Posts Tagged programming
Edit: As of 8 January, 2021, @realdonaldtrump is no longer a Twitter user, but he was at the time of this post.
Version 2.0.1 of my iOS app NastyWriter has 184 different insults (plus two extra special secret non-insults that appear rarely for people who’ve paid to remove ads 🤫) which it can automatically add before nouns in the text you enter. “But Angela,” I hear you not asking, “you’re so incredibly nice! How could you possibly come up with 184 distinct insults?” and I have to admit, while I’ve been known to rap on occasion, I have not in fact been studying the Art of the Diss — I have a secret source. (This is a bonus joke for people with non-rhotic accents.)
My secret source is the Trump Twitter Archive. Since NastyWriter is all about adding gratuitous insults immediately before nouns, which Twitter user @realdonaldtrump is such a dab hand at, I got almost all of the insults from there. But I couldn’t stand to read it all myself, so I wrote a Mac app to go through all of the tweets and find every word that seemed to be an adjective immediately before a noun. I used NSLinguisticTagger, because the new Natural Language framework did not exist when I first wrote it.
Natural language processing is not 100% accurate, because language is complicated — indeed, the app thought ‘RT’, ‘bit.ly’, and a lot of twitter @usernames (most commonly @ApprenticeNBC) and hashtags were adjectives, and the usernames and hashtags were indeed used as adjectives (usually noun adjuncts) e.g. in ‘@USDOT funding’. One surprising supposed adjective was ‘gsfsgh2kpc’, which was in a shortened URL mentioned 16 times, to a site which Amazon CloudFront blocks access to from my country.
For each purported adjective the app found, I had a look at how it was used before adding it to NastyWriter’s insult collection. Was it really an adjective used before a noun? Was it used as an insult? Was it gratuitous? Were there any other words it was commonly paired with, making a more complex insult such as ‘totally conflicted and discredited’, or ‘frumpy and very dumb’? Was it often in allcaps or otherwise capitalised in a specific way?
But let’s say we don’t care too much about that and just want to know roughly which adjectives he used the most. Can you guess which is the most common adjective found before a noun? I’ll give you a hint: he uses it a lot in other parts of sentences too. Here are the top 35 as of 6 November 2020:
- ‘great’ appears 4402 times
- ‘big’ appears 1351 times
- ‘good’ appears 1105 times
- ‘new’ appears 1034 times
- ‘many’ appears 980 times
- ‘last’ appears 809 times
- ‘best’ appears 724 times
- ‘other’ appears 719 times
- ‘fake’ appears 686 times
- ‘American’ appears 592 times
- ‘real’ appears 510 times
- ‘total’ appears 509 times
- ‘bad’ appears 466 times
- ‘first’ appears 438 times
- ‘next’ appears 407 times
- ‘wonderful’ appears 375 times
- ‘amazing’ appears 354 times
- ‘only’ appears 325 times
- ‘political’ appears 310 times
- ‘beautiful’ appears 298 times
- ‘fantastic’ appears 279 times
- ‘tremendous’ appears 270 times
- ‘massive’ appears 268 times
- ‘illegal’ appears 254 times
- ‘incredible’ appears 254 times
- ‘nice’ appears 251 times
- ‘strong’ appears 250 times
- ‘greatest’ appears 248 times
- ‘true’ appears 247 times
- ‘major’ appears 243 times
- ‘same’ appears 236 times
- ‘terrible’ appears 231 times
- ‘presidential’ appears 221 times
- ‘much’ appears 217 times
- ‘long’ appears 215 times
So as you can see, he doesn’t only insult. The first negative word, ‘fake’, is only the ninth most common, though more common than its antonyms ‘real’ and ‘true’, if they’re taken separately (‘false’ is in 72nd position, with 102 uses before nouns, while ‘genuine’ has only four uses.) And ‘illegal’ only slightly outdoes ‘nice’.
He also talks about American things a lot, which is not surprising given his location. ‘Russian’ comes in 111st place, with 62 uses, so about a tenth as many as ‘American’. As far as country adjectives go, ‘Iranian’ is next with 40 uses before nouns, then ‘Mexican’ with 39, and ‘Chinese’ with 37. ‘Islamic’ has 33. ‘Jewish’ and ‘White’ each have 27 uses as adjectives before nouns, though the latter is almost always describing a house rather than people. The next unequivocally racial (i.e. referring to a group of people rather than a specific region) adjective is ‘Hispanic’, with 25. I’m not an expert on what’s unequivocally racial, but I can tell you that ‘racial’ itself has nine adjectival uses before nouns, and ‘racist’ has three.
“But Angela,” I hear you not asking, “why are you showing us a list of words and numbers? Didn’t you just make an audiovisual word cloud generator a few months ago?” and the answer is, yes, indeed, I did make a word cloud generator that makes visual and audio word clouds, So here is an audiovisual word cloud of all the adjectives found at least twice before nouns in tweets by @realdonaldtrump in The Trump Twitter Archive, with Twitter usernames filtered out even if they are used as adjectives. More common words are larger and louder. Words are panned left or right so they can be more easily distinguished, so this is best heard in stereo.
There are some nouns in there, but they are only counted when used as attributive nouns to modify other nouns, e.g. ‘NATO countries’, or ‘ObamaCare website’.
For my comprehensive channel trailer, I created a word cloud of the words used in titles and descriptions of the videos uploaded each month. Word clouds have been around for a while now, so that’s nothing unusual. For the soundtrack, I wanted to make audio versions of these word clouds using text-to-speech, with the most common words being spoken louder. This way people with either hearing or vision impairments would have a somewhat similar experience of the trailer, and people with no such impairments would have the same surplus of information blasted at them in two ways.
I checked to see if anyone had made audio word clouds before, and found Audio Cloud: Creation and Rendering, which makes me wonder if I should write an academic paper about my audio word clouds. That paper describes an audio word cloud created from audio recordings using speech-to-text, while I wanted to create one from text using text-to-speech. I was mainly interested in any insights into the number of words we could perceive at once at various volumes or voices. In the end, I just tried a few things and used my own perception and that of a few friends to decide what worked. Did it work? You tell me.
There’s a huge variety of English voices available on macOS, with accents from Australia, India, Ireland, Scotland, South Africa, the United Kingdom, and the United States, and I’ve installed most of them. I excluded the voices whose speaking speed can’t be changed, such as Good News, and a few novelty voices, such as Bubbles, which aren’t comprehensible enough when there’s a lot of noise from other voices. I ended up with 30 usable voices. I increased the volume of a few which were harder to understand when quiet.
I wondered whether it might work best with only one or a few voices or accents in each cloud, analogous to the single font in each visual word cloud. That way people would have a little time to adapt to understand those specific voices rather than struggling with an unfamiliar voice or accent with each word. On the other hand, maybe it would be better to have as many voices as possible in each word cloud so that people could distinguish between words spoken simultaneously by voice, just as we do in real life. In the end I chose the voice for each word randomly, and never got around to trying the fewer-distinct-voices version. Being already familiar with many of these voices, I’m not sure I would have been a good judge of whether that made it easier to get used to them.
Arranging the words
It turns out making an audio word cloud is simpler than making a visual one. There’s only one dimension in an audio word cloud — time. Volume could be thought of as sort of a second dimension, as my code would search through the time span for a free rectangle of the right duration with enough free volume. I later wrote an AppleScript to create ‘visual audio word clouds’ in OmniGraffle showing how the words fit into a time/volume rectangle. I’ve thus illustrated this post with a visual word cloud of this post, and a few audio word clouds and visual audio word clouds of this post with various settings.
However, words in an audio word cloud can’t be oriented vertically as they can in a visual word cloud, nor can there really be ‘vertical’ space between two words, so it was only necessary to search along one dimension for a suitable space. I limited the word clouds to five seconds, and discarded any words that wouldn’t fit in that time, since it’s a lot easier to display 301032 words somewhat understandably in nine minutes than it is to speak them. I used the most common (and therefore louder) words first, sorted by length, and stopped filling the audio word cloud once I reached a word that would no longer fit. It would sometimes still be possible to fit a shorter, less common word in that cloud, but I didn’t want to include words much less common than the words I had to exclude.
I set a preferred volume for each word based on its frequency (with a given minimum and maximum volume so I wouldn’t end up with a hundred extremely quiet words spoken at once) and decided on a maximum total volume allowed at any given point. I didn’t particularly take into account the logarithmic nature of sound perception. I then found a time in the word cloud where the word would fit at its preferred volume when spoken by the randomly-chosen voice. If it didn’t fit, I would see if there was room to put it at a lower volume. If not, I’d look for places it could fit by increasing the speaking speed (up to a given maximum) and if there was still nowhere, I’d increase the speaking speed and decrease the volume at once. I’d prioritise reducing the volume over increasing the speed, to keep it understandable to people not used to VoiceOver-level speaking speeds. Because of the one-and-a-bit dimensionality of the audio word cloud, it was easy to determine how much to decrease the volume and/or increase the speed to fill any gap exactly. However, I was still left with gaps too short to fit any word at an understandable speed, and slivers of remaining volume smaller than my per-word minimum.
I experimented with different minimum and maximum word volumes, and maximum total volumes, which all affected how many voices might speak at once (the ‘hubbub level’, as I call it). Quite late in the game, I realised I could have some voices in the right ear and some in the left, which makes it easier to distinguish them. In theory, each word could be coming from a random location around the listener, but I kept to left and right — in fact, I generated separate left and right tracks and adjusted the panning in Final Cut Pro. Rather than changing the logic to have two separate channels to search for audio space in, I simply made my app alternate between left and right when creating the final tracks. By doing this, I could increase the total hubbub level while keeping many of the words understandable. However, the longer it went on for, the more taxing it was to listen to, so I decided to keep the hubbub level fairly low.
The algorithm is deterministic, but since voices are chosen randomly, and different voices take different amounts of time to speak the same words even at the same number of words per minute, the audio word clouds created from the same text can differ considerably. Once I’d decided on the hubbub level, I got my app to create a random one for each month, then regenerated any where I thought certain words were too difficult to understand.
In my visual word clouds, I kept the algorithm case-sensitive, so that a word with the same spelling but different capitalisation would be counted as a separate word, and displayed twice. There are arguments for keeping it like this, and arguments to collapse capitalisations into the same word — but which capitalisation of it? My main reason for keeping the case-sensitivity was so that the word cloud of Joey singing the entries to our MathsJam Competition Competition competition would have the word ‘competition’ in it twice.
Sometimes these really are separate words with different meanings (e.g. US and us, apple and Apple, polish and Polish, together and ToGetHer) and sometimes they’re not. Sometimes these two words with different meanings are pronounced the same way, other times they’re not. But at least in a visual word cloud, the viewer always has a way of understanding why the same word appears twice. For the audio word cloud, I decided to treat different capitalisations as the same word, but as I’ve mentioned, capitalisation does matter in the pronunciation, so I needed to be careful about which capitalisation of each word to send to the text-to-speech engine. Most voices pronounce ‘JoCo’ (short for Jonathan Coulton, pronounced with the same vowels as ‘go-go’) correctly, but would pronounce ‘joco’ or ‘Joco’ as ‘jocko’, with a different vowel in the first syllable. I ended up counting any words with non-initial capitals (e.g. JoCo, US) as separate words, but treating title-case words (with only the initial letter capitalised) as the same as all-lowercase, and pronouncing them in title-case so I wouldn’t risk mispronouncing names.
A really smart version of this would get the pronunciation of each word in context (the same way my rhyming dictionary rhyme.science finds rhymes for the different pronunciations of homographs, e.g. bow), group them by how they were pronounced, and make a word cloud of words grouped entirely by pronunciation rather than spelling, so ‘polish’ and ‘Polish’ would appear separately but there would be no danger of, say ‘rain’ and ‘reign’ both appearing in the audio word cloud and sounding like duplicates. However, which words are actually pronounced the same depend on the accent (e.g. whether ‘cot’ and ‘caught’ sound the same) and text normalisation of the voice — you might have noticed that some of the audio word clouds in the trailer have ‘aye-aye’ while others have ‘two’ for the Roman numeral ‘II’.
Similarly, a really smart visual word cloud would use natural language processing to separate out different meanings of homographs (e.g. bow🎀, bow🏹, bow🚢, and bow🙇🏻♀️) and display them in some way that made it obvious which was which, e.g. by using different symbols, fonts, styles, colours for different parts of speech. It could also recognise names and keep multi-word names together, count words with the same lemma as the same, and cluster words by semantic similarity, thus putting ‘Zoe Keating’ near ‘cello’, and ‘Zoe Gray’ near ‘Brian Gray’ and far away from ‘Blue’. Perhaps I’ll work on that next.
I’ve recently been updated to a new WordPress editor whose ‘preview’ function gives a ‘page not found’ error, so I’m just going to publish this and hope it looks okay. If you’re here early enough to see that it doesn’t, thanks for being so enthusiastic!
A few months ago I wrote an app to download my YouTube metadata, and I blogged some statistics about it and some haiku I found in my video titles and descriptions. I also created a few word clouds from the titles and descriptions. In that post, I said:
Next perhaps I’ll make word clouds of my YouTube descriptions from various time periods, to show what I was uploading at the time. […] Eventually, some of the content I create from my YouTube metadata will make it into a YouTube video of its own — perhaps finally a real channel trailer.Me, two and a third months ago
TL;DR: I made a channel trailer of audiovisual word clouds showing each month of uploads:
It seemed like the only way to do justice to the number and variety of videos I’ve uploaded over the past thirteen years. My channel doesn’t exactly have a content strategy. This is best watched on a large screen with stereo sound, but there is no way you will catch everything anyway. Prepare to be overwhelmed.
Now for the ‘too long; don’t feel obliged to read’ part on how I did it. I’ve uploaded videos in 107 distinct months, so creating a word cloud for each month using wordclouds.com seemed tedious and slow. I looked into web APIs for creating word clouds automatically, and added the code to my app to call them, but then I realised I’d have to sign up for an account, including a payment method, and once I ran out of free word clouds I’d be paying a couple of cents each. That could easily add up to $5 or more if I wanted to try different settings! So obviously I would need to spend many hours programming to avoid that expense.
I have a well-deserved reputation for being something of a gadget freak, and am rarely happier than when spending an entire day programming my computer to perform automatically a task that it would otherwise take me a good ten seconds to do by hand. Ten seconds, I tell myself, is ten seconds. Time is valuable and ten seconds’ worth of it is well worth the investment of a day’s happy activity working out a way of saving it.Douglas Adams in ‘Last chance to see…’
I searched for free word cloud code in Swift, downloaded the first one I found, and then it was a simple matter of changing it to work on macOS instead of iOS, fixing some alignment issues, getting it to create an image instead of arranging text labels, adding some code to count word frequencies and exclude common English words, giving it colour schemes, background images, and the ability to show smaller words inside characters of other words, getting it to work in 1116 different fonts, export a copy of the cloud to disk at various points during the progress, and also create a straightforward text rendering using the same colour scheme as a word cloud for the intro… before I knew it, I had an app that would automatically create a word cloud from the titles and descriptions of each month’s public uploads, shown over the thumbnail of the most-viewed video from that month, in colour schemes chosen randomly from the ones I’d created in the app, and a different font for each month. I’m not going to submit a pull request; the code is essentially unrecognisable now.
In case any of the thumbnails spark your curiosity, or you just think the trailer was too short and you’d rather watch 107 full videos to get an idea of my channel, here is a playlist of all the videos whose thumbnails are shown in this video:
It’s a mixture of super-popular videos and videos which didn’t have much competition in a given month.
Of course, I needed a soundtrack for my trailer. Music wouldn’t do, because that would reduce my channel trailer to a mere song for anyone who couldn’t see it well. So I wrote some code to make an audio version of each word cloud (or however much of it could fit into five seconds without too many overlapping voices) using the many text-to-speech voices in macOS, with the most common words being spoken louder. I’ll write a separate post about that; I started writing it up here and it got too long.
The handwritten thank you notes at the end were mostly from members of the JoCo Cruise postcard trading club, although one came with a pandemic care package from my current employer. I have regaled people there with various ridiculous stories about my life, and shown them my channel. You’re all most welcome; it’s been fun rewatching the concert videos myself while preparing to upload, and it’s always great to know other people enjoy them too.
I put all the images and sounds together into a video using Final Cut Pro 10.4.8. This was all done on my mid-2014 Retina 15-inch MacBook Pro, Sneuf.
I’ve developed a bit of a habit of recording entire concerts of musicians who don’t mind their concerts being recorded, splitting them into individual songs, and uploading them to my YouTube channel with copious notes in the video descriptions. My first upload was, appropriately, the band featured in the first image on the web, Les Horribles Cernettes, singing Big Bang. I first got enough camera batteries and SD cards to record entire concerts for the K’s Choice comeback concert in Dranouter in 2009, though the playlist is short, so perhaps I didn’t actually record that entire show.
I’ve also developed a habit of going on a week-long cruise packed with about 25 days of entertainment every year, and recording 30 or so hours of that entertainment. So my YouTube channel is getting a bit ridiculous. I currently have 2723 publicly-visible videos on my channel, and 2906 total videos — the other 183 are private or unlisted, either because they’re open mic or karaoke performances from JoCo Cruise and I’m not sure I have the performer’s permission to post them, or they’re official performances that we were requested to only share with people that were there.
I’ve been wondering just how much I’ve written in my sometimes-overly-verbose video descriptions over the years, and the only way I found to download all that metadata was using the YouTube API. I tested it out by putting a URL with the right parameters in a web browser, but it’s only possible to get the data for up to 50 videos at a time, so it was clear I’d have to write some code to do it.
Late Friday evening, after uploading my last video from JoCo Cruise 2020, I set to writing a document-based CoreData SwiftUI app to download all that data. I know my way around CoreData and downloading and parsing JSON in Swift, but haven’t had many chances to try out SwiftUI, so this was a way I could quickly get the information I wanted while still learning something. I decided to only get the public videos, since that doesn’t need authentication (indeed, I had already tried it in a web browser), so it’s a bit simpler.
By about 3a.m, I had all the data, stored in a document and displayed rather simply in my app. Perhaps that was my cue to go to bed, but I was too curious. So I quickly added some code to export all the video descriptions in one text file and all the video titles in another. I had planned to count the words within the app (using enumerateSubstrings byWords or enumerateTags, of course… we’re not savages! As a linguist I know that counting words is more complicated than counting spaces.) but it was getting late and I knew I wanted the full text for other things, so I just exported the text and opened it in Pages. The verdict:
- 2723 public videos
- 33 465 words in video titles
- 303 839 words in video descriptions
The next day, I wanted to create some word clouds with the data, but all the URLs in the video descriptions got in the way. I quite often link to the playlists each video is in, related videos, and where to purchase the songs being played. I added some code to remove links (using stringByReplacingMatches with an NSDataDetector with the link type, because we’re not savages! As an internet person I know that links are more complicated than any regex I’d write.) I found that Pages counts URLs as having quite a few words, so the final count is:
- At least 4 633 links (this is just by searching for ‘http’ in the original video descriptions, like a savage, so might not match every link)
- 267 567 words in video descriptions, once links are removed. I could almost win NaNoWriMo with the links from my video descriptions alone.
I then had my app export the publish dates of all the videos, imported them into Numbers, and created the histogram shown above. I actually learnt quite a bit about Numbers in the process, so that’s a bonus. I’ll probably do a deeper dive into the upload frequency later, with word clouds broken down by time period to show what I was uploading at any given time, but for now, here are some facts:
- The single day when I uploaded the most publicly-visible videos was 25 December 2017, when I uploaded 34 videos — a K’s Choice concert and a Burning Hell concert in Vienna earlier that year. I’m guessing I didn’t have company for Christmas, so I just got to hang out at home watching concerts and eating inexpertly-roasted potatoes.
- The month when I uploaded the most publicly-visible videos was April 2019. This makes sense, as I was unemployed at the time, and got back from JoCo Cruise on March 26.
So, onto the word clouds I cleaned up that data to make. I created them on wordclouds.com, because wordle has rather stagnated. Most of my video titles mention the artist name and concert venue and date, so some words end up being extremely common. This huge variation in word frequency meant I had to reduce the size from 0 all the way to -79 in order for it to be able to fit common words such as ‘Jonathan’. Wordclouds lets you choose the shape of the final word cloud, but at that scale, it ends up as the intersection of a diamond with the chosen shape, so the shape doesn’t end up being recognisable. Here it is, then, as a diamond:
The video descriptions didn’t have as much variation between word frequencies, so I only had to reduce it to size -45 to fit both ‘Jonathan’ and ‘Coulton’ in it. I still don’t know whether there are other common words that didn’t fit, because the site doesn’t show that information until it’s finished, and there are so many different words that it’s still busy drawing the word cloud. Luckily I could download an image of it before that finished. Anyway, at size -45, the ‘camera’ shape I’d hoped to use isn’t quite recognisable, but I did manage a decent ‘YouTube play button’ word cloud:
One weird fact I noticed is that I mention Paul Sabourin of Paul and Storm in video descriptions about 40% more often than I mention Storm DiCostanzo, and I include his last name three times as much. To rectify this, I wrote a song mentioning Storm’s last name a lot, to be sung to the tune of ‘Hallelujah’, because that’s what we do:
We’d like to sing of Paul and Storm.
It’s Paul we love to see perform.
The other member’s name’s the one that scans though.
So here’s to he who plays guitar;
let’s all sing out a thankful ‘Arrr!’
for Paul and Storm’s own Greg “Storm” DiCostanzo!
DiCostanzo, DiCostanzo, DiCostanzo, DiCostanzo
I’m sure I’ll download more data from the API, do some more analysis, and mine the text for haiku (if Haiku Detector even still runs — it’s been a while since I touched it!) later, but that’s enough for now!
In November 2018 I created NanoRhymo (inspired by NaNoWriMo), in which I wrote and tweeted a very short rhyming poem every day. I did the same thing in April 2019 for Global Poetry Writing Month. I started pretty late with NanoRhymo in 2019, and didn’t end up with a poem for each day of November, but I’ve started it again on January 1 and made up for the missing poems. In November, I mostly stuck to writing something based on a random rhyme from the rhyming dictionary I made, rhyme.science — either a new one I’d found each day, or one generated earlier for the @RhymeScience twitter feed. In January, I’ve often been inspired by other things.
I’ll continue writing a NanoRhymo a day for as long as I can. Here’s what I’ve written so far:
Day 1, inspired by the rhymes later, translator, and (in non-rhotic accents) convey to:
When you’ve got a thought to convey to
many mortals, sooner or later,
then you ought to get a translator.
Sailors lying in their bunks
would shout “Ahoy there, mate… watch under!”
and then let loose digested chunks
on hapless seamen sleeping under.
That’s why even now, down under,
[I am lying; truth debunks!]
some refer to puke as chunder.
[This is half-digested junk
Please accept my weak apology
and not this doubtful etymology.]
Day 3, inspired by a friend’s experience learning flying trapeze:
My friend Robert Burke tried the flying trapeze.
It meant lots of work mulling hypotheses,
and then much amusement and catching catchees,
to end up all bruised on the backs of the knees.
Looking at small things up close and myopically,
one might prevent overgrowth with a germicide.
But looking at large things afar, macroscopically,
one must prevent unchecked growth with a spermicide.
As soon as the bug is explainable,
we can hope that it might be containable,
and our neural nets will be retrainable,
and our code is so very maintainable
that this progress is surely sustainable!
Mouth agape, stunned, unspeaking
Eyes wide open, silent freaking,
What could this strange vision be?
a music video, on MTV?!
Over much terrain they trekked;
specimens they did collect,
to show just how diverse life was
before we killed it off, just ‘cause.
If rhyming couplets leave you peeved,
here, I tried ABAB.
Now the rhymes are interleaved!
This rhyme and rhythm’s reason-free.
If rhyming couplets leave you peeved,
Then try to make them interleaved
Or don’t, and then just let the hate flow through ya
Just AAB, then CCB
This rhyme and rhythm’s reason-free.
At least it can be sung to Hallelujah.
The most Hallelujest Joey Marianer sang that version:
I’m just fine with the end-of-year platitudes —
“Happy Holidays”, nice and generic,
but please, be inclusive of latitudes:
“Happy Winter” is too hemispheric!
Day 11, another Hallelujah, inspired by Joey’s singing of the previous Hallelujah:
A kitchen scale, a petrol gauge,
a cylinder, a final page
will tell you up to what things have amounted.
An abacus, a quipu string,
some tally sticks, to always sing,
are all things on which Joey can be counted.
Day 12, inspired by the rhyme deprecations and lamentations, some deprecated code I was removing from the software I develop at work, and also complaints about macOS Catalina dropping support for 32-bit applications. I imagine it sung to the tune of Camp Bachelor Alma Mater:
Hear the coders’ lamentations
over apps that will not run,
due to years-old deprecations,
updates that they’ve never done.
Have some more whoops on me,
hearing the Sloop John B
as JoCo Cruise comes to an end.
You still have all night.
Hang loose, or sleep tight.
Well, we feel so broke up
but you’ll stay my friend.
Something is broken;
look at that warning!
Raise the exceptions.
Erase all the warnings.
Raze preconceptions wrongly inferred.
The rooms are all full for as far out as they can see;
such a big guest house to fill, but oh well.
What’s this? Nonetheless, there’s a sign saying vacancy!
There’s always more room at the Hilbert hotel.
Clap along if you feel like a room without a roof. 👏
Please applaud if you think you’re a chamber with no ceiling. 👏
Clap along If you feel like happiness is the truth. 👏
Please applaud if you think there’s veracity in good feelings. 👏
For day 17, I let Pico, emacs, ed, vi count as the NanoRhymo, even though it does not mention the text editor nano.
November ended with no more rhymes, but I started it up again on January 1, simply because I was inspired to, and I continued to get ideas every day since. I’m not promising to keep this up daily all year (indeed, I promise not to keep it up during MarsCon and JoCo Cruise 2020) but I’ll post NanoRhymi whenever I feel inspired to.
Don’t worry that you might incur the
sentence, “That person’s unworthy.”
Just try what you wish, and try plenty,
and have a great year twenty-twenty.
If you’d punch down, or kick to the curb
for verbing a noun, or nouning a verb,
researching the past will amount your disturb.
So many of the words we used today, including some in that poem, were once strictly parts of speech other than the ones they’re used as without a second thought today, and people objected to their shifts in usage just as they object to all manner of language change today.
Day 20, inspired by the rhymes occur to, Berta, and (in non-rhotic accents) subverter:
If it were to occur to Berta the subverter to hurt Alberta,
she’d prefer to assert a slur to refer to her to stir internal murder.
(Stones break bones but names make shame — heals more slowly, hurts the same.)
While you’re growing in the field,
all your goodness is concealed,
till some lovely creature picks you,
doesn’t think they have to fix you,
lets you chill, let down your shield;
then, when you are fully peeled,
your sweetest inner self revealed,
that cunning rascal bites and licks you.
Day 22, inspired by the rhymes for fish, dwarfish, and (maybe in some non-rhotic accents with the cot-caught merger) standoffish, the ‘teach a man to fish‘ metaphor, and of course, my own poem, They Might Not Be Giants:
If a person’s always asking for fish,
don’t give them one, and go away, standoffish.
Teach techniques that they’ll expand on.
Be the shoulders they will stand on.
Not a giant — generous and dwarfish.
And then the same thing as a limerick:
There once was a man asking for fish,
who got one from someone standoffish.
Then shoulders to stand on
and tricks to expand on,
were given by someone quite dwarfish.
Day 23, inspired by… certain kinds of transphobic people, I guess:
Some folk seem to be offended
by the thought the queerly gendered
might themselves become offended
when they’re purposely misgendered,
so they’ve boorishly defended
all the hurt that they intended
towards the “easily offended”
who are “wimps” to try to end it.
Day 24, a double dactyl inspired by a conversation with someone who’s considering hormone therapy with one aim being a reduction of schlength, during which we noticed that ‘endocrinologist’ is a double dactyl, and also inspired by Paul and Storm’s habit of calling Jonathan Coulton ‘Dr. Smallpenis‘ (with the ‘e’ unstressed) which began on JoCo Cruise 2013:
Dr. Jon Smallpənis,
helps you to shrink all the
parts that aren’t you.
Piss off, dysphoria!
soon make you tinkle the
whole darn day through.
Spironolactone is a medication that blocks the effect of testosterone, which as a side effect can increase urinary frequency.
Dear Father, a prayer I remember, amen.
Another, sincere from a vendor, again.
As if by reciting just ten or eleven words
I’ll lift myself quite transcendentally heavenwards.
Day 26, inspired by what I was actually told at my first comprehensive annual checkup:
Sit up straight!
Lose some weight!
Take these pills!
Cure your ills!
Your heart is beating!
You’re good at breathing!
With those two habits kept up,
We’ll see you at the next year’s checkup.
They really did seem impressed by how well I could breathe. I wasn’t too good at it when I started, but I have been practising my whole life, and if I’m good then I may as well continue the habit.
Here’s a technique that is quite underhand
to beam gadgets speaking they might understand,
and give an unsound and light-fingered command.
This one works best in accents without the trap-bath split, so that ‘command’ rhymes with ‘understand’ and ‘underhand’.
Day 28, inspired by a container of those little dowel things to hold up shelves, which was labelled ‘Safety trans.’, and the song The Safety Dance, by Men Without Hats. This parody is presumedly to be sung by Women and Nonbinary People Without Hats:
You can trans[ition] iff you want to.
You can leave your assigned gender behind.
‘Cause your assigned gender ain’t trans and if you don’t trans[ition],
Well your assigned gender stays assigned.
Acquired savants suffer pain,
to wake up with a better brain.
Get a bump, or have a seizure,
then end up with synaesthesia —
not the grapheme-colour kind,
rather, an amazing mind!
Day 30 is a version of day 29’s poem which can be sung to the tune of Hallelujah, with a second verse reminding people that synaesthesia is actually pretty common, affecting about 4.4% of people, (I have the grapheme-colour kind) and doesn’t necessarily confer superpowers:
Acquired savants suffer pain,
to wake up with a better brain
by healing from an injury or seizure.
They sometimes get amazing minds
associating different kinds
of input in a thing called synaesthesia.
Synaesthesia, synaesthesia, synaesthesia, synaesthesia.
But synaesthetes are everywhere,
not magical, or even rare.
It doesn’t make them smart or make things easier.
It just makes Thursday forest green,
or K maroon and 7 mean.
Your ‘the’-tastes-like-vanilla synaesthesia
Synaesthesia, synaesthesia, synaesthesia…
This refers to time-unit-color synaesthesia, grapheme-colour synaesthesia, ordinal linguistic personification (also known as sequence-personality synaesthesia), and lexical-gustatory synaesthesia, but there are many other kinds.
Did you hear he goes commando?
I remember long ago another starry night like this.
In the firelight, commando,
he was wearing his new kilt and playing bagpipes by the fire.
I could hear his sudden screams
and sounds of mountain oysters sizzling in the fryer.
Day 32, inspired by two tweets I saw, each quoting the same tweet where someone had contrasted pictures of Prince Harry in the army with pictures of him with his wife, and claimed that getting out of the army and getting married was somehow emasculation caused by ‘toxic’ Hollywood feminism:
The two tweets happened to rhyme with each other and follow the same structure, from the ‘fellas, is it gay’ meme, so I put them together, and added a few lines:
Fellas, is is gay to have a wife?
Fellas, is it gay to be a human being with a life?
Fellas, is it gay to wear a suit?
Fellas, is it gay to dress to socialise instead of shoot?
(Fellas, is it toxic to be gay?
Fellas, why frame questions with a word she didn’t say?)
Day 33, another Hallelujah parody, inspired by Joey’s observation that NanoRhymo scans:
You want to practise writing verse.
The secret’s to be very terse.
You don’t have to try hard, just have to try mo’.
You write some dogg’rel every day
and some you’ll toss, but some will stay.
An atom at a time; it’s NanoRhymo.
NanoRhymo, NanoRhymo, NanoRhymo, NanoRhymo.
I love when it complies,
regards me with deference,
and bravely compiles
my unguarded dereference.
Day 35, inspired by… tea. I feel so rich when I make a pot of tea and top it up all day, having unlimited tea without feeling like maybe it’s wasteful to be using my eighth teabag of the day:
If hot tea’s an oddity,
the tea bag’s your commodity,
but if you drink a lot of tea,
you should make a pot of tea.
(To add some boiling water t’
whenever you want hotter tea.)
Day 36, inspired by my efforts to write an AppleScript to copy all my NanoRhymi and GloPoWriMo poems from Notes into a spreadsheet in Numbers, which initially failed because I had accidentally addressed the script to Pages instead, and Pages don’t know sheet:
👩🏻💻Hello there! Your finest Greek corpus, to go!
👩🍳The what now? Not understand corpus, no no!
👩🏻💻The active Greek corpus, the frontmost, the first, display all the corpora you have; am I cursed?
👩🍳I’m sorry? Your question is Greek to me… how?
👩🏻💻Okay then, just show me your bookcases, now!
👩🍳Bookcases? I have none; you’ve made a mistake.
👩🏻💻Ah, frack! You’re no linguist! You’re actually the baker!
The spreadsheet, by the way, shows I’ve written about a hundred of these small poems in total so far, in the course of my NanoRhymo and GloPoWriMo stints. I haven’t gone through it checking for notes that didn’t contain completed poems, so I don’t know the exact number yet. In the next roundup of these things, I’ll probably start numbering them based on that total, rather than the ‘days’ of any particular run of them.
Day 37 (today, as I write this), a parody of Taylor Swift’s ‘Shake it Off‘ inspired by another tweet by Rob Rix, in which he notices that a calculation done in Spotlight Search which should give the result zero does not, and remarks, ‘computers gonna compute’:
’Cause the bugs are are gonna ship, ship, ship, ship, ship
And an on bit is a blip, blip, blip, blip, blip
I’m just gonna flip, flip, flip, flip, flip
I flip it off ⌽, I flip it off 🖕🏻
That’s all of the NanoRhymi I have so far; I’ll post more here occasionally, but follow me on Twitter if you want to see them as they happen.
In other news, please consider buying one or all of the MarsCon Dementia Track Fundraiser albums, which are albums of live comedy music performances from previous MarsCon Dementia Tracks, sold to raise funds for the performers’ hotel costs for the next one. The 2020 fundraiser album (with the concerts from MarsCon 2019) is nearly four hours of live comedy music for $20, and includes my performances of Chicken Monkey Duck and Why I Perform at Open Mics.
For yet more music, Joey and I will be participating in round #16 of SpinTunes, a songwriting competition following in the footsteps of Masters of Song Fu. I’ve been following it since the beginning, but never had the accompaniment to actually enter.
I released a new version of NastyWriter today! It fixes the various bugs I found while posting nastified text every day on the NastyWriter Tumblr and Twitter, and some that other people kindly told me about. I also added new, all-natural insults sustainably gathered from the wild, and state restoration so you won’t risk losing what you were working on every time you switch to another app. Given how simple that was to implement, I am now even more annoyed at the many better-funded apps that don’t do it.
There are still a few issues that I’m aware of, but I decided the issues I’d already fixed were worse, so it was more important to get the fixes to them out. Anyway, check out the new app on the App Store, or if you like, read more about the bug fixes in this version on my company blog.
I’ve been writing Mac software for fun and occasional profit for decades, and freelancing writing an iOS app for use in-house, but don’t you think it’s about time I wrote an iOS app for the App Store?
Surprise! I just released one. It’s called NastyWriter, and it inserts insults before nouns as you type. I see people online who can barely mention people or things they don’t like without insulting them, and I figured I may as well automate that and have some fun with it. It’s always fun to play with natural language processing!
This was mostly an experiment, a learning exercise, and a way to feel better about applying for jobs which have ‘must have app in the App Store’ in the requirements. The experiment is to see how a silly free app with ads and an in-app purchase to turn off ads does, although James Thomson already ran that experiment so I don’t expect it to pay for very many kilos of rice.
The learning exercise was a huge success. I learnt many things, about natural language processing in macOS/iOS, about how many other things there are to think of that take much more effort than the actual adding-insults-before-nouns part, about how awesome automated testing is in a small project by a single person, about how testing accessibility can make flaws in the regular interface more apparent (I didn’t even realise dictation was broken until I tested with VoiceOver!), about the most common adjectives used directly before nouns in the Trump Twitter Archive (‘great’ outnumbers the next most common by about a factor of three), about fastlane, and about the App Store, AdMob and in-app purchases. I might write blog posts about those things later. Do any of these topics seem particular interesting to you?
However, hours after I submitted it, the ‘e’ key on my MacBook’s keyboard stopped working, and while it’s not one of those new butterfly switch keyboards that can apparently need replacing after seeing a speck of dust (or maybe it is? It’s a 2014 model), somehow it turns out that in addition to that my Mac’s battery is swollen and it’ll have to go to the Apple Store and have the battery and the whole keyboard part of the case replaced. This will make it rather difficult to tend to any serious issues in NastyWriter or write as much about it as I wanted to just yet. I can use my iPad (which I am currently typing this on) or, until the Mac goes into the shop, an external keyboard, but neither is quite as comfortable.
And since many people have asked: no, there is no Android version yet, but I’m freelancing and I like learning new things so I would be happy to write one iff somebody pays me to. It would be cheaper for you to buy an iOS device.
I might make a Mac version for fun, though!
Last Towel Day, I posted a poem I had written using 42 -ation rhymes which an app I wrote found in Douglas Adams’ book ‘Last Chance to See‘. Later that day, Joey Marianer posted a video of himself singing the poem[cetacean needed], and while I did eventually mention that in another post, Towel Day had long passed by then. So strap yourself into your Poetry Appreciation Chair, because here it is for Towel Day this year:
Here are the words again:
Earth’s vegetation made slow transformation as each confrontation or new situation provoked adaptation in each generation for eons duration.
Until civilisation, and its acceleration of our population at high concentration with great exhortation and disinclination to make accommodations with administration of conservation.
Then Adams’ fascination and realisation that with elimination of echolocation no cetacean reincarnation will save our reputation; his bold exploration to spread information and fuel education and his determination to stop exploitation by identification and communication of each dislocation of species, his observation and growing frustration we reduce speciation to bone excavation with every temptation to favor our nation and not immigration of distant relations… was his speculation we’d reduce penetration mere hallucination?
The app which found these rhymes was made to create the data for my accent-aware online rhyming dictionary rhyme.science. I’ve made some improvements to the app and the rhymes it finds, and I am looking forward to updating the website to reflect the improvements, but for the last few months I’ve spent my free time working on an unrelated iOS app instead. I’ll be submitting that to the App Store soon, and will announce it here when it’s available, so watch this space. Or watch outer space, and look out for Vogons.
Have a great Towel Day, don’t forget your towel, and don’t panic!
I’ve been away in the Bay Area, on JoCo Cruise, on trains, and at MarsCon, and too many things have happened for one blog post, but here are a few of them. Just before the cruise, Joey Marianer sang ‘Accessible‘, my parody of James Blunt’s ‘Beautiful’ about accessibility:
Joey sang a few other songs of mine during and after the cruise, but I’m going to save them for other posts so that this one is less of a mish-mash. If you would like a preview of those along with a recap of other things I wrote that he sang, here’s a playlist.
But Joey is not the only person whose name starts with ‘Jo’ who has sung words that I wrote! A while ago, my friend Joseph sang ‘Back to the Future Song‘, my parody of Moxy Früvous’s ‘Gulf War Song‘ as part of his Patreon. Lately he’s been opening up older posts to be visible to non-patrons, so now you can also hear Joseph singing Back to the Future Song. I changed that one line that I didn’t like very much.
You can also hear the cover of Moxy Früvous’s ‘Downsizing’ which Joseph sang for me after I lost my last job. If you like these covers, check out some of his other covers, short stories and poems on patron, and become a patron; I’m sure he’d appreciate the support, and you, too, would be able to request things like this.
I’ll post a few more times to update you on some other cool things, and who knows, perhaps I’ll participate in National Poetry Writing Month again. As is usual at this time of year, I’m spending most of my free time lately uploading videos from the JoCo Cruise, so if you want me to entertain you in some way and you can’t wait for the next blog post, subscribe to me on YouTube to see my latest uploads.
The other day I was reading some developer documentation about nodes while I had Jonathan Coulton’s ‘Sunshine’ from the album ‘Solid State’ in my head. ‘The road we’re on’ became ‘the node we’re on’ and the next thing I know I’m writing a parody about the runtime, and errors that could have been detected by the compiler in other languages or with stricter compiler settings but instead aren’t detected until runtime. I didn’t end up using the ‘node’ line. The first verse barely needed changing, so for fun I tried to keep as many words from the original as I could in the rest of the parody as well. Whether that’s a good thing depends on your taste and how well you know the original. Feel free to sing it if that’s a thing you can do!
We were blind to every sign
That we should have seen
In a clearly broken line
Machine to machine
Our mistakes were the future
But no one could tell
Lots of errors to detect
The tests didn’t show
The things compilers could have checked
We just let it go
Walked away as assumptions
Crumbled and fell
We bust our arse
To find the errors the machine could parse
Catch it just-in-time
Here in the runtime
Cast the pointer to a type it’s not (runtime)
Walk the edge case of the code we’ve got (runtime)
Every bit was filed away
There’s memory to fill
There’s no message that could say
Receiver is nil
We don’t heed any warnings
Try it and bail
Cast from Any type to this
The object’s a tease
Reflect to find out what it is
A sudden unease
If the data’s corrupt
Then when did it fail?
We soldier on (just a flesh wound)
Heap space and registers are almost gone (memory use ballooned)
Watch the stack unwind (maybe we’re doomed)
Here in the runtime
Cast the pointer to a type it’s not (runtime)
Walk the edge case of the code we’ve got (runtime)
The caller of the method must not do this
Everything’s your fault, you have been remiss
Code is fine, the world has something amiss
All the tests have slipped away
Just garbage I/O
I won’t last another day
And neither will O
EOF of a stateless mutable thing
The data’s blitzed (blame the new hire)
There’s nothing left that can remember it (cut the red wire)
But this is fine (halt and catch fire)
Here in the runtime
Cast the pointer to a type it’s not (runtime)
Walk the edge case of the code we’ve got (runtime)
Cast the pointer to a type it’s not (runtime)
Walk the edge case of the code we’ve got (runtime)
I’m not so sure about ‘EOF of a stateless mutable thing’ and whether it would be better stateful or immutable. It doesn’t exactly make sense (does anything, when it’s that far gone?) but it sounds cool, and a lot like the original. I’ll release it like this and patch it in production if necessary. 😉