The slides and transcript from a great talk by Maggie Appleton, including this perfect description of the vibes we get from large language models:
It feels like they’re either geniuses playing dumb or dumb machines playing genius, but we don’t know which.
A plea to let users do web things on websites. In other words, stop over-complicating everything with buckets of JavaScript.
Honestly, this isn’t wishlist isn’t asking for much, and it’s a damning indictment of “modern” frontend development that we’ve come to this:
- Let me copy text so I can paste it.
- If something navigates like a link, let me do link things.
- …
Chat is rarely a suitable interface for most tasks. Here, Maggie Appleton explores and prototypes some alternatives.
No one says “information superhighway” anymore, but whenever anyone explains net neutrality, they do so in terms of fast lanes and tolls. Twitter is a “town square,” a metaphor that was once used for the internet as a whole. These old metaphors had been joined by a few new ones: I have a feeling that “the cloud” will soon feel as dated as “cyberspace.”
In some ways, the fervor around AI is reminiscent of blockchain hype, which has steadily cooled since its 2021 peak. In almost all cases, blockchain technology serves no purpose but to make software slower, more difficult to fix, and a bigger target for scammers. AI isn’t nearly as frivolous—it has several novel use cases—but many are rightly wary of the resemblance. And there are concerns to be had; AI bears the deceptive appearance of a free lunch and, predictably, has non-obvious downsides that some founders and VCs will insist on learning the hard way.
This is a good level-headed overview of how generative language model tools work.
If something can be reduced to patterns, however elaborate they may be, AI can probably mimic it. That’s what AI does. That’s the whole story.
There’s very practical advice on deciding where and when these tools make sense:
The sweet spot for AI is a context where its choices are limited, transparent, and safe. We should be giving it an API, not an output box.
The pros and cons of dependencies in your toolchain.
Mandy takes a deep dive into the treatment of altruism in Ursula Le Guin’s The Dispossessed.
Of course, users can learn over time what prompts work well and which don’t, but the burden to learn what works still lies with every single user. When it could instead be baked into the interface.
I didn’t know the Washington Post had a design system or that the system has this good section on accessibility.
I like how Luke is using a large language model to make a chat interface for his own content.
This is the exact opposite of how grifters are selling the benefits of machine learning (“Generate copious amounts of new content instantly!”) and instead builds on over twenty years of thoughtful human-made writing.
Stick a singularity in your “effective altruism” pipe and smoke it.
There’s a time for linguistics, and there’s a time for grabbing the general public by the shoulders and shouting “It lies! The computer lies to you! Don’t trust anything it says!”
Imagine a collaboratively developed, universal content style guide, based on usability evidence.
Stuart has written this fantastic concise practical guide to privacy for developers and designers. A must-read!
Here’s an aggregator of components from multiple design systems.
So then the question becomes: how do you most effectively communicate designs, to facilitate the best discussions about those designs? My answer is: lots of little prototypes built with HTML, CSS, and JavaScript.
This is a terrific walkthrough from Andy showing how smart fundamentals in your CSS can give you a beautiful readable document without much work.
We use metaphors all the time. To quote George Lakoff, we live by them.
We use analogies some of the time. They’re particularly useful when we’re wrapping our heads around something new. By comparing something novel to something familiar, we can make a shortcut to comprehension, or at least, categorisation.
But we need a certain amount of vigilance when it comes to analogies. Just because something is like something else doesn’t mean it’s the same.
With that in mind, here are some ways that people are describing generative machine learning tools. Large language models are like…
A very handy guide to considering privacy at all stages of digital product design:
This guidance is written for technology professionals such as product and UX designers, software engineers, QA testers, and product managers.
This is a great step-by-step guide to HTML by Estelle.