2026-03-21 03:00:00
What happened was, soon after New Year’s, friends and colleagues in the UK and Germany started letting us know that their emails to us were bouncing. Our “textuality.com” family domain is a Google Workspace (or whatever they call it this year) for email and docs and so on. Its Web presence, including DNS, has for many years been handled by a local outfit I’ll call “CWH” for some absurdly low monthly price, and has been trouble-free.
So, what could be wrong? We investigated and discovered that Google was offering a new-and-improved MX-record option, although they emphasized that the old setup should still work. Anyhow, we installed the New Thing and it didn’t help.
So, we filed a ticket with CWH tech support and somebody got back to us pretty quick, saying they’d changed a firewall
setting that was blocking connections to Germany. I detect the scent of GDPR, but whatever.
Euro-email: Bounce, bounce.
CWH: Probably an MX-record issue, and we should wait for DNS propagation. Several days passed and
bounce, bounce, bounce.
Us: “Not DNS propagation.”
CWH: “Still could be.”
So we VPN’ed to Germany and discovered we couldn’t ping Textuality’s IP address. Smells like a firewall to me. We told CWH that.
CWH: We have made some changes to firewall settings.
EMail: bounce, bounce, bounce.
VPN+Ping: Request timeout, request timeout, request timeout.
CWH: Try traceroute?
VPN+Traceroute: 14 hops, no joy.
CWH: Your VPN settings must be wrong. Here are instructions to use Windows PC VPN correctly.
Us: Thanks but no.
CWH: Your MX records are configured incorrectly.
Us: No, they are correct per Google guidance. We sent an email beginning
“Please believe us.”
CWH: It must be DNSSEC. Check to see if your registrar implements DNSSEC.
Us: We are using your DNS servers.
CWH: Perhaps your registrar is broadcasting an old record?
Us: Our registrar doesn’t do DNSSEC.
At this point we consulted a friend who’s an expert on DNS and Email and even DNSSEC. He verified that not only could you not ping Textuality from Germany, you also couldn’t ping CWH or its name servers. Firewall firewall firewall!
CWH: “I did test the site access using a 3rd party application, and it seems to be accessible on all parts.”
Us: Look at the
output, it shows we can’t be reached from anywhere in Germany.
Also, for all the remaining messages in the email trail, we prefixed our input with bold face extra-large text reading: Systems located in Germany cannot ping Textuality.com’s IP address, nor can they ping the IP addresses of textuality.com’s designated name servers. This is the problem.
CWH: Let’s try migrating you to a different server; try pinging these hostnames.
VPN+Ping: Nope.
CWH: Are you sure it’s not your VPN settings?
Us: Are you sure it’s not your GDPR settings?
CWH: Raising your issue to Tier 3.
20 hours pass, then we get email from:
…who said “This has been fixed.” It was. Nash’s email signature was “Nash(Rajaneesh) B”. What a great name, though. Thanks, Nash.
Not really. Consumer-facing tech support is hard. None of their suggestions were unreasonable. Doing GDPR correctly is hard. They’ve been just fine for years and were having a bad week. Could we expect better from any of CWH’s local competitors? Probably not.
It wasn’t funny at the time, but looking back, it kind of is.
2026-03-17 03:00:00
This last weekend we attended a concert entitled Lenten Reflection at Vancouver’s Catholic Holy Rosary Cathedral featuring the Belle Voci vocal group and the Cantare Super Orchestram early-music band. The music was fine and it was the most beautiful sound I’ve heard in a long time. Twenty-two months, to be precise (see below). And so I get to report on good music and yell at production people.
A cathedral is a nice place for a concert!
The concert opened with just the singers, their voices drifting down from a high place behind us, a balcony or choir loft. There was no incremental accompaniment and no amplification; the music flowed from vocal cords to eardrums — not directly, of course, there was lots of reflection and reverberation introduced by the Cathedral space. The singers were polished and expressive and the sound, drifting through the vast space, beyond exquisite.
They sang a lovely piece by Byrd (1539-1623). Then the instrumentalists played a number by von Biber (1644-1704) while the singers snuck downstairs. Joined, they performed Bach’s BWV 229 and 150, then pieces by Pergolesi (1710-1736) and Steffani (1654-1728).
The Bach pieces, as usual, had more music in the music, but the others were also fun. It was a small ensemble: In the choir, five sopranos, five altos, a countertenor, four each tenors and basses. The band had five baroque violins, a baroque viola, a baroque cello, a violone (think, string bass with frets), a baroque bassoon, and a player doubling on harpsichord and organ. Thus, an ensemble quite likely not too much bigger or smaller than the ones playing this music in the 1700s, when it was new.
Once again, the sound was something special and yeah, the musicians were excellent, but for me, the key thing was the lack of amplification: vocal cord to eardrum via cathedral. It’s always seemed obvious to me that you can’t run music through a bunch of electronics and speaker mechanics without changing it; if only spatially, with the sounds coming from speaker diaphragms located somewhere away from the human musician. To my ears, there is a fragile magic in pure unamplified sound. I lack the words to describe the difference but it’s not subtle.
Does this mean that everything was perfect? No; the choir was a little bit male-heavy; some of the soprano and especially alto lines were part-hidden behind the massed male voices. Also, the bassoon was right at the front of the stage; While the playing was fine, it felt as though it were musically, not just physically “in front of” the band and singers.
Both of these could have been fixed, by telling the men to take it down a notch or having one or two fewer of them. And by moving the bassoon back to the usual woodwinds spot behind the strings. Still, these were very minor imperfections.
Oh, and the performance and sound of the bass line on that violone was absolutely awesome; clearly audible as a thing on its own while it wove all the other musical threads together.
I’ve discovered that few classical musicians share my passion for unamplification. I hear things like “I want a full sound or “The soloists need to cut through the orchestra.” Which, well, OK, but somehow people managed to accomplish those things for centuries, before amplifiers and speakers were invented.
That’s since May of 2024 when I took in the Tedeschi-Trucks Band, whose music couldn’t be more different from anything called “Lenten Reflections”: electric not acoustic, profane not sacred. But crystal clear and perfectly balanced sound; so much better than most electric bands achieve.
My sincere thanks to the musicians and their leaders for a lovely experience. And my message to everyone co-ordinating and leading live music performances: Of course the first priority has to be the quality of the music, but think about the sound and try to be better. Better than than most performances manage, these days.
We know it’s possible.
2026-03-06 04:00:00
Recently I read Because Internet by Gretchen McCulloch and Algospeak by Adam Aleksic. The language we speak (and text) to each other is at the core of who and what we are, and the Internet is the strongest among the forces that channel and fertilize its growth. So there’s scope for plenty of books on the subject. Both books educated and entertained, one made me angry.
Its approach is historical and its voice fairly uninflected. It smiles and argues, but it doesn’t ROFL nor does it YELL AT YOU. The history is longer, perhaps, than most people reading this have been online (or even alive). Ms McCulloch goes back to the days of BBSes (“bulletin-board systems”) and ListServs and IRC. Some of the jargon and formulations of those days live on; you’d be surprised.
Here’s her table of contents.
The analysis is grounded in the formalisms of the author’s profession, academic linguistics. Nothing wrong with that.
Let’s look at a couple of her ideas, beginning with Chapter 1’s “Informal Writing”. A few of us, back in the late Eighties, noticed that computers in general and the then-nascent Internet in particular were driving a writing renaissance.
Before computers, a knowledge worker who had laboriously constructed essays in college quite likely wrote almost nothing for the rest of their working life. People talked face-to-face or on the phone, and dictated to secretaries. Written communication was seen as necessarily formal and disjoint from the way we spoke, or that we wrote in personal correspondence. Then, suddenly, everyone was sitting at a keyboard only seconds away from everyone else’s screen. McCulloch goes deep on this:
In the future, the era of writing between the invention of the printing press and the internet may come to be seen as an anomaly—an era when there arose a significant gap between how easy it was to be a writer versus a reader. An era when we collectively stopped paying attention to the informal, unedited side of writing and let typography become static and disembodied.
The internet didn’t create informal writing, but it did make it more common, changing some of our previously spoken interactions into near-real-time text exchanges.
From which all of this follows. It feels like a central insight. I suppose you could argue that centrality of informal text is fading in the face of short-form video. Maybe, it’s too soon to tell.
Then consider chapter 5, about emojis. Linguists obviously need to think about them because now they’re an integral part of written language. McCulloch’s insight is that they correspond almost exactly to gestures, the way we use our hands to add force to our speech. Obviously, for example, “👍”. Or when you’re talking about something completely loopy and you twirl your index finger by your ear? You meant “🤪”.
I offer the emoji story for flavor, an example of a linguist’s approach to what we’re doing to our language with our networks.
McCulloch has lots more of this stuff. I enjoyed Because Internet a lot, partly because I’m old and my memories stretch back to those BBS and IRC days and I had a front-row seat for the decades of linguistic seething and heaving. And also because I’m a Unicode geek.
The subtitle is “How Social Media Is Transforming the Future of Language”. OK, but… Social media is a fertile field for language evolution. Thing is, corporate social media discourse lives in the dire grip of the proprietors’ algorithms. And that’s where Adam Aleksic focuses. He treats all of them as a single opaque object, “The Algorithm”, which I think is fair because they all are designed with one goal: To maximize the effectiveness of human conversation at generating advertising revenue.
First, the Table of Contents.
Aleksic knows whereof he speaks: As “Etymology Nerd”, his aggregate following across TikTok, Instagram, and YouTube is over three million. He’s all about cool bits and pieces of linguistics, often Internet-specific usages. If I had the patience for podcasts I suppose his would be near the top of my list.
He really enjoys his work and has fun talking about some of Social Media’s more colorful linguistic extrusions; check that Table of Contents. I’m kind of old and I learned a lot about the words and emojis younger folk emit, and I think most folks, even those just out of their teens, would too. I’m on a Discord for a Major League Soccer team’s fans, and while it’s totally all-ages, I can say I am regularly less mystified than I was before I read Algospeak. For example, now I know what it means when someone tosses “💀” into a chat. Do you?
Aleksic isn’t averse to a little history himself. Looking back over the successive online-jargon volcanoes, he argues convincingly that two stand out as extra productive. First of all, the short-lived (but hot stuff at the time) Vine video platform. Second, the incel cesspool; sad but (apparently) true.
Remember, it’s all about what advertisers want. And wow, do they ever want a lot of things. I’ll just touch on a few of Aleksic’s points.
First of all, they don’t want to find themselves next to downers. So if you want to talk about death or suicide or rape or racism or rage, you need to fool The Algorithm. Thus “unalive” and many other dodges. Of course, The Algorithm learns about them so you have to keep dodging. Neither side of this struggle can stay ahead for long.
Here’s another thing I didn’t know: Apparently written Chinese is particularly rich in techniques for euphemizing, making it easier for users of that language to evade, for a time, The Algorithm.
Another big thing The Algorithm likes is grouping people into smaller and smaller baskets based on interests, generations, and many other criteria. This is because advertisers can aim very specific campaigns at just exactly the right cohort of people who are likely to buy what they’re selling. Here’s a quote; See how the language fills in behind advertisers’ pressure?
It doesn’t matter how much I label myself. If I’m a demisexual goblincore Gen Z Swiftie, I guarantee there are still others like me. The only thing these labels really change about me is that they make me easier to classify and market to. Ironically, true individuality may come out of a lack of labels and stories, because there’s greater freedom of expression with a blank slate. If everybody’s the “main character,” then nobody is.
Algospeak, unlike Because Internet, doesn’t limit itself to written language. One of its most compelling studies concerns the vocal techniques of podcasters and YouTubers. The finding is simple: It’s hard to build and hold an audience for your show unless you sound like MrBeast. No, really.
Anyhow, they’re both good books. Because Internet educated and entertained me. Algospeak is way more intense, intentionally more like the subject it addresses. Also it made me angry. I am a lover of human language and of its patterns of growth and mutation and simplification and complexification. Linguistics is one of the disciplines I regret not having chosen.
Aleksic makes it clear that there’s an amusing narrative about how the people living and speaking in the shade of the Algorithm can never defeat it, but they can still manage to get their messages across. But they shouldn’t have to struggle!
In fact, a few million of us have found a place to talk to each other that isn’t in The Algorithm’s shadow: Decentralized social media. Specifically the Fediverse (what people mean when they say “Mastodon”) and maybe the ATmosphere (same for “Bluesky”).
I want to see how language grows in a place where new forms arrive when they’re needed, to say new things that need to be said. Not to either serve or resist The Algorithm.
2026-02-28 04:00:00
Block announced that it’s cutting 40% of its workforce. It didn’t say it was replacing those people with GenAI. Not out loud. Jack Dorsey did say “I believe the majority of companies will reach the same conclusion and make similar structural changes.” Wall Street loved it, bidding up the share price by 24%. Which reminded me of Kansas in 2010.
As long as I can remember, a certain class of right-wing evangelists has preached that cutting taxes would stimulate business growth and everyone would come out ahead. There are a couple of problems with this theory. First, mainstream economists almost universally think it’s just wrong. Second, most of the people pushing it are rich and would benefit from the cuts.
Anyhow, in 2010 US Senator Sam Brownback won the race for governor of Kansas on what was then called the “Tea Party” program: Prosperity through tax cuts. Tea-party Republicans also won a large majority in the state legislature. Unsurprisingly they immediately did what they said they were going to do: Slashed a wide variety of taxes, some to zero.
The predicted prosperity failed to happen. The state government’s revenue plunged and it had to dig deep into rainy-day reserves just to keep the doors open. There were brutal cuts to policing, road repair, and schools. Also a nasty feedback loop: As the state’s fiscal position worsened, its credit rating fell and interest rates rose, leading to yet more brutal austerity measures.
Another result was that affluent Kansans made out like bandits; the cost of running the state was substantially transferred to the less financially fortunate.
In 2017, the legislature threw in their cards and repealed the tax cuts, overriding Brownback’s veto.
While this was a terrible experience for most Kansans, it is historically useful, because whenever you encounter a tax-cut nut (probably self-interestedly wealthy) you can say “But, Kansas!” Having said that, there are still plenty of those nuts, and they’ll tell you that the Kansas experiment failed because of one fine-tuning effort or another. That’s a position that’s hard to defend, though.
Before I move onto the AI angle, I gotta pause to acknowledge this week’s news story about the Kansas government’s vicious, brutal, and ignorant assault on trans people. To be clear, I think the shitty people who hate trans folk are aren’t necessarily the same shitty people as the shitty people who don’t want to contribute to the public good. But, something about Kansas seems to attract both flavors.
The core value proposition of contemporary AI technology is exactly what Dorsey seems to think: Fire half your employees and profits will soar! If that’s true, the trillion dollars or so invested so far will seem like small potatoes. Since we don’t know if this will actually work, anyone who actually does it is conducting an experiment. Just like Sam Brownback did. Unsurprisingly, the investor class loves this experiment and is putting their money on it working.
To be fair, voices have been raised to argue that the tech sector is a special case: That following on feverish over-hiring during the Covid lockdown, they need to slash headcount anyhow, and are using AI as an excuse. For example John Gruber.
I personally am unconvinced, but even if they’re right, it’s irrelevant. The shareholding class won’t be able to see past that 24% payoff. So as of today, they’ll be yelling at every CEO on the planet to start pulling the mass-firing trigger. Or else.
I think I know how the experiment will turn out. Just like in Kansas, it’s not going to be fun.
2026-02-25 04:00:00
I’ve run early-spring pictures of these little purple guys almost every year since this blog’s birth in early 2003. Except for last year. Because we moved and the new place didn’t have any. Only now it does, and they’re (just barely) up. [Update: Up and open, too.]
Long-time followers may note that they’re pale and fragile compared to the exuberant blossoms of previous years. Not sure why, but our new place faces north and there’s this enormous White Ash tree right in front of it, so they’re not getting as much sun as at the south-facing former joint.
And also this is their first spring. We bought the bulbs and hired a professional with the right tools to jam them into the earth last autumn, between the big tree’s roots. So they really haven’t had a chance to get their own root systems going.
And finally, it really is the first day that’s bright and warm enough to get out the camera. Maybe they’ll be better in another few days. And quite likely next Spring.
This would be the place to introduce whatever metaphor this year’s blossoms, fighting their way through the leaf cover in chilly air toward the sun, fit into, but I’m not gonna.
I, like many, am not dealing very well with what I see when I look at the world in either the big or the ultra-local landscapes. The world in tough shape and its worst people are making it worse. People I love are in ugly corners and not finding help.
But you know, the flowers, in their low-key way, look great and so does the tree, still in wintersleep. Today the sun was shining on them. It’ll be warmer and nicer soon.
Metaphors can go to hell. It’s just late-winter light on pale violet petals. Enjoy the moments you have with it.
Update: Now open for business.
2026-02-17 04:00:00
I’ve been puttering away on my Quamina project since 2023. In the last few weeks GenAI has intervened. Quamina + Claude, Case 1 describes a series of Claude-generated human-curated PRs, most of which I’ve now approved and merged. Quamina + Claude, Case 2 considers quamina-rs, a largely-Claude-driven port from Go to Rust. Both of these stories seem to have happy endings and negligible downsides. So empirically, I can apply LLM technology usefully to software development. But should I?
Let me be clear: In the big GenAI picture, I’m a contra. Why? I’ll pass the mike to Baldur Bjarnason, my favorite among GenAI’s blood enemies.: “AI” is a dick move. His tl;dr is something like “GenAI is environmentally devastating and has the goal of throwing millions of knowledge workers onto the street and is being sold by the worst people and is used for horrible applications and will increase society’s already-intolerable level of inequality!” To which I reply “Yes, yes, yes, yes, and yes.”
At the end of the day, the business goal of GenAI is to boost monopolist profits by eliminating decent jobs, and damn the consequences. This is a horrifying prospect (although I’m somewhat comforted by my belief that it basically won’t work and most of the investment capital is heading straight down the toilet).
But. All that granted, there’s a plausible case, specifically in software development, for exempting LLMs from this loathing.
First of all, size. JetBrains thinks that the world has 21 million or so software developers, i.e. less than 1% of the earth’s working population. Vanishingly small in the context of the lunatic tsumani of LLM overinvestment. Training and operating the models required for a market this small is rounding error measured on the Great GenAI Overbuild scale. There aren’t enough geeks to create a detectable bump in the global carbon load.
Another odious aspect of LLMs is RLHF, “Reinforcement Learning From Human Feedback”, which relies on underpaying Third-Worlders to polish the models’ outputs. My guess is that much less is required for code-oriented LLMs. The combination of the compiler and your unit tests provide good starter guardrails. Then skilled professional intervention is required to deal with the remaining misfires, as with those Quamina PRs.
Finally, it seems making billionaires into multibillionaires is intrinsic to GenAI dreams. But software-development tools won’t do that. Once again, the market is just too small. But even if it weren’t, consider this from Steve Yegge:
For this blog post, “Claude Code” means “Claude Code and all its identical-looking competitors”, i.e. Codex, Gemini CLI, Amp, Amazon Q-developer ClI, blah blah, because that’s what they are. Clones.
(GenAI, overbuilding wherever you look.) None of these products have moats and the chance that any of them become extractive monopolies is about zilch. Nobody’s ever built a major cash-cow on developer tooling
One reason is (*gasp*) Open Source. Does anybody doubt that in the near future, there will be entirely open-source versions of what Yegge means by “Claude”?
So, if you want to condemn the use of GenAI in software development, I think you need arguments other than the fact that it’s also being promoted for societally-toxic business purposes.
I have a few. But stand by, let me push that on the stack and turn to technology for a bit.
Question: Can LLMs even participate in quality software engineering? Baldur doesn’t think so: “The gigantic, impossible to review, pull requests. Commits that are all over the place. Tests that don’t test anything. Dependencies that import literal malware. Undergraduate-level security issues. Incredibly verbose documentation completely disconnected from reality.”
I’m not saying that these pathologies can’t or don’t happen. But in my personal experience with Quamina, they didn’t. (Mind you, it’s a hobby project.)
And when they do happen, I would assume that mature open-source projects will use a network of trust, as big operations like Linux already do. PRs that don’t have the imprimatur of someone known to be clueful will be ignored. When I saw the first of those incoming Quamina PRs, I took the time for a serious look because I knew Rob and had seen evidence that he was technically competent. If I see an incoming PR that’s nontrivial and from some rando and doesn’t pass a 120-second sanity check, it’s unlikely to get any more attention.
In fact, some essentials don’t change. If you’re not requiring that PRs be clean and test coverage be good and code reviews not be skipped and dependencies be curated, you’re going to get a lousy result whether the upstream code is coming from a human or an LLM.
But it’d be naive to think that a big change in the shape of that upstream isn’t going to affect the profession.
Speaking from personal experience, reviewing the PRs from Claude&Rob was neither faster nor slower, easier nor harder, than what I’m used to pre-GenAI. The number of my disagreements with the diffs, and the amount of arguing it took to resolve them, was also about as usual. Which creates a big problem. Because if we can generate code a whole lot faster but review doesn’t speed up, all we’ve done is moved the bottleneck in the system.
Speaking of which, Armin Ronacher offers The Final Bottleneck, from which: “When one part of the pipeline becomes dramatically faster, you need to throttle input.” Think about that.
Meanwhile, evidence is piling up that LLM-based software development is driving developers to overwork and burnout. Here’s a cool-eyed take from Harvard Business Review. Then there’s Steve Yegge’s frantic, overly-long The AI Vampire. But my favorite, and I think a must-read, is Siddhant Khare’s AI fatigue is real and nobody talks about it. From which: “AI reduces the cost of production but increases the cost of coordination, review, and decision-making. And those costs fall entirely on the human.”
The argument we’re hearing is that GenAI makes development more efficient. And more efficient is better. Until it’s not.
I’m not sure the profession I joined last century would attract me today. And on Mastodon, @GordWait said “At our office, we are noticing a huge drop in Comp Sci co-op applications. The next generation is convinced there’s no future in programming thanks to AI hype.”
Here’s another conundrum. Suppose we can build a whole lot more stuff, faster. Should we? I don’t know about you, but I am regularly enraged at tools that work just fine popping up “wonderful new features” modals in front of what I’m trying to get accomplished. Also at damaging UI churn, driven by product managers trying to get promoted. It’s just not obvious that speeding up software development is, in the big picture, a good thing.
And I can’t help noting that every attempt to measure the productivity boost due to GenAI has shown zero (or worse) improvement. Of course, Claude’s cheering section will point out that those studies date to 2024 which is the stone age. Maybe they’re right.
(In which I once again go all class-reductionist.) The real problem here is late-stage capitalism, and I think is best addressed in Yegge’s AI Vampires piece, from which I quote: “…dollar-signs appear in their [employers’] eyeballs, like cartoon bosses. I know that look. There’s no reasoning with the dollar-eyeball stare.” Yeah.
Thus the ancient question: cui bono? Assuming GenAI genuinely boosts productivity, who gets the benefits? Because the ownership class sure doesn’t think they should go to their newly-more-efficient employees.
I know that you gotta have test coverage or your software is an unmaintainable tangle of festering tech debt. I know you gotta have code review or your quality is on inexorable downhill drift. I don’t know how to build LLMs into a sane, sustainable software engineering culture. Nor what to do about capitalism’s AI Vampires.
And I absolutely do not believe the wild-eyed claims of 10× productivity gains, assuming we demand (as we should) that they’re sustainable at scale.
So, would I advise executives to tell software engineering shops to discard their culture in favor of vibe coding in the expectation of monstrous productivity wins? Nope. Vibe engineering, maybe. Centaurs, not reverse centaurs? Indeed.
But would I say “Stay away, don’t even look”? Nope. I’d probably suggest pointing the LLM at well-delimited non-strategic issues and optimizations, and emphasize no shortcuts on reviewing or CI/CD standards.
Also note that the GenAI apostles are at one in saying that this year’s tools are so much better than last year’s, and next year’s are guaranteed to be qualitatively still better! So why would you rush in and risk getting locked into soon-to-be-outmoded tooling?
Rob Sayre wrote “I would never bother to type out these patches by hand. But I read them all.” I probably wouldn’t have either and I read them too. And now Quamina is roughly twice as fast. Which is to say, I got good results on a hobby project. That’s not nothing.
But, also not conclusive. Once the AI bubble pops and we’ve recovered from the systemic damage, I think there’ll probably be a place for open-source LLM automation in developer toolkits.
But maybe not. Wouldn’t surprise me much, either way.