But there are a lot of academic and research institutions that actually do have good Linux user management. I worked at a pediatric hospital, and the RHEL HPC admins did not mess around in terms of who was allowed to access which patients' data. As someone who was not an admin, it was a huge pain and it should have been. So this bug has pretty serious implications, seems like anyone at that hospital can abscond with a lot of deidentified data. [research HPC not as sensitive as the clinical stuff, which I think was all Windows Server]

Of course it would take them a while to learn facts about datetime that the LLM doesn't need to learn. If your argument is about cost optimization then congrats, you win. The point is that it doesn't take a huge amount of C expertise to do this successfully - the standard implementation is nothing you wouldn't see in K&R: https://raw.githubusercontent.com/util-linux/util-linux/refs... It's routine.

But a nontrivial database, even a simple one like SQLite, really does require professional-level C expertise. It is not routine. So your comparison to ProgramBench still seems apple-to-oranges.

But even if its putatively implementing the same algorithm, LLMs certainly do not output basically the same finance Python as they would mechanical engineering Python. The style will be a little different. Sometimes the performance/clarity tradeoffs will be different. Sometimes it'll be fairly fancy and object-oriented, other times it'll be more low-level "objects are just dicts."

It's way more than a higher abstraction layer: LLM codegen involves a nontechnical tangling of concerns that doesn't exist with even the hoitiest-toitiest proof-checking compilers. It's a complete sea change. I find it incredibly disconcerting... for the same reason, by the way, that assembly programmers found Fortran and C disconcerting, and continued to reliably find employment for a good 40 years after higher-level languages were invented :) Actually even today. The assembly programmers who got hosed by C tended to be electricians who learned on the job - it's kind of cool to read old manuals from the 70s, carefully (and correctly!) explaining to electricians that a computer program is essentially an ephemeral circuit.

But I think there are specific skills around scientific thinking (learned at a formal college) and engineering carefulness (learned via hard knocks) that aren't going anywhere.

Eg cal is totally routine. I would expect most sophomores to be able to write a perfectly good cal. In fact the only program you tested which actually has anywhere close to the complexity of SQLite or FFmpeg is is Pkl, and it looks like Opus 4.6 totally failed.

I think your results are consistent. You're just measuring different things. Your benchmarks mostly tests LLMs ability to write technically routine programs of moderate length - yes the bioinformatics package involves specialized domain knowledge, but not specialized Go engineering. ProgramBench is harder.

My belief is that the Turing test (and LLMs in particular) are not categorically different. Language is a tiny part of the human brain because it's a tiny part of human cognition, despite its outsized impact socially.

"Oh! That explains it!"

"Uh..."

"I didn't want to say anything rude, but the whole time I was like 'yikes, how did this idiot become a professor at Notre Dame?'"

"Actually -"

"Heh! You got me good! Of course it was written with AI. Duh. These ideas are so vacuous and shallow, there's no way a fancy professor like you-"

"Actually, I asked the AI to write an essay summarizing my arguments from social media."

"...oh."

"..."

"...hey you should log off BlueSky, it's not healthy there."

> Researchers say that climate models may need to be updated to account for the warming effect of plastic, but the new study is far from conclusive.

So it's not scientific make-work, they are looking into whether climate models are missing something. That seems important. Perhaps local effects in India are more severe than "a fraction of the impact of soil" - India produces a huge amount of new plastic while also scavenging and recycling international plastic imports, all with very poor oversight and corrupt regulation.

Freedom for who, exactly? Coinbase's executives, I suppose.

  We see that frontier models are intelligent enough to manage humans

And this is just egregious:

  Despite the learning curve, the café is working. In the first two weeks of operation, Andon Café has brought in 44,000 SEK in sales. Mona’s inbox has been flooded with messages from customers asking questions or pitching different business proposals. In one case, a customer emailed wanting to prepay for 300 coffees to give away. Mona negotiated a deal where he paid 9,000 SEK in exchange for 300 QR codes that people could redeem for a free coffee. In another case, a startup paid her 3,000 SEK to rename a pastry after them for three months.

   By running this experiment, we shift the discussion of how we want this future to look earlier in time, so we can better prepare.

  no one’s livelihood depends on the judgment of an AI alone.

I hate the proliferation of audiobooks too, by the way. It's the exact same problem.

  So… are the neurons on that chip seeing?

  We all desperately want to say no.

Again I share the ethical concern about this stuff. But your blog post is quite misleading.

   trained them to play DOOM - honestly better than I do.

>> While the neurons can play the game better than a randomly firing player, they’re not very good. “Right now, the cells play a lot like a beginner who’s never seen a computer—and in all fairness, they haven’t,” Brett Kagan, chief scientific officer at Cortical Labs, says in the video. “But they show evidence that they can seek out enemies, they can shoot, they can spin. And while they die a lot, they are learning.” [https://www.smithsonianmag.com/smart-news/a-clump-of-human-b... ]

  To play DOOM, the system feeds visual data to the neurons. For the neurons to react, they have to interpret that data in some way. 

>> So how does a petri dish of brain cells play Doom when it doesn’t have any eyes? Or fingers? "We take a snapshot of the game with information like the player’s health and the position of enemies, pass it through a neural network, convert it into numbers, and send the data,” explains Cole. “This is called encoding – essentially turning the game state into signals the neurons can understand. The neurons then fire an output – move left, move right, walk forward, shoot or not shoot – which the system decodes and converts back into actions in the game." [https://www.theguardian.com/games/2026/mar/16/petri-dish-bra...]

I am also concerned about neuronal computing. But it doesn't really help anyone to spread childish ghost stories about it.

I really hate YouTube, by the way. My dad used to read newspapers and had interesting ideas. Now he watches a bunch of YouTube and he's a huge idiot. It's not (directly) because of age: nobody is immune to narcotic slop. I had to delete my account when I realized how much of my life and cognition I was wasting. I wish others would do the same.

  Cognitive Stack

  System 3 - Long Term Drives
  I love being petted.

  System 2 - Emotionally-Aware Reasoning
  Goal: Receive pets.

  IWM - Internal World Model
  Guardian in view (right, moderate distance). Being petted.

  My Guardian is paying attention to me. My Guardian is reaching a hand toward me.

And considering how much I love my cats because of how little they care if I'm busy, this just seems cynical bordering on evil:

  Jones describes two scenarios for when a Familiar’s owner comes home: In the first, the person bends down with arms wide open, ready for a hug. In the second, the person is rushed, with an armload of groceries, no time for robo-snuggles. The Familiar will know whether to run up or to hang back.

  Is a person happy or sad? Are they having an argument? When does a sad person want comfort... or solitude? The Familiar must be programmed to handle these scenarios, like an autonomous vehicle is programmed to handle tricky intersections.
  All of a Familiar’s behaviors are intended to be approachable and inoffensive, says Morgan Pope, a Familiar Machines roboticist who spent almost eight years at Disney Research. Some of the ways the robot moves are based on the moves of dogs and other animals: how close it comes to a person, the speed at which it approaches, even the way it bats its eyes and twitches its ears.
  Training a robot on the intricate timing of initiating interaction with a new person is an "enormously hard" problem, says Pope.

Now: the CEO gets paid the big bucks and has the least direct accountability, very much because it's their job to take responsibility for people more powerful than them, and likewise the CTO with major commercial software contracts like a Claude subscription. That's why this guy was so hard to take seriously: okay fine, you got burned by Anthropic, stop being a baby about it. Take responsibility for not listening to the critics.

But - to be a little more neutral about my personal distaste - I do think vibe coders are making a very similar mistake to C developers throughout the 90s, where problems with the tooling were not merely dismissed, but actively valorized.

Real Devs use buffers freely and don't make overflow errors.

Real Devs use hands-free agentic development and don't delete production databases.

What I find strange about this is that in 2020 nobody would be this openly cynical and selfish about, say, good Python idioms, a useful emacs configuration, git shortcuts, etc. This attitude of "your job is to deliver value for the customer, anything else is a distraction, and if you share your hard-earned value-delivery techniques with others then you are a sucker" - this is new, and very disconcerting.

I understand there's not much we can do to stop the cyberpunk dystopia, but do we have to leap in head-first?

"Does Unemployment Make It More Likely for Late Middle-Aged People, Particularly Men, To Drink Alcohol? Evidence From We Obviously Should Have Considered This In The Paper, Perhaps We Are Too Sheltered"

To be clear I am not being pedantic. The paper explicitly endorses the policy of pushing back the retirement age specifically because doing so likely reduces cognitive decline. I agree with this, in the same sense that shooting car thieves in the street without a trial reduces automotive theft. "Reducing cognitive decline in people near retirement age" might be better met with psychiatric intervention, so that unemployed people also get some of the benefits. Ignoring this confounding variable and prattling about "causal explanation" - while endorsing the policy of snatching away people's pensions until they work a few more years - is evil born from ignorance.