Automation

I just described prototype code as code to be thrown away. On that topic…

I’ve been observing how people are programming with large language models and I’ve seen a few trends.

The first thing that just about everyone agrees on is that the code produced by a generative tool is not fit for public consumption. At least not straight away. It definitely needs to be checked and tested. If you enjoy debugging and doing code reviews, this might be right up your street.

The other option is to not use these tools for production code at all. Instead use them for throwaway code. That could be prototyping. But it could also be the code for those annoying admin tasks that you don’t do very often.

Take content migration. Say you need to grab a data dump, do some operations on the data to transform it in some way, and then pipe the results into a new content management system.

That’s almost certainly something you’d want to automate with bespoke code. Once the content migration is done, the code can be thrown away.

Read Matt’s account of coding up his Braggoscope. The code needed to spider a thousand web pages, extract data from those pages, find similarities, and output the newly-structured data in a different format.

I’ve noticed that these are just the kind of tasks that large language models are pretty good at. In effect you’re training the tool on your own very specific data and getting it to do your drudge work for you.

To me, it feels right that the usefulness happens on your own machine. You don’t put the machine-generated code in front of other humans.

AI wants to rule the World, but it can’t handle dairy.

AI has the same problem that I saw ten year ago at IBM. And remember that IBM has been at this AI game for a very long time. Much longer than OpenAI or any of the new kids on the block. All of the shit we’re seeing today? Anyone who worked on or near Watson saw or experienced the same problems long ago.

THE AI CON - How to Fight Big Tech’s Hype and Create the Future We Want

A shame that this must-read book won’t be out in time for Christmas—’twould make a great stocking filler for a lot of people I know.

A smart, incisive look at the technologies sold as artificial intelligence, the drawbacks and pitfalls of technology sold under this banner, and why it’s crucial to recognize the many ways in which AI hype covers for a small set of power-hungry actors at work and in the world.

Tabloid: the clickbait headline programming language

Tabloid is a turing-complete programming language for writing programs in the style of clickbait news headlines.

Lessons learned in 35 years of making software – Jim Grey

Number one:

Do things in the most straightforward way possible. It’s easy to fall into the trap of clever solutions, or clever applications of technology, or overbuilding something because you’re anticipating the future. Don’t do it. You will hate yourself for it later when you have to maintain it.

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.