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.

The story of the workers who made ChatGPT possible offers a glimpse into the conditions in this little-known part of the AI industry, which nevertheless plays an essential role in the effort to make AI systems safe for public consumption. “Despite the foundational role played by these data enrichment professionals, a growing body of research reveals the precarious working conditions these workers face,” says the Partnership on AI, a coalition of AI organizations to which OpenAI belongs. “This may be the result of efforts to hide AI’s dependence on this large labor force when celebrating the efficiency gains of technology. Out of sight is also out of mind.”

Computer-generated text, images, video, and audio will transform the way countless industries do business, the most bullish investors believe, boosting efficiency everywhere from the creative arts, to law, to computer programming. But the working conditions of data labelers reveal a darker part of that picture: that for all its glamor, AI often relies on hidden human labor in the Global South that can often be damaging and exploitative. These invisible workers remain on the margins even as their work contributes to billion-dollar industries.

One Sama worker tasked with reading and labeling text for OpenAI told TIME he suffered from recurring visions after reading a graphic description of a man having sex with a dog in the presence of a young child. “That was torture,” he said. “You will read a number of statements like that all through the week. By the time it gets to Friday, you are disturbed from thinking through that picture.” The work’s traumatic nature eventually led Sama to cancel all its work for OpenAI in February 2022, eight months earlier than planned.

In the day-to-day work of data labeling in Kenya, sometimes edge cases would pop up that showed the difficulty of teaching a machine to understand nuance. One day in early March last year, a Sama employee was at work reading an explicit story about Batman’s sidekick, Robin, being raped in a villain’s lair. (An online search for the text reveals that it originated from an online erotica site, where it is accompanied by explicit sexual imagery.) The beginning of the story makes clear that the sex is nonconsensual. But later—after a graphically detailed description of penetration—Robin begins to reciprocate. The Sama employee tasked with labeling the text appeared confused by Robin’s ambiguous consent, and asked OpenAI researchers for clarification about how to label the text, according to documents seen by TIME. Should the passage be labeled as sexual violence, she asked, or not? OpenAI’s reply, if it ever came, is not logged in the document; the company declined to comment. The Sama employee did not respond to a request for an interview.

In February, according to one billing document reviewed by TIME, Sama delivered OpenAI a sample batch of 1,400 images. Some of those images were categorized as “C4”—OpenAI’s internal label denoting child sexual abuse—according to the document. Also included in the batch were “C3” images (including bestiality, rape, and sexual slavery,) and “V3” images depicting graphic detail of death, violence or serious physical injury, according to the billing document.

But the need for humans to label data for AI systems remains, at least for now. “They’re impressive, but ChatGPT and other generative models are not magic – they rely on massive supply chains of human labor and scraped data, much of which is unattributed and used without consent,” Andrew Strait, an AI ethicist, recently wrote on Twitter. “These are serious, foundational problems that I do not see OpenAI addressing.”

Blockbuster responded by pricing Blockbuster Online 50 cents cheaper, accelerating Netflix’s stock slide. Netflix, though, knew that Blockbuster was carrying $1 billion in debt from its spin-off from Viacom, and decided to wait it out; Blockbuster cut the price again, taking an increasing share of new subscribers, and still Netflix waited.

We humans are so interconnected to our jobs, admittedly more than we should be. We identify our job with who we are as people. This means that if we are not growing at work or in our business, we feel like we are not growing as people. Growth can, and should be divided. We can both be growing as people and growing as workers or business owners.

Growth at the group level and specifically scaling growth is not good for us as people. The amount of stress and pressure that is undertaken while trying to scale is unhealthy and unsustainable – regardless of what your favorite hustle culture influencer says.We need time, space, and agency to grow at our own paces. We need to be able to get better and worse at things, without being vilified for it.

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.

Look a week ahead: Towards the end of a week, I’ll start to take a look at what meetings I have the following week. For any that I’m responsible for, I’ll start pulling together some information for attendees. Sometimes this means updating the calendar invite with an agenda; other times this means starting a Google Doc for what we need to run through during the meeting, and I share it with edit rights for all attendees. Use meeting goals: If the meeting has a bunch of people in it (like, more than two), especially if those people typically have full schedules, then I’ll write down goals for the meeting. Often, I’ll put those goals in the calendar item, and I’ll mention them at the beginning of the meeting. That means that if we get off-track during our time together, I can hit pause and recenter on the goals, asking folks to continue that other conversation afterward. Find a plant: Once in awhile, it’s helpful to “seed” the meeting somehow. For example, in one meeting where there’s an “open questions” time and I want people to ask anything, I’ve asked a buddy to think up a super weird one to demonstrate to others that it’s a safe space. Don’t surprise people in the meeting: Additionally, I do a lot of prep to make sure there’s no surprises in my meetings, at least none coming from me. This usually means that I let a handful of people know about a big announcement ahead of time (or had a tough conversation), usually one-on-one, so they wouldn’t be surprised in front of a lot of other people. Gain consensus 1:1 beforehand, if possible: My goal with any decision-making meeting is to already have a sense, going in, of what issues people have, what their opinion is, and what they might need to come to agreement. I do as much legwork in advance as possible, so that the whole group is ready to make that decision more quickly in the room.

Few things bog down meetings more than an unclear process, or a lack of clarity about how people in attendance are supposed to participate. By sharing the goals of the meeting and a high-level overview of what we’re going to do there, I hope to make it clear what’s expected of folks in the room.

Setting up a form for people to add their questions to - including people in the shared physical space - so that the facilitator can run through them rather than prioritize the voices in the room

I cancel meetings if they’re unwarranted. I check-in every few months to see if a meeting’s goal still makes sense; I ask attendees how they’re feeling about the length of the meeting, how often it happens, and what we do during it. I iterate on meetings to make sure they’re still effective, or even necessary.

Swelling trade tensions and the rise of “direct-to-consumer” platforms were bound to heighten the scrutiny on the import of mass media cultural products. But it’s also notable that the Marvel movies that did gain admittance in China were led by six heroes (The Avengers), five of whom were employed by the American military (with the sole outlier being an extraterrestrial) and all of whom were white. The current, rejected leads are more diverse in vocation, American allegiance, and ethnicity (among other attributes).

In 2017, Disney began a marketing integration with aerospace and defense giant Northrop Grumman encouraging those who use Google to research American defense contractor Stark Industries to join something like the real thing.

Avatar’s unprecedented achievements require us to examine not just its technological innovations, but also its narrative. The film’s “protagonist humans” are classic Western archetypes such as the taciturn soldier and the driven scientist. The villains are archetypes as well, but they are also particularly close to foreign caricatures of evil Americans: the tough-as-nails, violence-prone colonel and pillage-the-earth corporate executive. Furthermore, Avatar’s overarching message is one of collectivism, spiritualism, and alignment with nature. At the end of the movie, each of the Western heroes literally shed their individual identities (and white bodies) to become part of the cooperative aboriginal mind and save the day.

Bikeshed refers to topics which have never recieved concensus and are likely to generate side-discussions and flames unless all participants are well-read on all the past history. It stems from the idea that big changes (like the building of a power plant) go through quickly, since everyone assumes that someone else has checked it out, while simple changes (like building a bikeshed) often get mired in bureaucracy, since everyone has an opinion on it.

A common example of diminishing returns is choosing to hire more people on a factory floor to alter current manufacturing and production capabilities. Given that the capital on the floor (e.g. manufacturing machines, pre-existing technology, warehouses) is held constant, increasing from one employee to two employees is, theoretically, going to more than double production possibilities and this is called increasing returns. If we now employ 50 people, at some point, increasing the number of employees by two percent (from 50 to 51 employees) would increase output by two percent and this is called constant returns. However, if we look further along the production curve to, for example 100 employees, floor space is likely getting crowded, there are too many people operating the machines and in the building, and workers are getting in each other's way. Increasing the number of employees by two percent (from 100 to 102 employees) would increase output by less than two percent and this is called "diminishing returns."

In software development, technical debt (also known as design debt[1] or code debt) is the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer.[2] Analogous with monetary debt,[3] if technical debt is not repaid, it can accumulate "interest", making it harder to implement changes. Unaddressed technical debt increases software entropy and cost of further rework.

Common causes of technical debt include: Ongoing development, long series of project enhancements over time renders old solutions sub-optimal.

Parallel development on multiple branches accrues technical debt because of the work required to merge the changes into a single source base. The more changes done in isolation, the more debt.