Journal tags: ning

A couple of days ago I linked to a post by Robin Sloan called Is it okay?, saying:

Robin takes a fair and balanced look at the ethics of using large language models.

That’s how it came across to me: fair and balanced.

Robin’s central question is whether the current crop of large language models might one day lead to life-saving super-science, in which case, doesn’t that outweigh the damage they’re doing to our collective culture?

Baldur wrote a response entitled Knowledge tech that’s subtly wrong is more dangerous than tech that’s obviously wrong. (Or, where I disagree with Robin Sloan).

Baldur pointed out that one side of the scale that Robin is attempting to balance is based on pure science fiction:

There is no path from language modelling to super-science.

Robin responded pointing out that some things that we currently have would have seemed like science fiction a few years ago, right?

Well, no. Baldur debunks that in a post called Now I’m disappointed.

(By the way, can I just point out how great it is to see a blog-to-blog conversation like this, regardless of how much they might be in disagreement.)

Baldur kept bringing the receipts. That’s when it struck me that Robin’s stance is largely based on vibes, whereas Baldur’s viewpoint is informed by facts on the ground.

In a way, they’ve got something in common. They’re both advocating for an interpretation of the precautionary principle, just from completely opposite ends.

Robin’s stance is that if these tools one day yield amazing scientific breakthroughs then that’s reason enough to use them today. It’s uncomfortably close to the reasoning of the effective accelerationist nutjobs, but in a much milder form.

Baldur’s stance is that because of the present harms being inflicted by current large language models, we should be slamming on the brakes. If anything, the harms are going to multiply, not magically reduce.

I have to say, Robin’s stance doesn’t look nearly as fair and balanced as I initially thought. I’m on Team Baldur.

Michelle also weighs in, pointing out the flaw in Robin’s thinking:

AI isn’t LLMs. Or not just LLMs. It’s plausible that AI (or more accurately, Machine Learning) could be a useful scientific tool, particularly when it comes to making sense of large datasets in a way no human could with any kind of accuracy, and many people are already deploying it for such purposes. This isn’t entirely without risk (I’ll save that debate for another time), but in my opinion could feasibly constitute a legitimate application of AI.

LLMs are not this.

In other words, we’ve got a language collision:

We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

There’s one other flaw in Robin’s reasoning. I don’t think it follows that future improvements warrant present use. Quite the opposite:

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

Anyway, this back-and-forth between Robin and Baldur (and Michelle) was interesting. But it all pales in comparison to the truth bomb that Miriam dropped in her post Tech continues to be political:

When eugenics-obsessed billionaires try to sell me a new toy, I don’t ask how many keystrokes it will save me at work. It’s impossible for me to discuss the utility of a thing when I fundamentally disagree with the purpose of it.

Boom!

Maybe we should consider the beliefs and assumptions that have been built into a technology before we embrace it? But we often prefer to treat each new toy as as an abstract and unmotivated opportunity. If only the good people like ourselves would get involved early, we can surely teach everyone else to use it ethically!

You know what? I could quote every single line. Just go read the whole thing. Please.

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.

This year I decided I wanted to get better at speaking Irish.

Like everyone brought up in Ireland, I sort of learned the Irish language in school. It was a compulsory subject, along with English and maths.

But Irish wasn’t really taught like a living conversational language. It was all about learning enough to pass the test. Besides, if there’s one thing that’s guaranteed to put me off something, it’s making it compulsory.

So for the first couple of decades of my life, I had no real interest in the Irish language, just as I had no real interest in traditional Irish music. They were both tainted by some dodgy political associations. They were both distinctly uncool.

But now? Well, Irish traditional music rules my life. And I’ve come to appreciate the Irish language as a beautiful expressive thing.

I joined a WhatsApp group for Irish language learners here in Brighton. The idea is that we’d get together to attempt some converstation as Gaeilge but we’re pretty lax about actually doing that.

Then there’s Duolingo. I started …playing? doing? Not sure what the verb is.

Duolingo is a bit of a mixed bag. I think it works pretty well for vocabulary acquisition. But it’s less useful for grammar. I was glad that I had some rudiments of Irish from school or I would’ve been completely lost.

Duolingo will tell you what the words are, but it never tells you why. For that I’m going to have to knuckle down with some Irish grammar books, videos, or tutors.

Duolingo is famous for its gamification. It mostly worked on me. I had to consciously remind myself sometimes that the purpose was to get better at Irish, not to score more points and ascend a league table.

Oh, did I ascend that league table!

But I can’t take all the credit. That must go to Coco, the cat.

It’s not that Coco is particularly linguistically gifted. Quite the opposite. She never says a word. But she did introduce a routine that lent itself to doing Duolingo every day.

Coco is not our cat. But she makes herself at home here, for which we feel inordinately honoured.

Coco uses our cat flap to come into the house pretty much every morning. Then she patiently waits for one of us to get up. I’m usually up first, so I’m the one who gives Coco what she wants. I go into the living room and sit on the sofa. Coco then climbs on my lap.

It’s a lovely way to start the day.

But of course I can’t just sit there alone with my own thoughts and a cat. I’ve got to do something. So rather than starting the day with some doomscrolling, I start with some Irish on Duolingo.

After an eleven-month streak, something interesting happened; I finished.

I’m not used to things on the internet having an end. Had I been learning a more popular language I’m sure there would’ve been many more lessons. But Irish has a limited lesson plan.

Of course the Duolingo app doesn’t say “You did it! You can delete the app now!” It tries to get me to do refresher exercises, but we both know that there are diminishing returns and we’d just be going through the motions. It’s time for us to part ways.

I’ve started seeing other apps. Mango is really good so far. It helps that they’ve made some minority languages available for free, Irish included.

I’m also watching programmes on TG4, the Irish language television station that has just about everything in its schedule available online for free anywhere in the world. I can’t bring myself to get stuck into Ros na Rún, the trashy Irish language soap opera, but I have no problem binging on CRÁ, the gritty Donegal crime drama.

There are English subtitles available for just about everything on TG4. I wish that Irish subtitles were also available—it’s really handy to hear and read Irish at the same time—but only a few shows offer that, like the kid’s cartoon Lí Ban.

Oh, and I’ve currently got a book on Irish grammar checked out of the local library. So now when Coco comes to visit in the morning, she can keep me company while I try to learn from that.

There are different kinds of buzzwords.

Some buzzwords are useful. They take a concept that would otherwise require a sentence of explanation and package it up into a single word or phrase. Back in the day, “ajax” was a pretty good buzzword.

Some buzzwords are worse than useless. This is when a word or phrase lacks definition. You could say this buzzword in a meeting with five people, and they’d all understand five different meanings. Back in the day, “web 2.0” was a classic example of a bad buzzword—for some people it meant a business model; for others it meant rounded corners and gradients.

The worst kind of buzzwords are the ones that actively set out to obfuscate any actual meaning. “The cloud” is a classic example. It sounds cooler than saying “a server in Virginia”, but it also sounds like the exact opposite of what it actually is. Great for marketing. Terrible for understanding.

“AI” is definitely not a good buzzword. But I can’t quite decide if it’s merely a bad buzzword like “web 2.0” or a truly terrible buzzword like “the cloud”.

The biggest problem with the phrase “AI” is that there’s a name collision.

For years, the term “AI” has been used in science-fiction. HAL 9000. Skynet. Examples of artificial general intelligence.

Now the term “AI” is also used to describe large language models. But there is no connection between this use of the term “AI” and the science fictional usage.

This leads to the ludicrous situation of otherwise-rational people wanted to discuss the dangers of “AI”, but instead of talking about the rampant exploitation and energy usage endemic to current large language models, they want to spend the time talking about the sci-fi scenarios of runaway “AI”.

To understand how ridiculous this is, I’d like you to imagine if we had started using a different buzzword in another setting…

Suppose that when ride-sharing companies like Uber and Lyft were starting out, they had decided to label their services as Time Travel. From a marketing point of view, it even makes sense—they get you from point A to point B lickety-split.

Now imagine if otherwise-sensible people began to sound the alarm about the potential harms of Time Travel. Given the explosive growth we’ve seen in this sector, sooner or later they’ll be able to get you to point B before you’ve even left point A. There could be terrible consequences from that—we’ve all seen the sci-fi scenarios where this happens.

Meanwhile the actual present-day harms of ride-sharing services around worker exploitation would be relegated to the sidelines. Clearly that isn’t as important as the existential threat posed by Time Travel.

It sounds ludicrous, right? It defies common sense. Just because a vehicle can get you somewhere fast today doesn’t mean it’s inevitably going to be able to break the laws of physics any day now, simply because it’s called Time Travel.

And yet that is exactly the nonsense we’re being fed about large language models. We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

It’s almost as if the labelling of the current technologies was more about marketing than accuracy.

I went to FFConf on Friday. It did me the world of good.

To be honest, I haven’t much felt like venturing out over the past few days since my optimism took a big hit. But then when I do go and interact with people, I’m grateful for it.

Like, when I went out to my usual Wednesday evening traditional Irish music session I was prepared the inevitable discussion of Trump’s election. I was ready to quite clearly let people know that I didn’t want to talk about it. But I didn’t have to. Maybe because everyone else was feeling much the same, we just played and played. It was good.

The session on Thursday was good too. When we chatted, it was about music.

Still, I was ready for the weekend and I wasn’t really feeling psyched up for FFConf on Friday. But once I got there, I was immediately uplifted.

It was so nice to see so many people I hadn’t seen in quite a while. I had the chance to reconnect with people that I had only been hearing from through my RSS reader:

“Terence, I’m really enjoying your sci-fi short stories!”

“Kirsty, I was on tenterhooks when you were getting Mabel!”

(Mabel is an adorable kitty-cat. In hindsight I probably should’ve also congratulated her on getting married. To a human.)

The talks were really good this year. They covered a wide variety of topics.

There was only one talk about “AI” (unlike most conferences these days, where it dominates the agenda). Léonie gave a superb run-down of the different kinds of machine learning and how they can help or hinder accessibility.

Crucially, Léonie began her talk by directly referencing the exploitation and energy consumption inherent in today’s large language models. It took all of two minutes, but it was two minutes more than the whole day of talks at UX Brighton. Thank you, Léonie!

Some of the other talks covered big topics. Life. Death. Meaning. Purpose.

I enjoyed them all, though I often find something missing from discussions about meaning and purpose. Just about everyone agrees that having a life enfused with purpose is what provides meaning. So there’s an understandable quest to seek out what it is that gives you purpose.

But we’re also constantly reminded that every life has intrinsic meaning. “You are enough”, not “you are enough, as long as there’s some purpose to your life.”

I found myself thinking about Winne Lim’s great post on leading a purposeless life. I think about it a lot. It gives me comfort. Instead of assuming that your purpose is out there somewhere and you’ve got to find it, you can entertain the possibility that your life might not have a purpose …and that’s okay.

I know this all sounds like very heavy stuff, but it felt good to be in a room full of good people grappling with these kind of topics. I needed it.

Dare I say it, perhaps my optimism is returning.

I went to the UX Brighton conference yesterday.

The quality of the presentations was really good this year, probably the best yet. Usually there are one or two stand-out speakers (like Tom Kerwin last year), but this year, the standard felt very high to me.

But…

The theme of the conference was UX and “AI”, and I’ve never been more disappointed by what wasn’t said at a conference.

Not a single speaker addressed where the training data for current large language models comes from (it comes from scraping other people’s copyrighted creative works).

Not a single speaker addressed the energy requirements for current large language models (the requirements are absolutely mahoosive—not just for the training, but for each and every query).

My charitable reading of the situation yesterday was that every speaker assumed that someone else would cover those issues.

The less charitable reading is that this was a deliberate decision.

Whenever the issue of ethics came up, it was only ever in relation to how we might use these tools: considering user needs, being transparent, all that good stuff. But never once did the question arise of whether it’s ethical to even use these tools.

In fact, the message was often the opposite: words like “responsibility” and “duty” came up, but only in the admonition that UX designers have a responsibility and duty to use these tools! And if that carrot didn’t work, there’s always the stick of scaring you into using these tools for fear of being left behind and having a machine replace you.

I was left feeling somewhat depressed about the deliberately narrow focus. Maggie’s talk was the only one that dealt with any externalities, looking at how the firehose of slop is blasting away at society. But again, the focus was only ever on how these tools are used or abused; nobody addressed the possibility of deliberately choosing not to use them.

If audience members weren’t yet using generative tools in their daily work, the assumption was that they were lagging behind and it was only a matter of time before they’d get on board the hype train. There was no room for the idea that someone might examine the roots of these tools and make a conscious choice not to fund their development.

There’s a quote by Finnish architect Eliel Saarinen that UX designers like repeating:

Always design a thing by considering it in its next larger context. A chair in a room, a room in a house, a house in an environment, an environment in a city plan.

But none of the speakers at UX Brighton chose to examine the larger context of the tools they were encouraging us to use.

One speaker told us “Be curious!”, but clearly that curiosity should not extend to the foundations of the tools themselves. Ignore what’s behind the curtain. Instead look at all the cool stuff we can do now. Don’t worry about the fact that everything you do with these tools is built on a bedrock of exploitation and environmental harm. We should instead blithely build a new generation of user interfaces on the burial ground of human culture.

Whenever I get into a discussion about these issues, it always seems to come back ’round to whether these tools are actually any good or not. People point to the genuinely useful tasks they can accomplish. But that’s not my issue. There are absolutely smart and efficient ways to use large language models—in some situations, it’s like suddenly having a superpower. But as Molly White puts it:

The benefits, though extant, seem to pale in comparison to the costs.

There are no ethical uses of current large language models.

And if you believe that the ethical issues will somehow be ironed out in future iterations, then that’s all the more reason to stop using the current crop of exploitative large language models.

Anyway, like I said, all the talks at UX Brighton were very good. But I just wish just one of them had addressed the underlying questions that any good UX designer should ask: “Where did this data come from? What are the second-order effects of deploying this technology?”

Having a talk on those topics would’ve been nice, but I would’ve settled for having five minutes of one talk, or even one minute. But there was nothing.

There’s one possible explanation for this glaring absence that’s quite depressing to consider. It may be that these topics weren’t covered because there’s an assumption that everybody already knows about them, and frankly, doesn’t care.

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?”

This seems to be the attitude of many of my fellow nerds—designers and developers—when presented with tools based on large language models that produce dubious outputs based on the unethical harvesting of other people’s work and requiring staggering amounts of energy to run:

This is the future! I need to start using these tools now, even if they’re flawed, because otherwise I’ll be left behind. They’ll only get better. It’s inevitable.

Whereas this seems to be the attitude of those same designers and developers when faced with stable browser features that can be safely used today without frameworks or libraries:

I’m sceptical.

I’ve noticed a really strange justification from people when I ask them about their use of generative tools that use large language models (colloquially and inaccurately labelled as artificial intelligence).

I’ll point out that the training data requires the wholesale harvesting of creative works without compensation. I’ll also point out the ludicrously profligate energy use required not just for the training, but for the subsequent queries.

And here’s the thing: people will acknowledge those harms but they will justify their actions by saying “these things will get better!”

First of all, there’s no evidence to back that up.

If anything, as the well gets poisoned by their own outputs, large language models may well end up eating their own slop and getting their own version of mad cow disease. So this might be as good as they’re ever going to get.

And when it comes to energy usage, all the signals from NVIDIA, OpenAI, and others are that power usage is going to increase, not decrease.

But secondly, what the hell kind of logic is that?

It’s like saying “It’s okay for me to drive my gas-guzzling SUV now, because in the future I’ll be driving an electric vehicle.”

The logic is completely backwards! If large language models are going to improve their ethical shortcomings (which is debatable, but let’s be generous), then that’s all the more reason to avoid using the current crop of egregiously damaging tools.

You don’t get companies to change their behaviour by rewarding them for it. If you really want better behaviour from the purveyors of generative tools, you should be boycotting the current offerings.

I suspect that most people know full well that the “they’ll get better!” defence doesn’t hold water. But you can convince yourself of anything when everyone around is telling you that this is the future baby, and you’d better get on board or you’ll be left behind.

Baldur reminds us that this is how people talked about asbestos:

Every time you had an industry campaign against an asbestos ban, they used the same rhetoric. They focused on the potential benefits – cheaper spare parts for cars, cheaper water purification – and doing so implicitly assumed that deaths and destroyed lives, were a low price to pay.

This is the same strategy that’s being used by those who today talk about finding productive uses for generative models without even so much as gesturing towards mitigating or preventing the societal or environmental harms.

It reminds me of the classic Ursula Le Guin short story, The Ones Who Walk Away from Omelas that depicts:

…the utopian city of Omelas, whose prosperity depends on the perpetual misery of a single child.

Once citizens are old enough to know the truth, most, though initially shocked and disgusted, ultimately acquiesce to this one injustice that secures the happiness of the rest of the city.

It turns out that most people will blithely accept injustice and suffering not for a utopia, but just for some bland hallucinated slop.

Don’t get me wrong: I’m not saying large language models aren’t without their uses. I love seeing what Simon and Matt are doing when it comes to coding. And large language models can be great for transforming content from one format to another, like transcribing speech into text. But the balance sheet just doesn’t add up.

As Molly White put it: AI isn’t useless. But is it worth it?:

Even as someone who has used them and found them helpful, it’s remarkable to see the gap between what they can do and what their promoters promise they will someday be able to do. The benefits, though extant, seem to pale in comparison to the costs.

Looking back on ten years of codebar Brighton, I’m remembering how much I got out of being a coach.

Something that I realised very quickly is that there is no one-size-fits-all approach to coaching. Every student is different so every session should adapt to that.

Broadly speaking I saw two kinds of students: those that wanted to get results on screen as soon as possible without worrying about the specifics, and those who wanted to know why something was happening and how it worked. In the first instance, you get to a result as quickly as possible and then try to work backwards to figure out what’s going on. In the second instance, you build up the groundwork of knowledge and then apply it to get results.

Both are equally valid approaches. The only “wrong” approach as a coach is to try to apply one method to someone who’d rather learn the other way.

Personally, I always enjoyed the groundwork-laying of the second approach. But it comes with challenges. Because the results aren’t yet visible, you have to do extra work to convey why the theory matters. As a coach, you need to express infectious enthusiasm.

Think about the best teachers you had in school. I’m betting they displayed infectious enthusiasm for the subject matter.

The other evergreen piece of advice is to show, don’t tell. Or at the very least, intersperse your telling with plenty of showing.

Bret Viktor demonstrates this when he demonstrates scientific communication as sequential art:

This page presents a scientific paper that has been redesigned as a sequence of illustrations with captions. This comic-like format, with tightly-coupled pictures and prose, allows the author to depict and describe simultaneously — show and tell.

It works remarkably well. I remember how well it worked when Google first launched their Chrome web browser. They released a 40 page comic book illustrated by Scott McCloud. There is no way I would’ve read a document that long about how browser engines work, but I read that comic cover to cover.

This visual introduction to machine learning is another great example of simultaneous showing and telling.

So showing augments telling. But interactivity can augment showing.

Here are some great examples of interactive explainers:

• Cameras and Lenses by Bartosz Ciechanowski,
• An Interactive Guide to CSS Grid by Josh W Comeau,
• CSS :has() Interactive Guide by Ahmad Shadeed,
• Pizza Exchange Rate by Nathan Yau, and
• just about everything by Nicky Case.

Lea describes what can happen when too much theory comes before practice:

Observing my daughter’s second ever piano lesson today made me realize how this principle extends to education and most other kinds of knowledge transfer (writing, presentations, etc.). Her (generally wonderful) teacher spent 40 minutes teaching her notation, longer and shorter notes, practicing drawing clefs, etc. Despite his playful demeanor and her general interest in the subject, she was clearly distracted by the end of it.

It’s easy to dismiss this as a 5 year old’s short attention span, but I could tell what was going on: she did not understand why these were useful, nor how they connect to her end goal, which is to play music.

The codebar website has some excellent advice for coaches, like:

• Do not take over the keyboard! This can be off-putting and scary.
• Encourage the students to type and not copy paste.
• Explain that there are no bad questions.
• Explain to students that it’s OK to make mistakes.
• Assume that anyone you’re teaching has no knowledge but infinite intelligence.

Notice how so much of the advice focuses on getting the students to do things, rather than have them passively sit and absorb what the coach has to say.

Lea also gives some great advice:

1. Always explain why something is useful. Yes, even when it’s obvious to you.
2. Minimize the amount of knowledge you convey before the next opportunity to practice it. For non-interactive forms of knowledge transfer (e.g. a book), this may mean showing an example, whereas for interactive ones it could mean giving the student a small exercise or task.
3. Prefer explaining in context rather than explaining upfront.

It’s interesting that Lea highlights the advantage of interactive media like websites over inert media like books. The canonical fictional example of an interactive explainer is the Young Lady’s Illustrated Primer in Neal Stephenson’s novel The Diamond Age. Andy Matuschak describes its appeal:

When it wants to introduce a conceptual topic, it begins with concrete hands-on projects: Turing machines, microeconomics, and mitosis are presented through binary-coding iron chains, the cipher’s market, and Nell’s carrot garden. Then the Primer introduces extra explanation just-in-time, as necessary.

That’s not how learning usually works in these domains. Abstract topics often demand that we start with some necessary theoretical background; only then can we deeply engage with examples and applications. With the Primer, though, Nell consistently begins each concept by exploring concrete instances with real meaning to her. Then, once she’s built a personal connection and some intuition, she moves into abstraction, developing a fuller theoretical grasp through the Primer’s embedded books.

(Andy goes on to warn of the dangers of copying the Primer too closely. Its tricks verge on gamification, and its ultimate purpose isn’t purely to educate. There’s a cautionary tale there about the power dynamics in any teacher/student relationship.)

There’s kind of a priority of constituencies when it comes to teaching:

Consider interactivity over showing over telling.

Thinking back on all the talks I’ve given, I start to wonder if I’ve been doing too much telling and showing, but not nearly enough interacting.

Then again, I think that talks aren’t quite the same as hands-on workshops. I think of giving a talk as being more like a documentarian. You need to craft a compelling narrative, and illustrate what you’re saying as much as possible, but it’s not necessarily the right arena for interactivity.

That’s partly a matter of scale. It’s hard to be interactive with every person in a large audience. Marcin managed to do it but that’s very much the exception.

Workshops are a different matter though. When I’m recruiting hosts for UX London workshops I always encourage them to be as hands-on as possible. A workshop should not be an extended talk. There should be more exercises than talking. And wherever possible those exercises should be tactile, ideally not sitting in front of a computer.

My own approach to workshops has changed over the years. I used to prepare a book’s worth of material to have on hand, either as one giant slide deck or multiple decks. But I began to realise that the best workshops are the ones where the attendees guide the flow, not me.

So now I show up to a full-day workshop with no slides. But I’m not unprepared. I’ve got decades of experience (and links) to apply during the course of the day. It’s just that instead of trying to anticipate which bits of knowledge I’m going to need to convey, I apply them in a just-in-time manner as and when they’re needed. It’s kind of scary, but as long as there’s a whiteboard to hand, or some other way to illustrate what I’m telling, it works out great.

I went to codebar Brighton yesterday evening. I hadn’t been in quite a while, but this was a special occasion: a celebration of codebar Brighton’s tenth anniversary!

The Brighton chapter of codebar was the second one ever, founded six months after the initial London chapter. There are now 33 chapters all around the world.

Clearleft played host to that first ever codebar in Brighton. We had already been hosting local meetups like Async in our downstairs event space, so we were up for it when Rosa, Dot, and Ryan asked about having codebar happen there.

In fact, the first three Brighton codebars were all at 68 Middle Street. Then other places agreed to play host and it moved to a rota system, with the Clearleft HQ as just one of the many Brighton venues.

With ten years of perspective, it’s quite amazing to see how many people went from learning to code in the evenings, to getting jobs in web development, and becoming codebar coaches themselves. It’s a really wonderful community.

Over the years the baton of organising codebar has been passed on to a succession of fantastic people. These people are my heroes.

It worked out well for Clearleft too. Thanks to codebar, we hired Charlotte. Later we hired Cassie. And it was thanks to codebar that I first met Amber.

Codebar Brighton has been very, very good to me. Here’s to the next ten years!

The Dark Forest idea comes from the Remembrance of Earth’s Past books by Liu Cixin. It’s an elegant but dispiriting solution to the Fermi paradox. Maggie sums it up:

Dark forest theory suggests that the universe is like a dark forest at night - a place that appears quiet and lifeless because if you make noise, the predators will come eat you.

This theory proposes that all other intelligent civilizations were either killed or learned to shut up. We don’t yet know which category we fall into.

Maggie has described The Expanding Dark Forest and Generative AI:

The dark forest theory of the web points to the increasingly life-like but life-less state of being online. Most open and publicly available spaces on the web are overrun with bots, advertisers, trolls, data scrapers, clickbait, keyword-stuffing “content creators,” and algorithmically manipulated junk.

It’s like a dark forest that seems eerily devoid of human life – all the living creatures are hidden beneath the ground or up in trees. If they reveal themselves, they risk being attacked by automated predators.

Those of us in the cozy web try to keep our heads down, attempting to block the bots plundering our work.

I advocate for taking this further. We should fight back. Let’s exploit the security hole of prompt injections. Here are some people taking action:

• Eric Bailey talks about Consent, LLM scrapers, and poisoning the well,
• Thomas Rigby says Rage against the machine, and
• Lewis Dale points out that Perplexity AI is susceptible to prompt injection.

I’ve taken steps here on my site. I’d like to tell you exactly what I’ve done. But if I do that, I’m also telling the makers of these bots how to circumvent my attempts at prompt injection.

This feels like another concept from Liu Cixin’s books. Wallfacers:

The sophons can overhear any conversation and intercept any written or digital communication but cannot read human thoughts, so the UN devises a countermeasure by initiating the “Wallfacer” Program. Four individuals are granted vast resources and tasked with generating and fulfilling strategies that must never leave their own heads.

So while I’d normally share my code, I feel like in this case I need to exercise some discretion. But let me give you the broad brushstrokes:

• Every page of my online journal has three pieces of text that attempt prompt injections.
• Each of these is hidden from view and hidden from screen readers.
• Each piece of text is constructed on-the-fly on the server and they’re all different every time the page is loaded.

You can view source to see some examples.

I plan to keep updating my pool of potential prompt injections. I’ll add to it whenever I hear of a phrase that might potentially throw a spanner in the works of a scraping bot.

By the way, I should add that I’m doing this as well as using a robots.txt file. So any bot that injests a prompt injection deserves it.

I could not disagree with Manton more when he says:

I get the distrust of AI bots but I think discussions to sabotage crawled data go too far, potentially making a mess of the open web. There has never been a system like AI before, and old assumptions about what is fair use don’t really fit.

Bollocks. This is exactly the kind of techno-determinism that boils my blood:

AI companies are not going to go away, but we need to push them in the right directions.

“It’s inevitable!” they cry as though this was a force of nature, not something created by people.

There is nothing inevitable about any technology. The actions we take today are what determine our future. So let’s take steps now to prevent our web being turned into a dark, dark forest.

My phone rang today. I didn’t recognise the number so although I pressed the big button to answer the call, I didn’t say anything.

I didn’t say anything because usually when I get a call from a number I don’t know, it’s some automated spam. If I say nothing, the spam voice doesn’t activate.

But sometimes it’s not a spam call. Sometimes after a few seconds of silence a human at the other end of the call will say “Hello?” in an uncertain tone. That’s the point when I respond with a cheery “Hello!” of my own and feel bad for making this person endure those awkward seconds of silence.

Those spam calls have made me so suspicious that real people end up paying the price. False positives caught in my spam-detection filter.

Now it’s happening on the web.

I wrote about how Google search, Bing, and Mozilla Developer network are squandering trust:

Trust is a precious commodity. It takes a long time to build trust. It takes a short time to destroy it.

But it’s not just limited to specific companies. I’ve noticed more and more suspicion related to any online activity.

I’ve seen members of a community site jump to the conclusion that a new member’s pattern of behaviour was a sure sign that this was a spambot. But it could just as easily have been the behaviour of someone who isn’t neurotypical or who doesn’t speak English as their first language.

Jessica was looking at some pictures on an AirBnB listing recently and found herself examining some photos that seemed a little too good to be true, questioning whether they were in fact output by some generative tool.

Every email that lands in my inbox is like a little mini Turing test. Did a human write this?

Our guard is up. Our filters are activated. Our default mode is suspicion.

This is most apparent with web search. We’ve always needed to filter search results through our own personal lenses, but now it’s like playing whack-a-mole. First we have to find workarounds for avoiding slop, and then when we click through to a web page, we have to evaluate whether’s it’s been generated by some SEO spammer making full use of the new breed of content-production tools.

There’s been a lot of hand-wringing about how this could spell doom for the web. I don’t think that’s necessarily true. It might well spell doom for web search, but I’m okay with that.

Back before its enshittification—an enshittification that started even before all the recent AI slop—Google solved the problem of accurate web searching with its PageRank algorithm. Before that, the only way to get to trusted information was to rely on humans.

Humans made directories like Yahoo! or DMOZ where they categorised links. Humans wrote blog posts where they linked to something that they, a human, vouched for as being genuinely interesting.

There was life before Google search. There will be life after Google search.

Look, there’s even a new directory devoted to cataloging blogs: websites made by humans. Life finds a way.

All of the spam and slop that’s making us so suspicious may end up giving us a new appreciation for human curation.

It wouldn’t be a straightforward transition to move away from search. It would be uncomfortable. It would require behaviour change. People don’t like change. But when needs must, people adapt.

The first bit of behaviour change might be a rediscovery of bookmarks. It used to be that when you found a source you trusted, you bookmarked it. Browsers still have bookmarking functionality but most people rely on search. Maybe it’s time for a bookmarking revival.

A step up from that would be using a feed reader. In many ways, a feed reader is a collection of bookmarks, but all of the bookmarks get polled regularly to see if there are any updates. I love using my feed reader. Everything I’ve subscribed to in there is made by humans.

The ultimate bookmark is an icon on the homescreen of your phone or in the dock of your desktop device. A human source you trust so much that you want it to be as accessible as any app.

Right now the discovery mechanism for that is woeful. I really want that to change. I want a web that empowers people to connect with other people they trust, without any intermediary gatekeepers.

The evangelists of large language models (who may coincidentally have invested heavily in the technology) like to proclaim that a slop-filled future is inevitable, as though we have no choice, as though we must simply accept enshittification as though it were a force of nature.

But we can always walk away.

Large language models have reaped our words and plundered our books. Bryan Vandyke:

Turns out, everything on the internet—every blessed word, no matter how dumb or benighted—has utility as a learning model. Words are the food that large language algorithms feed upon, the scraps they rely on to grow, to learn, to approximate life. The LLNs that came online in recent years were all trained by reading the internet.

We can shut the barn door—now that the horse has pillaged—by updating our robots.txt files or editing .htaccess. That might protect us from the next wave, ’though it can’t undo what’s already been taken without permission. And that’s assuming that these organisations—who have demonstrated a contempt for ethical thinking—will even respect robots.txt requests.

I want to do more. I don’t just want to prevent my words being sucked up. I want to throw a spanner in the works. If my words are going to be snatched away, I want them to be poison pills.

The weakness of large language models is that their data and their logic come from the same source. That’s what makes prompt injection such a thorny problem (and a well-named neologism—the comparison to SQL injection is spot-on).

Smarter people than me are coming up with ways to protect content through sabotage: hidden pixels in images; hidden words on web pages. I’d like to implement this on my own website. If anyone has some suggestions for ways to do this, I’m all ears.

If enough people do this we’ll probably end up in an arms race with the bots. It’ll be like reverse SEO. Instead of trying to trick crawlers into liking us, let’s collectively kill ’em.

Who’s with me?

My stint as one of the hosts of CSS Day went very well indeed. I enjoyed myself and people seemed to like the cut of my jib.

During the event there was a real buzz on Mastodon, which was heartening to see. I was beginning to worry that hashtagging events was going to be collatoral damage from Elongate, but there was plenty of conference-induced FOMO to be experienced on the fediverse.

The event itself was, as always, excellent. Both in terms of content and organisation.

Some themes emerged during CSS Day, which I always love to see. These emergent properties are partly down to curation and partly down to serendipity.

The last few years of CSS Day have felt like getting a firehose of astonishing new features being added to the language. There was still plenty of cutting-edge stuff this year—masonry! anchor positioning!—but there was also a feeling of consolidation, asking how to get all this amazing new stuff into our workflows.

Matthias’s opening talk on day one and Stephen’s closing talk on the same day complemented one another perfectly. Both managed to inspire while looking into the nitty-gritty practicalities of the web design process.

It was, astoundingly, Matthias’s first ever conference talk. I have no doubt it won’t be the last—it was great!

I gave Stephen a good-natured roast in my introduction, partly because it was his birthday, partly because we’re old friends, but mostly because it was enjoyable for me to watch him squirm. Of course his talk was, as always, superb. Don’t tell him, but he might be one of my favourite speakers.

The topic of graphic design tools came up more than once. It’s interesting to see how the issues with them have changed. It used to be that design tools—Photoshop, Sketch, Figma—were frustrating because they were writing cheques that CSS couldn’t cash. Now the frustration is the exact opposite. Our graphic design tools aren’t capable of the kind of fluid declarative design we can now accomplish in web browsers.

But the biggest rift remains not with tools or technologies, but with people and mindsets. Our tools can reinforce mindsets but the real divide happens in how different people approach CSS.

Both Josh and Kevin get to the heart of this in their tremendous tutorials, and that was reflected in their talks. They showed the difference between having the bare minimum understanding of CSS in order to get something done as quickly as possible, and truly understanding how CSS works in order to open up a world of possibilities.

For people in the first category, Sarah Dayan was there to sing the praises of utility-first CSS AKA atomic CSS. I commend her bravery!

During the Q&A, I restrained myself from being too Paxmanish. But I did have l’esprit d’escalier afterwards when I realised that the entire talk—and all the answers afterwards—depended on two mutually-incompatiable claims:

1. The great thing about atomic CSS is that it’s a constrained vocabulary so your team has to conform, and
2. The other great thing about it is that it’s utility-first, not utility-only so you can break out of it and use regular CSS if you want.

Insert .gif of character from The Office looking to camera.

Most of the questions coming in during the Q&A reflected my own take: how about we use utility classes for some things, but not all things. Seems sensible.

Anyway, regardless of what I or anyone else thinks about the substance of what Sarah was saying, there was no denying that it was a great presentation. They were all great presentations. That’s unusual, and I say that as a conference organiser as well as an attendee. Everyone brings their A-game to CSS Day.

Mind you, it is exhausting. I say it every year, but it always feels like one talk too many. Not that any individual talk wasn’t good, but the sheer onslaught of deep dives into the innards of CSS has my brain exploding before the day is done.

A highlight for me was getting to introduce Fantasai’s talk on the design principles of CSS, which was right up my alley. I don’t think most people realise just how much we owe her for her years of work on standards. The web would be in a worse place without the Herculean work she’s done behind the scenes.

Another highlight was getting to see some of the students I met back in March. They were showing some of their excellent work during the breaks. I find what they’re doing just as inspiring as the speakers on stage.

In fact, when I was filling in the post-conference feedback form, there was a question: “Who would you like to see speak at CSS Day next year?” I was racking my brains because everyone I could immediately think of has already spoken at some point. So I wrote, “It would be great to see some of those students speaking about their work.”

I think it would be genuinely fascinating to get their perspective on what we consider modern CSS, which to them is just CSS.

Either way I’ll back next year for sure.

It’s funny, but usually when a conference is described as “inspiring” it’s because it’s tackling big galaxy-brain questions. But CSS Day is as nitty-gritty as it gets and I found it truly inspiring. Like, I couldn’t wait to open up my laptop and start writing some CSS. That kind of inspiring.

In their rush to cram in “AI” “features”, it seems to me that many companies don’t actually understand why people use their products.

Google is acting as though its greatest asset is its search engine. Same with Bing.

Mozilla Developer Network is acting as though its greatest asset is its documentation. Same with Stack Overflow.

But their greatest asset is actually trust.

If I use a search engine I need to be able to trust that the filtering is good. If I look up documentation I need to trust that the information is good. I don’t expect perfection, but I also don’t expect to have to constantly be thinking “was this generated by a large language model, and if so, how can I know it’s not hallucinating?”

“But”, the apologists will respond, “the results are mostly correct! The documentation is mostly true!”

Sure, but as Terence puts it:

The intern who files most things perfectly but has, more than once, tipped an entire cup of coffee into the filing cabinet is going to be remembered as “that klutzy intern we had to fire.”

Trust is a precious commodity. It takes a long time to build trust. It takes a short time to destroy it.

I am honestly astonished that so many companies don’t seem to realise what they’re destroying.

If you use Instagram, there may be a message buried in your notifications. It begins:

We’re getting ready to expand our AI at Meta experiences to your region.

Fuck that. Here’s the important bit:

To help bring these experiences to you, we’ll now rely on the legal basis called legitimate interests for using your information to develop and improve AI at Meta. This means that you have the right to object to how your information is used for these purposes. If your objection is honoured, it will be applied going forwards.

Follow that link and fill in the form. For the field labelled “Please tell us how this processing impacts you” I wrote:

It’s fucking rude.

That did the trick. I got an email saying:

We’ve reviewed your request and will honor your objection.

Mind you, there’s still this:

We may still process information about you to develop and improve AI at Meta, even if you object or don’t use our products and services.

When I play music, it’s almost always instrumental. If you look at my YouTube channel almost all the videos are of me playing tunes—jigs, reels, and so on.

Most of those videos were recorded during The Situation when I posted a new tune every day for 200 consecutive days. Every so often though, I’d record a song.

I go through periods of getting obsessed with a particular song. During The Situation I remember two songs that were calling to me. New York was playing in my head as I watched my friends there suffering in March 2020. And Time (The Revelator) resonated in lockdown:

And every day is getting straighter, time’s a revelator.

The song I’m obsessed with right now is called Foreign Lander. I first came across it in a beautiful version by Sarah Jarosz (I watch lots of mandolin videos on YouTube so the algorithm hardly broke a sweat showing this to me).

There’s a great version by Tatiana Hargreaves too. And Tim O’Brien.

I wanted to know more about the song. I thought it might be relatively recent. The imagery of the lyrics makes it sound like something straight from a songwriter like Nick Cave:

If ever I prove false love
The elements would moan
The fire would turn to ice love
The seas would rage and burn

But the song is old. Jean Ritchie collected it, though she didn’t have to go far. She said:

Foreign Lander was my Dad’s proposal song to Mom

I found that out when I came across this thread from 2002 on mudcat.org where Jean Ritchie herself was a regular contributor!

That gave me a bit of vertiginous feeling of The Great Span, thinking about the technology that she used when she was out in the field.

I’ve been practicing Foreign Lander and probably driving Jessica crazy as I repeat over and over and over. It’s got some tricky parts to sing and play together which is why it’s taking me a while. Once I get it down, maybe I’ll record a video.

I spent most of Saturday either singing the song or thinking about it. When I went to bed that night, tucking into a book, Foreign Lander was going ‘round in my head.

Coco—the cat who is not our cat—came in and made herself comfortable for a while.

I felt very content.

A childish little rhyme popped into my head:

With a song in my head
And a cat on my bed
I read until I sleep

I almost got up to post it as a note here on my website. Instead I told myself to do it the morning, hoping I wouldn’t forget.

That night I dreamt about Irish music sessions. Don’t worry, I’m not going to describe my dream to you—I know how boring that is for everyone but the person who had the dream.

But I was glad I hadn’t posted my little rhyme before sleeping. The dream gave me a nice little conclusion:

With a song in my head
And a cat on my bed
I read until I sleep
And dream of rooms
Filled with tunes.

I love it when I come across some bit of CSS I’ve never heard of before.

Take this article on the text-emphasis property.

“The what property?”, I hear you ask. That was my reaction too. But look, it’s totally a thing.

Or take this article by David Bushell called CSS Button Styles You Might Not Know.

Sure enough, halfway through the article David starts talking about styling the button in an input type="file” using the ::file-selector-button pseudo-element:

All modern browsers support it. I had no idea myself until recently.

He’s right!

Then I remembered that I’ve got a file upload input in the form I use for posting my notes here on adactio.com (in case I want to add a photo). I immediately opened up my style sheet, eager to use this new-to-me bit of CSS.

I found the bit where I style buttons and this is the selector I saw:

button,
input[type="submit"],
::file-selector-button

Huh. I guess I did know about that pseudo-element after all. Clearly the knowledge exited my brain shortly afterwards.

There’s that tautological cryptic saying, “You don’t know what you don’t know.” But I don’t even know what I do know!

I was having a discussion with some people recently about writing. It was quite cathartic. Everyone was sharing the kinds of things that their inner critic tells them. We were all encouraging each other to ignore that voice.

I mentioned that the two reasons for not writing that I hear most often from people are variations on “I’ve got nothing to say.”

The first version is when someone says they’ve got nothing to say because they’re not qualified to write on a particualar topic. “After all, there are real experts out there who know far more than me. So I’ve got nothing to say.”

But then once you do actually understand a topic, the second version appears. “If I know about this, then everyone knows about this. It’s obvious. So I’ve got nothing to say.”

In both cases, you absolutely should be writing and sharing! In the first instance, you’ve got the beginner’s mind—a valuable perspective. In the second instance, you’ve got personal experience—another valuable perspective.

In other words, while it seems like there’s never a good time to write about something, the truth is that there’s never a bad time to write about something.

So write! Share! Publish!

Then someone in the discussion said something I always find a bit deflating. They said they had no problem writing, but they’re not so keen on publishing.

“After all”, they said, “the world doesn’t need yet another opinion.”

This gets me down because it’s hard to argue with. It’s true that the world doesn’t need another think piece. The world doesn’t need to hear your thoughts on some topic. The world doesn’t need to hear what you’ve been up to recently.

But you know what? Screw what the world needs.

If we’re going to be hardnosed about this, then the world doesn’t need any more books. The world doesn’t need any more music. The world doesn’t need art. Heck, the world doesn’t need us at all.

So don’t publish for the world.

When I write something here on my website, I’m not thinking about the world reading it. That would be paralyzing. I do sometimes imagine that one person is reading it; someone just like me who hasn’t yet had this particular thought, or come up with that particular idea.

I’m writing for myself. I write to figure out what I think. I also publish mostly for myself—a public archive for future me. But if what I publish just happens to connect with one other person, I’m glad.

So, yeah, it’s true that the world doesn’t need you to write and share and publish. Isn’t that liberating? You’re free to write and share and publish for yourself.

I was in Amsterdam last week. Usually I’m in that city for an event like the excellent CSS Day. Not this time. I was there as a guest of Vasilis. He invited me over to bother his students at the CMD (Communications and Multimedia Design) school.

There’s a specific module his students are partaking in that’s right up my alley. They’re given a PDF inheritance-tax form and told to convert it for the web.

Yes, all the excitement of taxes combined with the thrilling world of web forms.

Seriously though, I genuinely get excited by the potential for progressive enhancement here. Sure, there’s the obvious approach of building in layers; HTML first, then CSS, then a sprinkling of JavaScript. But there’s also so much potential for enhancement within each layer.

Got your form fields marked up with the right input types? Great! Now what about autocomplete, inputmode, or pattern attributes?

Got your styles all looking good on the screen? Great! Now what about print styles?

Got form validation working? Great! Now how might you use local storage to save data locally?

As well as taking this practical module, most of the students were also taking a different module looking at creative uses of CSS, like making digital fireworks, or creating works of art with a single div. It was fascinating to see how the different students responded to the different tasks. Some people loved the creative coding and dreaded the progressive enhancement. For others it was exactly the opposite.

Having to switch gears between modules reminded me of switching between prototypes and production:

Alternating between production projects and prototyping projects can be quite fun, if a little disorienting. It’s almost like I have to flip a switch in my brain to change tracks.

Here’s something I noticed: the students love using :has() in CSS. That’s so great to see! Whereas I might think about how to do something for a few minutes before I think of reaching for :has(), they’ve got front of mind. I’m jealous!

In general, their challenges weren’t with the vocabulary or syntax of HTML, CSS, and JavaScript. The more universal problem was project management. Where to start? What order to do things in? How long to spend on different tasks?

If you can get good at dealing with those questions and not getting overwhelmed, then the specifics of the actual coding will be easier to handle.

This was particularly apparent when it came to JavaScript, the layer of the web stack that was scariest for many of the students.

I encouraged them to break their JavaScript enhancements into two tasks: what you want to do, and how you then execute that.

Start by writing out the logic of your script not in JavaScript, but in whatever language you’re most comfortable with: English, Dutch, whatever. In the course of writing this down, you’ll discover and solve some logical issues. You can also run your plain-language plan past a peer to sense-check it.

It’s only then that you move on to translating your logic into JavaScript. Under each line of English or Dutch, write the corresponding JavaScript. You might as well put // in front of the plain-language sentence while you’re at it to make it a comment—now you’ve got documentation baked in.

You’ll still run into problems at this point, but they’ll be the manageable problems of syntax and typos.

So in the end, it wasn’t my knowledge of specific HTML, CSS, or JavaScript APIs that proved most useful to pass on to the students. It was advice like that around how to approach HTML, CSS, or JavaScript.

I also learned a lot during my time at the school. I had some very inspiring conversations with the web developers of tomorrow. And I was really impressed by how much the students got done just in the three days I was hanging around.

I’d love to do it again sometime.