While we use a lot of CAD tools, many of us are fans of Tinkercad — especially for working with kids or just doing something quick. But many people dislike having to work across the Internet with their work stored on someone’s servers. We get it. So does [CommonWealthRobotics], which offers CaDoodle. It is nearly a total clone of Tinkercad but runs on Windows, Linux, Mac, or even Chrome OS.

Is it exactly Tinkercad? No, but that’s not always a bad thing. For example, CaDoodle can work with Blender, FreeCAD, OpenSCAD, and more. However, on the business end, it sure looks like the core functions of Tinkercad.

The program appears fairly new, so you have to make some allowances. For example, the Linux AppImage seems to have difficulty loading plugins (which it needs to import many of its file formats). In addition, on at least some systems, you have to resize the window after it starts, or it won’t respond. But, overall, it is pretty impressive. The Settings, by the way, has a checkbox for advanced features, and there are some other goodies there, too.

One reason we found this interesting is that we sometimes go into schools, and they don’t want us to have kids on the Internet. Of course, they don’t like us installing random software either, so you can pick your battles.

Tinkercad, of course, continues to add features. Not all of which you’d expect in a drawing package.

I recently realized that it has been a year since I started writing regularly on Substack (can I now officially call myself a “Substacker”?), and one of the unexpected joys of sitting down week after week to write these articles is the insight it’s given me on how important editing can be.

I make no claim to be a great prose stylist. I claim only that the versions of the article I hit “send” on are much better than their first drafts.

Learning to write well for the internet has been a long process, though, and I’m nowhere near the end of it. But I’d never have got this far without an amazing editor. Through our work together, I’ve learned how even seemingly small changes to the prose can make accessible topics out of things which many would consider too dry or difficult for a general audience.

When the sentences flow smoothly, the reader’s attention flows along with them. When the prose stumbles, the reader stumbles too.

So I’ve become a little bit obsessed with the kinds of changes that have come out of the editing process. And I noticed that the changes I would make were most often at the ends of sentences or clauses. I found that a clumsy sentence could often be made much more graceful by changing nothing but the phrase at the finish.

But for a long time, I had no real theory about what I was changing or why.

This bothered me for a while — I like my theories, after all — until, some weeks later, with my mind steeped in theories of poetic rhythm, I realized that my editor and I had been doing by instinct something that has thousands of years of theory behind it. But it’s largely disappeared from the discussions we have about writing style today.

I’m talking about the study of prose rhythm, and more particularly, theories of cadence: the rhythm of the end of sentences (and other things which we’ll talk about soon).

Writers have cared about “sticking the landing” — that is, ending sentences on a satisfying rhythm — since the days of orators of Ancient Greece.1 They had very clear ideas about which rhythms worked at the end of sentences and which ones didn’t, as did the ancient Romans and the medieval churchmen. But none of them wrote in English.

So I set out to see if anyone had examined how these ancient and medieval theories of prose rhythm might apply to English. This article is the result of my investigations.

One caveat before we begin: I want to emphasize that I have found no recipe for good prose, no step-by-step list to follow that will guarantee a good result. But what I have found is a way of analysing and classifying some of the rhythmic ingredients of English prose, to give you a better idea of what exactly — to continue the metaphor — you’re cooking with.

You’re reading The Dead Language Society. I’m Colin Gorrie, linguist, ancient language teacher, and your guide through the history of the English language and its relatives.

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This too starts in ancient Greece

It seems to have been en vogue in the Edwardian period (1900–1919) for scholars to examine the rhythm of English prose.

They were drawing from recent studies that had elucidated first the rhythmic tools used by classical orators such as Cicero, and the subsequent development of those tools into the Latin of the Middle Ages.

To understand how all this applies to English, however, we’ll need to acquaint ourselves with some of the terminology that originally applied to the study of prose rhythm in Latin and Ancient Greek.

Today, we divide prose into paragraphs, sentences, and clauses. We mark these divisions typographically: paragraphs are signalled to us through the use of indentation and spacing, sentences by capitalization at the beginning and the use of the period or full stop (.) at the end, and clauses by the use of punctuation like semicolons (;), and colons (:) at the end, although not entirely consistently.

The ancients understood prose divisions differently. Their definitions were based on oratorical practice, that is, the art of public speaking. What these ancient concepts of prose organization care about is rhythm. And rhythm is determined by how the words hang together into larger units, and where it’s appropriate to take a breath.

Their largest division in this ancient system did not correspond to our modern paragraph,2 but to our modern sentence. They called this division a period, from the Greek περίὁδος períhodos ‘cycle.’

The period was a unit of organization which could stand on its own, just as we understand the modern sentence today. Those of us who call the punctuation at the end of a sentence a period have taken the name from the chunk of language that the punctuation mark concludes.

The period was composed of smaller units called cola (singular, colon). The word colon comes from the Greek κῶλον ‘limb’.3 A colon was a unit of prose which could be pronounced in a single breath, after which there would be a pause.

As we can tell from the etymology of the word, a colon was not a complete thought: it was considered as a part of the whole, like a limb of a body, where the body corresponds to the period.

We might think of the colon as equivalent to the modern concept of a clause, although the comparison is not exact. The clause is fundamentally a grammatical concept: a unit consisting of a subject and a predicate. The colon, on the other hand, is defined in terms of when a speaker would have to take a breath.

Like the period, the colon has given its name to a unit of punctuation, although the modern function of the punctuation mark we call a colon (:) is not simply to mark the end of a colon in the rhetorical sense.

Cola, too, were divided into smaller units. Each of these was called a comma (plural, commata), from the Greek κόμμα kómma ‘piece.’ The comma corresponds roughly to the modern concept of a phrase, that is, a group of words that act together as a single unit.

For example, a big matzoh ball is a noun phrase, really very nice and good is an adjective phrase, and put an end to maritime oil spills is a verb phrase.

Phrases in the modern sense can nest: maritime oil spills is a noun phrase which exists inside another noun phrase, an end to maritime oil spills, which is itself a part of the larger verb phrase put an end to maritime oil spills.

The classical comma doesn’t worry about nesting in this way, since it has its origin in oratory: for an orator, the relevant thing is not the grammatical structure of the phrase, which is indeed hierarchical, but rather its rhythmic structure.

In other words: where are the places where a small pause could be inserted to let the speaker take a breath? These are the comma divisions. And, as you’ve no doubt noticed by now, the ancient rhetorical comma has given a name to a modern punctuation mark (,), which is used in some places to mark pauses in speech, and in others to mark particular grammatical relationships.

Why no one knows how to use commas today

I’ll pause here to note that modern punctuation has a dual heritage: on the one hand, it descends from marks used to indicate the rhythm of the sentence, and therefore what kind of breath a speaker should take at various points; on the other, it seeks to express grammatical relationships.

Modern punctuation standards are a sometimes uncomfortable blend of both systems. For example, in the sentence, I had a good time and she did too, the use of punctuation to mark grammatical relationships forbids a comma (that is, the punctuation mark) between these two conjoined clauses, but the use of punctuation to signal rhythm and breathing points seems to tempt us to add a comma after time, which many writers do indeed do.

As we’ve seen, the way the ancients divided prose had much more to do with rhythm than with grammatical relationships. And this was not just a characteristic of oratory, which would be primarily delivered verbally, but also of writing. There was no great difference between the two modes of delivering prose in how the ancients thought of them: a letter or a work of historical narrative was divided into periods, cola, and commata just as a speech was.

For our purposes, these divisions were of special importance because of another feature of ancient rhetoric: writers and speakers paid special attention to the rhythm at the end of each period, and often of each colon as well. Each period was thought of as akin to a flight, rising at the beginning and falling at the end, and, as I’m sure you’ll all agree, the most important part of any flight is an orderly and controlled landing.

So in classical Greek and Roman practice, it was considered a matter of good style to end a period in one of only a few rhythmic patterns. These patterns were called by the Romans clausulae, but I’ll use the later term cadence to describe them, because our primary interest is English, where the term used for these period-ending patterns is cadence rather than clausula.

And, as it turns out, these cadences were crucial for understanding not just the style of the ancient Greeks and Romans, but of many of the great English writers as well, even if these writers were wholly unconscious of this system of rhythmic cadences.

“Shave and a haircut” is a classical cadence

But before we talk about individual English writers, we need to understand which cadences were considered appropriate for ending a period, and which were not.

The first thing to know is that the ancient Greeks and Romans defined rhythmic patterns in prose in the same way they did in poetry: in terms of long and short syllables.

These patterns of long and short syllables grew out of the nature of the Ancient Greek and Latin languages, which distinguished between long and short vowels (much like Old and Middle English did).

For someone who hasn’t studied Ancient Greek or Latin, these patterns of long and short syllables can be obscure. Fortunately, we can avoid talking about them because the Romans themselves eventually switched to a different way of determining rhythm.

That is, they started to use the stress pattern of the words to determine the rhythm, just like we do in English today — by the way, if you’re not familiar with the concept of stress and rhythm in English, you can brush up on that here. It’ll come in handy as we proceed.

Let’s look at some examples of what the late Romans — and their medieval descendants4 — considered acceptable cadences.

There were three main cadences considered acceptable, and each had a name: there was the cursus planus ‘level cadence,’ the cursus tardus ‘slow cadence,’ and the cursus velox ‘fast cadence.’ Each of these referred to a different arrangement of stressed and unstressed syllables, which I’ll notate with the symbols / for stressed and x for unstressed, respectively.5

• Cursus planus: / x x / x, e.g. Latin vóces testántur ‘voices bear witness’

• Cursus tardus: / x x / x x, e.g. Latin méa curátio ‘my guardianship’

• Cursus velox: / x x / x / x, e.g. Latin gáudia pèrveníre ‘to arrive (at) the joys’6

If you don’t know Latin, listen to the rhythm of the stress as I read them here — note that I’m reading them in a reconstructed classical pronunciation, which is not how the medieval church would have read them. But what matters here is not the precise details of the vowels and the consonants, but the pattern of the stress accent, which is the same in all pronunciation schemes:

Along with these three basic cadences, there were some acceptable variations, but we’ll discuss these later on.

The use of these cadences in writing rhythmic Latin prose continued through the latter half of the Middle Ages, until the rediscovery of classical learning in the Renaissance — including the practice of basing rhythm on long and short syllables rather than the stress accent — brought an end to the practice.

But, for a long time, scholars have wondered whether the practice truly died out everywhere. In particular, many writers have wondered whether the system survived in the writing of English prose, even if the influence was unconscious. After all, English is a language which, like Medieval Latin, uses a stress-based system of accent.

One of the first places they looked for this influence is in the Book of Common Prayer, first published in 1549 as a product of the English Reformation. New versions followed in 1552, 1559, and 1604, amid (and as a result of) the religious and political turmoil of that period in English history, before it finally stabilized in the 1662 edition.

The language of the Book of Common Prayer is worth an issue unto itself, because it’s been one of the most influential works on English prose style, with a level of influence similar to the Authorized (i.e., King James) Version of the Bible.

But for now, I’ll just say that a set of prayers7 from the Book of Common Prayer was used as the data set for a 1912 paper by John Shelly, which examined the influence of the Latin cadences on English prose. These prayers had been translated into English from Latin by Thomas Cranmer, although Cranmer made many adaptations to fit his own ideas about the Church.

Shelly’s contention was that Cranmer was replicating in his translations the cadences that he found in the original Latin — Latin that worked according to the three cursus patterns of the Middle Ages.

Corresponding to the Latin cursus planus vóces testántur, we have the English hélp and defénd us. The Latin cursus tardus méa curátio is echoed by the English góverned and sánctified, and the cursus velox of Latin gáudia pèrveníre corresponds to the English púnished for òur offénces.

In Shelly’s analysis, around 50% of the English prayers in the data set ended in one of these three cadences. Other scholars, including Albert C. Clark and Oliver Elton, expanded the investigation to encompass English prose more generally, and extended the analysis to include the following variations of the three classical cadences:

• variant cursus planus: / x x x / x, e.g. Latin dóna sentiámus ‘may we experience the gifts,’ English wrítten for our léarning

• variant cursus tardus: / x x x / x x, e.g. Latin (vir)tútis operátio ‘the working of power,’ English dángers and advérsities

• trispondaic (extended cursus velox): / x x / x / x / x, e.g. Latin (er)rántium córda rèsipíscant ‘may the hearts of the erring repent,’ English páss to our jóyful rèsurréction.

These scholars found that these classical cadences do indeed appear — and in great number — in some of the most classically influenced English authors: Edward Gibbon (1737–1794), the author of The Decline and Fall of the Roman Empire, comes to mind as an especially good example of such an author, as do Thomas Browne (1605–1682), author of Urn Burial, and Thomas Babington Macaulay (1800–1859), author of The History of England.8

Classical cadences in the hands of a master

Let’s look now at how Gibbon, to take the best-known of these authors as an example, manages his cadences to see how many of them are in accordance with the classical forms.

I’ve broken up the first paragraph of The Decline and Fall of the Roman Empire according to cola, and marked the stress pattern of the cadences, as I understand them:9

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In the decade since the neural architecture version of Google Translate and the invention of transformer architecture for neural networks, we have experienced the most rapid technological breakthroughs since at least World War II, and possibly ever. Artificial intelligence has proven to be a satisfactory substitute for the labor of translators and illustrators and looks to soon be an adequate substitute for call-center workers, computer programmers, paralegals, reference librarians, and radiologists. The best within all these professions can still exceed artificial intelligence, and indeed may find it a complement rather than a substitute for their labor, but the typical member of such occupations should expect artificial intelligence to behave as competition. As capital continues to invest enormous sums in training artificial intelligence, it will provide a usually adequate and often superior substitute for the human capital of more and more workers.

That improving AI is a substitute for labor is widely appreciated, and indeed, megalomaniacal mad-scientist visions of mass white-collar unemployment is part of the pitch decks shown to investors. What is less widely appreciated is that even if the technology stopped improving tomorrow, it would still be an increasingly good substitute for human capital. This is because it is already capable of giving human beings, and especially young people, the choice to idle in stupidity and ignorance.

People use consumer-facing artificial intelligence for all sorts of things. Every few days one learns about some horrific application like communing with the dead, cultivating psychosis, or substituting sycophantic waifubots and simpchats for the frustrations of romance with human beings. But the real killer app for LLMs is cheating on homework.

A graph of OpenRouter data that went viral this August shows that tokens processed by OpenAI dropped precipitously with the end of the school year. I checked the current data on the OpenRouter dashboard and tokens skyrocketed again with the beginning of this school year. OpenAI’s own analysis does not break out queries by date, but it shows that 40 percent of all queries are for what the researchers call “doing” tasks. These tasks are what would traditionally be considered work, mostly writing or editing prose and summarizing or translating texts.

“The user interface asks what dost thou want?”

Like a Great Oxygenation Event for the intellect, the first application of a technology that mirrors human intelligence is to undermine the cultivation of the real thing. John Henry beat the steam-powered drill, albeit at the expense of a heart attack, but the machine would have won by forfeit if years earlier it had done John Henry’s labor and exercise for him and so he never developed the strapping physique necessary to be a steel-driving man in the first place. And the temptation is ever present. Google Docs, Microsoft Outlook, Microsoft Word, and many other apps or browser windows into which one regularly types more than a hundred characters of text have user interface features that let AI do the writing. At every turn, the baby god actively solicits opportunities to do your work for you, no matter how important it is for developing your capacities or maintaining your integrity. The user interface asks what dost thou want? Wouldst thou like thine term paper done? A bit of homework? Wouldst thou like to live LLMiciously?

In my conversations with deans responsible for academic discipline, they have told me they enjoyed only a brief return to normal case loads after clearing the backlog of misconduct cases from Covid-era remote instruction before reaching a new normal of nonstop misconduct cases related to LLM usage. The problem is difficult because whereas cloud services like TurnItIn make it relatively trivial to detect and conclusively establish traditional ctrl-C/ctrl-V plagiarism, one can usually only strongly suspect but not conclusively prove unauthorized LLM usage. TurnItIn has created an AI detector, but many universities decline to use it as it has a low but nontrivial false positive rate: If the true rate of misconduct is 5 percent and the AI detector has a false positive rate of 5 percent and false negative rate of 5 percent, then half of all cases flagged by the AI detector will be innocent.

The obvious solution is to base grades on in-class assignments, but this has both intellectual and practical problems. The intellectual problem is that we sometimes want sustained engagement with a project, as with a term paper. The practical problem is that the marginal demand for education is in online education, where it is impossible to proctor to the same level that one can with blue books in a lecture hall.

One sometimes hears that instead of waging the impossible fight of getting kids not to use AI, we should teach them how to use it. There is a logic to this. When a technology becomes more available, wages go up for those who have human capital that complements the technology. But this raises the question of what sort of human capital is a complement to—as opposed to a substitute for—artificial intelligence and the corollary of whether such human capital is best cultivated through use of artificial intelligence or abstention from it? The usual assumption is that the most valuable skill one can acquire is prompt engineering. This is indeed an important skill to have, but I am skeptical that one learns to interact well with an AI through off-loading reading and writing tasks to it during one’s education.

My experience when I have caught university students making unauthorized use of AI is that the cheaters are too ignorant and lazy to know what good output would look like. Sometimes these errors are very obvious, as when two of my students turned in memos that did not summarize the assigned reading but one with a similar title. Knowing what good output looks like requires skills and knowledge that can only be acquired the old-fashioned way, by doing one’s own work. And I am talking about students at a selective university a few years into the AI boom. How much worse must it be at a junior high chosen at random? And how much worse will it be when students who used AI for their entire time in junior high and high school age into first college and then the labor force?

Personally, I find that judicious and careful use of AI helps me at work, but that is because I completed my education decades ago and have been actively studying ever since. Thirty years in higher education gives me the skills to complement an LLM rather than have it substitute for my own. Decades of doing so without an LLM as a substitute for my labor means I know how to write, how to read, and how to code so I can have an LLM aid me in this. Most important of all, my accumulated knowledge gives me inspiration for new research questions and techniques. I can then ask the AI very focused questions about if anyone has ever previously approached things in a similar way and to provide citations that I can then read for myself. As the economy adapts to AI, those of us who can take a complementary approach to LLMs will be more productive than those who know nothing but how to ask “@grok, is this true?” The problem is that developing the skills needed to interact with AI in a complementary fashion depends on not relying on them for one’s education.

As a friend who works in AI told me, AI heightens the contradictions. It is a boon to those with the motivation and background to cultivate knowledge but it spells total destruction for the system of universal education and credentialing. My worry is that we may run out of people with motivation and background to learn, know, and do. In the future, Gen X and millennial knowledge workers will be the human capital equivalent to pre-war steel. Just as particle detectors need steel forged before atmospheric nuclear testing gave all newly forged steel unacceptable background radiation, we will discover that even if your job mostly consists of interacting with LLMs, doing so well will require people who remember what it was like to read and interpret a document or contrast two ideas without asking an LLM to do it for you.

As AI might ask: Would you like me to expand on the theme of what happens to social stability when the relationship between social classes changes rapidly and the young find their labor superfluous to the needs of capital?

You’ve just pushed a PR after hours of careful work. You’re feeling pretty good about it too. Then the review comes in.

“ChatGPT thinks that {wall of AI-generated text}”

No context or specifics. Just a copy-paste job from someone who couldn’t be bothered to form their own thoughts.

I’m seeing this everywhere now: PR reviews, design docs, Slack threads. But here’s the thing: I don’t care what AI said. I care what you think.

ChatGPT isn’t on the team. It won’t be in the post-mortem when things break. It won’t get paged at 2 AM. It doesn’t understand the specific constraints, tech debt, or your business context. It doesn’t have skin in the game. You do.

When you paste an AI response instead of writing your own feedback, you’re not being helpful. You’re being lazy. Worse, you’re creating more work for everyone else. Now I have to parse through generic AI advice, figure out if it even applies to our situation, extract anything useful, and then guess what parts you actually agree with. Did you even read what you pasted? Do you understand it? Do you think it’s right?

Good feedback looks like this: “This nested loop is O(n²) and will blow up when we hit production scale. Consider using a hash map here.” Not: “I asked ChatGPT about your code and here’s what it said” followed by three paragraphs about algorithmic complexity that may or may not apply.

Look, I’m not anti-AI. I use it all the time. It’s incredible for exploring ideas, getting unstuck, learning new concepts. But there’s a massive difference between using AI to help you think and using it to avoid thinking altogether.

When you review someone’s work, you owe them real engagement. Specific feedback based on your understanding of the code and the context. If AI helps you spot an issue or articulate a concern, great! But then write it in your own words (if you agree!). Explain why it matters for this specific case. Show that you actually understand what you’re suggesting.

You’re the one with context. You’re the one who understands the codebase, the team dynamics, and your technical constraints. You’re the one whose name is on the review. You’re the one accountable, so own it.

The outage that hit Amazon Web Services and took out vital services worldwide was the result of a single failure that cascaded from system to system within Amazon’s sprawling network, according to a post-mortem from company engineers.

The series of failures lasted for 15 hours and 32 minutes, Amazon said. Network intelligence company Ookla said its DownDetector service received more than 17 million reports of disrupted services offered by 3,500 organizations. The three biggest countries where reports originated were the US, the UK, and Germany. Snapchat, AWS, and Roblox were the most reported services affected. Ookla said the event was “among the largest internet outages on record for Downdetector.”

It’s always DNS

Amazon said the root cause of the outage was a software bug in software running the DynamoDB DNS management system. The system monitors the stability of load balancers by, among other things, periodically creating new DNS configurations for endpoints within the AWS network. A race condition is an error that makes a process dependent on the timing or sequence events that are variable and outside the developers’ control. The result can be unexpected behavior and potentially harmful failures.

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Imagine you’re holding two equal-size dice. Is it possible to bore a tunnel through one die that’s big enough for the other to slide through? Perhaps your instinct is to say “Surely not!” If so, you’re not alone. In the late 1600s, an unidentified person placed a bet to that effect with Prince Rupert of the Rhine. Rupert — a nephew of Charles I of England who commanded the Royalist forces in the…

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