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Introduction & Setting
: Recorded on a sunny August afternoon in a Merton garden with Trurl exploring a wide-ranging philosophical and analytical journey through mathematics, games, and literature.





Literary & Sci-Fi References
:




Stanislaw Lem
: Discussion of the robot character
Trurl
from
The Cyberiad
, who alphabetically creates everything before reaching the letter "N" for "nothing" and destroying everything. Learn more about Lem on
Wikipedia
.





Vladimir Nabokov
: Analysis of
The Luzhin Defense
, a novel exploring chess obsession, psychological breakdown, and the "horror of chess." Explore the book on
Wikipedia
.





Edgar Allan Poe & Thomas Hobbes
: Discussion on 19th-century precision in writing, stylistic inflections, and the value of rigorous language.





Martin Amis
: Praise for his modern precision, with highlights including
Money
and
Information
.









Core Philosophical & Mathematical Themes
:




Socratic Epistemology
: Exploring the ancient Greek view from Plato's dialogues that equates knowledge directly with practical skill.





Active vs. Empirical Learning
: Trurl's theoretical aversion to traditional science labs in favor of pure conceptual frameworks, contrasted with hands-on computational tinkering.





Stephen Wolfram
: Discussion of
A New Kind of Science
and the computational approach to universal systems.









Applied Puzzles & Strategic Games
:




Chess Ratings
: Investigating the rating system, Elo mechanics, and statistical anomalies suggesting top players are statistically underrated.





The Drake Equation & Astrobiology
: Quantitative modeling of extraterrestrial life inspired by Stephen Webb's
If the Universe Is Teeming with Aliens... Where Is Everybody?
.





Economics & Libertarianism
: Transition from early socialist upbringing to decentralized economic structures, cryptocurrency architectures, and the Lightning Network.











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This show has been flagged as Clean by the host.

In a bonus episode for the reserve queue, Kevie and Dave talk about how they review beers:





Aroma - what do you smell from the glass and bottle or can



Taste - take a sip, swirl it around your mouth and allow it rest a second before drinking; don't gulp. What flavours are you getting.



Wait - What flavours are appearing in the aftertaste.



Opinions - what are your personal thoughts on this beer.



Score - pick a score and rarely give any beer full marks.









Connect with the guys on Untappd:







Dave






Kevie










The intro sounds for the show are used from:







https://freesound.org/people/mixtus/sounds/329806/






https://freesound.org/people/j1987/sounds/123003/






https://freesound.org/people/greatsoundstube/sounds/628437/








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This show has been flagged as Clean by the host.

As we are currently going through a warm patch (well, at the time of recording anyway), the HPR Beer Garden returns to provide some refreshment in the heat with a glass of cold and refreshing lager. Dave chooses
Super Bock
from Portugal, whilst Kevie picks
City Limits Amber Lager
from the tiny
Dog Falls Brewing Co
in Inverness.

















Connect with the guys on
Untappd
:







Dave






Kevie










The intro sounds for the show are used from:







https://freesound.org/people/mixtus/sounds/329806/






https://freesound.org/people/j1987/sounds/123003/






https://freesound.org/people/greatsoundstube/sounds/628437/










Upcoming beers:





Mild



Bitter



Pale Ale



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This show has been flagged as Clean by the host.


Lieven Blancke:


https://mastodon-belgium.be/@lieven





asterisk:


https://www.asterisk.org/





Asterisk audio files:


https://downloads.asterisk.org/pub/telephony/sounds/





Audio of PSTN call:


https://en.wikipedia.org/wiki/File:1TR110-1_Kap8.3_Freiton1.ogg
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This show has been flagged as Explicit by the host.

Obviously, I'll need to introduce why I'm the best person to explain this concept and how I did my research.



After that, I dive into the core of he episode: the research.









The Ultimate Guide To Spotting AI Generated Text That Every Writer Needs To Read.




That title already has two tells:







Title case
: every word capitalised, which conveniently cannot be read out loud





Hyperbole
: a promise of ultimacy, for a ten minute podcast





For most of this episode I am not myself, I am the machine, and every sentence I say is an example of the thing it describes. This page is the key to that part of the show: the tells in the order you hear them, with what they sound like in the recording.



Most names come from


tropes.fyi


, so you can look them up there and get the longer explanation.



As also stated in the podcast: these aren't errors. They are ordinary figures of speech that human writers have used for centuries. The tell is the density.





Part one: Vocabulary








Prestige vocabulary
: words nobody uses out loud. e.g. delve, harness, utilise, robust, foster





Grand nouns for ordinary things
: e.g. tapestry, landscape, realm, ecosystem, paradigm





Magic adverbs
: imply significance, carry no information. e.g. quietly, fundamentally, arguably, remarkably





Avoiding the verb "to be"
: plain "is" swapped for something that sounds busier. e.g. serves as, stands as, represents, marks





Invented concept labels
: an abstract problem noun (trap, creep, paradox, inversion) welded onto a domain word, then used as if it were established. e.g. "the fluency trap", "prestige creep"





Vague attributions
: authority without a name. e.g. "experts agree", "industry reports suggest", "observers have noted"





Appeal to familiarity
: consensus claimed rather than proven. e.g. "a classic", "famously", "as we all know"





Promotional register
: describing turns into selling. e.g. unlocks, seamless, unprecedented







Part two: Sentence shapes








Negative parallelism
: the "it's not X, it's Y" pattern, and the most reported tell of all. e.g. "This isn't a stylistic quirk, it's a structural signature."





Not X. Not Y. Just Z.
: the same move tripled, building tension for a reveal that needed none. e.g. "Not a habit. Not a tic. A tell."





Sets of three
: one tricolon is elegant, three in a row is a stuck needle. e.g. "it arrives in threes, it lands in threes, it exhausts in threes"





Short punchy fragments
: emphasis manufactured by full stops instead of earned by content. e.g. "Short. Parallel. Relentless."





Self-answered questions
: asks what nobody asked, answers it immediately. e.g. "The problem? Nobody asked."





Anaphora
: the same sentence opening until it becomes furniture. e.g. "They open sentences the same way. They open the next one the same way."





Trailing "-ing" analysis
: shallow significance bolted onto a finished sentence. e.g. "highlighting its enduring importance", "reflecting broader trends"





False ranges
: "from X to Y" where there is no scale and no middle. e.g. "from cesspit to citation"





Filler transitions
: connect nothing to nothing. e.g. "it's worth noting", "importantly", "notably"





The interjection reflex
: every sentence broken in half, whatever the punctuation. e.g. em-dash, double hyphen, comma pair, colon





Comma-clipped tail
: a short phrase hung off a comma instead of landing the point. e.g. "above the content, and save."







Part three: Tone








Manufactured suspense
: the buildup arrives, the revelation does not. e.g. "here's the thing", "here's the kicker"





Patronising analogy
: teacher mode, uninvited. e.g. "think of it as a Swiss Army knife for condescension"





Forced figurative language
: a metaphor picked for cleverness rather than clarity. e.g. "Picture a bicycle. Now picture that the bicycle is prose."





Stakes inflation
: everything is historic. e.g. "a pivotal moment in the evolution of reading itself"





False vulnerability
: honesty performed, never risked. e.g. "let me be honest with you here. Genuinely honest."





Assertion in place of evidence
: e.g. "history is unambiguous on this point", "the evidence is clear"





"Imagine a world where..."
: the futurism sales pitch, pleasant outcomes if you accept the premise. e.g. "imagine a world where every paragraph carries a quiet intelligence"





Quotable one-liners
: slide bait that survives being pulled out of context because it never had any. e.g. "Every bite is a chance to be here, now, fully."





The collaborative "we"
: one author, plural pronoun. e.g. "we will unpack each one in turn"





Sycophancy
: not a figure of speech but a training artefact, and the fastest way to spot raw chatbot output. e.g. "What a genuinely profound question."







Part four: Formatting








Title case headings
: see the top of this page





Bullet lists where prose would do
: the document turns into a slide deck. e.g. this page, from here to the bottom





Bold-first bullets
: every item opens with a bolded keyword and a colon. e.g. yes, also this page, I know





Emoji as punctuation
: rocket for aspiration, key for insight, sparkles because the paragraph ended





Em-dashes
: mid-sentence, twice, sometimes three times, where a comma would have done the job





The find and replace tell
: double hyphens everywhere, because someone removed the character and kept the habit





Unicode decoration
: arrows and curly quotes nobody typed on a keyboard. e.g. Input → Processing → Output





One thought per line
: paragraphs abolished. e.g. "It happened. Alone. On a Tuesday."







Part five: Composition








Announce then answer
: says what the text is about to do instead of doing it. e.g. "This guide will explore what those signs look like."





Rapid-fire historical analogies
: authority borrowed from a list, with no argument underneath. e.g. "Marconi. Sony. Apple. Print, radio, web, mobile."





The metaphor that will not end
: e.g. the garden that stays a garden for six sentences, then becomes a north star that moves the needle





Self-echo
: a word from earlier returning as if it were being paid off, when it is the same narrow vocabulary surfacing again





One point diluted
: the same idea in five coats, sounding like progress. e.g. "put differently", "to rephrase", "in other words"





Fractal summaries
: every section summarises itself, including the one you are inside. e.g. "In this section we explored composition."





The tie-back
: closes by looping the answer back to the original question, long after the point was made. e.g. "So, to answer your question..."





Concede and dismiss
: e.g. "despite its fluency, it faces challenges typical of automated systems. Despite those challenges, it endures."





Signposted conclusion
: competent writing does not need to announce that it is ending. e.g. "in conclusion", "to sum up", "in summary"





The conclusion that refuses to end
: clause stacked on clause, quieter and more meaningful each time. e.g. "the real AI generated text was the paragraphs we scrolled past along the way"





Engagement bait
: e.g. "Agree?" plus a pointing emoji







Tells on the list that did not fit in the episode




The recording would have run to forty minutes. These are worth knowing anyway.







Reasoning leak
: the text narrating its own decisions instead of just making them. e.g. "I want to be exact about my own role here."





Premise stacking
: a paragraph of evidence in front of the point, so the point has been made twice before it arrives





Compulsive counting
: states the number of items before listing them. e.g. "Five things we wish to discuss"





Excessive enumeration
: a listicle disguised as prose. e.g. "The first wall is... The second wall is..."





Belaboring the unnecessary
: defending a minor point against an objection nobody was going to raise





Synonym cycling
: refusing to repeat a noun. e.g. the dashboard, then the interface, then the portal, then the analytics hub





Wh-headings
: the default shape a model reaches for when naming a section. e.g. "What this means", "Why it matters"





Content duplication
: the same paragraph appearing twice, because the model lost track of what it had written







Why this is not a detector guide




Liang, Yuksekgonul, Mao, Wu and Zou, Stanford, published in Patterns (Cell Press) on 10 July 2023. Seven commercial GPT detectors were run over 91 TOEFL essays written by non-native English speakers and 88 US eighth grade essays.





Average false positive rate on the TOEFL essays: 61.3 percent.



89 of the 91 essays were flagged as AI generated by at least one detector. All seven agreed on 18 of them.



The US eighth grade essays came back near perfect. Plainer word choice, lower perplexity, reads as machine to the software.



Feeding the TOEFL essays through ChatGPT with the prompt "enhance the word choices to sound more like that of a native speaker" dropped the false positive rate to 11.6 percent.





So the way to stop being accused of writing like a machine was to have a machine rewrite you. Do not take my word for it, the paper is open access:









https://www.cell.com/patterns/fulltext/S2666-3899(23)00130-7






Preprint:


https://arxiv.org/abs/2304.02819










The pointers, for real






Write with things in it: names, dates, prices, the model number of the drive that died.



Say something a machine has no way to know, because it happened to you.



Be willing to be wrong out loud.



Keep one weird sentence that your editor would cut.







Links and references






The list this episode leaned on:


tropes.fyi


, by


ossama.is






The detector study: Liang et al., "GPT detectors are biased against non-native English writers", Patterns, 2023



"Not an Addict", K's Choice, 1995. It's not a habit, it's cool, I feel alive.



"Friends, Romans, countrymen" and "I come to bury Caesar, not to praise him": Shakespeare, Julius Caesar, act 3 scene 2



"Veni, vidi, vici", attributed to Julius Caesar by Suetonius and Plutarch







Over to you




There is a comment section under this episode. Tell me which tell I missed, and which one you are guilty of. Better yet, record it. Read your worst, most machine sounding paragraph out loud and post it to Hacker Public Radio. The queue is always hungry.

Provide feedback on this episode.