software gives you its own set of abstractions and basic vocabulary with which to understand every experience. It sort of smells like mathematics in some ways. But software’s way of looking at the world is more about abstractions modeling underlying complexities in systems; signal vs. noise; scale and orders of magnitude; and information — how much there is, what we can do it with, how we can learn from it and model it. Software’s interpretation of reality is particularly important because software drives the world now, and the people who write the software that runs it see the world through this kind of “software’s worldview” — scaling laws, information theory, abstractions and complexity. I think over time I’ve come to believe that understanding this worldview is more interesting than learning to wield programming tools.

DAOs are effectively owned and governed by people who hold a sufficient number of a DAO’s native token, which functions like a type of cryptocurrency. For example, $FWB is the native token of popular social DAO called Friends With Benefits, and people can buy, earn, or trade it.

Contributors will be able to use their DAO’s native tokens to vote on key decisions. You can get a glimpse into the kinds of decisions DAO members are already voting on at Snapshot, which is essentially a decentralized voting system. Having said this, existing voting mechanisms have been criticized by the likes of Vitalik Buterin, founder of Ethereum, the open-source blockchain that acts as a foundational layer for the majority of Web3 applications. So, this type of voting is likely to evolve over time.

I think when you're starting a company, it's really useful to ask the question, why now? And this is like, pretty cliche for startup stuff, so if you've heard of it already, apologies, but I do think it's a really useful framework. And that question of why now? Can be societal. Maybe there's some new cultural trend, perhaps it's regulatory. Some law has been passed or appealed. I like the technological version. And for us we saw drones in 2012 and WebGL as few technologies that were happening where because these technologies were happening, new possibilities were suddenly there.

But we're like, why would you do anything in photo editing if you're not on the phone? And so we felt like we were kind of building the wrong place and then eventually sort of shifted our attention to design, which I had been a design internet flipboard and that helped kind of help me realize what would be possible there.

And I would just say don't just go for an idea because it's kind of working. Go for an idea that you really care about because even if it doesn't work, you'll still learn from it and you'll still have one.

those most likely to become entrepreneurs are well-rounded. A 2005 study from Edward Lazear suggests that the self-employed tend to be generalists, not specialists.

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.

Stories like these are unhelpful at best, and damaging at worst. Someone skimming the Insider homepage uncritically might see this headline and chuckle at how coddled the kids are these days. That simply is not the case. The kids are alright. It is, as ever, the rich and truly powerful — the actual managerial bureaucracy — who are enriching themselves at the expense of the rest of us.

Gen Z has its own problems to confront: its members began their adult lives in the middle of a pandemic with a whiplashing economy and, in many parts of the world, an overheated market for renting or owning a home. Surveys show they want a healthier balance between their work and personal lives, and they understand developing a successful career takes time and constant learning. They do not want to be pandered to; they just want a reasonable level of respect, as with pretty much everyone else.

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.