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even more important, many advocates argue, is how the two reforms together can change how candidates and elected officials of all stripes approach their jobs, by adjusting the incentive structure they operate under. Increasingly, many states and districts are solidly red or blue, meaning the general election is uncompetitive, and the key race takes place in the primary. That’s a problem, because the primary electorate is by and large smaller, more partisan and more extreme than the general electorate. Right now, with politicians worrying more about the primary than the general, they’re more focused on playing to their base than on reaching beyond it and solving problems, critics argue.

By allowing multiple candidates to advance, Final Four/Five shifts the crucial election from the primary to the general. And RCV means the votes of Democrats in red districts and Republicans in blue ones still matter, even if their top choice remains unlikely to win. Together, it means candidates are rewarded for paying attention to the entire general electorate, not just a small slice of staunch supporters. As a result, it encourages candidates — and elected officials, once in office — toward moderation and problem-solving, and away from extremism.

There is nothing wrong with wanting a big fanbase or making money with your talent. In fact, I’d argue that we all have this desire (as a writer, I definitely do). The problem arises when these things become goals in and of themselves. Artistry is the soul of creation, and over-engineering it for the sake of a certain outcome kills that soul.

There are writers and there are grifters. There are artists and there are content creators.

This isn’t about highbrow versus lowbrow art, but rather algorithmic versus authentic creation.

creative works cannot be deeply transformational for your audience if they are not meaningful to you first.

Resist being context-collapsed to a one-dimensional being.

Technologists intervene in our present realities and forge the future, and in doing so, choose how best to model the world and impress their will upon it. The public must insist that technologists are responsible for thinking about the human implications of their work.

You will find in every case that these technologies were only possible because of active decisions taken by governments, corporations, and individuals. They never just “happened”, whatever that might mean. People make history happen, and by reading history, you can see yourself as one of those people. What better motivation could there be for working to make things happen yourself?

The umwelt theory states that the mind and the world are inseparable because it is the mind that interprets the world for the organism. Because of the individuality and uniqueness of the history of every single organism, the umwelten of different organisms differ.

We can take nature’s superpowers and expand them across many more vectors that are interesting to humans: Across scale — far and near, binoculars, zoom, telescope, microscope Across wavelength — UV, IR, heatmaps, nightvision, wifi, magnetic fields, electrical and water currents Across time — view historical imagery, architectural, terrain, geological, and climate changes Across culture — experience the relevance of a place in books, movies, photography, paintings, and language Across space — travel immersively to other locations for tourism, business, and personal connections Across perspective — upside down, inside out, around corners, top down, wider, narrower, out of body Across interpretation — alter the visual and artistic interpretation of your environment, color-shifting, saturation, contrast, sharpness

Headset displays connect sensory extensions directly to your vision. Equipped with sensors that perceive beyond human capabilities, and access to the internet, they can provide information about your surroundings wherever you are. Until now, visual augmentation has been constrained by the tiny display on our phone. By virtue of being integrated with your your eyesight, headsets can open up new kinds of apps that feel more natural. Every app is a superpower. Sensory computing opens up new superpowers that we can borrow from nature. Animals, plants and other organisms can sense things that humans can’t

The first mass-market bicycle for the senses was Apple’s AirPods. Its noise cancellation and transparency mode replace and enhance your hearing. Earbuds are turning into ear computers that will become more easily programmable. This can enable many more kinds of hearing. For example, instantaneous translation may soon be a reality

For the past seven decades, computers have been designed to enhance what your brain can do — think and remember. New kinds of computers will enhance what your senses can do — see, hear, touch, smell, taste. The term spatial computing is emerging to encompass both augmented and virtual reality. I believe we are exploring an even broader paradigm: sensory computing. The phone was a keyhole for peering into this world, and now we’re opening the door.

What happens when put on a headset and open the “Math” app? How could seeing the world through math help you understand both better?

Advances in haptics may open up new kinds of tactile sensations. A kind of second skin, or softwear, if you will. Consider that Apple shipped a feature to help you find lost items that vibrates more strongly as you get closer. What other kinds of data could be translated into haptic feedback?

It may sound far-fetched, but converting olfactory patterns into visual patterns could open up some interesting applications. Perhaps a new kind of cooking experience? Or new medical applications that convert imperceptible scents into visible patterns?

Evergreen notes turn ideas into objects. By turning ideas into objects you can manipulate them, combine them, stack them. You don’t need to hold them all in your head at the same time.

Evergreen notes allow you to think about complex ideas by building them up from smaller composable ideas.

Much of these problems with Google today stem from a lack of visionary leadership from Sundar Pichai, and his clear lack of interest in maintaining the cultural norms of early Google. A symptom of this is the spreading contingent of inept middle management. Take Jeanine Banks, for example, who manages the department that somewhat arbitrarily contains (among other things) Flutter, Dart, Go, and Firebase. Her department nominally has a strategy, but I couldn't leak it if I wanted to; I literally could never figure out what any part of it meant, even after years of hearing her describe it. Her understanding of what her teams are doing is minimal at best; she frequently makes requests that are completely incoherent and inapplicable. She treats engineers as commodities in a way that is dehumanising, reassigning people against their will in ways that have no relationship to their skill set. She is completely unable to receive constructive feedback (as in, she literally doesn't even acknowledge it). I hear other teams (who have leaders more politically savvy than I) have learned how to "handle" her to keep her off their backs, feeding her just the right information at the right time. Having seen Google at its best, I find this new reality depressing.

In the first approach, the primary interface affordance is an input that directly (for the most part) instructs an AI model(s). In this paradigm, people are authoring prompts that result in text, image, video, etc. generation. These prompts can be sequential, iterative, or un-related. Marquee examples are OpenAI's ChatGPT interface or Midjourney's use of Discord as an input mechanism. Since there are few, if any, UI affordances to guide people these systems need to respond to a very wide range of instructions. Otherwise people get frustrated with their primarily hidden (to the user) limitations.

The second approach doesn't include any UI elements for directly controlling the output of AI models. In other words, there's no input fields for prompt construction. Instead instructions for AI models are created behind the scenes as people go about using application-specific UI elements. People using these systems could be completely unaware an AI model is responsible for the output they see.

The third approach is application specific UI with AI assistance. Here people can construct prompts through a combination of application-specific UI and direct model instructions. These could be additional controls that generate portions of those instructions in the background. Or the ability to directly guide prompt construction through the inclusion or exclusion of content within the application. Examples of this pattern are Microsoft's Copilot suite of products for GitHub, Office, and Windows.

they could be overlays, modals, inline menus and more. What they have in common, however, is that they supplement application specific UIs instead of completely replacing them.

complexity can make it difficult for both domain novices and experts alike to learn how to use the interface. LLMs can help reduce this barrier by being leveraged to prove assistance to the user if they’re trying to accomplish something, but don’t exactly know how to navigate the interface.The user could tell the program what they’re trying to do via a text or voice interface, or perhaps, the program may be able to infer the user’s intent or goals based on what actions they’ve taken so far.Modern GUI apps are slowly starting to add in more features for assisting users with navigating the space of available commands and actions via command palettes; popularised in software iA Writer and Superhuman.

for executing a sequence of tasks as part of a complex workflow, LLM powered interfaces afford a richer opportunity for learning and using complex software.The program could walk them through the task they’re trying to accomplish by highlighting and selecting the interface elements in the correct order to accomplish the task, along with explanations provided.

Expert interfaces that take advantage of LLMs may end up looking like they currently do - again, complex tasks require complex interfaces. However, it may be easier and faster for users to learn how to use these interfaces thanks to built-in LLM-powered assistants. This will help them to get into flow faster, improving their productivity and feeling of satisfaction when using this complex software.

unlike Clippy, these new types of assistant would be able to act on the interface directly. These actions will be made in accordance to the goals of the person using them, but each discrete action taken by the assistant on the interface will not be done according to explicit human actions - the goals are directed by he human user, but the steps to achieve those goals are unknown to the user, which is why they’re engaging with the assistant in the first place