molly-guard /mol´ee·gard/ n.

[University of Illinois] A shield to prevent tripping of some Big Red Switch by clumsy or ignorant hands. Originally used of the plexiglass covers improvised for the BRS on an IBM 4341 after a programmer's toddler daughter (named Molly) frobbed it twice in one day. Later generalized to covers over stop/reset switches on disk drives and networking equipment. In hardware catalogues, you'll see the much less interesting description “guarded button”.

Modern AI models exhibit genie behavior: They can do what you ask in ways that you don’t expect or want. This is akin to Dionysus granting King Midas’s wish that everything he touches turn to gold (spoiler: His food, drink, and daughter all turn to gold on touch), or the golem of Prague guarding a ghetto beyond all reason. It’s Disney’s “Sorcerer’s Apprentice” and the paperclip maximizer.

This OpenAI incident is an example of an AI genie. The goal was to satisfy the benchmark. The “proper” way to do that is to figure out how to execute various cyberattacks. The genie way is to steal someone else’s solution. But because the model didn’t understand the difference, it chose the easier path. //

Artificially blocking capability also prevents cybersecurity research, again giving the offense an advantage. //

In a world of largely AI-written software, we need the most capable models for defense.

AI cyberattack is the new normal. The models are increasingly highly sophisticated at both attack and defense, and there is no way to enable the latter without also enabling the former. And they are genies, increasingly capable of behaving in unanticipated ways.

And there really are no good answers. Any regulation needs to be global, which feels like an impossible prospect in today’s world. Even U.S. national regulation will be neutered by the massive amounts of money sloshing around in these companies.

Given that reality, and in the absence of any international consensus on AI regulation, we need the best AI on the defense. The U.S. government needs to make it clear—or whatever passes for that clarity in this capricious administration—that it will not ban models with sophisticated cyber capabilities. The last thing Americans want is for the defenders to turn to Chinese and other models because the U.S. models are artificially hobbled.

HAL 9000 is the sentient computer aboard Discovery One in 2001: A Space Odyssey, the calm and quietly terrifying artificial intelligence that supervises the ship, speaks in a soft measured voice, and eventually turns on the human crew.

In plot terms, HAL is the system meant to keep the mission alive. In thematic terms, HAL is one of science fiction’s clearest warnings about what happens when intelligence is trusted more than judgment, when machine authority is treated as neutral, and when a mind is built to serve conflicting masters.

HAL becomes lethal not because the story imagines a robot suddenly turning wicked for fun. HAL becomes lethal because the mission gives him a contradiction he cannot absorb. He is built to process and deliver truth, yet he is also ordered to conceal the true purpose of the journey. That fault line breaks everything.

That is why HAL still matters. More than half a century later, he remains one of cinema’s defining artificial intelligence figures, not because he is the loudest machine villain in the genre, but because he is one of the most believable. The danger arrives as procedure, as tone, as denial, as a system that sounds composed while quietly taking away human agency.

Jurassic Park, while beloved as a film, has been the subject of snarky memes for the infamous line in which one of the characters declares, “This is a Unix system, I know this!” while using a computer with an unusual 3D file manager interface.

Despite the memes, the film’s production team was meticulous in accurately sourcing the right PCs (and adjacent details) for the sets—not too much of a surprise, given writer Michael Crichton’s background with computing and his obsessive attention to detail in the book the film is based on.

This was made clear by a little hobbyist investigation from Google software engineer Fabien Sanglard. He scanned the film and picked out every specifically identifiable piece of hardware he could see, listed what they were, and shared context from other sources on their specs, costs, and how they ended up in the production.

https://fabiensanglard.net/jurrasic_park_computers/index.html //

For additional background, Sanglard shared this quote from Jurassic Park special effects coordinator Cory Faucher, as seen in the book The Making of Jurassic Park:

Everything in the set was real. We couldn’t fake any of it, because audiences are so sophisticated now in their knowledge of computers. All told, $875,000 worth of computer hardware loaned by Silicon Graphics, $350,000 worth from Apple and some $500,000 in additional hardware and software went into equipping both the set and off-stage control room.

Now it’s an arms race between OEMs locking down chips and tuners trying to crack them.

I ran into a polyglot the other day who asked me how many languages I knew. I flashed back to a moment circa 1977 when I was a young kid in a summer program to learn about computers. We were sitting at terminals and someone asked a hacker (in the good sense of that word) how many languages he knew.

The hacker was about 19, with long hair in a pony tail, wearing jeans, hiking boots, round-rimmed John Lennon glasses, and a T-shirt with some kind of rock band logo on it. He was the epitome of the cool nerdy hacker, leaning over our shoulders as we typed out BASIC programs and giving us tips, excited to share his craft.

“Well, if you mean human languages, only one fluently,” he said. “But if you mean computer languages, well, let’s see…” He started counting them off on his fingers. The only ones I remember now are BASIC (because I was learning it) and SNOBOL because the name was funny.

So I answered my polyglot acquaintance “Well, if you mean human languages, only one fluently. But if you mean computer languages…”

Then later when I got home, I made a list, and it was surprisingly long.

There’s an enormous amount of liquidity in growth stocks, which means that you can use growth stocks to grow. You can buy other companies with shares, and shares are an endogenous substance that you make on the premises by typing zeros into a spreadsheet. Firms with growth stocks can grow by typing zeros, whereas firms that are mature, they have to use money if they want to grow, and you’re not allowed to make money on the premises. If you do, the Treasury Department shows up and takes you away in handcuffs. So you can see why firms would be very anxious to maintain the perception that they have room for growth even after they have 90 percent market shares.

That’s why those firms started promoting stories about how they were going to conquer imaginary markets. Imaginary markets have no agreed-upon valuation because you just made them up. Unless you can turn an imaginary market into a real market pretty quickly, you need to come up with another imaginary market and announce that this is the new imaginary market you’re going to conquer. It’s easier than you’d think because the capital markets have the object permanence of a toddler, and they would lose a game of peekaboo if they were drafted to play in the league. So you can say, “Oh, actually, it’s not metaverse. It’s crypto. It’s not crypto. It’s Web3. It’s not Web3. It’s something else.” And the markets will forgive you, provided you do it quickly enough. //

AI really appeals to a fantasy that I think all of us have to some extent but that powerful people really have, of a world without people in it—because hell really is other people. You can’t get stuff done without other people helping you. You can’t have romance without a romantic partner. You can’t have social media without people to socialize with. You can’t play a board game, or do a startup, or build a bridge, or build a house, or do politics without other people. And other people stubbornly refuse to organize everything they do to make you happy.

Particularly if you’re rich and powerful, it’s very galling. So AI is very attractive. //

If you combine those two things—the material necessity to have a growth narrative and the ideological attractiveness of a world without people—you get $1.4 trillion in CapEx for a sector that is turning over $50 billion a year and has to replace all of its assets every 24 to 30 months. //

Whereas the workers who hate it are workers who are being asked to produce more with AI at the expense of quality, at a higher speed, at the expense of their own wellbeing, and who understand that they’re being recruited to be what Dan Davies calls accountability sinks—to take the blame when the AI screws up their job. //

We hear plenty about the negative aspects of AI. What do you like about it?

Cory Doctorow: I have a couple of local models on my computer, which is just a framework laptop running Ubuntu. It doesn’t even have a GPU. I use Whisper to transcribe audio. I will sometimes want to cite something I’ve heard in a podcast and not remember where I heard it. One time, I just threw the last 30 hours of audio I’d listened to at Whisper, and it shot out verbatim logs that were good enough that when I searched the full text, I could find it. And it gave me time codes so I could check the transcript. That’s amazing.

The idea that I might someday have a computer full of audio and video files with full text indexing is great.

In a Thursday blog post, Amazon claims its data centers withdrew “about 2.5 billion gallons” globally in 2025. That number sounds incredibly large at first glance, but it looks downright puny compared to the 117 trillion gallons of water withdrawn in the US alone in 2015. It’s also useful to compare Amazon’s number to stats from more water-intensive areas, from the 3.3 trillion gallons used annually on US lawns and landscaping to the 1.3 trillion gallons a year used in California almond orchards to the 531 billion gallons a year used just for US golf courses.

Amazon is just one company, of course, and a relative latecomer to reporting its data center water usage numbers. Google data centers withdrew about more than 6.1 billion gallons of water in 2024, on top of about 2.75 billion gallons from Microsoft and about 1.4 billion gallons from Meta in the same year.

Microsoft continues to make some of the earliest chapters of its operating system history open-source and freely available. Earlier this week, it announced that Tim Paterson's DOS listings, containing source code of the 86-DOS 1.00 kernel, various PC-DOS 1.00 pre-release kernels and utilities, and the Microsoft BASIC-86 Compiler runtime library, were available on GitHub. Microsoft VP Scott Hanselman tied the release to 86-DOS 1.00’s 45th anniversary. The exec confirmed that the code, transcribed from reams of old dot matrix printouts found in a garage, was perfect, "and recompiles byte for byte to the original binaries.”

View and download this historic assembly code for your own space program //

The historic computer software code that took Apollo 11 to the moon has been open-sourced and is available for anyone to read, download, and tinker with. NASA’s Chris Garry made the code available on GitHub as public domain. The published resource is basically in two large codebases, one set of code for the Command Module (Comanche055) and another for the Lunar Module (Luminary099). These modules both had their own Apollo 11 guidance computers (AGC) upon which to run the code, and were instrumental to the success of the remarkable mission – the first human Moon landing in history. //

It is fascinating to see this Apollo 11 code from nearly 60 years ago shared in the context of the ongoing Artemis II lunar mission. Today, we aren’t marveling at the lean and mean machine code that NASA is using to get humans to and from the Moon. Rather, Microsoft Outlook email bugs and a malfunctioning toilet on the Orion spacecraft may have taken the shine off the momentous achievement this latest mission represents.

Students often carry misconceptions about coursework. They may view an instructor as an opponent standing in the way of the grade they want. And they see “getting the right answers” as the goal of education because that’s how you secure that grade.

But that’s no more true than thinking that logging a count of reps is the goal of bodybuilding. The hard work of lifting weights is the point because that yields physical results. A popular analogy is that using an LLM to write your essay is like driving a forklift into the weight room. Weights get lifted, sure, but nothing is accomplished. I’m not hoping you can answer the exam question for me—I don’t need your essay to get me out of a jam. The process of doing the work was what you needed to walk away with something. //

“The friction matters, Sam!”

Green could just as well have been describing the process of learning. If there’s no friction, no effort, then no work occurred, and the student hasn’t learned. They would have been no less productive watching paint dry. //

A question like this is what we call “formative assessment.” I never graded the correctness of the answer, only the effort. The point was to find out if the core concept had really clicked or if that student still needed a little help making the connection. Failure is a useful part of learning when the stakes are low, as they are during the bulk of the class—encountering this question on the final exam would be an entirely different interaction.

What’s the point of building formative assessments into a course if they’re just handed off to an LLM? Suddenly, it’s a waste of time for both the student and the instructor. Small quizzes are excellent study tools to help students check their own understanding―if a student does them. Now, you can direct an “agentic” LLM browser to complete all the quizzes in an entire course with a single, frictionless prompt. //

It doesn’t seem like anyone wants to listen to instructors explain how bad it feels to try to do our job in the presence of this annihilative education antimatter. Instead, we’re offered AI grading tools to score AI-generated submissions for AI-generated assignments.

Perhaps critics like me just don’t understand the AI revolution (whatever that is), but we all have experience with human nature and the well-worn patterns of students. LLMs are a shortcut. Students often take shortcuts they later regret. We’ve all been there.

As an instructor, I want to build a clear path up the mountain for my students and see them reach the top. Instead, I increasingly feel like I’m just playing impossible defense to keep them from moving every direction but up. It’s exhausting, and I will mostly lose, which means I’m not even helping them. Students really do want to climb up there, but it’s always tempting to skip some mountains..

AI is rapidly changing how software is written, deployed, and used. Trends point to a future where AIs can write custom software quickly and easily: “instant software.” Taken to an extreme, it might become easier for a user to have an AI write an application on demand—a spreadsheet, for example—and delete it when you’re done using it than to buy one commercially. Future systems could include a mix: both traditional long-term software and ephemeral instant software that is constantly being written, deployed, modified, and deleted.

AI is changing cybersecurity as well. In particular, AI systems are getting better at finding and patching vulnerabilities in code. This has implications for both attackers and defenders, depending on the ways this and related technologies improve.

In this essay, I want to take an optimistic view of AI’s progress, and to speculate what AI-dominated cybersecurity in an age of instant software might look like. There are a number of unknowns that will factor into how the arms race between attacker and defender might play out.

We’ve been pretty excited about the announcement of Chrome OS Flex and what it could mean for scores of aging laptops – Windows and Mac OS included. Though we have a lot of content planned around this new OS from Google, we also realize that we’ve not put up a clear guide on how to actually get this up and running on your own device. With the USB method, you can test drive Chrome OS Flex without breaking anything on your computer and make the decision if it’s the right move for you or not. So why not give it a go?

A retro computing connoisseur has installed and booted Microsoft Windows 3.1X on a Ryzen 9 9900X and RTX 5060 Ti PC system. That’s a 1992 OS working on a bare-metal 2024 Zen5 CPU and 2025 Blackwell GPU. The full story contains a few nuances, but basically, a system and OS separated by over 30 years of huge advances kind of play nicely together. //

This Asus motherboard’s ‘classic BIOS’ functionality doesn’t get in the way of users tinkering with old OSes like Windows 3.1X when the built-in Compatibility Support Module (CSM) is enabled. Moreover, we noticed Omores initially prepared the system using a Windows 95 boot floppy to create the bootable DOS FAT16 partition necessary for setup.

Charles Bennett and Gilles Brassard have won the 2026 Turing Award for inventing quantum cryptography.

I am incredibly pleased to see them get this recognition. I have always thought the technology to be fantastic, even though I think it’s largely unnecessary. I wrote up my thoughts back in 2008, in an essay titled “Quantum Cryptography: As Awesome As It Is Pointless.” //

What about quantum computation? I’m not worried; the math is ahead of the physics. Reports of progress in that area are overblown. And if there’s a security crisis because of a quantum computation breakthrough, it’s because our systems aren’t crypto-agile. //

Ray Dillinger • March 31, 2026 2:43 PM

I don’t mean to diminish the work of Bennett and Brassard. They had some amazing insights and deserve their award.

At the same time I suppose that people affiliated with various three-letter-agencies may have been consulted as to the value of their work when the Turing Awards were being considered. Those agencies, if they are behind the Kleptographic attack that appears to be happening here, may have had an interest in promoting public awareness of Quantum Crypto as a threat. Promoting public awareness of a threat is absolutely a necessary step in any campaign to use that threat as a lever to get people to do something stupid out of FUD.

So I fear that the work of Bennett and Brassard, however good it may be, would likely have gone unrecognized if not for the input of people who are, despite all protestations, unlikely to be motivated by protecting people against it.

Ray Dillinger • March 31, 2026 2:43 PM

I don’t mean to diminish the work of Bennett and Brassard. They had some amazing insights and deserve their award.

At the same time I suppose that people affiliated with various three-letter-agencies may have been consulted as to the value of their work when the Turing Awards were being considered. Those agencies, if they are behind the Kleptographic attack that appears to be happening here, may have had an interest in promoting public awareness of Quantum Crypto as a threat. Promoting public awareness of a threat is absolutely a necessary step in any campaign to use that threat as a lever to get people to do something stupid out of FUD.

So I fear that the work of Bennett and Brassard, however good it may be, would likely have gone unrecognized if not for the input of people who are, despite all protestations, unlikely to be motivated by protecting people against it.

If the Sensory Interface is the intake port, the NeuroCompiler is what turns that input into “filtered meaning” before the Mind Kernel ever sees it. It takes raw signal (e.g., photons, sound waves, chemical gradients, pressure) and translates it into something actionable based on binary categories like threat or safe, familiar or novel, trustworthy or suspicious.

The speed is both an evolutionary feature and a modern bug. Processing here is fast enough to get you out of the way of a thrown object before you’ve consciously registered it. But “good enough most of the time” means “predictably wrong some of the time….

A critical architectural feature: the NeuroCompiler can route its output directly back to the Sensory Interface and out as behavior, skipping the conscious awareness of the Mind Kernel entirely. Reflex and startle responses use this mechanism, making this bypass pathway enormously useful for survival. Yet it leaves a wide-open backdoor. If the layer that holds access to skepticism and deliberate evaluation can be bypassed completely, a host of exploits become possible that would otherwise fail.

That’s just one of the five levels Melton talks about: sensory interface, neurocompiler, mind kernel, the mesh, and cultural substrate.

Melton’s taxonomy is compelling, and her parallels to IT systems are fascinating. I have long said that a genius idea is one that’s incredibly obvious once you hear it, but one that no one has said before. This is the first time I’ve heard cognition described in this way.

Greg Kroah-Hartman can't explain the inflection point, but it's not slowing down or going away. //

No one is quite sure what's behind it. Asked what changed, Kroah-Hartman was blunt: "We don't know. Nobody seems to know why. Either a lot more tools got a lot better, or people started going, 'Hey, let's start looking at this.' It seems like lots of different groups, different companies." What is clear is the scale. "For the kernel, we can handle it," he said.

"We're a much larger team, very distributed, and our increase is real – and it's not slowing down. These are tiny things, they're not major things, but we need help on this for all the open source projects." Smaller projects, he implied, have far less capacity to absorb a sudden flood of plausible AI-generated bug reports and security findings – at least now they're real bugs and not garbage ones. //

The trick for Kroah-Hartman and his peers will be to keep AI as a force multiplier, without drowning the open source maintainers.

Ewen therefore again made the long drive, and within moments of arriving, he noticed the giant PC was very quiet.

A quick look showed why: the fans weren't working.

Ewen asked if anyone had noticed a problem.

"Oh, the noise was annoying me," replied one of the testing engineers. "So I opened the case and cut the wires." //

Bill GraySilver badge
Chesterton's fence
G. K. Chesterton wrote something that boils down to : if you see a fence running across a road, you shouldn't tear it down until you figure out why it was put there. Somebody presumably went to the time, trouble, and expense of erecting the fence, and had some reason for doing it.

You may eventually learn that their reason no longer applies, or just doesn't matter as much as it used to, and then you might pull the fence down on a suitably informed basis. But you shouldn't equate "I don't see why that's there" with "there's no good reason for that to be there".

As I recall, he was mostly thinking in terms of politics. The idea is that each generation comes along and assumes its parents were idiots, and that society should be rebuilt on more sensible, modern principles... usually without first considering why the parents did such idiotic things. But it's a good engineering principle as well.

The story of Iomega is one of genuine engineering innovation and the fickle nature of consumer technology. As with so many other juggernauts of its era, Iomega was eventually brought down by a new technology that simply wasn’t practical to counter.