Cryosleep

On the last day of UX London this year, I was sitting and chatting with Rachel Coldicutt who was going to be giving the closing keynote. Inevitably the topic of converstation worked its way ’round to “AI”. I remember Rachel having a good laugh when I summarised my overall feeling:

I kind of wish I could go into suspended animation and be woken up when all this is over and things have settled down one way or another.

I still feel that way. Like Gina, I’d welcome a measured approach to this technology. As Anil puts it:

Technologies like LLMs have utility, but the absurd way they’ve been over-hyped, the fact they’re being forced on everyone, and the insistence on ignoring the many valid critiques about them make it very difficult to focus on legitimate uses where they might add value.

I very much look forward to using language models (probably small and local) to automate genuinely tedious tasks. That’s a very different vision to what the slopagandists are pushing. Or, like Paul Ford says:

Make it boring. That’s what’s interesting.

Fortunately, my cryosleep-awakening probably isn’t be too far off. You can smell it in the air, that whiff of a bubble about to burst. And while it will almost certainly be messy, it’s long overdue.

Paul Ford again:

I’ve felt so alienated from tech over the past couple of years. Part of it is the craven authoritarianism. It dampens the mood. But another part is the monolithic narrative—the fact that we live in a world where there seem to be only a few companies, only a few stories going at any time, and everything reduces to politics. God, please let it end.

Vibe code is legacy code | Val Town Blog

When you vibe code, you are incurring tech debt as fast as the LLM can spit it out. Which is why vibe coding is perfect for prototypes and throwaway projects: It’s only legacy code if you have to maintain it!

The worst possible situation is to have a non-programmer vibe code a large project that they intend to maintain. This would be the equivalent of giving a credit card to a child without first explaining the concept of debt.

If you don’t understand the code, your only recourse is to ask AI to fix it for you, which is like paying off credit card debt with another credit card.

Vibe coding and Robocop

The short version of what I want to say is: vibe coding seems to live very squarely in the land of prototypes and toys. Promoting software that’s been built entirely using this method would be akin to sending a hacked weekend prototype to production and expecting it to be stable.

Remy is taking a very sensible approach here:

I’ve used it myself to solve really bespoke problems where the user count is one.

Would I put this out to production: absolutely not.

Keeping up appearances | deadSimpleTech

Looking at LLM usage and promotion as a cultural phenomenon, it has all of the markings of a status game. The material gains from the LLM (which are usually quite marginal) really aren’t why people are doing it: they’re doing it because in many spaces, using ChatGPT and being very optimistic about AI being the “future” raises their social status. It’s important not only to be using it, but to be seen using it and be seen supporting it and telling people who don’t use it that they’re stupid luddites who’ll inevitably be left behind by technology.

What I’ve learned about writing AI apps so far | Seldo.com

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.

AI and Asbestos: the offset and trade-off models for large-scale risks are inherently harmful – Baldur Bjarnason

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.