Back in the 1980s and early 1990s, I had a collection of 5¼” and later 3½” floppy disks1 on which were stored a variety of games and utilities that I’d
collected over the years2.
I had lots of floppy disks that looked almost-exactly like this: a scrawled label of their contents and notes on how to make use of them that would perhaps only make sense to me.
I remember that at some point I acquired a program called INSULTS.COM. When executed, this tool would spoof a basic terminal prompt and then, when the user pressed any key,
output a randomly-generated assortment of crude insults.
Do you feel thoroughly insulted yet?
As far as prank programs go, it was far from sophisticated. I strongly suspect that the software, which was released for free in 1983, was intended to be primarily a vehicle to promote
sales of a more-complex set of tools called PRANKS, which was advertised within.
In any case: as a pre-pubescent programmer I remember being very interested in the mechanism by which INSULTS.COM was generating its output.
I partially-reverse-engineered the permutations by polling the output and looking for parts I hadn’t seen before, and tallying them up. Mostly in an effort to validate the program’s
claim that it’s capable of generating “more than 22 million insults”3.
Of course, nowadays I understand reverse-engineering better than I did as a child. So I downloaded a copy of INSULTS.COM from this Internet Archive image, ran it through Strings, and pulled out the data.
Easy!
Wait for it, and you can be be insulted all over again!
Why did I do this? Why do I do anything? Reimplementing a 42-year-old piece of DOS software that nobody remembers is even stranger than that time I reimplemented a 16-year old Flash advertisement! But I hope it gave you a moment’s joy to be told that you’re… an annoying load of
festering parrot droppings, or whatever.
Footnotes
1 Also some 3″ floppy disks – a weird and rare format – but that’s another story.
2 My family’s Amstrad PC1512
had two 5¼” disk drives, which made disk-to-disk copying much easier than it was on computers with a single disk drive, on which you’d have to copy as much data as possible
to RAM, swap disks to write what had been copied so far, swap disks back again, and repeat. This made it less-laborious for me to clone media than it was for most other folks I knew.
3 Assuming the random number generator is capable of generating a sufficient diversity of
seed values, the claim is correct: by my calculation, INSULTS.COM can generate 22,491,833 permutations of insults.
I have A Plan for today. Step #2 involves a deep-dive into Algolia search indexing, ranking, and priority, to understand how one might optimise for a diverse and complex dataset.
So obviously step #1 involves a big ol’ coffee and a sugary breakfast. Here we go…
It turns out my seriesofefforts to improve the BBC News RSS feeds are more-popular
than I thought. People keep asking for variants of them, and it’s probably time I stopped hosting the resulting feeds on my NAS (which does a good job,
but it’s in a highly-kickable place right under my desk).
The new site isn’t pretty. But it works.
So I’ve launched BBC-Feeds.DanQ.dev. On a 20-minute schedule, it generates both UK and World editions of the BBC News feeds,
filtered to remove iPlayer, Sounds, app “nudges”, duplicates, and other junk, and optionally with the sports news filtered out too.
Their inclusion of non-news content such as plugs for iPlayer and their apps,
Their repeating of identical news stories with marginally-different GUIDs, and
All of the sports news, which I don’t care about one jot.
Well, it turns out that some people want #3: the sport. But still don’t want the other two.
Some people actually want to read this crap, apparently.
I shan’t be subscribing to this RSS feed, and I can’t promise I’ll fix it if it gets broken. But if “without the crap, but with the sports” is the way you like your BBC News RSS feed,
I’ve got you covered:
the world needs more recreational programming.
like, was this the most optimal or elegant way to code this?
no, but it was the most fun to write.
Yes. This.
As Baz Luhrmann didn’t say, despite the implications of this video: code one thing every day that amuses you.
There is no greater way to protest the monetisation of the Web, the descent into surveillance capitalism, and the monoculture of centralised social media silos… than to create things
just for the hell of it. Maybe that’s Kirby eating a blog post. Maybe that’s whatever slippy stuff Lu put out this week. Maybe it’s a podcast exclusively about random things that interest one person.
The pre-corporate Web was fun and weird. Nowadays, keeping the Internet fun and weird is relegated to a
counterculture.
But making things (whether code, or writing, or videos, or whatever) “just because” is a critical part of that counterculture. It’s beautiful art flying in the face of rampant
commercialism. The Web provides a platform where you can share what you create: it doesn’t have to be good, or original, or world-changing… there’s value in just creating and giving
things away.
Even when it’s technical, not all of my International Volunteer Day work for Three Rings has been spent using our key technologies (LNMR [Linux, Nginx, MariaDB, Ruby] stacks).
Today, I wrote some extra PHP for our WordPress-powered contact form to notify our Support Team volunteers via Slack when messages are sent. We already aim to respond to every message
within 24 hours, 365 days a year, and are often faster than that… but this might help us to be even more-responsive to the needs of the charities who we help look after.
Not every code review is fireworks, but most Three Rings changes come from the actual needs of the voluntary organisations that Three Rings supports. Some of our users were confused by
the way the Admin > Roles page was laid out, so one of our volunteers wrote an improved version.
And because we’re all about collaboration, discussion, learning from one another, and volunteer-empowerment… I’ve added a minor suggestion… but approved their change “with or without”
it. I trust my fellow volunteer to either accept my suggestion (if it’s right), reject it (if it’s wrong), or solicit more reviews or bring it to Slack or our fortnightly dev meeting
(if it requires discussion).
Good news! It turns out that the new code to fix the mail merge fields in Three Rings doesn’t introduce an inconsistency with established behaviour. It was important to check, but it
turns out all is well.
I touched bases with a fellow volunteer on Slack. Three Rings volunteers primarily communicate via Slack: it helps us to work asynchronously, which supports the fact that our volunteers
all have different schedules and preferences. Some might do a couple of evenings a week, others might do the odd weekend, others still might do an occasional intense solid week of
volunteering with us and then nothing for months! A communication model that works both synchronously and asynchronously is really important to make that volunteering model work, and
Slack fits the bill.
We get together in person sometimes, and we meet on Zoom from time to time too, but Slack is king of communication at Three Rings
My first task this International Volunteer Day is to test a pull request that aims to fix a bug with Three Rings’ mail merge fields functionality. As it’s planned to be a hotfix (direct
into production) we require extra rigor and more reviewers than code that just goes into the main branch for later testing on our beta environment.
My concern is that fixing this bug might lead to a regression not described by our automated tests, so I’m rolling back to a version from a couple of months ago to compare the behaviour
of the affected tool then and now. Sometimes you just need some hands-on testing!
The Beeb continue to keep adding more and more non-news content to the BBC News RSS feed (like this ad for the iPlayer app!), so I’ve once again had to update my script to “fix” the feed so that it only contains, y’know, news.
This morning’s actual breakfast order from the 7-year-old: “A sesame seed bagel with honey, unless there aren’t any sesame seed bagels, in which case a plain bagel with honey on one
half and jam on the other half, unless there aren’t any plain bagels, in which case a cinnamon and raisin bagel with JimJams on one half and Biscoff on the other half.”
Some day, this boy will make a great LISP programmer. 😂
I’ve never been even remotely into Sex and the City. But I can’t help but love that this developer was so invested in the characters and their relationships that when
he asked himself “couldn’t all this drama and heartache have been simplified if these characters were willing to consider polyamorous relationships rather than serial
monogamy?”1,
he did the maths to optimise his hypothetical fanfic polycule:
As if his talk at !!Con 2024 wasn’t cool enough, he open-sourced the whole thing, so you’re free to try the calculator online for yourself or expand upon or adapt it to your heart’s content. Perhaps you disagree with his assessment of the
relative relationship characteristics of the characters2: tweak them and
see what the result is!
Or maybe Sex and the City isn’t your thing at all? Well adapt it for whatever your fandom is! How I Met Your Mother,Dawson’s Creek, Mamma
Mia and The L-Word were all crying out for polyamory to come and “fix” them3.
Perhaps if you’re feeling especially brave you’ll put yourself and your circles of friends, lovers, metamours, or whatever into the algorithm and see who it matches up. You never know,
maybe there’s a love connection you’ve missed! (Just be ready for the possibility that it’ll tell you that you’re doing your love life “wrong”!)
Footnotes
1 This is a question I routinely find myself asking of every TV show that presents a love
triangle as a fait accompli resulting from an even moderately-complex who’s-attracted-to-whom.
2 Clearly somebody does, based on his commit “against his will” that increases Carrie and Big’s
validatesOthers scores and reduces Big’s prioritizesKindness.
3 I was especially disappointed with the otherwise-excellent The L-Word, which
did have a go at an ethical non-monogamy storyline but bungled the “ethical” at every hurdle while simultaneously reinforcing the “insatiable bisexual” stereotype. Boo!
Anyway: maybe on my next re-watch I’ll feed some numbers into Juan’s algorithm and see what comes out…
Perhaps inspired by my resharing of Thomas‘s thoughts about the biggest problem in
AI (tl;dr: he thinks it’s nomenclature; I agree that’s a problem but I don’t know if it’s the biggest issue), Ruth posted some thoughts to LinkedIn that I think are quite well-put:
I was going to write about something else but since LinkedIn suggested I should get AI to do it for me, here’s where I currently stand on GenAI.
As a person working in computing, I view it as a tool that is being treated as a silver bullet and is probably self-limiting in its current form. By design, it produces average
code. Most companies prior to having access to cheap average code would have said they wanted good code. Since the average code produced by the tools is being fed back into those
tools, mathematically this can’t lead anywhere good in terms of quality.
However, as a manager in tech I’m really alarmed by it. If we have tools to write code that is ok but needs a lot of double checking, we might be tempted to stop hiring
people at that level. There already aren’t enough jobs for entry level programmers to feed the talent pipeline, and this is likely to make it worse. I’m not sure where the next
generation of great programmers are supposed to come from if we move to an ecosystem where the junior roles are replaced by Copilot.
I think there’s a lot of potential for targeted tools to speed up productivity. I just don’t think GenAI is where they should come from.
This is an excellent explanation of no fewer than four of the big problems with “AI” as we’re seeing it marketed today:
It produces mediocre output, (more on that below!)
It’s a snake that eats its own tail,
It’s treated as a silver bullet, and
By pricing out certain types of low-tier knowledge work, it damages the pipeline for training higher-tiers of those knowledge workers (e.g. if we outsource all first-level tech
support to chatbots, where will the next generation of third-level tech support come from, if they can’t work their way up the ranks, learning as they go?)
Let’s stop and take a deeper look at the “mediocre output” claim. Ruth’s right, but if you don’t already understand why generative AI does this, it’s worth a
little bit of consideration about the reason for it… and the consequences of it:
Mathematically-speaking, that’s exactly what you would expect for something that is literally statistically averaging content, but that still comes as a surprise to people.
Bear in mind, of course, that there are plenty of topics in which the average person is less-knowledgable than the average of the content that was made available to the model.
For example, I know next to noting about fertiliser application in large-scale agriculture. ChatGPT has doubtless ingested a lot of literature about it, and if I ask it what
fertiliser I should use for a field of black beans in silty soil in the UK, it delivers me a confident-sounding answer:
Who knows if this answer is right, of course! If the answer mattered to me – because I was about to drill my field – I’d have to do my own research to check, by which point I
might as well have just done the research in the first place. If all I cared about was a quick sense-check to an answer I already knew, and it didn’t matter too much, this might be
okay output. (It’s pretty verbose and repeats itself a lot, like it’s learned how to talk from YouTube tutorials: I’m surprised it didn’t finish by exhorting me to like and
subscribe!)
When LLMs produce exceptional output (I use the term exceptional in the sense of unusual and not-average, not to mean “good”), it appears more-creative and interesting but is even
more-likely to be riddled with fanciful hallucinations.
There’s a fine line in getting the creativity dial set just right, and even when you do there’s no guarantee of accuracy, but the way in which many chatbots are told to talk makes them
sound authoritative on basically every subject. When you know it’s lying, that’s easy. But people don’t always use LLMs for subjects they’re knowledgeable about!
I asked ChatGPT to define five words for me. Two (“quantifiable” and “ethology”) are real words that somebody might have trouble with. One (“no cap”) is a slang term. One
(“erinaceiophobia” is a logically-sound construction from the Latin name for the biological family that hedgehogs belong to and the Greek suffix that’s applied to irrational fears).
ChatGPT came up with perfectly reasonable definitions of all of these. But it also confidently defined “undercontrastised”, a word I made up and which I can’t find used anywhere at
all!
In my example above, a more-useful robot would have stated that it didn’t know the answer to the question rather than, y’know, lying. But the nature
of the statistical models used by LLMs means that they can’t know what they don’t know: they don’t have a “known unknowns” space.
Regarding the “damages the training pipeline”: I’m undecided on whether or not I agree with Ruth. She might be on to something there, but I’m not sure. Needs more
thought before I commit to an opinion on that one.
Oh, and an addendum to this – as a human, I find the proliferation of AI tools in spaces that are all about creating connections with other humans deeply concerning. I saw a lot of
job applications through Otta at my previous role, and they were all kind of the same – I had no sense of the person behind the averaged out CV I was looking at. We already have a
huge problem with people presenting inauthentic versions of themselves on social media which makes it harder to have genuine interactions, smoothing off the rough edges of real people
to get something glossy and processed is only going to make this worse.
AI posts on social media are the chicken nuggets of human interaction and I’d rather have something real every time.
Emphasis mine… because that’s a fantastic metaphor. Content generated where a generative AI is trying to “look human” are so-often bland, flat, and unexciting: a mass-produced
most-basic form of social sustenance. So yeah: chicken nuggets.
Ironically, I might’ve gotten a better picture here if I’d asked AI to draw this for me, because I couldn’t find any really unappetising-looking McDonalds-grade chicken nuggets on the
stock photography site I used.
