive UX is to “reduce time-to-value and reframe AI as an adaptive partner that anticipates user’s intent as you type.”

Imagine a little widget that appears within the chatbox as you type. Kind of a cool idea.

Thinking about this morning’s link about web forms, if you abstract why it’s so powerful, you get to the point of human-computer interaction: the computer should do what the user intends, not the buttons they push.

Matt Webb reminds us about the DWIM, or Do What I Mean philosophy in computing that was coined by Warren Teitelman in 1966. Webb quotes computer scientist Larry Masinter:

DWIM is an embodiment of the idea that the user is interacting with an agent who attempts to interpret the user’s request from contextual information. Since we want the user to feel that he is conversing with the system, he should not be stopped and forced to correct himself or give additional information in situations where the correction or information is obvious.

Webb goes on to say:

Squint and you can see ChatGPT as a DWIM UI: it never, never, never says “syntax error.”

Now, arguably it should come back and ask for clarifications more often, and in particular DWIM (and AI) interfaces are more successful the more they have access to the user’s context (current situation, history, environment, etc).

But it’s a starting point. The algo is: design for capturing intent and then DWIM; iterate until that works. AI unlocks that.

Forms is one of the fundamental things we make users do in software. Whether it’s the login screen, billing address form, or a mortgage application, forms are the main method for getting data from users and into computer-accessible databases. The human is deciding what piece of information to put into which column in the database. With AI, form filling should be much simpler.

Luke Wroblewski makes the argument:

With Web forms, the burden is on people to adapt to databases. Today’s AI models, however, can flip this requirement. That is, they allow people to provide information in whatever form they like and use AI do the work necessary to put that information into the right structure for a database.

How can it work?

With AgentDB connected to an AI model (via an MCP server), a person can simply say “add this” and provide an image, PDF, audio, video, you name it. The model will use AgentDB’s template to decide what information to extract from this unstructured input and how to format it for the database. In the case where something is missing or incomplete, the model can ask for clarification or use tools (like search) to find possible answers.

Josh Miller, CEO, and Hursh Agrawal, CTO, of The Browser Company:

Today, The Browser Company of New York is entering into an agreement to be acquired by Atlassian in an all-cash transaction. We will operate independently, with Dia as our focus. Our objective is to bring Dia to the masses.

Super interesting acquisition here. There is zero overlap as far as I can tell. Atlassian’s move is out of left-field. Dia’s early users were college students. The Browser Company more recently opened it up to former Arc users. Is this bet for Atlassian—the company that makes tech-company-focused products like Jira and Confluence—around the future of work and collaboration? Is this their first move against Salesforce? 🤔

DOC is a publication from Fabricio Teixeira and Caio Braga that I’ve linked to before. Their latest reflection is on interfaces.

A good user interface is a good conversation.

Interfaces thrive on clarity, responsiveness, and mutual understanding. In a productive dialogue, each party clearly articulates their intentions and receives timely, understandable responses. Just as a good conversationalist anticipates the next question or need, a good interface guides you smoothly through your task. At their core, interfaces translate intent into action. They’re a bridge between what’s in your head and what the product can do.

Reflection is the best word I’ve found to describe these pieces. They’re hype-free, urging us to take a step back, and—at least for me—a reminder about our why.

In the end, interfaces are also a space for self-expression.

The ideal of “no interface” promises ultimate efficiency and direct access—but what do we lose in that pursuit? Perhaps the interface is not just a barrier to be minimized, but a space for human expression. It’s a canvas; a place to imbue a product with personality, visual expression, and a unique form of art.

When we strip that away, or make everything look the same, we lose something important. We trade the unique and the delightful for the purely functional. We sacrifice a vital part of what makes technology human: the thoughtful, and sometimes imperfect, ways we present ourselves to the world.

Hard to believe that the Domino’s Pizza tracker debuted in 2008. The moment was ripe for them—about a year after the debut of the iPhone. Mobile e-commerce was in its early days.

Alex Mayyasi for The Hustle:

…the tracker’s creation was spurred by the insight that online orders were more profitable – and made customers more satisfied – than phone or in-person orders. The company’s push to increase digital sales from 20% to 50% of its business led to new ways to order (via a tweet, for example) and then a new way for customers to track their order.

Mayyasi weaves together a tale of business transparency, UI, and content design, tracing—or tracking?—the tracker’s impact on business since then. “The pizza tracker is essentially a progress bar.” But progress bars do so much for the user experience, most of which is setting proper expectations.

I have always wanted to read 6,200 words about color! Sorry, that’s a lie. But I did skim it and really admired the very pretty illustrations. Dan Hollick is a saint for writing and illustrating this chapter in his living book called Making Software, a reference manual for designers and programmers that make digital products. From his newsletter:

I started writing this chapter just trying to explain what a color space is. But it turns out, you can’t really do that without explaining a lot of other stuff at the same time.

Part of the issue is color is really complicated and full of confusing terms that need a maths degree to understand. Gamuts, color models, perceptual uniformity, gamma etc. I don’t have a maths degree but I do have something better: I’m really stubborn.

And here are the opening sentences of the chapter on color:

Color is an unreasonably complex topic. Just when you think you’ve got it figured out, it reveals a whole new layer of complexity that you didn’t know existed.

This is partly because it doesn’t really exist. Sure, there are different wavelengths of light that our eyes perceive as color, but that doesn’t mean that color is actually a property of that light - it’s a phenomenon of our perception.

Digital color is about trying to map this complex interplay of light and perception into a format that computers can understand and screens can display. And it’s a miracle that any of it works at all.

I’m just waiting for him to put up a Stripe link so I can throw money at him.

Interesting piece from Vaughn Tan about a critical thinking framework that is disguised as a piece about building better AI UIs for critical thinking. Sorry, that sentence is kind of a tongue-twister. Tan calls out—correctly—that LLMs don’t think, or in his words, can’t make meaning:

Meaningmaking is making inherently subjective decisions about what’s valuable: what’s desirable or undesirable, what’s right or wrong. The machines behind the prompt box are remarkable tools, but they’re not meaningmaking entities.

Therefore when users ask LLMs for their opinions on matters, e.g., as in the therapy use case, the AIs won’t come back with actual thinking. IMHO, it’s semantics, but that’s another post.

Anyhow, Tan shares a pen and paper prototype he’s been testing, which breaks down a major decision into guided steps, or put another way, a framework.

This user experience was designed to simulate a multi-stage process of structured elicitation of various aspects of strongly reasoned arguments. This design explicitly addresses both requirements for good tool use. The structured prompts helped students think critically about what they were actually trying to accomplish with their custom major proposals — the meaningmaking work of determining value, worth, and personal fit. Simultaneously, the framework made clear what kinds of thinking work the students needed to do themselves versus what kinds of information gathering and analysis could potentially be supported by tools like LLMs.

This guided or framework-driven approach was something I attempted wtih Griffin AI. Via a series of AI-guided prompts to the user—or a glorified form, honestly—my tool helped users build brand strategies. To be sure, a lot of the “thinking” was done by the model, but the idea that an AI can guide you to critically think about your business or your client’s business was there.

Designer Tey Bannerman writes that when he hears “human in the loop,” he’s reminded of a story about Lieutenant Colonel Stanislav Petrov, a Soviet Union duty watch officer who monitored for incoming missile strikes from the US.

12:15 AM… the unthinkable. Every alarm in the facility started screaming. The screens showed five US ballistic missiles, 28 minutes from impact. Confidence level: 100%. Petrov had minutes to decide whether to trigger a chain reaction that would start nuclear war and could very well end civilisation as we knew it.

He was the “human in the loop” in the most literal, terrifying sense.

Everything told him to follow protocol. His training. His commanders. The computers.

But something felt wrong. His intuition, built from years of intelligence work, whispered that this didn’t match what he knew about US strategic thinking.

Against every protocol, against the screaming certainty of technology, he pressed the button marked “false alarm”.

Twenty-three minutes of gripping fear passed before ground radar confirmed: no missiles. The system had mistaken a rare alignment of sunlight on high-altitude clouds for incoming warheads.

His decision to break the loop prevented nuclear war.

Then Bannerman shares an awesome framework he developed that allows humans in the loop in AI systems “genuine authority, time to think, and understanding the bigger picture well enough to question” the system’s decision. Click on to get the PDF from his site.

Cap Watkins, Head of Product Design at Lattice, was catching up with a former top-performing designer who was afraid other designers were mad at her for getting all the “cool” projects.

What made those projects glamorous and desirable was her and how she approached the work. There’s that old nugget about making your own luck and that is something she excelled at. She had a unique ability to take really hard or nebulous problems (both design and team-related) and morph them into something amazing that got people excited. Instead of getting discouraged, she’d respond to friction with more energy, more enthusiasm. In so many ways, she was a transformative presence on any team and project.

In other words, this designer cared and made the best of all her assignments.

I enjoyed this interview with Notion’s CEO, Ivan Zhao over at the Decoder podcast, with substitute host, Casey Newton. What I didn’t quite get when I first used Notion was the “LEGO” aspect of it. Their vision is to build business software that is highly malleable and configurable to do all sorts of things. Here’s Zhao:

Well, because it didn’t quite exist with software. If you think about the last 15 years of [software-as-a-service], it’s largely people building vertical point solutions. For each buyer, for each point, that solution sort of makes sense. The way we describe it is that it’s like a hard plastic solution for your problem, but once you have 20 different hard plastic solutions, they sort of don’t fit well together. You cannot tinker with them. As an end user, you have to jump between half a dozen of them each day.

That’s not quite right, and we’re also inspired by the early computing pioneers who in the ‘60s and ‘70s thought that computing should be more LEGO-like rather than like hard plastic. That’s what got me started working on Notion a long time ago, when I was reading a computer science paper back in college.

From a user experience POV, Notion is both simple and exceedingly complicated. Taking notes is easy. Building the system for a workflow, not so much.

In the second half, Newton (gently) presses Zhao on the impact of AI on the workforce and how productivity software like Notion could replace headcount.

Newton: Do you think that AI and Notion will get to a point where executives will hire fewer people, because Notion will do it for them? Or are you more focused on just helping people do their existing jobs?

Zhao: We’re actually putting out a campaign about this, in the coming weeks or months. We want to push out a more amplifying, positive message about what Notion can do for you. So, imagine the billboard we’re putting out. It’s you in the center. Then, with a tool like Notion or other AI tools, you can have AI teammates. Imagine that you and I start a company. We’re two co-founders, we sign up for Notion, and all of a sudden, we’re supplemented by other AI teammates, some taking notes for us, some triaging, some doing research while we’re sleeping.

Zhao dodges the “hire fewer people” part of the question and instead, answers with “amplifying” people or making them more productive.

Ben Davies-Romano argues that the AI chat box is our new design interface:

Every interaction with a large language model starts the same way: a blinking cursor in a blank text field. That unassuming box is more than an input — it’s the interface between our human intent and the model’s vast, probabilistic brain.

This is where the translation happens. We pour in the nuance, constraints, and context of our ideas; the model converts them into an output. Whether it’s generating words, an image, a video sequence, or an interactive prototype, every request passes through this narrow bridge.

It’s the highest-stakes, lowest-fidelity design surface I’ve ever worked with: a single field that stands between human creativity and an engine capable of reshaping it into almost any form, albeit with all the necessary guidance and expertise applied.

In other words, don’t just say “Make it better,” but guide the AI instead.

That’s why a vague, lazy prompt, like “make it better”, is the design equivalent of telling a junior designer “make it intuitive” and walking away. You’ll get something generic, safe, and soulless, not because the AI “missed the brief,” but because there was no brief.

Without clear stakes, a defined brand voice, and rich context, the system will fill in the blanks with its default, most average response. And “average” is rarely what design is aiming for.

And he makes a point that designers should be leading the charge on showing others what generative AI can do:

In the age of AI, it shouldn’t be everyone designing, per say. It should be designers using AI as an extension of our craft. Bringing our empathy, our user focus, our discipline of iteration, and our instinct for when to stop generating and start refining. AI is not a replacement for that process; it’s a multiplier when guided by skilled hands.

So, let’s lead. Let’s show that the real power of AI isn’t in what it can generate, but in how we guide it — making it safer, sharper, and more human. Let’s replace the fear and the gimmicks with clarity, empathy, and intentionality.

The blank prompt is our new canvas. And friends, we need to be all over it.

Christopher K. Wong argues that desirability is a key part of design that helps decide which features users really want:

To give a basic definition, desirability is a strategic part of UX that revolves around a single user question: Have you defined (and solved) the right problem for users?

In other words, before drawing a single box or arrow, have you done your research and discovery to know you’re solving a pain point?

The way the post is written makes it hard to get at a succinct definition, but here’s my take. Desirability is about ensuring a product or feature is truly wanted, needed, and chosen by users—not just visual appeal—making it a core pillar for impactful design decisions and prioritization. And designers should own this.

Coincidentally, I was considering adding a service designer to my headcount plan when this article came across my feeds. Perfect timing. It’s hard to imagine that service design as a discipline is so young—only since 2012 according to the author.

Joe Foley, writing in Creative Bloq:

As a discipline, service design is still relatively new. A course at the Royal College of Art in London (RCA) only began in 2012 and many people haven’t even heard of the term. But that’s starting to change.

He interviews designer Clive Grinyer, whose new book on service design has just come out. He was co-founder of the design consultancy Tangerine, Director of Design and Innovation for the UK Design Council, and Head of Service Design at the Royal College of Art.

Griner:

Great service design is often invisible as it solves problems and removes barriers, which isn’t necessarily noticed as much as a shiny new product. The example of GDS (Government Digital Service) redesigning every government department from a service design perspective and removing many frustrating and laborious aspects of public life from taxing a car to getting a passport, is one of the best.

The key difference between service design and UX is that it’s end product is not something on a screen:

But service design is not just the experience we have through the glass of a screen or a device: it’s designed from the starting point of the broader objective and may include many other channels and touchpoints. I think it was Colin Burns who said a product is just a portal to a service.

In other words, if you open the aperture of what user experience means, and take on the challenge of designing real-world processes, flows, and interaction—that is service design.

Luke Wroblewski, writing in his blog:

Across several of our companies, software development teams are now “out ahead” of design. To be more specific, collaborating with AI agents (like Augment Code) allows software developers to move from concept to working code 10x faster. This means new features become code at a fast and furious pace.

When software is coded this way, however, it (currently at least) lacks UX refinement and thoughtful integration into the structure and purpose of a product. This is the work that designers used to do upfront but now need to “clean up” afterward. It’s like the development process got flipped around. Designers used to draw up features with mockups and prototypes, then engineers would have to clean them up to ship them. Now engineers can code features so fast that designers are ones going back and cleaning them up.

This is what I’ve been secretly afraid of. That we would go back to the times when designers were called in to do cleanup. Wroblewski says:

Instead of waiting for months, you can start playing with working features and ideas within hours. This allows everyone, whether designer or engineer, an opportunity to learn what works and what doesn’t. At its core rapid iteration improves software and the build, use/test, learn, repeat loop just flipped, it didn’t go away.

Yeah, or the feature will get shipped this way and be stuck this way because startups move fast and move on.

My take is that as designers, we need to meet the moment and figure out how to build design systems and best practices into the agentic workflows our developer counterparts are using.

Sonos announced yesterday that interim CEO Tom Conrad was made permanent. From their press release:

Sonos has achieved notable progress under Mr. Conrad’s leadership as Interim CEO. This includes setting a new standard for the quality of Sonos’ software and product experience, clearing the path for a robust new product pipeline, and launching innovative new software enhancements to flagship products Sonos Ace and Arc Ultra.

Conrad surely navigated this landmine well after the disastrous app redesign that wiped almost $500 million from the company’s market value and cost CEO Patrick Spence his job. My sincere hope is that Conrad continues to rebuild Sonos’s reputation by continuing to improve their products.

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