A synthesizer produces music very differently than an acoustic instrument. It produces music at the lowest level of abstraction, as mathematical models of sound waves.

Once we started understanding writing as a mathematical object, our vocabulary for talking about ideas expanded in depth and precision.

An idea is composed of concepts in a vector space of features, and a vector space is a kind of marvelous mathematical object that we can write theorems and prove things about and deeply and fundamentally understand.

Synthesizers enabled entirely new sounds and genres of music, like electronic pop and techno. These new sounds were easier to discover and share because new sounds didn’t require designing entirely new instruments. The synthesizer organizes the space of sound into a tangible human interface, and as we discover new sounds, we could share it with others as numbers and digital files, as the mathematical objects they’ve always been.

Because synthesizers are electronic, unlike traditional instruments, we can attach arbitrary human interfaces to it. This dramatically expands the design space of how humans can interact with music. Synthesizers can be connected to keyboards, sequencers, drum machines, touchscreens for continuous control, displays for visual feedback, and of course, software interfaces for automation and endlessly dynamic user interfaces. With this, we freed the production of music from any particular physical form.

Recently, we’ve seen neural networks learn detailed mathematical models of language that seem to make sense to humans. And with a breakthrough in mathematical understanding of a medium, come new tools that enable new creative forms and allow us to tackle new problems.

Heatmaps can be particularly useful for analyzing large corpora or very long documents, making it easier to pinpoint areas of interest or relevance at a glance.

If we apply the same idea to the experience of reading long-form writing, it may look like this. Imagine opening a story on your phone and swiping in from the scrollbar edge to reveal a vertical spectrogram, each “frequency” of the spectrogram representing the prominence of different concepts like sentiment or narrative tension varying over time. Scrubbing over a particular feature “column” could expand it to tell you what the feature is, and which part of the text that feature most correlates with.

What would a semantic diff view for text look like? Perhaps when I edit text, I’d be able to hover over a control for a particular style or concept feature like “Narrative voice” or “Figurative language”, and my highlighted passage would fan out the options like playing cards in a deck to reveal other “adjacent” sentences I could choose instead. Or, if that involves too much reading, each word could simply be highlighted to indicate whether that word would be more or less likely to appear in a sentence that was more “narrative” or more “figurative” — a kind of highlight-based indicator for the direction of a semantic edit.

Browsing through these icons felt as if we were inventing a new kind of word, or a new notation for visual concepts mediated by neural networks. This could allow us to communicate about abstract concepts and patterns found in the wild that may not correspond to any word in our dictionary today.

What visual and sensory tricks can we use to coax our visual-perceptual systems to understand and manipulate objects in higher dimensions? One way to solve this problem may involve inventing new notation, whether as literal iconic representations of visual ideas or as some more abstract system of symbols.

Photographers buy and sell filters, and cinematographers share and download LUTs to emulate specific color grading styles. If we squint, we can also imagine software developers and their package repositories like NPM to be something similar — a global, shared resource of abstractions anyone can download and incorporate into their work instantly. No such thing exists for thinking and writing. As we figure out ways to extract elements of writing style from language models, we may be able to build a similar kind of shared library for linguistic features anyone can download and apply to their thinking and writing. A catalogue of narrative voice, speaking tone, or flavor of figurative language sampled from the wild or hand-engineered from raw neural network features and shared for everyone else to use.

We’re starting to see something like this already. Today, when users interact with conversational language models like ChatGPT, they may instruct, “Explain this to me like Richard Feynman.” In that interaction, they’re invoking some style the model has learned during its training. Users today may share these prompts, which we can think of as “writing filters”, with their friends and coworkers. This kind of an interaction becomes much more powerful in the space of interpretable features, because features can be combined together much more cleanly than textual instructions in prompts.

Artists never complete a single, perfect artwork, and a single work never instigates an absolute transcendence in viewers. We may aspire toward this quasi-theological ideal, but art only has the ability to suggest the sublime. The real sustenance of the artistic is the scope of experience it provides, the cumulative sense of growth and cultivation of ourselves through art, a tendency toward a good that we can never capture but only assist in radiating itself and existence.

I quickly realized that my habits were more suited to going to galleries every week than to working regularly on longer pieces, that there weren’t very many shows I wanted to write about at length, and that a regular stream of blithe, off-the-cuff reviews would attract more attention than intermittent longer essays

Film, music, food and book critics write for a general public that can be swayed to spend their money one way or another, whereas the general public cannot afford to buy the art that is written about in Artforum.

I've noticed that most devs, anyway, are either front-loaded or back-loaded."Front-loaded" means that the part of a project they really enjoy is the beginning part, design work, etc. Once those problems are largely worked out, the project becomes less interesting to them. A common refrain from this personality is "the rest is just implementation details"."Back-loaded" is the opposite of that. They hate the initial work of a project and prefer to do the implementation details, after the road is mapped out.Both sorts of devs are critical. Could it be that you're a front-loaded sort? If so, maybe the thing to do is to bring in someone who's back-loaded and work on the projects together?

Even if it's just a personal project, think about the time and money you'll need to invest, and the benefits and value it will provide. Think on why you should prioritize this over other tasks or existing projects. Most importantly, sleep on it. Get away from it and do something else. Spend at least a couple of days on and off planning it. Outline and prioritize features and tasks. Decide on the most important ones and define the MVP. If, after this planning process, you still feel motivated to pursue the project, go ahead!

Quick win is to ask yourself: What have I learned from this project? And make that the result of the project.

Find a job/role/gig where you think of the solutions and let other people implement them. Just always remember that it is no longer your project. You might have thought of something, but without the efforts of others it will never amount to anything, ever. So as long as you can respect the work of others and your own limitations in doing what they do you will do fine.

Find more challenging problems. I usually do this by trying to expand something that spiked my interest to make it more generically applicable or asking myself if the problem is actually worth a solution ('faster horses') and if the underlying problem is not more interesting (mobility).

it helps to promise other people something: Present your findings, write a paper, make a POC by an agreed upon deadline. Now you have to be empatic enough to want to meet their deadline and thus create what you promised with all the works that comes with it. That is your result. You also have to be selfish enough to tell people that is where you end your involvement, because it no longer interests you, regardless of the plans they have pursuing this further

Avoid getting too specific at a feature level. For example, it's too specific if you say "Page navigator" and it's too high level if you try to explore "A blog builder app." The sweet spot to go for is something that is conceptual where you can explore an interaction for a concept, such as, "Exploring spatial viewing of pages".