Jim Nielsen’s Blog

I made a note of it, but Bryan Cantrill wrote a post about the ex-Anthropic employee who pegged the probability of AI killing humanity in the next decade at >10%.

The article is a good one and you should read it, but what I want to capture here is a side story from Cantrill on the Oxide and Friends podcast where he talks about how proud he was of this phrase from his post: “These ghoulish claims strike brazenly at the hearth…”.

He notes that someone online commented on that phrase, saying it sounded like a mix of classic literature and a line from The Simpsons.

Amused, Cantrill says he came up with the line all by himself, so he knew it wasn’t a classic literature reference nor a line from The Simpsons.

But just to be sure, he Google’d it: “These ghoulish claims strike brazenly at the hearth” with “The Simpsons” tagged on to the end.

To his surprise, Google’s AI told him that the line was, in fact, a line from The Simpsons. Here’s Google’s response (as reported by Cantrill):

During the Springfield mayoral debate on Channel 6, Sideshow Bob dramatically delivers the line “these ghoulish claims strike brazenly at the hearth of Springfield” while turning the debate against Mayor Joe Quimby. Instead of denying his criminal history, Bob exploits Quimby’s record and boldly tells the audience they secretly long for a cold-hearted Republican to rule them like a king.

(Here’s my homemade graphic for illustration purposes.)

Cantrill was astonished. Not knowing the episode enough by heart to discern whether this claim was true, he had a little bit of a panic attack:

If I am having what I feel to be original thoughts that are, in fact, just Simpsons lines regurgitating from 30 years ago, that’s troubling […] How am I not an LLM?

So he set out to find the truth. He finds the episode and watches it, but doesn’t hear the line.

But what if the episode he watched was an edited version and the full-length one is out there somewhere? He’s gotta get to the bottom of this.

So he goes back to Google and pushes on the AI’s answer, asking for the full script of the episode. At that point, the LLM folds and says the line is not from The Simpsons, but is actually from a post by — drum roll… — Bryan Cantrill.

Here’s Cantrill, again, reporting Google’s AI response:

What happened here is a classic AI “hallucination”. Because the phrase you shared used highly stylized, melodramatic language, “ghoulish claims”, “strike brazenly at the hearth”, my system misidentified the tone as belonging to Sideshow Bob who is famous for speaking in exactly that kind of grandiloquent Shakespearean style.

I have to say, I found the whole story amusing and relatable.

• You write something on the internet.
• Somebody says, “This sounds like something from the Simpsons.”
• You wonder, “Do I even have original thoughts?”
• AI gas lights you.
• You have to spend your time refuting AI assertions.
• Some teenage part of you gets a kick out of being conflated with Sideshow Bob for your choice of language.

I just wanted to write this all down and save it as a way for future me to remember, “This is what it was like to be online in 2026.”

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John Gruber makes his picks for the iPhone Hall of Fame:

1. iPhone (original)
2. iPhone 4
3. iPhone 5S
4. iPhone X
5. iPhone 17 Pro

Ok, my turn. Here’s my iPhone Hall of Fame:

1. iPhone (original)
2. iPhone 4
3. iPhone X
4. iPhone 12 Mini
5. iPod Touch

Notes:

• How do you not put the OG iPhone at the top spot? It’s like doing a Mount Rushmore and not putting the first president first. It’s gotta be George.
• Aesthetically, the 4 was the best. Just an absolute beauty to behold (and use, so long as you held it right).
• Aesthetically, the X was a close second to the 4. But in terms of form, X was best. (Shout out to the 3GS, that curved back felt great in the palm.)
• The Minis (12/13) stand as proof that commercial success doesn’t always equate with greatness. Ahead of their time, those ones.

Oh, and you’re probably wondering: “The Touch? That’s not even an iPhone!”

I disagree. The Touch was The iPhone For The Rest of Us™.

When you couldn’t get one of those early iPhones (because of price, because of geographic location, because of ___) the Touch was your gateway drug.

For me, it was price. I was a poor kid in college in 2007, and the iPhone was $$ (and it was on AT&T, which was even more $$).

Eventually, I was able to get an original iPhone on eBay (and jailbreak it for T-Mobile — remember Cydia?). But the Touch was my first taste of iPhone.

That’s my Hall of Fame list. What’s yours?

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“Artificial” things can get a bad rap next to their counterparts, but they have their place.

For example, artificial light (i.e. not from the sun)? Super useful, especially at night.

And when it comes to skiing, I’ll take artificial snow if I have to. Beats no snow.

Artificial isn’t necessarily inferior in all cases, everywhere, all the time. But it is optimized for different values. For example:

• Artificial grass (i.e. turf) is great for certain contexts, but I don’t want a golf course of artificial grass.
• Artificial flowers are great for certain scenarios, but I don’t want them at my wedding.
• Artificial flavoring is yummy (love me some peach rings), but I’d prefer a real peach pie over one with artificially flavored filling.

Artificial is an alternative. It’s understood as a trade-off, not a substitute.

You trade the real for the artificial when you’re concerned about attributes like cost, convenience, and availability.

Which brings me to “artificial” intelligence: surely a trade-off, not a substitute.

Ain’t nothing like the real thing.

These labs should consider ripping off (cause they’re good at that) Mastercard’s old tagline: “There’s some intelligence money can’t buy; for everything else, there’s Claude.”

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Poor bottlenecks. Always seen as problematic, antithetical to efficiency.

But bottlenecks aren’t universally bad.

Think about it: a bottle’s neck is designed to constrain the amount of liquid that can flow out. A decrease in bandwidth is its entire purpose! Otherwise an overwhelming amount of liquid flows out and makes a big mess.

We humans have a particular anatomy. We can only consume so much liquid at a time. The neck of a bottle works with that fact.

We could make machines to produce so much wine that we’re drowning in it. But that wouldn’t change the fact that we’re only capable of consuming so much liquid at a time (not to mention digestion, etc.).

When it comes to liquid consumption, the bottle’s neck isn’t the bottleneck — our neck is!

So if you’re having a hard time drinking out of a firehouse, perhaps the question isn’t, “How do I modify my biology to accommodate the bandwidth of the firehose?” But rather, “Why am I trying to drink out of a firehose in the first place?”

Maybe a bottleneck isn’t your problem. In fact, it might just be the solution.

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I’ve got Netlify Analytics turned on for a few of my websites.

I use it send myself a little daily digest that alerts me when folks link to my writing, be it an individual sending me a few referrals, a newsletter sending me a few hundred, or Hacker News sending me thousands.

But sometimes I want to know more than, “What happened with traffic in the last 24 hours?”

Sometimes I want to know, “What happened with traffic in the last month?” I can log in to Netlify to answer that question.

Sometimes I want to know, “What happened with traffic in the last six months?” Netlify doesn’t have an answer for that because they only store the last thirty days of traffic data.

I’ve known about this limitation for years and always thought, “I really should just start saving that data for myself.”

But, you know, laziness. How bad do I really want it? The answer, it seems, was “I only want it if it’s _easy_”.

Enter Val Town.

Val Town makes this kind of task easy. Each “Val” is it’s own little bucket of scoped computing primitives for just such a task:

• Code (run a script that fetches data)
• Secrets (securely store my Netlify access token)
• Storage (my own little SQLite database for persisting data over time)
• Automation (run this task every 30 days)

All of this might seem like overkill for a single-script task like mine. But I like it. The scoped nature of each of these primitives makes composition easy without the kind of entanglement I’d have to fight to keep isolated if I were co-locating this task with other small tasks.

I don’t want to think about a shared schema for this task and whatever other tasks I might think of in six months. I don’t want to have to reason about evolving an architecture and maintaining a codebase of miscellaneous, unrelated tasks. Vals are perfect for this.

So the way I have this setup is: run the .ts file every 30 days which fetches the last 30 days worth of analytics data from Netlify and stores it in SQLite. (You can see my Val here.)

At that point, I have the data I need to curiously ask any question, e.g.

• What are the top referring sites to my blog?
• What post has received the most page views over time?
• Where does most of my traffic come from?

The answers to these questions get more and more interesting over time, as the long tail of web dynamics play out: search engine traffic, serendipitous rediscovery of old posts in light of new trends, etc.

“But how do you actually view the data you’re storing to get answers to these kinds of questions?”

Great question. Val Town has a UI where I can see the raw data I’m storing (which I chose to store exactly as I got it from Netlify, rather than doing some pre-processing first):

But that’s not very useful. I need to parse that data and turn it into some kind of useful analysis.

And you know what tool is super useful for understanding data? A spreadsheet.

If I were using Excel, I’d probably have to figure out a way to dump the SQL, convert the JSON to some kind of CSV, then import it. And every time I wanted to see the “latest” data, I’d have to run that workflow.

Enter Quadratic.

Val Town has an API where I can fetch the latest data via code, and Quadratic is a spreadsheet that runs code. So seeing “the latest data” from my pipeline in Val Town is as easy as pressing a “re-run code” button in Quadratic.

Quadratic serves as a kind of “front-end” to my data pipeline. Easy to pull the raw data, transform it however I need, and create a “dashboard” I can revisit whenever I want to see the latest insights or ask new questions.

And, since I’m storing data for a couple different sites in Netlify, I have a single cell in my spreadsheet where I can swap out the domain — say from blog.jim-nielsen.com to iosicongallery.com — and the entire dataset and dashboard redraw.

I know, I know. There are lots of products for stuff like this. And there’s probably a million other ways to do what I’m doing, all peculiar in their own way. But this one is mine.

And now I can sleep at night knowing that every 30 days my little Val Town robot is gonna go store the last 30 days of data that Netlify is throwing away.

One person’s garbage is another person’s treasure trove of data.

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A URL is a technical thing: a hostname, a path, a port, an origin, some query parameters, etc.

But a link is a cultural thing: an invitation, a citation, a gift, a shortcut, a connection.

All from a simple idea: here’s a thing that points to another thing, off you go!

We’ve built an entire culture around links. Just look at the language we use for them:

• “Drop me a link.”
• “Send me the link.”
• “Got a link?”
• “Link in bio.”
• “Link in the description.”
• “Here’s the link.”
• “Don’t click that link.”
• “Sorry, wrong link.”

Or even just, “Link?”

Our language spans the spectrum of meaning, from one end to the other:

• We save links. We lose links.
• We share links. We hide links.
• We send links. We receive links.
• We collect links. We discard links.
• We open links. We close links.
• We add links. We remove links.
• We make links. We break links.
• We shorten links. We expand links.
• We follow links. We ignore links.
• We click links. We don’t click links.
• We link in. We link out.

We make links. Links make the web. The web makes Us.

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Speaking of being on the internet and finding things that make you go, “Hey! It’s not just me!” I scrolled across this skeet from Tyler Gaw:

I just dug to the bottom of my Big Box of Cables to find two cables that I needed for something. They've been in the bottom for 10+ years. So, "when are you ever gonna use these?" was today. Don't ever let anyone take your Big Box of Cables away.

I laughed. I cried. I felt inspired.

So inspired, in fact, that I decided I was gonna do something about it.

Something that would make me forever remember the value of that advice.

• I screenshotted that skeet.
• I printed it (in black and white with the ole’ trusty Brother).
• I cut it out.
• I pulled out my big box of cables (the one my wife lovingly labeled “FAMILY TECHNO BOX”).
• I cut some clear packing tape.
• And I plastered that thing on the front on my box.

Now every time I pull that box out to add YAC (yet another cable), instead of asking myself, “Why do I keep this box?” I’ll see that skeet and I’ll be reminded why I do what I do. I’ll feel a spark of joy, motivation, and purpose rekindled. (Not to mention the fact that it will serve as a warning to anyone in the family who thinks “I should just throw this away…”)

One day my kids will find that box in the attic amongst my other belongings. I hope they will pay heed to the timeless advice lovingly taped on the outside.

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Marcin Wichary has a lovely article titled “Photoshop’s challenges with focus, pt. 2”. If you’re into the minutiae of interface design, this is a good ‘un. It perfectly illustrates how impossible it is to try and compare two interfaces side by side as static images.

What matters in an interface is how it works, i.e. how you interface with it.

And when you bulldoze an interface to build a new one, comparing the old vs. new side-by-side to make sure you “captured everything” is a lie. It’s never obvious what has been lost because the interactive pieces are missing from the static images — and those are what matter most because it’s how the feature works!

I’ve found this to be true a lot as of late. Lots of tiny details that were meticulously crafted over years with specific rationales tied to real-world use cases, all completely bulldozed in a giant refactor. (All made possible by the great omniscient power of AI.)

Few notice what’s gone because few can see what’s being lost in the first place.

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I was reading Jason Gorman’s article “The Wall Confronting Reliable Coding Agent Autonomy” and he says:

the journey from 90% to 99% reliability is just as hard as it was to get to 90%. And from 99% to 99.9% is just as hard again.

This stood out, as I’ve been experiencing more and more “downtime” in my day-to-day work. GitHub’s down. CI’s down. AI’s down. Slack’s down. Downtime’s going mainstream! More and more I find myself visiting service status pages, where I’m confronted with a wall of colors and numbers like this:

100%? 99.72%? 99.09%? 98.98%? Those don’t all seem so different or bad? I mean, those are all an A in grade school.

Then I have to remind myself of Jason’s point and re-interpret the numbers, which is more like reading earthquake measurements. The difference between a 6.2 and a 7.8 might not seem that big, but it represents a massive difference in magnitude. Uptime percentages have a similar problem: 99.9% and 99.99% look pretty much the same, but the latter is 10× less!

Infrastructure people understand this. They even have a shorthand for it: two nines, three nines, four nines. They intuitively grasp the difference because they swim in these numbers every day.

But the audience for status pages isn’t just infra people anymore. It’s increasingly everybody.

I understand the math is straightforward. Percent uptime is a good metric for those in the industry. But it’s a lousy interface for people who don’t care about the best way to measure infrastructure reliability in a standardized, reliable, compliant way.

And status pages are the public interface for understanding the reliability of a service. I just want to know, “Dude, how much have you been down lately? Seems like a lot…”

So how about, and I’ll just throw this out there, instead of:

GitHub Actions: 98.31% uptime.

We say something like:

GitHub Actions: 12 hours affected in the last 30 days (98.31% uptime).

One requires you to understand the nonlinear significance of numbers near 100%. The other requires knowing what an hour is.

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Speaking of things we know individually, but let slide collectively, here’s Jeremy Keith talking about how we all used Flash:

Remember when almost every website had a Flash intro? Everyone knew they were annoying and uneccessary, but everyone else was doing it.

And here’s John Gruber writing about his experience with those marketing schemes that trade your personal information for a discount:

The marketing shitbirds who press for these schemes  […]  do so by pointing to data that shows that they do convert some number of users. “It works” they claim, pointing to data. What doesn’t show up in their data are interactions like mine. They don’t have analytics that measure that I now consider their website an antagonist to avoid at all costs.

There’s a funny thing about us humans: we place trust in systems that our own individual experience tells us are flawed. For example:

• KPIs: we know metrics get optimized at the expense of reality, but leadership treats dashboards as a reflection of truth.
• Performance reviews: we know they’re political and subjective, yet companies leverage them for promotions, compensation, even firings.
• Standardized testing: we know tests don’t measure real ability, but institutions rely on them for admissions, funding, and rankings.
• Terms of Service: nobody reads them, but the legal system acts like informed consent is given.
• Privacy notices / cookie banners: everybody clicks through them mindlessly while companies claim transparency and compliance.

This behavior is best summed up by Scott Berkun in Confessions of a Public Speaker:

I know I avoid most surveys I’m asked to fill out, as do many of you, which begs the question why we place so much faith in survey-based research.

What’s convenient at scale becomes a substitute for the reality we experience as individuals (because scale excludes the parts of reality it cannot conveniently capture).

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