I’d posit that a reason why people love CarPlay so much is because the media, communication, and navigation experiences have traditionally been pretty poor. CarPlay supplants those, and it does so with aplomb because people use those same media, communication, and navigation features that are personalized to them with their phones when they’re not in their cars.

No one is walking around with a speedometer and a tachometer on their iPhone that need to have a familiar look and feel, rendered exclusively in San Francisco. As long as automakers supply the existing level of CarPlay support, which isn’t a given, then customers like us would be content with the status quo, or even a slight improvement.

In my humble opinion, Next-Gen CarPlay is dead on arrival. Too late, too complicated, and it doesn’t solve the needs of automakers or customers. Instead of letting the vehicle’s interface peak through, Apple should consider letting CarPlay peak through for the non-critical systems people prefer to use with CarPlay.

Design a CarPlay that can output multiple display streams (which Apple already over-designed) and display that in the cluster. Integrate with the existing controls for managing the interfaces in the vehicle. When the phone isn’t there, the vehicle will still be the same vehicle. When the phone is there, it’s got Apple Maps right in the cluster how you like it without changing the gauges, or the climate controls, or where the seat massage button is.

The everyday irritations people have are mundane, practical, and are not related to how Apple-like their car displays can look.

LinkedIn, Reddit, and Blind abound with enraged job applicants and employees sharing tales of how difficult it is to book paid leave, how Kafkaesque it is to file an expense, how nerve-racking it is to close out a project. "I simply hate Workday. Fuck them and those who insist on using it for recruitment," one Reddit user wrote. "Everything is non-intuitive, so even the simplest tasks leave me scratching my head," wrote another. "Keeping notes on index cards would be more effective." Every HR professional and hiring manager I spoke with — whose lives are supposedly made easier by Workday — described Workday with a sense of cosmic exasperation.

If candidates hate Workday, if employees hate Workday, if HR people and managers processing and assessing those candidates and employees through Workday hate Workday — if Workday is the most annoying part of so many workers' workdays — how is Workday everywhere? How did a software provider so widely loathed become a mainstay of the modern workplace?

This is a saying in systems thinking: The purpose of a system is what it does (POSIWID), not what it fails to do. And the reality is that what Workday — and its many despised competitors — does for organizations is far more important than the anguish it causes everyone else.

In 1988, PeopleSoft, backed by IBM, built the first fully fledged Human Resources Information System. In 2004, Oracle acquired PeopleSoft for $10.3 billion. One of its founders, David Duffield, then started a new company that upgraded PeopleSoft's model to near limitless cloud-based storage — giving birth to Workday, the intractable nepo baby of HR software.

Workday is indifferent to our suffering in a job hunt, because we aren't Workday's clients, companies are. And these companies — from AT&T to Bank of America to Teladoc — have little incentive to care about your application experience, because if you didn't get the job, you're not their responsibility. For a company hiring and onboarding on a global scale, it is simply easier to screen fewer candidates if the result is still a single hire.

A search on a job board can return hundreds of listings for in-house Workday consultants: IT and engineering professionals hired to fix the software promising to fix processes.

For recruiters, Workday also lacks basic user-interface flexibility. When you promise ease-of-use and simplicity, you must deliver on the most basic user interactions. And yet: Sometimes searching for a candidate, or locating a candidate's status feels impossible. This happens outside of recruiting, too, where locating or attaching a boss's email to approve an expense sheet is complicated by the process, not streamlined. Bureaucratic hell is always about one person's ease coming at the cost of someone else's frustration, time wasted, and busy work. Workday makes no exceptions.

Workday touts its ability to track employee performance by collecting data and marking results, but it is employees who must spend time inputting this data. A creative director at a Fortune 500 company told me how in less than two years his company went "from annual reviews to twice-annual reviews to quarterly reviews to quarterly reviews plus separate twice-annual reviews." At each interval higher-ups pressed HR for more data, because they wanted what they'd paid for with Workday: more work product. With a press of a button, HR could provide that, but the entire company suffered thousands more hours of busy work. Automation made it too easy to do too much. (Workday's "customers choose the frequency at which they conduct reviews, not Workday," said the spokesperson.)

At the scale of a large company, this is simply too much work to expect a few people to do and far too user-specific to expect automation to handle well. It's why Workday can be the worst while still allowing that Paychex is the worst, Paycom is the worst, Paycor is the worst, and Dayforce is the worst. "HR software sucking" is a big tent.

Workday finds itself between enshittification steps two and three. The platform once made things faster, simpler for workers. But today it abuses workers by cutting corners on job-application and reimbursement procedures. In the process, it provides the value of a one-stop HR shop to its paying customers. It seems it's only a matter of time before Workday and its competitors try to split the difference and cut those same corners with the accounts that pay their bills.

Workday reveals what's important to the people who run Fortune 500 companies: easily and conveniently distributing busy work across large workforces. This is done with the arbitrary and perfunctory performance of work tasks (like excessive reviews) and with the throttling of momentum by making finance and HR tasks difficult. If your expenses and reimbursements are difficult to file, that's OK, because the people above you don't actually care if you get reimbursed. If it takes applicants 128% longer to apply, the people who implemented Workday don't really care. Throttling applicants is perhaps not intentional, but it's good for the company.

The most powerful thing a product can do is give its user a premise.1 A premise is the foundational belief that shapes a user’s behavior. A premise can normalize actions that people otherwise might not take, held back by some existing norm

AirBnb. The premise: It’s ok to stay in strangers’ homes.

the idea of staying in strangers’ homes for short stays was doubted even by the founders. Crashing in someone’s spare room wasn’t unheard of, but it might be seen as weird, taboo, or even dangerous.

Bumble. The premise: It’s ok for women to ask men out.

The best way to follow through on a premise is to make it the core feature of the app. Bumble did, requiring that women make the first move on the app. A woman would be presented with a list of her matches and would have to make the first "move" before men could reply. This of course became a powerful differentiating feature and marketing hook.

Substack. The premise: It’s ok to charge for your writing.

Substack's premise aimed to normalize the hardest part of internet writing: getting paid. They aimed to show that independent authors could succeed at making a living (and subscription models aligned with this ethos). In doing so, Substack also made the less-hard parts of internet writing even easier. You could start a newsletter and keep it free until you felt confident about going paid. This not only normalized the end goal but also lowered the barrier to getting started.

A premise is valuable not only for “products,” but also for experiences.As I recently shouted, people still underestimate the power of giving a social event a premise. Hackathons, housewarmings, happy hours and the like are hangouts with a narrative. They have a good premise — a specific context that makes it more comfortable to do something that can be hard: socialize. (Side note: some of the best tv series and films are built on great premises.)

Premises work best on end consumers, prosumers, small business freelancers, and the like. Many two-sided marketplaces serving two of these stakeholder groups tend to have a good premise. For example, Kickstarter's premise for the creator might be: It’s ok to ask for money before you've built a product.

in 2023, I switched gears to emphasize intimacy. Instead of statistical analysis and summative interviews, I sat next to individuals for hours, as they used one-off prototypes which I’d made just for them. And I got more insight in the first few weeks of this than I had in all of 2022

I’d been building systems and running big experiments, and I could tell you plenty about forgetting curves and usage patterns—but very little about how those things connected to anything anyone cared about.

I could see, in great detail, the texture of the interaction between my designs and the broader learning context—my real purpose, not some proxy.

Single-user experiments like this emphasize problem-finding and discovery, not precise evaluation.

a good heuristic for evaluating my work seems to be: try designs 1-on-1 until they seem to be working well, and only then run more quantitative experiments to understand how well the effect generalizes.

My aim is to invent augmented reading environments that apply to any kind of informational text—spanning subjects, formats, and audiences. The temptation, then, is to consider every design element in the most systematic, general form. But this again confuses aims with methods. So many of my best insights have come from hoarding and fermenting vivid observations about the particular—a specific design, in a specific situation. That one student’s frustration with that one specific exercise.

It’s often hard to find “misfits” when I’m thinking about general forms. My connection to the problem becomes too diffuse. The object of my attention becomes the system itself, rather than its interactions with a specific context of use. This leads to a common failure mode among system designers: getting lost in towers of purity and abstraction, more and more disconnected from the system’s ostensible purpose in the world.

I experience an enormous difference between “trying to design an augmented reading environment” and “trying to design an augmented version of this specific linear algebra book”. When I think about the former, I mostly focus on primitives, abstractions, and processes. When I think about the latter, I focus on the needs of specific ideas, on specific pages. And then, once it’s in use, I think about specific problems, that specific students had, in specific places. These are the “misfits” I need to remove as a designer.

Of course, I do want my designs to generalize. That’s not just a practical consideration. It’s also spiritual: when I design a system well, it feels like I’ve limned hidden seams of reality; I’ve touched a kind of personal God. On most days, I actually care about this more than my designs’ utilitarian impact. The systems I want to build really do require abstraction and generalization. Transformative systems really do often depend on powerful new primitives. But more and more, my experience has been that the best creative fuel for these systematic solutions often comes from a process which focuses on particulars, at least for long periods at a time.

Also? The particular is often a lot more emotionally engaging, day-to-day. That makes the work easier and more fun.

Throughout my career, I’ve struggled with a paradox in the feeling of my work. When I’ve found my work quite gratifying in the moment, day-to-day, I’ve found it hollow and unsatisfying retrospectively, over the long term. For example, when I was working at Apple, there was so much energy; I was surrounded by brilliant people; I felt very competent, it was clear what to do next; it was easy to see my progress each day. That all felt great. But then, looking back on my work at the end of each year, I felt deeply dissatisfied: I wasn’t making a personal creative contribution. If someone else had done the projects I’d done, the results would have been different, but not in a way that mattered. The work wasn’t reflective of ideas or values that mattered to me. I felt numbed, creatively and intellectually.

Progress often doesn’t look like progressIt often feels like I’m not making any progress at all in my work. I’ll feel awfully frustrated. And then, suddenly, a tremendous insight will drive months of work. This last happened in the fall. Looking back at those journals now, I’m amused to read page after page of me getting so close to that central insight in the weeks leading up to it. I approach it again and again from different directions, getting nearer and nearer, but still one leap away—so it looks to me, at the time, like I’ve got nothing. Then, finally, when I had the idea, it felt like a bolt from the blue.

This paper maps concepts from AI alignment onto a basic, three step interaction cycle, yielding a corresponding set of alignment objectives: 1) specification alignment: ensuring the user can efficiently and reliably communicate objectives to the AI, 2) process alignment: providing the ability to verify and optionally control the AI's execution process, and 3) evaluation support: ensuring the user can verify and understand the AI's output.

the notion of a Process Gulf, which highlights how differences between human and AI processes can lead to challenges in AI control.

what I cheered as an analyst was Altman’s clear articulation of the company’s priorities: lower price first, speed later. You can certainly debate whether that is the right set of priorities (I think it is, because the biggest need now is for increased experimentation, not optimization), but what I appreciated was the clarity.

The fact that Microsoft is benefiting from OpenAI is obvious; what this makes clear is that OpenAI uniquely benefits from Microsoft as well, in a way they would not from another cloud provider: because Microsoft is also a product company investing in the infrastructure to run OpenAI’s models for said products, it can afford to optimize and invest ahead of usage in a way that OpenAI alone, even with the support of another cloud provider, could not. In this case that is paying off in developers needing to pay less, or, ideally, have more latitude to discover use cases that result in them paying far more because usage is exploding.

You can, in effect, program a GPT, with language, just by talking to it. It’s easy to customize the behavior so that it fits what you want. This makes building them very accessible, and it gives agency to everyone.

Stephen Wolfram explained: For decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But now—thanks to the success of ChatGPT—as well as all the work we’ve done in making Wolfram|Alpha understand natural language—there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own.

This new model somewhat alleviates the problem: now, instead of having to select the correct plug-in (and thus restart your chat), you simply go directly to the GPT in question. In other words, if I want to create a poster, I don’t enable the Canva plugin in ChatGPT, I go to Canva GPT in the sidebar. Notice that this doesn’t actually solve the problem of needing to have selected the right tool; what it does do is make the choice more apparent to the user at a more appropriate stage in the process, and that’s no small thing.

ChatGPT will seamlessly switch between text generation, image generation, and web browsing, without the user needing to change context. What is necessary for the plug-in/GPT idea to ultimately take root is for the same capabilities to be extended broadly: if my conversation involved math, ChatGPT should know to use Wolfram|Alpha on its own, without me adding the plug-in or going to a specialized GPT.

the obvious technical challenges of properly exposing capabilities and training the model to know when to invoke those capabilities are a textbook example of Professor Clayton Christensen’s theory of integration and modularity, wherein integration works better when a product isn’t good enough; it is only when a product exceeds expectation that there is room for standardization and modularity.

To summarize the argument, consumers care about things in ways that are inconsistent with whatever price you might attach to their utility, they prioritize ease-of-use, and they care about the quality of the user experience and are thus especially bothered by the seams inherent in a modular solution. This means that integrated solutions win because nothing is ever “good enough”

the fact of the matter is that a lot of people use ChatGPT for information despite the fact it has a well-documented flaw when it comes to the truth; that flaw is acceptable, because to the customer ease-of-use is worth the loss of accuracy. Or look at plug-ins: the concept as originally implemented has already been abandoned, because the complexity in the user interface was more detrimental than whatever utility might have been possible. It seems likely this pattern will continue: of course customers will say that they want accuracy and 3rd-party tools; their actions will continue to demonstrate that convenience and ease-of-use matter most.

Prompt engineers not only need to get the model to respond to a given question but also structure the output in a parsable way (such as JSON), in case it needs to be rendered in some UI components or be chained into the input of a future LLM query. They scaffold the raw input that is fed into an LLM so the end user doesn’t need to spend time thinking about prompting at all.

From the user’s side, it’s hard to decide what to ask while providing the right amount of context.From the developer’s side, two problems arise. It’s hard to monitor natural language queries and understand how users are interacting with your product. It’s also hard to guarantee that an LLM can successfully complete an arbitrary query. This is especially true for agentic workflows, which are incredibly brittle in practice.

When we speak to other people, there is a shared context that we communicate under. We’re not just exchanging words, but a larger information stream that also includes intonation while speaking, hand gestures, memories of each other, and more. LLMs unfortunately cannot understand most of this context and therefore, can only do as much as is described by the prompt

most people use LLMs for ~4 basic natural language tasks, rarely taking advantage of the conversational back-and-forth built into chat systems:Summarization: Summarizing a large amount of information or text into a concise yet comprehensive summary. This is useful for quickly digesting information from long articles, documents or conversations. An AI system needs to understand the key ideas, concepts and themes to produce a good summary.ELI5 (Explain Like I'm 5): Explaining a complex concept in a simple, easy-to-understand manner without any jargon. The goal is to make an explanation clear and simple enough for a broad, non-expert audience.Perspectives: Providing multiple perspectives or opinions on a topic. This could include personal perspectives from various stakeholders, experts with different viewpoints, or just a range of ways a topic can be interpreted based on different experiences and backgrounds. In other words, “what would ___ do?”Contextual Responses: Responding to a user or situation in an appropriate, contextualized manner (via email, message, etc.). Contextual responses should feel organic and on-topic, as if provided by another person participating in the same conversation.

Prompting nearly always gets in the way because it requires the user to think. End users ultimately do not wish to confront an empty text box in accomplishing their goals. Buttons and other interactive design elements make life easier.The interface makes all the difference in crafting an AI system that augments and amplifies human capabilities rather than adding additional cognitive load.Similar to standup comedy, delightful LLM-powered experiences require a subversion of expectation.

Users will expect the usual drudge of drafting an email or searching for a nearby restaurant, but instead will be surprised by the amount of work that has already been done for them from the moment that their intent is made clear. For example, it would a great experience to discover pre-written email drafts or carefully crafted restaurant and meal recommendations that match your personal taste.If you still need to use a text input box, at a minimum, also provide some buttons to auto-fill the prompt box. The buttons can pass LLM-generated questions to the prompt box.

Replacing the lock icon with a neutral indicator prevents the misunderstanding that the lock icon is associated with the trustworthiness of a page, and emphasizes that security should be the default state in Chrome. Our research has also shown that many users never understood that clicking the lock icon showed important information and controls.

Duolingo had already implemented several gamification mechanics successfully, such as the progression system on the home screen, streaks, and an achievements system. And second, top digital games at the time had much higher retention rates than our product, which I took as evidence that we hadn’t yet reached the ceiling for gamification’s impact.

The moves counter allowed users only a finite number of moves to complete a level, which added a sense of scarcity and urgency to the gameplay. We decided to incorporate the counter mechanic into our product. We gave our users a finite number of chances to answer questions correctly before they had to start the lesson over.

Depressingly, the result of all that effort was completely neutral. No change to our retention. No increase in DAU. We hardly got any user feedback at all.

When you are playing Gardenscapes, each move feels like a strategic decision, because you have to outmaneuver dynamic obstacles to find a path to victory. But strategic decision-making isn’t required to complete a Duolingo lesson—you mostly either know the answer to a question or you don’t. Because there wasn’t any strategy to it, the Duolingo moves counter was simply a boring, tacked-on nuisance. It was the wrong gamification mechanic to adopt into Duolingo. I realized that I had been so focused on the similarities between Gardenscapes and Duolingo that I had failed to account for the importance of the underlying differences.

Referrals work for Uber because riders are paying for rides on a never-ending pay-as-you-go system. A free ride is a constant incentive. For Duolingo, we were trying to incentivize users by offering a free month of Super Duolingo. However, our best and most active users already had Super Duolingo, and we couldn’t give them a free month when they were already in a plan. This meant that our strategy, which needed to rely on our best users, actually excluded them.

Now when looking to adopt a feature, I ask myself:Why is this feature working in that product?Why might this feature succeed or fail in our context, i.e. will it translate well?What adaptations are necessary to make this feature succeed in our context?

Our failure with the Gardenscapes-style moves counter hadn’t actually disproved any of the original reasons why we believed gamification still had upside for Duolingo—we had only learned that the moves counter was a clumsy attempt at it. This time, we would be more methodical and intelligent about features we added or borrowed.

We deliberately made our leaderboard as casual and frictionless as possible; users were automatically opted in and could progress to the top of the first league by merely engaging consistently in their regular language study. By keeping the game mechanic exciting, but making it simpler than in FarmVille 2, we felt like we had struck the right balance of adopting and adapting.

as I’ve wrestled with writing this, it’s clear that many just don’t see the problem, as they assume a cheap button is nearly as good as a proper dial. They’ll openly admit a dial is indeed better but a cheap button is “good enough” and that a dial is “just too expensive.” That actually may be true! There are cases when using a push button is the right choice. But not always. We need to understand when to try a bit harder. Yes, you’re spending a tiny bit more on hardware, but you’re creating a product that is usually much easier to use, reduces returns, and builds your brand which improves sales. Is this positive outcome a given? Of course not, nothing is guaranteed but we need to stop pretending there is NO COST to cheaping out on buttons.

The dial changes the frequency with a simple twist. The push button device “Deconstructs” the twist dial into two up/down buttons. Each press increments the frequency a tiny amount. This means a twist is replaced with many button presses. Again, they are ‘functionally equivalent’ but the expression and ease of use are quite different.

“Adding a feature” is never free. Always start with the user’s problems first. If pressed into using one of these four abuses, make sure to fully appreciate its impact, the friction it creates, and what you can do to work around it.  Adding a feature shouldn’t also “add a problem.”

As a professional UX Designer, I want devices to offer more. But UX Design isn’t about cramming everything into your product in the vague Hail Mary hope it’ll ship a few more units. That’s the sales team speaking, not the user. It’s the wrong motivation and creates monsters.

However the most controversial step I’ll be taking is removing a few of the functions. This usually gets pushback from most product managers as for them “features == sales”. The problem with this thinking is that it assumes a) all features are used and b) extra features has no negative impact.

When content is shorter and maximized for engagement, we often lose track of the origin, history, and context behind it: a new designer is more likely to hear about a UX law from a UX influencer on an Instagram carousel than through the actual research which brought it about.The lack of nuance from algorithm-suggested posts undermines any value we could get from them. For a discipline known for asking "why" and for striving to understand users’ context, it’s time we become more intentional about our own information sources.

Shifts in visual narratives happen every decade or so, so it’s not surprising that the design world is moving away from the corporate flatness of web2. Instead of reminding us of the problems of our current world and the harm that’s been caused by Big Tech, the new, abstract forms of web3 distract us from the crises of the day with the promise of a new virtual world.

TikTok looked at what makes their content unique. Applying an OOUX mindset, the most interesting object is the “post” populating the feed. Two things stand out. First, the videos are very short, with only a couple of seconds of runtime. Which meant the usual distinction between browsing and watching made little sense. Second, opting for a truly mobile experience, their videos would be portrait mode. This meant users could browse and watch in the same orientation, one video at a time. The design decision to merge the browse and watch experience into one stream with autoplay broke all kinds of conventions. Yet, by doing so, it created a unique and engaging experience that is even borderline addictive.

Tinder understood that the selection moment is what makes them unique. They wanted to provide a quick and easy method for their key interaction to decide if a user is a match or not.

n order for designers to do high-quality design work, they need to work at companies that truly understand design. Here’s the catch though, there’s a tiny amount of such companies out there.

They treat it as something that makes things look pretty, so they hire UI designers to do UX design for them.

Whenever Facebook changes its interface, it has an impact on how millions of people communicate. In this sense, user interfaces are cultural artefacts.

If I had to guess why "math reform" is misinterpreted as "math education reform", I would speculate that school is the only contact that most people have had with math. Like school-physics or school-chemistry, math is seen as a subject that is taught, not a tool that is used. People don't actually use math-beyond-arithmetic in their lives, just like they don't use the inverse-square law or the periodic table.

Teach the current mathematical notation and methods any way you want -- they will still be unusable. They are unusable in the same way that any bad user interface is unusable -- they don't show users what they need to see, they don't match how users want to think, they don't show users what actions they can take.

They are unusable in the same way that the UNIX command line is unusable for the vast majority of people. There have been many proposals for how the general public can make more powerful use of computers, but nobody is suggesting we should teach everyone to use the command line. The good proposals are the opposite of that -- design better interfaces, more accessible applications, higher-level abstractions. Represent things visually and tangibly. And so it should be with math. Mathematics, as currently practiced, is a command line. We need a better interface.

Anything that remains abstract (in the sense of not concrete) is hard to think about... I think that mathematicians are those who succeed in figuring out how to think concretely about things that are abstract, so that they aren't abstract anymore. And I believe that mathematical thinking encompasses the skill of learning to think of an abstract thing concretely, often using multiple representations – this is part of how to think about more things as "things". So rather than avoiding abstraction, I think it's important to absorb it, and concretize the abstract... One way to concretize something abstract might be to show an instance of it alongside something that is already concrete.

The mathematical modeling tools we employ at once extend and limit our ability to conceive the world. Limitations of mathematics are evident in the fact that the analytic geometry that provides the foundation for classical mechanics is insufficient for General Relativity. This should alert one to the possibility of other conceptual limits in the mathematics used by physicists.

be inspired by the untapped potential of human capabilities. Don't just extrapolate yesterday's technology and then cram people into it.

You can look at an interface and see it as a clearly signposted user journey you should follow. Or you can see it as a collection of functions and affordances to repurpose. As raw material, rather than a guided path.