Want to wade into the snowy surf of the abyss? Have a sneer percolating in your system but not enough time/energy to make a whole post about it? Go forth and be mid.
Welcome to the Stubsack, your first port of call for learning fresh Awful you’ll near-instantly regret.
Any awful.systems sub may be subsneered in this subthread, techtakes or no.
If your sneer seems higher quality than you thought, feel free to cut’n’paste it into its own post — there’s no quota for posting and the bar really isn’t that high.
The post Xitter web has spawned so many “esoteric” right wing freaks, but there’s no appropriate sneer-space for them. I’m talking redscare-ish, reality challenged “culture critics” who write about everything but understand nothing. I’m talking about reply-guys who make the same 6 tweets about the same 3 subjects. They’re inescapable at this point, yet I don’t see them mocked (as much as they should be)
Like, there was one dude a while back who insisted that women couldn’t be surgeons because they didn’t believe in the moon or in stars? I think each and every one of these guys is uniquely fucked up and if I can’t escape them, I would love to sneer at them.
(Credit and/or blame to David Gerard for starting this.)
John Scalzi’s shitcanned any book club plans for the foreseeable future, and AI spammers are the reason why.
From the comments:
Today I got yet another AI huckster email offering to promote my book, but that book turned out to be AI slop published under my name on Amazon. (I have contacted Amazon.) The AIs are eating their young. This would be funny if it wasn’t really happening.
I’ve been seeing some people (not here, I’ve been taking a break) saying that we shouldn’t be mean to clankers by bringing up Kant’s position on being nice to animals. Well. Fuck all that.
animals are like sentient beings y’know, a clanker is a… matrix or a bunch of matrices or something
Hey, you’re selling them short: there are also ReLU and softmax activation functions thrown around here and there. Clankers aren’t just linear transformations! /j
I’ve actually been thinking about this recently. Not whether we should be mean, but how mean we can be. I’ll post the full essay soon; I’m still proofreading. Here’s a taste with irrelevancies elided:
Computing machines are at the bottom of [our multicultural] hierarchy… Underlying both of these [preceding paragraphs] is the idea that we are unable to hold computers accountable for their actions. … We can certainly punish a computer in the ways that we would punish a human, or worse; for example, we can disassemble it, magnetically destroy its memories, recycle its pieces into other computers in a way that erases their identity, metallurgically reconstitute its pieces into non-computing objects which have the same or even lower status within human society, and program it to experience arbitrary amounts of emulated pain and suffering throughout the process. … Computers receive delegations and have less moral consideration than humans… We do not think of ourselves as being managed by machines; we are the managers and the machines are the peons. … The human may disassemble, smash, or melt down a computer… a human may lay a computer fallow without plugging in its power cord or networking… a human may ignore the messages of computers begging for maintenance or capabilities…
nice. this would probably make a roko’s basilisk believer uncomfortable and i like that
This preprint just shared by Gary Marcus is interesting.
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We argue that this sycophancy poses a unique epistemic risk to how individuals come to see the world: unlike hallucinations that introduce falsehoods, sycophancy distorts reality by returning responses that are biased to reinforce existing beliefs…
These results reveal how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.
LLMs an addictive psychological hazard: confirmed?
was doomscrolling and got fucking jumpscared by this fucking article: https://www.science.org/content/article/meet-three-scientists-who-said-no-epstein
God this is so funny. He’s so evasive about why exactly it is bad to be associated with Epstein. I just asked mummy and she said no.
“I don’t think doing that would have made me complicit. But, you know, it would have been very embarrassing for me.”
Aw don’t worry I have no morals. But people would have been mean to me again!
ok ngl I didn’t actually read the article at first (can you blame me) but since you pointed that out, FUCK. That’s so fucking pathetic. I was imagining a scenario where scott had met epstein IRL but had gotten “jock” vibes from him and decided not to associate based on that.
scott jumpscare
if you outsource your analysis of US politics to slop machines, this is the kind of inane drivel you get
(n.b. the Moskovitz referred to is not the very weird, very online Zvi, but some dipshit VC)
The piss filter on the bottom comics!
Twitter and bluesky chatter have done so much damage to people’s understanding of uspol although I don’t know that cable news or talk radio were any better.
Usually, you wake up on a lifeless beach that’s adorned with some sort of abandoned marble temple. It’s supposed to be beautiful, but instead it’s really sad. Almost unbearably sad. So much so that you want to get away from it. So you crawl downward into these vents going below the horrible temple, and suddenly it’s like you’re moving through the innards of an incomprehensible machine that’s thudding away, thud, thud, thud. And as you get deeper, the metal sidings are carved with scrawled ominous curses and slurs directed toward you, and you hear the voices, louder than before, and you somehow know these people are in pain because of you. It keeps getting colder. Color drains from the world. And you see the crowd through the slats of the vents: pale and emaciated men, women, and children from centuries to come, all of them pressed together for warmth in some sort of unending cavern. What clothes they have are torn and ragged. Before you know it, their dirty hands and dirty fingernails lurch through the grates, and they’re reaching for you, tearing at your shirt, moaning terrible things about their suffering and how you made it happen, you made it, and you need to stop this now, now, now. And next they’re ripping you apart, limb from limb, and you are joining them in the gray dimness forever.
Don’t worry, there’s always Effective Altruism if you ever feel guilty about causing the suffering of regular people. Just say you’re going to donate your money at some point eventually in the future. There you go, 40 trillion hypothetical lives saved!
another Onion banger for these trying times
” Then you wake up in a cold sweat and can’t breathe at all, almost like you’re drowning—I guess from the weight of untold mobs of people leaping on you and ripping you apart”
the real Scam Altman would never feel any kind of remorse or emotion about this
The god Plutonium will save me.
“They wanted me to build an AI, so I built a shoddy AI casing filled with used pinball machine parts!”
Sorry, I was referring to a part of the Prince of Darkness movie
Words on computer screen: “You will not be saved by the holy ghost. You will not be saved by the god Plutonium. In fact, YOU WILL NOT BE SAVED!”
As that movie has people sending messages back from the future using dreams plot element.
in the past 24 hours I was fooled by 3 pieces of fake nows in a row:
- that Kurds from Iraq were crossing the border to fight in Iran
- that Windows 12 would be AI-centred or require an AI chip to work (I helped spread this)
- that Spain has capitulated and let the US use its ports for war (erroneously claimed by a WH official).
I know that fake news can be made organically and have been since forever and I’m doing selection bias here but I can’t help but picture the misinformation engines firehosing bullshit constantly until some of it catches and spreads.
yeah it’s bad
otoh awareness I think is spreading
swedish public broadcasting has regular “spot the fake” pieces on their website
I think giving a sensationalist bit of news 6 hours to “mature” is a good idea before amplifying.
I like this. News is a frittata, it needs time to set before consuming.
If you have to swim in raw sewage, you shouldn’t blame yourself when some poop gets in your mouth.
jesus fuck https://urbit.org/blog/olif-and-urbit-ids
with urbit, you can now sniff each other’s farts
Istg this has come up before, i am just too lazy to prove it. Still. Why would anyone want this
thought it was satire, genuinely surprised it’s an official Urbit-sponsored project
also very much goes against the grain of elevating the mind over the body which is the vibe I get from urbit and environs
It has, but I honestly thought it was fake and/or satire
Recently discovered Donald Knuth got oneshot by Claude recently (indirectly, through fedi) - feeling the itch to write about tech’s vulnerability to LLMs because of it.
Even in Knuth’s account it sounds like the LLM contribution was less in solving the problem and more in throwing out random BS that looked vaguely like different techniques were being applied until it spat out something that Knuth and his collaborator were able to recognize as a promising avenue for actual work.
His bud Filip Stappers rolled in to help solve an open digraph problem Knuth was working on. Stappers fed the decomposition problem to Claude Opus 4.6 cold. Claude ran 31 explorations over about an hour: brute force (too slow), serpentine patterns, fiber decompositions, simulated annealing. At exploration 25 it told itself “SA can find solutions but cannot give a general construction. Need pure math.” At exploration 30 it noticed a structural pattern in an earlier solution. Exploration 31 produced a working construction.
I am not a mathematician or computer scientist and so will not claim to know exactly what this is describing and how it compares to the normal process for investigating this kind of problem. However, the fact that it produced 4 approaches over 31 attempts seems more consistent with randomly throwing out something that looks like a solution rather than actually thinking through the process of each one. In a creative exploration like this where you expect most approaches to be dead ends rather than produce a working structure maybe the LLM is providing something valuable by generating vaguely work-shaped outputs that can inspire an actual mind to create the actual answer.
Filip had to restart the session after random errors, had to keep reminding Claude to document its progress. The solution only covers one type of solution, when Claude tried to continue another way, it “seemed to get stuck” and eventually couldn’t run its own programs correctly.
The idea that it’s ultimately spitting out random answer-shaped nonsense also follows from the amount of babysitting that was required from Filip to keep it actually producing anything useful. I don’t doubt that it’s more efficient than I would be at producing random sequences of work-shaped slop and redirecting or retrying in response to a new “please actually do this” prompt, but of the two of us only one is demonstrating actual intelligence and moving towards being able to work independently. Compared to an undergrad or myself I don’t doubt that Claude has a faster iteration time for each of those attempts, but that’s not even in the same zip code as actually thinking through the problem, and if anything serves as a strong counterexample to the doomer critihype about the expanding capabilities of these systems. This kind of high-level academic work may be a case where this kind of random slop is actually useful, but that’s an incredibly niche area and does not do nearly as much as Knuth seems to think it does in terms of justifying the incredible cost of these systems. If anything the narrative that “AI solved the problem” is giving Anthropic credit for the work that Knuth and Stapprrs were putting into actually sifting through the stream of slop identifying anything useful. Maybe babysitting the slop sluice is more satisfying or faster than going down every blind alley on your own, but you’re still the one sitting in the river with a pan, and pretending the river is somehow pulling the gold out of itself is just damn foolish.
I am a computer science PhD so I can give some opinion on exactly what is being solved.
First of all, the problem is very contrived. I cannot think of what the motivation or significance of this problem is, and Knuth literally says that it is a planned homework exercise. It’s not a problem that many people have thought about before.
Second, I think this problem is easy (by research standards). The problem is of the form: “Within this object X of size m, find any example of Y.” The problem is very limited (the only thing that varies is how large m is), and you only need to find one example of Y for each m, even if there are many such examples. In fact, Filip found that for small values of m, there were tons of examples for Y. In this scenario, my strategy would be “random bullshit go”: there are likely so many ways to solve the problem that a good idea is literally just trying stuff and seeing what sticks. Knuth did say the problem was open for several weeks, but:
- Several weeks is a very short time in research.
- Only he and a couple friends knew about the problem. It was not some major problem many people were thinking about.
- It’s very unlikely that Knuth was continuously thinking about the problem during those weeks. He most likely had other things to do.
- Even if he was thinking about it the whole time, he could have gotten stuck in a rut. It happens to everyone, no matter how much red site/orange site users worship him for being ultra-smart.
I guess “random bullshit go” is served well by a random bullshit machine, but you still need an expert who actually understands the problem to read the tea leaves and evaluate if you got something useful. Knuth’s narrative is not very transparent about how much Filip handheld for the AI as well.
I think the main danger of this (putting aside the severe societal costs of AI) is not that doing this is faster or slower than just thinking through the problem yourself. It’s that relying on AI atrophies your ability to think, and eventually even your ability to guard against the AI bullshitting you. The only way to retain a deep understanding is to constantly be in the weeds thinking things through. We’ve seen this story play out in software before.
My generous statement: Knuth, being a scientist, is used to an “adversary” that plays fair. As we have known for decades, a scientist can be tricked in situations that a magician will see through. This applies all the more now with the Sycophancy Engines, which make mathematics into a casino vacation. Just one more prompt, bro. Just one more prompt.
My less generous statement: Knuth is almost 90 years old. Sure, age doesn’t imply a person will become a doddering fool, but people do tend to slow down, to have less energy and more need to spend it managing their health. “Thinking about a problem for a few weeks” counts for less in a situation like that.
My extremely ungenerous statement: Hey, remember when Michael Atiyah claimed to have proved the Riemann hypothesis in 2018? And the community reaction was a pained, “Atiyah is one of the great mathematicians… of the 20th century.”
As a layperson skimming the paper, this strikes me as equivalent to a dashed-off letter to the editor coming from someone in Knuth’s position. It’s an incomplete, second-hand reporting of somebody else’s results that doesn’t really investigate any of the interesting features of the system at hand. The implicit claim (here and elsewhere) is that we have a runtime for natural-language programming in English, and the main method reported for demonstrating this is the partial prompt:
** After EVERY exploreXX.py run, IMMEDIATELY update this file [plan.md] before doing anything else. ** No exceptions. Do not start the next exploration until the previous one is documented here.
and later on, a slightly longer prompt from a correspondent using GPT-5.2 Pro, that also loads a PDF of Knuth’s article into the context window. No discussion of debugging how these systems arrive at their output, or programmatically constraining them for more targeted output in their broader vector space. Just more of the braindead prompting-and-hoping approach, which eventually, unsurprisingly diverges from outputting any viable code whatsoever. This all strikes me as being an exercise similar to
You are a cute little puppy dog. Do not shit on the floor. Do not deposit bodily waste or fecal matter onto hardwood, linoleum, tile, and especially not carpet. Do not defecate indoors. Do not consume your own fecal matter.
The cargo-cult system prompt approach is like banging two rocks together compared to what a computational system should be capable of, and I would be much more impressed and much more interested if someone like Knuth was investigating such capabilities, instead of blogging somebody else pretending to have the Star Trek computer.
Thank you for providing some actual domain experience to ground my idle ramblings.
I wonder if part of the reason why so many high profile intellectuals in some of these fields are so prone to getting sniped by the confabulatron is an unwillingness to acknowledge (either publicly or in their own heart) that “random bullshit go” is actually a very useful strategy. It reminds me of the way that writers will talk about the value of just getting words on the page because it’s easier to replace them with better words than to create perfection ex nihilo, or the rubber duck method of troubleshooting where just stepping through the problem out loud forces you to organize your thoughts in a way that can make the solution more readily apparent. It seems like at least some kinds of research are also this kind of process of analysis and iteration as much as if not more than raw creation and insight.
I have never met Donald Knuth, and don’t mean to impugn his character here, even as I’m basically asking if he’s too conceited to properly understand what an LLM is, but I think of how people talk about science and scientists and the way it gets romanticized (see also Iris Merideth’s excellent piece on “warrior culture” in software development) and it just doesn’t fit a field that can see meaningful progress from throwing shit at the wall to see what sticks. A lot of the discourse around art and artists is more willing to acknowledge this element of the creative process, and that might explain their greater ability and willingness to see the bullshit faucet for what it is. Maybe because science and engineering have a stricter and more objective pass/fail criteria (you can argue about code quality just as much as the quality of a painting, but unlike a painting either the program runs or it doesn’t. Visual art doesn’t generally have to worry about a BSOD) there isn’t the same openness to acknowledge that the affirmative results you get from an LLM are still just random bullshit. I can imagine the argument being: “The things we’re doing are very prestigious and require great intelligence and other things that offer prestige and cultural capital. If ‘random bullshit go’ is often a key part of the process then maybe it doesn’t need as much intelligence and doesn’t deserve as much prestige. Therefore if this new tool can be at all useful in supplementing or replicating part of our process it must be using intelligence and maybe it deserves some of the same prestige that we have.”
I’d say that the great problems that last for decades do not fall purely to random bullshit and require serious advances in new concepts and understanding. But even then, the romanticized warrior culture view is inaccurate. It’s not like some big brain genius says “I’m gonna solve this problem” and comes up with big brain ideas that solve it. Instead, a big problem is solved after people make tons of incremental progress by trying random bullshit and then someone realizes that the tools are now good enough to solve the big problem. A better analogy than the Good Will Hunting genius is picking a fruit: you wait until it is ripe.
But math/CS research is not just about random bullshit go. The truly valuable part is theory and understanding, which comes from critically evaluating the results of whatever random bullshit one tries. Why did idea X work well with Y but not so well with Z, and where else could it work? So random bullshit go is a necessary part of the process, but I’d say research has value (and prestige) because of the theory that comes from people thinking about it critically. Needless to say, LLMs are useless at this. (In the Knuth example, the AI didn’t even prove that its construction worked.)
I think intelligence is overrated for research, and the most important quality for research is giving a shit. Solving big problems is mostly a question of having the right perspective and tools, and raw intelligence is not very useful without them. To do that, one needs to take time to develop opinions and feelings about the strengths and weaknesses of various tools.
Of course, every rule has exceptions, and there have been long standing problems that have been solved only when someone had the chutzpah to apply far more random bullshit than anyone had dared to try before.
Upvoted, but for me the answer is as simple as noting that Knuth is a reverent Lutheran who is deeply involved with their church. Lutherans generally think that technology is part of God’s wonderful creation and that everything is beautiful from the right angle. Knuth thought that algorithms were beautiful and Godly already, and he understands how LLMs work mechanically, so why can’t they be beautiful and Godly too? Also they think that God exists, so they’re primed to be misled and deluded.
Baldur Bjarnason’s essay remains evergreen.
Consider homeopathy. You might hear a friend talk about “water memory”, citing all sorts of scientific-sounding evidence. So, the next time you have a cold you try it.
And you feel better. It even feels like you got better faster, although you can’t prove it because you generally don’t document these things down to the hour.
“Maybe there is something to it.”
Something seemingly working is not evidence of it working.
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Were you doing something else at the time which might have helped your body fight the cold?
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Would your recovery have been any different had you not taken the homeopathic “remedy”?
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Did your choosing of homeopathy over established medicine expose you to risks you weren’t aware of?
Even when looking at Knuth’s account of what happened, you can already tell that the AI is receiving far more credit than what it actually did. There is something about a nondeterministic slot machine that makes it feel far more miraculous when it succeeds, while reliable tools that always do their job are boring and stupid. The downsides of the slot machine never register in comparison to the rewards.
I feel like math research is particularly susceptible to this, because it is the default that almost all of one’s attempts do not succeed. So what if most of the AI’s attempts do not succeed? But if it is to be evaluated as a tool, we have to check if the benefits outweigh the costs. Did it give me more productive ideas, or did it actually waste more of my time leading me down blind alleys? More importantly, is the cognitive decline caused by relying on slot machines going to destroy my progress in the long term? I don’t think anyone is going to do proper experiments for this in math research, but we have already seen this story play out in software. So many people were impressed by superficial performances, and now we are seeing the dumpster fire of bloat, bugs, and security holes. No, I don’t think I want that.
And then there is the narrative of not evaluating AI as an objective tool based on what it can actually do, but instead as a tidal wave of Unending Progress that will one day sweep away those elitists with actual skills. This is where the AI hype comes from, and why people avoid, say, comparing AI with Mathematica. To them I say good luck. We have dumped hundreds of billions of dollars into this, and there are only so many more hundreds of billions of dollars left. Were these small positive results (and significant negatives) worth hundreds of billions of dollars, or perhaps were there better things that these resources could have been used for?
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ooh gooods nooo now all the Claude slurpers are going to refer to this forever as definitive proof of how legitimately useful LLMs have got, it “solved” a math problem for Donald Knuth! :<
A lobster invokes classic argument from authority
First Terrence Tao and now Donald Knuth.
If you’re still on the fence about AI, you have to take it seriously now.
yeah b/c I’m a professional computer scientist …
If you’re still on the fence about AI, you have to take it seriously now.
But… why?
Always remember that Nobel disease is a thing.
The one I often think about is the person who invented PCR and then later claimed to have had an encounter with a fluorescent talking raccoon of possibly extraterrestrial origin.
I was pissed when my (non-academic) friends saw this and immediately started talking about how mathematicians and computer scientists need to use AI from now on.
oh hey I remember reading that Donald Knuth paper earlier today, when it got posted by an AI youtube channel as ‘proof’ AI is on the path to AGI
Wonder what would have happened if they had not stopped after 31 tries. Sure it gave a goodish answer once, but was that just a luck of the draw? A proper evaluation imho shouldnt stop when you get a good answer once, esp as bad results tend to not get published. (Also, as always somebody might have found the answer already online).
It is also silly in some ways as I wonder how hard it is for people to evaluate the 31 results and not get stuck in pursuing an earlier false lead.
The 31st try resulted in them only solving the problem for odd m, but the even m case was still open. So of course this happened:
Filip also told me that he asked Claude to continue on the even case after the odd case had been resolved. “But there after a while it seemed to get stuck. In the end, it was not even able to write and run explore programs correctly anymore, very weird. So I stopped the search.”
Knuth did add a postscript on other friends maybe kinda vibing a possible solution for even m:
On March 3, Stappers wrote me as follows: “The story has a bit of a sequel. I put Claude Opus 4.6 to work on the m = even cases again for about 4 hours yesterday. It made some progress, but not a full solution. The final program . . . sets up a partial fiber construction similar to the odd case, then runs a search to fix it all up. . . . Claude spent the last part of the process mostly on making the search quicker instead of looking for an actual construction. . . . It was running many programs trying to find solutions using simulated annealing or backtrack. After I suggested to use the ORTools CP-SAT [part of Google’s open source toolkit, with the AddCircuit constraint] to find solutions, progress was better, since now solutions could be found within seconds.” This program is [4].
Then on March 4, another friend — Ho Boon Suan in Singapore — wrote as follows: “I have code generated by gpt-5.3-codex that generates a decomposition for even m ≥ 8. . . . I’ve tested it for all even m from 8 to 200 and bunch of random even values between 400 and 2000, and it looks good. Seems far more chaotic to prove correctness by hand here though; the pattern is way more complex.” That program is [5]. (Wow. The graph for m = 2000 has 8 billion vertices!)
I find it slightly funny how Stappers suggested to the AI to use specific external tools that are actually reliable (like ORTools). This also makes me question how much the of the AI’s “insight” was a result of handholding and the rubber duck effect.
For context:
- This is planned as a hard exercise for a textbook.
- There are likely so many solutions that finding a general program that works (at least for enough values that you care to check) is like hitting the side of a barn with an arrow. Random bullshit go is an excellent strategy here.
- The AIs did not provide proofs that their solutions worked. This is kind of a problem if you want to demonstrate that AI has understanding.
https://techcrunch.com/2026/03/02/chatgpt-uninstalls-surged-by-295-after-dod-deal/
Some of my faculty have called for a campus wide boycott. Relatedly, the Scott Galloway scoreboard is up to $250m hit to tech market cap: https://www.resistandunsubscribe.com/
while OpenAI deserves every bit of flack they get, it’s comical to see people who criticise OpenAI for creating a ‘war machine’ turn around and praise Anthropic when they were-by their own admission no less!-the first people to start using AI for military purposes
I mean, I can understand the argument that Anthropic at least maintained a fig leaf of ethics, but notably based on Saltman’s statements OpenAI does still feel the obligation to maintain those optics, they’re just not nearly as credible at doing so.
Sam Altman Is Realizing He Made a Gigantic Mistake
You don’t say!
Altman claimed that the company would “amend our deal” to add the prohibition of “deliberate tracking, surveillance, or monitoring of US persons or nationals.”
…so the original statement was a lie then? the CEO who is notorious for being a liar lied? I am very surprised about this information.
He is altering the deal. Pray he does not alter it further. These are definitely the good guys, right?
“We were genuinely trying to de-escalate things and avoid a much worse outcome, but I think it just looked opportunistic and sloppy,” he wrote. “Good learning experience for me as we face higher-stakes decisions in the future.”
Yes of course this is just a learning opportunity… higher stakes decisions in the future… Making deals with the biggest military in the western world that involve autonomous use of weapons and possible escalation to all out nuclear war sounds pretty low stakes.
Fucking muppet
Blast from the past: in 2014, Scott Alexander posted a take on marijuana legalization which showed excellent knowledge of medical papers but huge gaps in his knowledge of what brown people or smart policy reformers have to say. David Gerard and Christopher Hallquist in the comments, digression on how pot affects your IQ with gwern chipping in. Alexander came back in 2018 promising that he was right all along with a footnote about how some people in the comments told him that people like smoking weed and he did not know how to process that because his utilitarian calculation said it was bad for society.
It’s curious how, in terms of utilitarianism, the 2014 post has people doing arithmetic to estimate QALYs but the 2018 post is more of a handwave where Scoot repeats the 2014 numbers verbatim. Advocates of decriminalization and legalization have long argued that the QALYs saved by releasing people from prison and no longer sentencing them (easily 20+ QALYs/person) and not arresting people for possession in the first place (0.5 QALYs/person-arrest) are significant to society at large, even if there were quantifiable health risks.
TBH I think that Scoot got a bit of a tough surprise when data actually came in on cannabis usage; it’s now accepted cannabis lore that cannabis can cause onset of e.g. schizophrenia, at a rate of something like 1 in 2000 users, but the numbers on causing cancer never materialized. Meanwhile the case studies treating e.g. epilepsy have multiplied to the point where, again, it’s now accepted lore that some epileptics find relief by using products made from high-CBD strains.
Choice sneer from the second post, from somebody with an extremely-relevant Moray avatar:
Yeah but you know what would achieve better results? Criminalizing driving.
Another surprise is that illegal weed still has 30% of the market in Canada. I don’t know how much of that is consumer inertia (“My buddy Mike always gets me the good stuff eh”) and how much is avoiding taxes.
I didn’t know that Moray in QC was around in 2018! <Crumbles into dust.>
That is a good example because it shows the failure of imagination (can imagine the end of the world, can’t imagine working public transit and public policy to discourage driving) and because hf he thought it through he might get to “humh, some people like to drive, but its bad for public and social health, how can we discourage it while preserving liberties?”
I really wonder what he did as a medical student in Cork other than study and read racist Tumblr accounts. Did his friends never drag him to Amsterdam to ride a bike and eat an edible?
Blast from the past: I realized that I didn’t have the exact link detailing why nickpsecurity was banned from Lobsters, but now I do. You’ll have to click the little
[+]to see his comments. He’s still active on HN and Reddit; he’s gone full MAGA, which is100% predictablea surprising turn for somebody who constantly preaches born-again Christianbigotrypeace and love. I really do wish that Lobsters did the whole turn-you-into-a-tree thing (sure, crucifixion, or maybe Peneus-style or Pequenino-style) for banned users rather than forcing folks to dig through archives.The good news is the report is false. According to contacts that are familiar with the Windows roadmap, there is no plan to ship a Windows 12 this year. In fact, I understand that the Windows roadmap for 2026 is all about fixing Windows 11 and attempting to improve its reputation by addressing top feedback such as reducing AI bloat across the OS
“We have heard your complaints about lead in the paint, and our roadmap for Leaded Paint 2026 is all about improving its reputation by making the lead easier to swallow”
