Tags: air

In an Irish session, tunes are almost never played in isolation. They’re played in sets.

A set of tunes might be as few as two. More usually, it’s three or more.

It’s unusual to change from one tune type into another. You tend to get a set of jigs, or a set of reels, or a set of hornpipes. But it’s very common to change key within a set. In fact, that’s often where a good set really stands out. There can be a real joy at that moment of switching. You might get a “Hup!” from someone listening to the session at that changeover.

So how do you decide what tunes to play in a set?

There are no real rules to this. Some people make up the set on the fly. Or you might try playing a set that you’ve heard other people play, maybe on a recording you like.

On the one hand, you’re looking for contrast. You probably don’t want to play three tunes all in the same key. On the other hand, it’s nice when there’s some kind of connection between the tunes—something about the phrasing or emphasis perhaps.

Pairing tunes for sets always reminds me of pairing typefaces. You don’t want the body copy and the headlines to be too similar, but you do want them to share some quality.

In his classic book, On Web Typography, Jason says:

When it comes to choosing and pairing typefaces, I keep two things in mind: distinction and harmony. To keep the system you’ve created for visual communication properly balanced, you need to choose typefaces that don’t compete too much with each other, but aren’t so similar as to be indistinguishable.

The same could be said for pairing tunes in sets!

Jason also says:

As another approach, opt for typefaces that share the same maker.

That can work for sets of tunes too. While most tunes are traditional, with no known composer, the really good composed tunes have entered the canon.

I’ve taken Jason’s advice for typefaces and applied to sets by playing a set of tunes by Junior Crehan or a set of tunes by Vincent Broderick.

Mostly though, there’s no real system to it. Or at least, not one that can be easily articulated. Like Jason says:

And we’re back to that old chestnut about rules: there are many right answers, and no answers are really wrong; there are just different degrees of good.

David Chisnall (*Now with 50% more sarcasm!*): “I think this needs to be repeated…”

Machine learning is amazing if … the value of a correct answer is much higher than the cost of an incorrect answer.

Related to Laissez-faire Cognitive Debt:

And that’s where I start to get really annoyed by a lot of the LLM hype. It’s pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it’s doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It’s great for translation! Unless a mistranslated word could kill a business deal or start a war. It’s great for summarisation! Unless missing a key point could cost you a load of money. It’s great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.

Laissez-faire Cognitive Debt – Smithery

I think of Cognitive Debt as ‘where we have the answers, but not the thinking that went into producing those answers’.

Lately, I have started noticing examples of not just where the debt is being accrued, but who then has the responsibility to pick it up and repay it.

Too often, an LLM doesn’t replace the need for thinking in a group setting, but simply creates more work for others.

I was supposed to be in Cork over the weekend.

Not only was it high time I paid my mother a visit, but the Cork Folk Festival was happening too. So I booked some relatively cheap plane tickets for myself and Jessica back in August and noted down the days in my calendar.

We didn’t end up getting our flight. This time it wasn’t because I messed up the flight times. We made it to Gatwick airport in plenty of time. That’s when we saw that our flight was delayed.

See, Storm Amy was moving in. We were hoping to get into Cork before the storm hit, but now with the flight delayed, that wasn’t likely. In fact, it was more likely that the flight would just get more and more delayed and possibly even get diverted.

To start with, we settled in at the airport, prepared to spend an hour or two more than we had planned. But, sure enough, the delays kept piling up. It was time to make decision; wait it out in the hopes that we’d eventually make a very bumpy landing in Cork, or cut our losses and run.

I quickly looked up flights for the next couple of weekends and spotted some cheap ones a fortnight away. I booked them and then called my mother to tell her we wouldn’t be showing up that night, but we’d see her in a couple of weeks.

Alright then, time to go home.

Wait a minute …how exactly are we supposed to leave the airport other than on an airplane?

It turns out there’s a process called “decontrolling”. You need to find someone from the airline (or in our case, a third-party contractor operating on the airline’s behalf) who has the security privileges to walk you back out. But you still need to go through passport control …even though you never left the building.

Luckily we didn’t have any checked luggage or it would’ve gotten complicated. As it was, it was just weird. It was all very City And The City.

That flight we abandoned did finally take off …six hours late. By that time we were back home on the sofa eating pizza and listening to the wind gusting outside. I think we made the right choice.

Dan wrote an interesting post with a somewhat clickbaity title; This Competition Exposed How AI is Reshaping Design:

I watched two designers go head-to-head in a high-speed battle to create the best landing page in 45 minutes. One was a seasoned pro. The other was a non-designer using AI.

If you can ignore the title (and the fact that Dan still actively posts on Twitter; something I find very hard to ignore), then there’s a really thoughtful analysis in there.

It’s less about one platform or tool vs. another more than it is a commentary on how design happens, and whether or not that’s changing in a significant way.

In particular, there’s a very revealing graph that shows the pros and cons of both approaches.

There’s no doubt about it, using a generative large language model helped a non-designer to get past the blank page. But it was less useful in subsequent iterations that rely on decision-making:

I’ve said it before and I’ll say it again: design is deciding. The best designers are the best deciders.

Dan finishes by saying that what he’d really like to see is an experienced designer/decider using these tools to turbo-boost their process:

AI raises the floor for non-designers. But can it raise the ceiling for designers?

Meanwhile, Matt has been writing about Vibe-designing. Matt is an experienced designer, but he’s not experienced with Figma. He’s found that he can work around that using a large language model:

Where in the past 30 years I might have had to cajole a more technically adept colleague into making something through sketches, gesticulating and making sound effects – I open up a Claude window and start what-iffing.

The “vibe” part of the equation often defaults to the mean, which is not a surprise when you think about what you’re asking to help is a staggeringly-massive machine for producing generally-unsurprising satisfactory answers quickly. So, you look at the output as a basis for the next sketch, and the next sketch and quickly, together, you move to something more novel as a result.

Interesting! Just as Dan insisted, the important work is making the decision and moving on to the next stage. If the actual outputs at each stage are mediocre, that seems to be okay, as long as they’re just good enough to inform a go/no-go decision.

This certainly seems more centaur-like than the usual boring uses of large language models to simply do what people are already doing.

Rich gets at something similar when he talks about using large language models for prototyping, where it’s okay if the code is kind of shitty:

If all you need is crappy code to try out a concept or a solution, then an LLM might well enable you (the designer) to do that.

Mind you, even if you do end up finding useful and appropriate ways to use these tools, you’re still using a tool built on exploitation and unfairness:

It’s hard (and reckless) to ignore the heartfelt and cogent perspective laid out by Miriam on the role of AI companies in the current geopolitical crisis:

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.

Another uncalled-for blog post about the ethics of using AI | Clagnut by Richard Rutter

This is a really thoughtful piece by Rich, who’s got conflicted feelings about large language models in the design process. I suspect a lot of people can relate to this.

What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.

“Wait, not like that”: Free and open access in the age of generative AI

Anyone at an AI company who stops to think for half a second should be able to recognize they have a vampiric relationship with the commons. While they rely on these repositories for their sustenance, their adversarial and disrespectful relationships with creators reduce the incentives for anyone to make their work publicly available going forward (freely licensed or otherwise). They drain resources from maintainers of those common repositories often without any compensation.

Even if AI companies don’t care about the benefit to the common good, it shouldn’t be hard for them to understand that by bleeding these projects dry, they are destroying their own food supply.

And yet many AI companies seem to give very little thought to this, seemingly looking only at the months in front of them rather than operating on years-long timescales. (Though perhaps anyone who has observed AI companies’ activities more generally will be unsurprised to see that they do not act as though they believe their businesses will be sustainable on the order of years.)

It would be very wise for these companies to immediately begin prioritizing the ongoing health of the commons, so that they do not wind up strangling their golden goose. It would also be very wise for the rest of us to not rely on AI companies to suddenly, miraculously come to their senses or develop a conscience en masse.

Instead, we must ensure that mechanisms are in place to force AI companies to engage with these repositories on their creators’ terms.

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.

Is it okay?

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

How Undersea Cables Connect the Global Internet

Cables!

DOJ, Nvidia, and why we restrict monopolies | Ian Betteridge

This observation seems intuitively obvious in Europe and pearl-clutchingly shocking in America:

What’s perfectly acceptable behaviour when you are a relatively small company becomes outright illegal (and rightly so) when you become dominant in an industry.

The invisible seafaring industry that keeps the internet afloat

A fascinating in-depth look at the maintenance of undersea cables:

The industry responsible for this crucial work traces its origins back far beyond the internet, past even the telephone, to the early days of telegraphy. It’s invisible, underappreciated, analog.

Snook’s Law:

It’s a truism that people don’t think about infrastructure until it breaks, but they tend not to think about the fixing of it, either.

CSS { In Real Life } | Stop Using AI-Generated Images

I have yet to meet anyone who wants to hang AI art on their walls (although I fully expect to see it in hotel chains).

Today was a good day. The weather was beautiful.

Jessica and I did a little bit of work in the garden—nothing too sweaty. Then Jessica cut my hair. It looks good. And it feels good to have my neck freed up.

We went for a Sunday roast at the nearest pub, which does a most excellent carvery. It was tasty and plentiful so after strolling home, I wanted to do nothing more than sit around.

I sat outside in the back garden under the dappled shade offered by the overhanging trees. I had a good book. I had my mandolin to hand. I’d reach for it occassionally to play a tune or two.

Coco the cat—not our cat—sat nearby, stretching her paws out lazily in the warm muggy air.

It was a good day.

Google AMP: how Google tried to fix the web by taking it over - The Verge

AMP succeeded spectacularly. Then it failed. And to anyone looking for a reason not to trust the biggest company on the internet, AMP’s story contains all the evidence you’ll ever need.

This is a really good oral history of how AMP soured Google’s reputation.

Full disclosure: I’m briefly cited:

“When it suited them, it was open-source,” says Jeremy Keith, a web developer and a former member of AMP’s advisory council. “But whenever there were any questions about direction and control… it was Google’s.”

As an aside, this article contains a perfect description of the company cultures of Facebook, Apple, and Google:

“You meet with a Facebook person and you see in their eyes they’re psychotic,” says one media executive who’s dealt with all the major platforms. “The Apple person kind of listens but then does what it wants to do. The Google person honestly thinks what they’re doing is the best thing.”

Spot. On.

What The Last of Us, Snowpiercer and ‘climate fiction’ get wrong - BBC Culture

I not only worry that “cli-fi” might not be an effective form of environmental expression – I have come to believe that the genre might be actively dangerous, stunting our cultural ability to imagine a future worth living in or fighting for.

The one about AI - macwright.com

Writing, both code and prose, for me, is both an end product and an end in itself. I don’t want to automate away the things that give me joy.

And that is something that I’m more and more aware of as I get older – sources of joy. It’s good to diversify them, to keep track of them, because it’s way too easy to run out. Or to end up with just one, and then lose it.

The thing about luddites is that they make good punchlines, but they were all people.

What happened when we disabled Google AMP at Tribune Publishing?

Shockingly little. So you should try it, too.

My comments to Competition and Markets Authority on mobile browser competition - Alistair Shepherd

A thoughtful response to the current CMA consultation:

The inability to compete with native apps using Progressive Web Apps fully—particularly on iOS—also has a big impact on my work and the businesses I have worked with. Progressive Web Apps are extremely accessible for development, allowing for the creation of a simple app in a fraction of the time and complexity of a native app. This is fantastic for allowing smaller agencies and businesses to innovate on the web and on mobile devices and to reach consumers. However the poor support for PWA features by Safari and by not allowing them in the App Store, Apple forces app development to be difficult, time consuming and extremely expensive. I have spoken with many companies who would have liked an app to compete with their larger competitors but are unable to afford the huge costs in developing a native app.

Get your response in by Friday by emailing [email protected].

What serif typeface would go well with Proxima Nova?

Mark Simonson goes into the details of his lovely new typeface Proxima Sera.