Now, more than ever, it’s critical to own your data. Really own it. Like, on your hard drive and hosted on your website.
Is taking control of your content less convenient? Yeah–of course. That’s how we got in this mess to begin with. It can be a downright pain in the ass. But it’s your pain in the ass. And that’s the point.
This is one way of putting things into perspective.
LLMs are good at transforming text into less text
Laurie is really onto something with this:
This is the biggest and most fundamental thing about LLMs, and a great rule of thumb for what’s going to be an effective LLM application. Is what you’re doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it’s probably going to be great at it. If you’re asking it to convert into a roughly equal amount of text it will be so-so. If you’re asking it to create more text than you gave it, forget about it.
Depending how much of the hype around AI you’ve taken on board, the idea that they “take text and turn it into less text” might seem gigantic back-pedal away from previous claims of what AI can do. But taking text and turning it into less text is still an enormous field of endeavour, and a huge market. It’s still very exciting, all the more exciting because it’s got clear boundaries and isn’t hype-driven over-reaching, or dependent on LLMs overnight becoming way better than they currently are.
Explore our hand-picked collection of 10,046 out-of-copyright works, free for all to browse, download, and reuse. This is a living database with new images added every week.
It always annoys me when a politician is accused of “flip-flopping” when they change their mind on something. Instead of admiring someone for being willing to re-examine previously-held beliefs, we lambast them. We admire conviction, even though that’s a trait that has been at the root of history’s worst attrocities.
When you look at the history of human progress, some of our greatest advances were made by people willing to question their beliefs. Prioritising data over opinion is what underpins the scientific method.
But I get it. It can be very uncomfortable to change your mind. There’s inevitably going to be some psychological resistance, a kind of inertia of opinion that favours the sunk cost of all the time you’ve spent believing something.
I was thinking back to times when I’ve changed my opinion on something after being confronted with new evidence.
In my younger days, I was staunchly anti-nuclear power. It didn’t help that in my younger days, nuclear power and nuclear weapons were conceptually linked in the public discourse. In the intervening years I’ve come to believe that nuclear power is far less destructive than fossil fuels. There are still a lot of issues—in terms of cost and time—which make nuclear less attractive than solar or wind, but I honestly can’t reconcile someone claiming to be an environmentalist while simultaneously opposing nuclear power. The data just doesn’t support that conclusion.
Similarly, I remember in the early 2000s being opposed to genetically-modified crops. But the more I looked into the facts, there was nothing—other than vibes—to bolster that opposition. And yet I know many people who’ve maintainted their opposition, often the same people who point to the scientific evidence when it comes to climate change. It’s a strange kind of cognitive dissonance that would allow for that kind of cherry-picking.
There are other situations where I’ve gone more in the other direction—initially positive, later negative. Google’s AMP project is one example. It sounded okay to me at first. But as I got into the details, its fundamental unfairness couldn’t be ignored.
I was fairly neutral on blockchains at first, at least from a technological perspective. There was even some initial promise of distributed data preservation. But over time my opinion went down, down, down.
Bitcoin, with its proof-of-work idiocy, is the poster-child of everything wrong with the reality of blockchains. The astoundingly wasteful energy consumption is just staggeringly pointless. Over time, any sufficiently wasteful project becomes indistinguishable from evil.
Speaking of energy usage…
My feelings about large language models have been dominated by two massive elephants in the room. One is the completely unethical way that the training data has been acquired (by ripping off the work of people who never gave their permission). The other is the profligate energy usage in not just training these models, but also running queries on the network.
My opinion on the provenance of the training data hasn’t changed. If anything, it’s hardened. I want us to fight back against this unethical harvesting by poisoning the well that the training data is drawing from.
But my opinion on the energy usage might just be swaying a little.
Michael Liebreich published an in-depth piece for Bloomberg last month called Generative AI – The Power and the Glory. He doesn’t sugar-coat the problems with current and future levels of power consumption for large language models, but he also doesn’t paint a completely bleak picture.
Effectively there’s a yet-to-decided battle between Koomey’s law and the Jevons paradox. Time will tell which way this will go.
The whole article is well worth a read. But what really gave me pause was a recent piece by Hannah Ritchie asking What’s the impact of artificial intelligence on energy demand?
When Hannah Ritchie speaks, I listen. And I’m well aware of the irony there. That’s classic argument from authority, when the whole point of Hannah Ritchie’s work is that it’s the data that matters.
In any case, she does an excellent job of putting my current worries into a historical context, as well as laying out some potential futures.
Don’t get me wrong, the energy demands of large language models are enormous and are only going to increase, but we may well see some compensatory efficiencies.
Personally, I’d just like to see these tools charge a fair price for their usage. Right now they’re being subsidised by venture capital. If people actually had to pay out of pocket for the energy used per query, we’d get a much better idea of how valuable these tools actually are to people.
Instead we’re seeing these tools being crammed into existing products regardless of whether anybody actually wants them (and in my anecdotal experience, most people resent this being forced on them).
Still, I thought it was worth making a note of how my opinion on the energy usage of large language models is open to change.
But I still won’t use one that’s been trained on other people’s work without their permission.
A ban on tracking-based personalised advertising will provide an incentive to reinforce sustainable alternative models and, in fact, will be a condition for making them viable. The advertising industry already has sustainable, proven concepts for effective online advertising that do not require targeted tracking and personalisation (e.g. contextual advertising).
The same small dataset visualised in a hundred different ways, with notes on the strengths and weaknesses of each one.
I think of myself of as an optimist. It makes me insufferable sometimes.
When someone is having a moan about something in the news and they say something like “people are terrible”, I can’t resist weighing in with a “well, actually…” Then I’ll start channeling Rutger Bregman, Rebecca Solnit, and Hans Rosling, pointing to all the evidence that people are, by and large, decent. I should really just read the room and shut up.
I opened my talk Of Time And The Web with a whole spiel about how we seem to be hard-wired to pay more attention to bad news than good (perhaps for valid evolutionary reasons).
I like to think that my optimism is rational, backed up by data. But if I’m going to be rational, then I also can’t become too attached to any particualar position (like, say, optimism). I should be willing to change my mind when I’m confronted with new evidence.
A truckload of new evidence got dumped on my psyche this week. The United States of America elected Donald Trump as president. Again.
Even here I found a small glimmer of a bright side: at least the result was clear cut. I was dreading weeks or even months of drawn-out ballot counting, lawsuits and uncertainty. At least the band-aid was decisively ripped away.
Back in 2016, I could tell myself all sorts of reasons why this might have happened. Why people might have been naïve or misled into voting a dangerous idiot into power. But the naïveté was all mine. The majority of America really is that sexist.
This feels very different to 2016. And hey, remember when we woke up to that election result and one of the first things we did was take out subscriptions to the New York Times and the Washington Post to “support real journalism”? Yeah, that worked out just great, didn’t it?
My faith in human nature is taking quite a hit. An electoral experiment has been run three times now—having this mysogistic racist narcissistic idiot run for the highest office in the land—and the same result came up twice.
I naïvely thought that the more people saw of his true nature, the less chance he would have. When he kept going off-script at his rallies, spouting the vilest of threats, I thought there was an upside. At least now people would see for themselves what he’s really like.
But in the end it didn’t matter one whit. Like I said in a different context:
To use an outdated movie reference, imagine a raving Charlton Heston shouting that “Soylent Green is people!”, only to be met with indifference. “Everyone knows Soylent Green is people. So what?”
I never liked talking about “faith” in human nature. To me, it wasn’t faith. It was just a rational assessment. Now I’m not so sure. Maybe I need some faith after all.
I wonder if my optimism will return. It probably will (see? I’m such an optimist). But if it does, perhaps it will have to be an optimism that exists despite the data, not because of it.
Speaking of serendipity, not long after I wrote about making a static archive of The Session for people to download and share, I came across a piece by Alex Chan about using static websites for tiny archives.
The use-case is slightly different—this is about personal archives, like paperwork, screenshots, and bookmarks. But we both came up with the same process:
I’m deliberately going low-scale, low-tech. There’s no web server, no build system, no dependencies, and no JavaScript frameworks.
And we share the same hope:
Because this system has no moving parts, and it’s just files on a disk, I hope it will last a long time.
You should read the whole thing, where Alex describes all the other approaches they took before settling on plain ol’ HTML files in a folder:
HTML is low maintenance, it’s flexible, and it’s not going anywhere. It’s the foundation of the entire web, and pretty much every modern computer has a web browser that can render HTML pages. These files will be usable for a very long time – probably decades, if not more.
I’m enjoying this approach, so I’m going to keep using it. What I particularly like is that the maintenance burden has been essentially zero – once I set up the initial site structure, I haven’t had to do anything to keep it working.
They also talk about digital preservation:
I’d love to see static websites get more use as a preservation tool.
I concur! And it’s particularly interesting for Alex to be making this observation in the context of working with the Flickr foundation. That’s where they’re experimenting with the concept of a data lifeboat
What should we do when a digital service sinks?
This is something that George spoke about at the final dConstruct in 2022. You can listen to the talk on the dConstruct archive.
Wow! Grace Hopper has always been a hero to me, but I had no idea she was such a fantastic presenter. She’s completely engaging, with the timing and deadpan delivery of a stand-up comedian at times.
The datalist element is good. It was a bit bumpy there for a while, but browser implementations have improved over time. Now it’s by far the simplest and most robust way to create an autocompleting combobox widget.
Hook up an input element with a datalist element using the list and id attributes and you’re done. You can even use a bit of Ajax to dynamically update the option elements inside the datalist in response to the user’s input. The browser takes care of all the interaction. If you try to roll your own combobox implementation, it’s almost certainly going to involve a lot of JavaScript and still probably won’t account for all use cases.
Safari on iOS—and therefore all browsers on iOS—didn’t support datalist for quite a while. But once it finally shipped, it worked really nicely. The options showed up just like automplete suggestions above the keyboard.
But that broke a while back.
The suggestions still appeared, but if you tapped on one of them, nothing happened. The input element didn’t get updated. You had to tap on a little downward arrow inside the input in order to see the list of options.
That was really frustrating for anybody on iOS using The Session. By far the most common task on the site is searching for a tune, something that’s greatly (progressively) enhanced with a dynamically-updating datalist.
I just updated to iOS 18 specifically to see if this bug has been fixed, and it has:
Fixed updating the input value when selecting an
optionfrom adatalistelement.
Hallelujah!
But now there’s some additional behaviour that’s a little weird.
As well as showing the options in the autocomplete list above the keyboard, Safari on iOS—and therefore all browsers on iOS—also pops up the options as a list (as if you had tapped on that downward arrow). If the list is more than a few options long, it completely obscures the input element you’re typing into!
I’m not sure if this is a bug or if it’s the intended behaviour. It feels like a bug, but I don’t know if I should file something.
For now, I’ve updated the datalist elements on The Session to only ever hold three option elements in order to minimise the problem. Seeing as the autosuggest list above the keyboard only ever shows a maximum of three suggestions anyway, this feels like a reasonable compromise.
As designers creating images to communicate complex ideas, we rationalize our processes, we bring objectivity to our craft, we want our clients to think that our decisions are based on reasoning. However, we should also defend our intuitions, our subjectivity. We should also defend pursuing beauty as it is one of our most powerful tools.
Here’s a nice HTML web component that uses structured data in the markup to populate a Leaflet map.
Personally I’d probably use microformats rather than microdata, but the princple is the same: progressive enhancement from plain old HTML to an interactive map.
I think it’s always worth revisiting accomplishments like this—it’s absolutely astounding that we don’t even think about polio (or smallpox!) in our day-to-day lives, when just two generations ago it was something that directly affected everybody.
The annual number of people paralyzed by polio was reduced by over 99% in the last four decades.
Maggie explores different ways of visualising journeys on the web, including browser histories:
Perhaps web browsing histories should look more like Git commit histories? Perhaps distinct branches could representing different topics and research avenues?
A collection of collections, this is a directory of design systems, with the handy option to browse by component type. The blueprints section is still a bit thin on the ground, but likes the most useful bit—an in-depth dissection of individual compenent types.
This is an interesting idea from Scott—a templating language that doesn’t just replace variables with values, but keeps the original variable names in there too.
Not sure how I feel about using data- attributes for this though; as far as I know, they’re intended to be site-specific, not for cross-site solutions like this.
Back in 2017 when I was in New York, I went on a self-guided infrastructure tour: 32 Avenue of the Americas, 60 Hudson Street, and the subject of this article, 33 Thomas Street. One of my pictures is used to illustrate its creepiness, both in real life and as an evil lair in fiction:
A windowless telecommunications hub, 33 Thomas Street in New York City embodies an architecture of surveillance and paranoia. That has made it an ideal set for conspiracy thrillers.
Analytics serves as a proxy for understanding people, a crutch we lean into. Until eventually, instead of solving problems, we are just sitting at our computer counting ghosts.
This article is spot-on!