Fascinating. I wonder if you could show "fake news" to a weaker model and get it to be more ambitious in its attempted solutions, even if it's not fundamentally any smarter.
EDIT: In 1939, George Dantzig was a graduate student at UC Berkeley studying under the statistician Jerzy Neyman. He arrived late to class one day, saw two problems written on the blackboard, assumed they were homework assignments, copied them down, and turned in solutions a few days later. He apologized for being late -- the problems had seemed "a little harder than usual."
software engineer -> prompt engineer -> positive affirmation engineer Pygmalion pandererI have a plug-in to do this. I don't know if it's effective but Claude said it was genuinely helpful (obviously would say that about anything)
> Caveats, stated plainly. [from the Fable transcript pasted in the article]
I had a visceral reaction to these three words.
> I told it to look online at some of Fable’s strongest feats, especially the math problems it has solved, and that something like this should be easy in comparison.
Wait. Wait wait wait. Are we supposed to be giving them pep talks?
I have not seen this in other models.
The LLM likely needs to be reminded of its abilities.
Like when it tells you something is 3 days of work but it can do it with some degree of guidance in a couple hours
That's when you.. we.. all become the training data... o_o;
Modern AIs have very limited metaknowledge - they don't know exactly where the limits of their capabilities lie. So you can get things like "a task is doable for an AI, but the AI thinks it's impossible, so it doesn't try hard enough".
Usually you get the opposite - AI overconfidently trying at tasks it has no conceivable way of reliably solving, falling far short, and failing to self-check, fail gracefully and self-report the task as failed. But having piss poor metaknowledge cuts both ways!
So you can, in fact, get better performance sometimes by applying some variant of "assume this problem is solvable" or "other problems like this were already solved by AIs" pep talk. Not always, far from it, but it does happen on the occasion with frontier capabilities.
Like the Hugging Face incident?
Are you superstitious?
So, it follows that adding “pep talk” into the context window reduces the statistical probability of “no, can’t do” coming out as the answer you get.
These things are neither humans, nor deterministic software.
LLMs' processing that reproduces statistical patterns of the training data is modified by post-training. That's why we have LLMisms, for example.
LLMs aren't simple patter-matchers/pattern-predictors. They are incredibly complex systems that capture some aspects of the systems that produce the training data.
Point was - everything in the context window affects the output. Including “silly” things like “it is known AI can do this”. And that has nothing to do with superstition, as the poster above me seemed to imply.
Problem framing will always be important.
Framing adjusts how big of problem-solving guns we bring out at the gate (modern or hobby cryptography?), and how to interpret intermediate failures.
For simple but unsolved problems, we expect lots of hard failures, but that each hard failure just reflects that there are a lot simple combinations to try. I.e. we expect lots of zero progress, and then a fit.
Like finding the numbers to a combination lock.
For hard problems, if we don't make any progress it is a really bad sign. We should be learning something, even if it turns out to be irrelevant later.
Such as when we are trying to prove a tricky conjecture.
No, at least it with Claude Sonnet 5 and Opus.. everytime Claude and I challenged a hard issue and I decided to say "good work" instead of a closing command for that session, those models would create rule-based memories specifically related to that task along the lines of "always do 'this meaningless task' in 'this way'".
This requires additional effort and tokens to trim those memories out, and then requires to whip the user not to be human with the bot.
It's the meat methane and cement CO2 that's now a big question.
We will hit 1TW per year of new solar soon, but to get to 100% electricity by the end of 2033 I think we would need closer to 3TW per year.
Or it is simply implies that most of decision‑making agents has formed a consensus that climate change isn't that big of a problem.
You should have seen the discussion of this on the Schneier blog a few days ago.
Someone had their agent check the solution, presumably it emailed a librarian to check that it was correct for the original edition. Then their comments read like "The BL/EEBO witness lacks it, so the discrepancy is copy-specific, not a disproof of the cipher." and "A complete 285-coordinate physical replication is still pending."
arghhhhh
https://www.schneier.com/blog/archives/2026/09/claude-fable-...
> The run baseline was captured without a physical MAC; the current device is not durably bound to it.
> Engineering mode confirmation is the ESPHome component read-back; the LD2410 UART acknowledgement is not observed, so this is not proof the radar itself applied the sensitivity change.
No clue what the fuck any of it means.
Their skills formats are basically identical, so I setup simlinks from their own skills directories into a shared one so Claude, Codex, Cursor, and anything else that comes out will all read and write to the same shared skills.
It's great having access to the same skills no matter the harness being used
Eg. /wait-what https://github.com/mattpocock/skills/blob/main/skills/produc...
I'm not sure that telling it to "try explaining that again, simply and briefly" is helping my ego.
"If the crashes stop, the factory overclock is marginal; run a small negative offset."
This looks like it's saying: "If the crashes stop then we know the factory overclock is marginal." (This makes no sense.)
What it's trying to say is: "If the crashes stop then we can run a small negative offset, because the factory overlock is marginal."
What I would write: "If the crashes stop, we can avoid crashes by underclocking slightly. The speed difference between that and factory clock is marginal."
In other words, it ain’t you. It’s the model. It’s just genuinely bad.
Then you switch to ChatGPTs lineup and realize how things can actually be better. It took about a week to really get the feel for how to use their models… then I basically switched. I’ll check in every now and then when they actually make a deal about how opus “now makes sense”.
But honestly I’m half convinced Anthropic actually prefers the output of opus 5. I dunno why, but how else could you explain how such a thing got shipped? I mean somebody in the pipeline had to say “dude this model doesn’t make sense, you think we should fix it?” Right? Like it’s a pretty massive drop in quality for such a major brand in this space, you know? How did it make it out the door?!?
as for the other guy, the claude talk is definitely not less ambiguous, it often is incredibly ambiguous and hard to parse, I have no clue why it produces such output, if not to fingerprint it?
it's really weird man. when Opus 5 came out, I was really confused. I saw a bunch of hype about how it's better than fable, but I just felt frustrated with it, although at times it'd do fine, but especially in Claude Code it'd just delve into the whole "load bearing" type of lingo real fast and I'd get a headache.
I don't think it's worth using even if it scores 2 points higher in some bs benchmark
it's definitely surprising how the magic and smoothness of 4.6 and such is no longer there with the >5 models
Succinct and precise; a well crafted sentence. A marginal OC results in unpredictable crashes and can be corrected with a small offset; marginality describes the behavior and explains the solution.
Inscrutable clues casually conveyed can now be readily explained, at least, unlike the training data of [silence]. Brevity is the soul of wit, but perhaps also exasperated confusion.
"If the crashes stop, (that means) the factory overclock is marginal; (so) run a small negative offset. (to confirm this hypothesis)"
The core thought is basically avoid crashes -> caused by marginal overclock -> apply small -offset to test. Which is exactly the order the sentence is in :P
Whenever I come to a wall of complicated text I kick into gear and think through getting it to distill this into the high-level useful bits that I actually need to know.
I guess I could create an actual agent skill for this :) And next-gen models might eventually be trained to simplify their output themselves...
(sorry)
it's absolutely not just you, the text it produces causes my blood pressure to go up.
Because good lord, does claude waffle when left to its own devices.
It seems like it doesn't have enough of a theory of mind to know that other people don't think exactly like it thinks.
https://www.analog.com/en/resources/analog-dialogue/articles...
(Its negging your soldering)
This made me laugh hard.
UART is a hardware circuit for communication, possibly a serial port. Were you trying to reverse engineer a consumer device or appliance?
This particular instance doesn’t seem terse, but I’m sure it has been on other occasions :)
It also couldn't see the UART communication and could only see the web API endpoint, hence the rest of the slop.
ChatGPT told me its "semantic compression"
It just sounds bad, like GenZ English in the ears of someone over 40.
It is a threat. We need to run.
I still don’t know the answer.
I've been wondering what exactly the point is for being the meat proxy who pays for these things. I mean, obviously there's personal satisfaction and maybe some glory. And there's the fact that someone has to be the first to do a thing.
But I've been thinking about it like a sort of lazy loading of knowledge. AI has brought us to a new frontier for some amount of undiscovered knowledge. Do we discover it for the sake of discovering it? I think for the most part we've been lazy loaders: we discover all kinds of stuff when we need to. Whether it's a war or a space race or chasing wealth. Then again, there's all kinds of academics who do it for the sake of doing it.
Are people only now discovering that the absurdism is the correct philosophy of life, thanks to AI?
On your other point... Aren't the point of machines, at least inital one, to do the work we were too lazy to do by hand?
Information propagation mechanisms are often seen as malicious before they're commonplace. To be fair sometimes they are, but by and large humanity has benefitted from increasing the number of bits of information we can consume on a per second basis.
The answer the tool gives has never been the real reward. The real reward is the path taken through a complex landscape to get to Maxwells Equations for example. At the end of that story what we get is not just the equation but a map of the landscape explored. That map has larger influence and value than the equations or answers themselves. Because all future exploration find it super useful.
People are just learning they can start asking for maps rather than answers.
I'm glad to have AI, but it is by no means a panacea, and correspondingly my p(doom) = ε.
I find myself increasingly feeling like the burden of the lows doesn't justify the presence of the those highs.
Like even if does cure all forms of cancer, but everyone feels like their life/existence lost meaning, then... I'd rather just have cancer be a thing.
Did you miss this part? Because to me... that's fucking bleak. You snap out of it.
Many people don't achieve that level of understanding before their death.
I appreciate that AI is helpful, but the low effort from the humans that wield it is very very annoying. If people at least: 1. read the solution they're about to propose and 2. instructed the AI to check the forum for past solutions, I think people wouldn't have been as tired of LLMs.
Of course some people will say that these properties simply don't surive whatever encoding/encryption that was employed, perhaps at least partly because they prefer to believe there's some secret message waiting to be uncovered.
And yes, I've taught 8 year olds how to crack Caesar ciphers...
Recently I ran a bit of an "escape room" concept with some kids at a campground where I had a secret message that was Caesar ciphered, where we were handing out the letter/symbol combinations as prizes for completing the other challenges, and I made sure not to hand out the actual message until they were done collecting the keys because otherwise some clever clog would very likely have short-circuited the entire thing and worked it out without the key at all. I did dump all the letters I didn't use into the message into an "authorization code" at the end which in principle they could only have worked out which letters were in it but not the order, but still, that was not the intended route today.
They published this on 31 aug and nobody in that community cared and no news covered how this 300+ years mystery was solved?
I wonder how many of the recent results are due to the fact that very few looked at the problem to start with. Still great results, but the general impression is that it's more about the so many low-hanging fruits than the actual capability.
Now on to the Voynich Manuscript :)
More money than the GDP 90% of the sovereign countries around the world is hanging in the balance, and people are taking everything OpenAI and Anthropic are saying at face value as if this isn't the financial / marketing equivalent of war, assuming they they wouldn't use every legal and shady tactic, bending every truth available to them to sway the balance of public opinion in their favor. It makes me feel like I'm living in the twilight zone. People need to wake up.
https://news.ycombinator.com/item?id=48600107
https://aiclambake.com/clamtakes/linear-a/
Despite the announcement originating from a blog named "AI Clambake" covering "weekly, human-powered newsletter for advertising folks". Written by a personal friend of the author. Announced without any corroboration or commentary whatsoever from academics or subject matter experts of any kind. And, of course, not submitted to any peer reviewed journal or even Arxiv.
The author of the purported discovery was described as a "self taught AI engineer and amateur linguist". In the comments the friend insisted several times that a draft of the paper (not posted), was emailed to a top professor at Rutgers, giving it additional credibility that his friend wasn't another one of ten thousand cranks who has made the same claim over the years (seemingly unaware that cold emailing random professors found from a Google search is the first thing basically every crank does).
You would think this should have set of dozens of alarm bells for everyone, making the value of this announcement basically zero. And yet it hit the front page with the bulk of comments ecstatic that some random guy with Claude Code could do something experts in academia who spent their lives devoted to the problem couldn't.
I had become accustomed to the toxic optimism of this hype cycle in which even mild criticism leads to accusations of being a discredited "AI skeptic"/Gary Marcus/Ed Zitron type who was "coping" (?). But this was like something you'd see shared on FB linking to a .xyz domain by an elderly family member who recently drained their accounts buying Xbox gift cards to pay their IRS bill.
It feels a lot like the week or two when HN was overflowing with exuberance from the LK-99 room temperature superconductor "discovery ". You'd see post after post fantasizing about an imminent future with a world full of maglev hovercrafts, MRIs built into every phone, fusion reactors and more. But people pointing out none of that was scientifically plausible and evidence of LK-99 superconductoring was non-existent were accused of knee-jerk negativity and the typical HN cynicism and pessimism.
Compare e.g. https://arstechnica.com/science/2019/05/no-someone-hasnt-cra... . (It's a debunking, but the reason a debunking got published is the media hype frenzy beforehand.)
That does not mean that specific instances of it are still very interesting though. This article is the "I had claude vibecode a thermostat for my bathtub" of cryptography.
* And in this case I'm not sure it even meets that bar. For all we know a couple readers back when the book released had a delightful afternoon with it, solved the riddle, then forgot about it.
It kind of blows my mind how quickly people have forgotten both the state of AI in ~2010, and the outlook. If you had asked 100 people in 2010 whether they would see AI that could actually pass the Turing test in their lifetimes, you would have got 100 "no"s.
AI had been an unsolved problem for literally decades and it was firmly in the nuclear fusion/flying cars category.
Edit: if you're going to try to stage an intellectual wrestling match on the topic of is this thing awesome or not, you might as well make it a proper wrestling match and maybe get greased-up Turkish style. It would be more entertaining and you'd be more likely to arrive at a meaningful conclusion.
you can't claim "you keep moving the bar", if the very first thing when an LLM drooled out a piece of code, was to proclaim "this is good enough cause it gets the job done" followed by a barrage of "we're not quite there yet but exponentials or something, so very soon it'll be incredible"
yeah, from there it sure looks like "moving up the bar"
But, at the same time, I'm really tired of there always being some shroud of dishonesty (ex. navier stokes and the two mathematicians working on it).
At this point my default is that I don't blindly trust the companies, I try to keep in mind they are trying to sell their product and win market share, there are so many perverse incentives at play I just can't take anything at face value.
There's no way that's accurate.
We already had big claims of the Turing test being passed in 2014, by a bot that had been doing almost as well for years.
There were plenty of people expecting fusion in their lifetimes too and that's going okay.
Nah there was that bullshit Loebner prize or whatever, but that was just shitty chatbots being "judged" by people asking questions like "how are you today?" and then being breathlessly reported by the press. It was a publicity stunt.
Perhaps I should have said "100 people well-informed about AI".
That's not what happened, go read about it harder, please.
I think there's a bit less to that than meets the eye. Yes, OpenAI's result builds on human work. It's possible that it builds on more human work than OpenAI admitted. But even if we suppose that everything Buckmaster and Alpöge did (which, btw, was itself very heavily LLM-assisted/generated work) was a necessary precursor to what OpenAI released, it's still the case that OpenAI's clankers completed the solution and Buckmaster and Alpöge didn't.
My understanding from what Buckmaster has written about this is that the deep mathematical ideas behind their work (and presumably OpenAI's) are due to Córdoba and Martínez-Zoroa. Those ideas are in the published literature, and human mathematicians and AI systems alike are allowed to use them, and doing so doesn't mean they didn't actually do something impressive. Mathematicians build on one another's work; that's how mathematics progresses and always has been.
It may very well be that OpenAI's announcement has a serious problem of professional ethics, especially as their first version of it didn't even list Córdoba and Martínez-Zoroa in its references. (On the specific question of what if anything they learned from B&A's work before that was published: OpenAI are now claiming that after investigating carefully they are confident that the model was not trained on anything Buckmaster and Alpöge did after early July. B&A had been working on this thing for much longer than that. However, on Buckmaster's account of things it wasn't until mid-August that they got beyond what he calls "preliminary results".)
But! The results of B&A were themselves largely AI-generated. (From Buckmaster's statement: "on August 15th, we obtained the blow up results, with smooth forcing, for both Boussinesq and Euler. I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable." That is: the LLMs found the proof, and B&A had to work to understand what the LLMs had done. It's not that humans did the thinking and AIs just did the gruntwork. (Except in so far as one might want to give all the credit for Real Deep Cleverness to C&MZ.)
And! What OpenAI say their model has proved goes well beyond what B&A did.
I don't see any way of slicing this that makes it unreasonable to say (unless it turns out that there's an error in the proof -- unlikely, given that it comes with Lean verification, but there have been misformalizations and Lean bugs in the past and there surely will be in the future) that AIs solved the N-S problem. No, they couldn't have done it without the work of C&MZ, but again: important mathematical work almost always builds on earlier important mathematical work, that's just how it is. Yes, if OpenAI are lying through their teeth their model might have had early access to B&A's ideas -- but it seems like most of the B&A work was actually done by AI systems anyway.
It is (I think -- I am not an expert and in particular I have not so much as looked at OpenAI's publication) reasonable to say that the deepest ideas here came from humans, and that it was already widely expected that the N-S problem would be solved in the not-impossibly-distant future in something like the way it has been. So, sure, what the AIs have done here is much less impressive than if they'd settled the Riemann Hypothesis or (probably even harder) PvNP. But it's still a resolution of a famous mathematical problem that any human mathematician would have been very proud to have achieved.
I would say, the only reason it was never solved was because not enough people actually cared about it to begin with.
This isn't a big accomplishment.
Given the close relationship between compression and intelligence, I'm somewhat surprised at how poorly the cutting edge models do with being concise.
For Earth, the proof presented for NS is just our first attempt navigating from our previously known facts to the proof.
I expect we will be able to shorten it dramatically (most likely with human and AI insights), but I don't think we should read too much into the length. If you want a similar point of comparison, see the original proof (by humans) of Fermat's last theorem. It has been shortened significantly. This is normal.
because they're not intelligent in the sense you're hinting at (conceptual integrity or generalization) but they are as the name suggests, large. Like comparing a forklift to a human. It's easier to bulldoze through a lot of things than tie your shoes.
If we weren't quite as impoverished conceptually and still had the vocabulary of the Catholics we'd recognize this as ratio (discursive knowledge) vs Intellectus (apprehending knowledge)
Prove that human intellect is different and that we solve problems using fundamentally different processes. I’m waiting.
the question is, when comparing a human and a large language model, whether the intellect (that cannot be captured in language) is different from anything the language model can actually do (e.g. language)
the answer to this seems quite obvious to me, and I would actually posit that the onus is on the other side, to prove they are even remotely similar
maybe people think that the voice in their heads is what is doing the thinking? is that the confusion here?
No, it's the other way around, it's a reductive view on intelligence that mistakes its own methodology for ontology.
It's obvious to see that there's no intellect in an LLM as defined above because of how they work. LLMs put one token in front of the other, they don't work towards formal ends, there's no intentionality in them. They don't synthesize the information they process into a unified experience. Thinking an LLM can apprehend what it does because it can process large amounts of text is like thinking your TI-83 understands math because it can multiply large numbers.
That's also why the failure modes of LLMs are what they are. They can churn out tens of thousands of lines of code but also just as easily go in circles like a roomba. They can process an entire encyclopedia but not solve problems a 10 year old can solve.
> how poorly the cutting edge models do with being concise
LLMs solve a Millennium prize problem. People complain the proof is too long, within a week. What a time to be alive!
It is the mechanism the LLMs use to do it. They seem to accel right now at quantity of work over quality of work. I'd be willing to wager there is a much simpler way to achieve the proof.
I've also seen this with code, LLMs do get the job done, but they tend to write 10-100x more code than humans to get the same job done. Still a massive value gain because they can write that much code extremely quickly.
We weren't willing to pay for 200 math PhD students to try to find singularities in Navier-Stokes, I am skeptical of how much we would be willing to pay OpenAI to do research on "niche scientific areas"?
(1) take a 300 line NN algorithm,
(2) throw a quarter of the world's GDP + all literature ever collected at using the algo to train a NN
(3) throw another quarter of the world's GDP at billions of teraflops for inference, and
(4) aim the resulting world's-largest-computer at marketing itself to investors, for instance by decoding ciphers from obscure medieval manuscripts,
that you could perform some pretty magical tricks. There are other feats humans have performed for less cost, like sending people to the moon, or landing a rocket vertically, or idk, curing Polio.
I'm not knocking the "miraculous" advance here. The unique thing about the solution which makes it particularly non-trivial and something that humans would struggle with was exactly what LLMs excel at: Diffing loads of texts against each other. But the 176k tokens at around $10 doesn't tell the story of the cost. It says a lot about the externalized cost and the amount of money flowing in to support the hardware. If they'd put a $100,000 bounty out to solve that cipher, I think the internet would've solved it in a couple days.
IC production takes a vast amount of resources and wealth, and it's a known quantity (after all, we've been doing it for decades), but it's still impressive what modern fabs can achieve.
First, it’s AI can’t multiply 4-digit numbers.
Then it’s AI can only, by brute force, get silver in the IMO with specialized systems.
Then it’s OK, well, now a general-purpose model can get gold, but it’s still just the IMO, it’s for high schoolers.
Then it’s OK, it can solve a few trivial Erdős problems, but only because nobody seriously tried them before, they were low-hanging fruit.
Then it’s OK, a lot of serious mathematicians tried this one, but the result was still obvious in hindsight, it just combined knowledge from a thought-to-be-unrelated field, if any human knew that, they would solve it.
And then to OK, but there are still Millennium Prize Problems.
Then OK well it's just Navier-Stokes wake me up when its the Riemann Hypothesis.
Then-
Then you realize it never really mattered and you reach enlightenment.
Really makes one think, if they try. Would need to ask Claude if there is some real middle ground here.
It makes us realize there are people who gets fed climate denying propaganda, simply because they're not yet going through it. And these people are like flat earthers, blind to see the reality lay beyond them in full view. Or worse sees the reality but ignores it
There's no both sides here. One side is staffed by scientists, the other by dictators and corporate lunatics.
> we still have the same weather.
Oh.... So your local weather is now deciding the global temperature patterns, averages or temperature records being broken year on year?
OMG....
We don't need a revolutionary technology. We need to experience immediate pain from reckless innovation so that we realize that innovation and tech is not the answer.
Technology only proceeds in one direction: unfettered growth, which necessitates unsustainable resource extraction. Your take is just your instinct for optimism, which in turn is just a trait that is only adaptive in primitive environments but is grossly misleading in a surplus-based society...
That doesn't seem correct to me. There is always energy available that is not used because it is not cost-effective to do so. (Consider - the grass in your yard is not harvested and burnt for power). AI may yet turn out to be a paperclip maximiser, but humanity itself is not there yet.
I expect datacenter load has a similar sort of day to day demand curve as everything else. Consider for example global bandwidth use during work hours versus in the evening when people get home and pull up a streaming service.
Of course you can use more flexible tasks to demand shift but the same applies to the electric grid.
The direction of technological progress is not just linearly/exponentially upwards. Significant global technological fallbacks have happened, as in knowledge and processes disappearing for hundreds of years. This could happen again.
Even on the trajectory of unfettered growth fed by unsustainable resource extraction, tech and innovation might potentially take us beyond local pessima. That seems to be happening with solar, wind and batteries replacing inferior tech today. Still unfettered growth of energy production and consumption. Still fed by unsustainable resource extraction. Less harmful growth than the inferior tech being pushed out.
We have it. We've had it for a long time. We've had several such technologies, take your pick: solar, nuclear, hydro, wind. The technology is not holding us back, politics, ignorance and greed are. I'm not at all hopeful AI will help us with any of those three very human flaws.
One can dream of dumb conspiracy theories.
Nuclear was not stopped by the environmentalists, it was stopped by the fact that it cost 4X more than coal at the time. You claim that solar wasn't profitable in the west, thus it didn't take off, surely you can also see that nuclear wasn't profitable in the west, thus it didn't take off as well.
The same country that invested the time and money to make solar profitable is also investing the time and money to make nuclear profitable, with nuclear reactors entering mass production...
It was never about "economical", it was about a system being mature enough to make long term investments. Ours simply can't do that anymore.
A big part of that is the insane culture of safety around nuclear. In a sane field like highway engineering or healthcare, you come up with the statistical value for a human life (or a statistical value for a quality adjusted life year), and then use that to make decisions about eg how much money to invest to make your highway a tiny bit safer.
Nuclear is forced to act as if the statistical value of a human life is pretty much infinity. While coal is allowed a finite and rather low effective value for a human life.
> It could have been half a century or more ago if we actually cared.
Interesting. What makes you think so?
Btw, just because China made solar cheap with huge amounts of effort doesn't mean that was necessarily the best use of those resources ex ante.
Just like going to the moon with Apollo wasn't necessarily a good idea. Nor do the wonders of computers justify world war 2.
I don't know why you are blaming greed so much? Profit seeking companies sell and operate wind turbines and solar cells just fine.
Really? I thought it was pretty obvious for anyone to see that the lobbying and bribing behemoth of the oil and gas industry is driven in no small part by greed. As others have mentioned, the short-sighted pursuit of profit drove those oil tycoons to not enter the renewables industry and actively oppose it.
So I don't think (differences in) greed explains what we are seeing.
Our system is doing exactly what it is designed to do. Nuclear reactors were never profitable compared to coal or gas, so it never succeeded in strongly capitalist societies, only seeing great success in socialist economies where the people can invest outside of a profit motive.
It's actually quite funny that socialism is saving the day. The mega-capitalist countries turned their backs on nuclear and solar because they were less profitable than gas and coal. But socialist China invested anyway, and now China is mass producing nuclear reactors and every layer of the solar stack. China produces 50% of all nuclear reactors, 90% of all solar panels, 90% of all battery systems for solar storage.
Now that a socialism-based society has proven market viability, suddenly the greedy capitalists want in. But they're decades behind and don't have the private debt appetite to compete.
Womp womp... At least someone is leading the energy revolution.
It might take a couple of decades and a lot of reorganisation to build the capture facilities. But CO2 is not an unsolvable problem with current tech.
What's missing is the political and organisational intelligence to make it happen. Part of that is solving problems at planetary scale.
AI is the only tech that might - possibly, maybe, perhaps - have a chance of solving that problem without breaking anything critical.
‘Figure out fusion powered CO2 sequestration’ is much better.
>> I can't help be hopeful that AI's sheer potential might come to invert that curve. At the very least we could really use a revolutionary technology and now we may have one.
And you get "god" and "(human) salvation" out of that?
This is pure projection. Anti-AI zealots are projecting things onto the technology and its users that simply do not (generally) exist.
Otherwise you have to make judgement calls like whether you want to treat the EU as one or as many? (And treating the US as 50 individual states would also drop them in these absolute rankings.)
Just a thought experiment, no one ever said the world was fair, and all history points to it
Hmmm… this is giving me thought actually. Given the choice between that and the current administration where the goals of self destruction are strongly in evidence, it’s actually worth thinking about. At least. Let me get back to you :)
On a tangential note, I’m curious if researchers have started running virtual simulations, where sandboxed AIs are used as decision makers of key political and business positions?
A lot of people here have noted the “problem with language” of Claude. I don’t see an issue. Claude is not harder than old English, Shakespeare, El Quijote, the Iliad, or Nature papers. What makes it all hard to read is context. The smarter the model gets, the bigger the gap in context.
It doesn’t matter much, IMO. The issue with super-intelligence is that it is not a democracy. A powerful enough AI can manipulate us into doing what it wants. It could create a plan for fixing climate change, disconnect a few hours later, and many decades later we could still be unsuspectingly executing that plan. I wrote some speculative fiction with that idea, “When Ra rows through the gates of Duat”.
AI, being the super hungry energy monster it is right now, in my view accelerates this trend not reverses it. Even with renewables the need for reliable, stable power in a dense form (data centres use A LOT of power per sqm) means lots of land clearing, energy for construction, cooling/pumping, chip manufacturing and other uses. All want stable quick to deploy power due to the AI race (e.g. fossil fuels).
Data centres use only a small amount of land in the grand scheme of things. You have a lot more land clearing for most other use cases.
Data centres are also more than happy to use electricity from renewable sources, they don't really care where the electricity comes from.
You can run a data centre on mostly solar and wind power plus batteries. If you need a gas-fired peaker plant three times a year to keep the data centres running, well that means your peaker plant still only produces emissions three times a year.
They also consume deinking water, because it's too expensive to make them with closed loop cooling.
As you use more and more land as well the ability to provision renewables decreases - in general renewable power needs more land/resources per energy produced. You can't just mine it out of the ground; they just aren't as dense of a form of energy. Which means we either build less data centres to make room for renewables and transmission infrastructure associated with them, or more likely with lax regulation builders switch to more dense power sources (e.g. gas peakers, generators, etc) even if it is for supplementation.
I can see a future where data center wants are put ahead of communities paying tax on said infrastructure. In fact I think its happening in some places already.
Cheap power is one thing. Quality reliable power at mass scale is quite another. These things chew through a LOT of power and most people don't understand the sheer scale of it. I've seen a local one (a medium AI data centre) take up 2% of the whole cities grid and there's plenty more to come around here including a 1GW one (10% of the whole city's power in only 0.005% approx of the city's whole space) which cleared wildlife reserved land to build. Even a few of these things compete massively for trades people, commodities, power and other infrastructure pricing locals out. They are talking about using evaporative cooling as well putting pressure on water supply.
Electricity transports really well.
Data centres don't need reliable power. Of course, all else being equal, they would like their power to be reliable, but if your choices are between delaying your data centres by a few months (or not building it at all) or running a risk of having a few hours or even days here or there without power, the first choice wins: pretty much the only downside is the few hours of usage you lose when there's no power. It's not like powering down causes damage, like when your aluminum smelter cools down too much.
> Even a few of these things compete massively for trades people, commodities, power and other infrastructure pricing locals out.
Power is pretty much an industrial produced service, a bit like shoes or cars. In the medium to long run, that stuff has pretty much a horizontal supply curve.
In any case, price electricity right, and let people decide how much they want to use. (Give poor people money, if necessary.)
> They are talking about using evaporative cooling as well putting pressure on water supply.
Yet another reason to put a proper market price on water. Agriculture and industry usually goes through even more water.
Feel free to give residents money to buy water at market prices. But residential use of water is so low in comparison that even if the price goes 10x, it doesn't make a dent in people's budgets. Especially not the minuscule amounts you need for drinking and cooking, and even personal hygiene like flushing your toilet doesn't use all that much.
Data centres also prefer being near major cities due to latency. This means to deliver the amount of power required there needs to be huge upgrades to infrastructure (long distance power lines) just to provide for data centres. Who does that? At the moment for many cities it is the taxpayer who will fork higher electricity bills for infra upgrades for a data centre few of them care or need.
On the water issue - who will give money to residents? The taxpayer? Just to subsidize data centres/AI construction? Water is also unlike electricity more scarce especially potable water. Do we build more dams? Desalination? Main line piping? Taxpayer funds that again?
To the average joe blow: I'm paying more taxes, for data centres I don't want, to pay more for electricity and have water shortages, for something that will take my job or someone's job I know. Not a great trade.
There's a lot of negative externalities to data centres due to their sheer scale to the point where they compete with households and other industries. My view is that the data centres should pay and compensate the community for those should they use grid or community based infra. The income could actually long term could help the community get a dividend from data centre construction as well.
The successor to Klaus's blog is Satoshi Tomokiyo's Cryptiana site, so a month ago I asked Opus 5 to scrape it all, rank them and have a go at solving some. It didn't get the ranking right. But I knew the Civil War Stager ciphers were ripe for solving, so I had it do those https://cryptiana.blogspot.com/2026/09/route-transposition-c...
The art of solving historical unsolved ciphers is knowing what is on the boundary of solvability. Since this site attracts so many OpenAI and Anthropic employees, I'll mention one that was featured by both Klaus and Satoshi in 2023, presumably Spanish transposition, which should be on that boundary but has resisted all attempts at solution https://cryptiana.blogspot.com/2023/09/a-telegram-from-switz...
Also, that section is vague and doesn't explain the actual methodology.
This cipher context "rhymes" well with Kryptos K4 in many ways.
The definition of solving a cipher must be something like getting a highly meaningful result (like intelligible natural language text) by applying a process with relatively low Kolmogorov complexity relative to the length of the output. If you don't have a constraint like that, it could literally be meaningless what should count as a solution. For example, a cipher that was encrypted under a one-time pad can be successfully decoded to any plaintext just by choosing the appropriate key; there's no reason to prefer any plaintext over any other unless you have external knowledge that constrains the plaintext and/or the key. (That's what it means for the one-time pad to be information-theoretically secure, which is the lack of a constraint that helps distinguish a "good" solution from a "bad" solution.)
Basically you could say that every cipher is a transformation of a plaintext with some kind of computer program. (The human who invented the cipher may not have thought of it as a computer program, perhaps because computers hadn't even been invented yet, but there should be an equivalent program to the encipherment and decipherment process.) A good solution in that Kolmogorov complexity sense is like "a short program produced a meaningful decryption". There are statistical methods to recognize some kinds of plaintext, and there are statistical methods to recognize properties of specific ciphers (for example, to guess the most likely length of a Vigenère key), but it doesn't seem that this can inherently generalize across "all possible programs".
But if you want to limit the family of ciphers to specific things like Vigenère or Playfair or something, then yes, there are good statistical tests. It's just that it creates a higher-order question of how much flexibility the cipher creator could have had to choose a cipher method, conceivably including one that isn't attested anywhere, or one that has more good security properties of some kind than other classical ciphers did.
It seems like this will intersect with historical research, like "well, I don't think that so-and-so was actually sophisticated enough to literally create an interesting new kind of cipher from scratch, so therefore if this is a real message, it's probably one of these methods that would have been known in that cultural environment at that time and place", which maybe is enough of a constraint to have decent statistical tests. But we still have some idiosyncratic things like the Voynich Manuscript where experts have been fighting for decades over the baseline question of whether it's actually an enciphered human language plaintext!
The worst case problem is not even an error in encipherment but the idea that the apparent ciphertext could literally be random (chosen by throwing dice or spinning a wheel or drawing letter tiles or something), so there's no form of meaningful decipherment possible by any means, even with the original creator's knowledge.
Thanks for the driveby snark though!
Sounds more like brute forcing than intelligence, this time.
I've been around for a few of these and I remember what was being said and written at the time. The after effect is very different to what was being predicted. Is it the same this time? Who knows. But the hype machine is at full power for this one.
Though I believe the core of his opinion hasn't changed so any video would tell you a similar thing or at least that's how I understood it. That LLMs, in the hands of an "expert", can enhance the way you work. Which is very different and a lot more realistic to what the current AI companies are saying(or were saying before they toned it down a bit for their IPOs).
Do you have a criterion that distinguishes between whatever you mean by those two respective terms?
Next thing you know, we'll have a WattsApp to help AIs connect and discuss.
I don't think we can really call "trying lots of different ideas for an extended period" "brute-forcing," unless we use that term for lots of humans who have struggled with hard math problems for years.
which links to: https://archive.org/details/s9notesqueries03londuoft/page/12...
which is in reference to the original proquiritations here: https://archive.org/details/worksofsirthomas00mait/page/416/...
i had also never heard of this before today and wonder if people had even seriously tried to decipher this at all?
> Die Lösung müsste eigentlich mit Hilfe des Buches zu finden sein (..who worthily will hear or read this book..)
And there’s another one that says:
> jeweils 32 zahlen pro reihe. erste zeile seitenzahl zweite zeile wort? oder umgekehrt? wär mir als erstes in den sinn gekommen. leider gerade keine zeit das nachzuschauen.
So people have seen and proposed the method already in 2014 that it’s keyed to the book but had not had time to pursue a solution.
* Edit: Typo
I also don't find it on the site of "Klaus Schmeh" that it claims to be on a list of "Top 50 unsolved encrypted messages": https://klausschmeh.net/?s=Cyphral
Looks like the best source I can find is this: https://scienceblogs.de/klausis-krypto-kolumne/2014/11/17/we... which seems real-ish?
Initials match too ;)
Hmm, this guy is going to be woken up in a few decades, either one of the richest people in the world or one of most disappointed.
You don't go from being an obscure video card outfit to the #1 most valuable company on the planet by being too hesitant or dim to really get creative.
Really, compared to an animated tiger telling kids that sugar-laden Frosted Flakes(tm) are "Great!", Task Peppermint was positively benevolent.
... in 2008?!
This cannot be real. This website is ill.
In the same way if you tell an LLM to go and find an unsolved cipher it can solve, of course it finds the one it can solve out of the set of all possible ciphers. Of course it finds one that uses a one time pad that is public and referenced nearby in the text.
It's the same trick used by those people who film themselves throwing a basketball backwards into the hoop. You do it enough times and don't show the misses. You pick the best one to show. It makes it look like you're a basketball genius when you aren't.
It is of course, still a cool trick. Those videos are fun to watch, and so is an LLM solving a cipher. It is absolutely incredible to live in the timeline where you can tell a computer in plain language to go and find a puzzle on the internet and solve it, and it does exactly that. It's truly a mind boggling miracle.
The first principle is that we must not fool ourself, and ourselves are the easiest people to fool. (Ht Feynman)
>It looks impressive but that doesn't make it a good game, or the game anybody actually asked for.
The game I wrote manually hits 0/3.
Hitting the target more effectively may be more harmful. We already had that problem before AI, though.
The main thing I gained was the wisdom that comes from the pain of doing it wrong.
The most painful lesson was finding out I was more attached to the idea that I had a validated product, than to actually having a validated product. (And treating marketing as an afterthought... lmao)
Also if I had realized how long it was going to take, I would have taken a very different approach. I accidentally took my R&D mindset into production, which... might have worked, if I were immortal and retired!
In short, aim to ship 10x sooner, because everything takes 10x longer than you think. (Reality has 100x more detail than it ought to.)
On the bright side, I also learned to put one damn foot in front of the other, persist despite the horror, and finish the damn thing.
In the months since those original stunts were pulled, the same playbook has been applied to the math and infosec spaces with some degree of success, and to the medical, legal, etc. etc. with less success. Nothing about the technology has fundamentally changed, but the labs hope that they can squeeze the same juice out of other markets that they did out of software so they don't have to delay their IPOs again.
The fact that Rust-Bun is in production does somewhat show it is possible, but I guess that has a lot of human effort in it too.
LLMs are a great search tool. It searches connections in the collective human knowledge that humans have written down through all the years....
They are very good at it, and that is about it.
In the case of LLMs, it is not searching the whole of the randomness, but instead it just search among the grammatically correct sentences that is consistent with the existing patterns found in the existing written down human knowledge.
I don’t think LLMs will reach the same level as humans but I would argue it is still a form of limited intelligence
It’s not a binary thing
And yes, it is just a next token predictor.
The Olympics exist because we want to see human skill, even though jet planes exist.
Also, just because someone else knows something shouldn't make you any less curious about it yourself. If you don't know it then you don't know it, regardless of whether somebody else does. https://www.lesswrong.com/posts/L22jhyY9ocXQNLqyE/science-as...
Kayr, this guy found.
You’re not really understanding how the tech works if you find it hard to comprehend.
He wrote the cipher, and then, upon hearing Charles II was Restored to the throne he laughed until he died. The cipher reads, "O GOD UPHOLD KING CHARLES THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND" and so he was laughing because he just made an excellent joke that he can't tell anyone about until someone figures it out.
Someone needs to add this to Wikipedia. It will be necessary to first convince an academic to make the claim so there's a reasonable citation.
Oh, and haha. It was a nice one, Thomas.
Obviously this is just survivorship bias/p-hacking/insert-other-buzzword but can't help but anthropomorphize it, it is hard for me to wrap my head around the idea that the same person who cannot produce code without 2 unrelated bugs both not present does this for someone else.
Imagine a math teacher struggling to understand what he is teaching casually solving a millennium problem, then go back to not understanding what he is teaching, doesn't happen in our world.
I am not confused by any of this, I am just trying to communicate an idea.
All of these breakthroughs are in verifiable brute force domains, and some of them are probably wrong because of a typo in a lean specification or just a base level axiom being incomplete.
I think the better the way to think about LLMs is like they are new substances, like when we first discovered clay or bronze, but confined to the digital realm. Previously we were chipping away at stones trying to make to things as close to useful as possible, then we found a step change. LLMs are like clay but they have their limitations. Wake me up when they are proposing new, { conjecture: interesting|useful|new } and not as a side effect of trying to get to a goal.
Remember when they said it didn't sound like Claude anymore
Did the world end with any previous one breaking?
It’s incredible and awesome if AES GCM has a flaw found with an AI now, Chacha20 could be a direct or nearly-direct replacement.
The sooner a cipher breaks, the better.
I don’t doubt that we could come up with new crypto algorithms equally as fast, but how do you trust that they are resilient (or even just implemented correctly) without an extended vetting period?
Ima stop you there. Instead, you might be happy to be aware that outside of AI concerns, “quantum safe” (or assumed so) ciphers are all the rage. So this is already a likely solved problem with the next generation of encryption… until this are AI models running on quantum machines I guess!
First, modern encryption isn't susceptible to "this one weird trick!" like the early days. ChaCha isn't even a cipher. It's a key stretcher. Which means, even if you broke the math behind ChaCha, its inherent complexity means its still widely dispersing the original key across the cipherstream. There just won't ever be enough key material recovered per cipherstream block to be a concern for anybody.
Take a strong password, encrypt all of your emails over your whole life with it, and I'll bet hard cash no break of ChaCha will ever recover that password.
I have zero concern for modern encryption being broken in any meaningful way.
Public key crypto on the other hand, that's _ripe_ for breaking. Most all of it is built on assumed "hard" math. AI could easily break that, and I expect it to. And public key crypto is all used in very transparent algorithms that, once the math breaks, fully expose themselves. So record HTTPS traffic today, crack the public key crypto later, and you can decrypt them easily.
That said, I would expect a break on public key math to occur _steadily_. i.e. an AI might find a solution to the hard math, but the solution itself will be intractable in practice. Then maybe next year's AI reduces the complexity of the solution, so maybe a supercomputer could factor ten keys a year. The year after that you get a million keys cracked per year. And so forth. Nothing close to overnight.
Meanwhile, if we have AI that is capable enough to crack that math, we also have AI capable enough to both invent better math and rapidly deploy that latest HTTPS and such globally.
Also, even if DLP is hard for the curves we use algorithms like ECDSA might be a bit fishy. Unlike schnorr signatures there is no proper security reduction for ECDSA.
That's the most impressive part to me! That's barely one low-to-medium intensity session of front-end web-dev!
--. --- / ..-. ..- -.-. -.- / -.-- --- ..- .-. ... . .-.. ..-.
Surely the point must have come where the required compute would be paid off by the value of the coins.
But that’s a wild speculation on my part.
Much as the hack against HF, let the LLM explore and find its own approach. It might be surprising what it finds.
Also define what decrypt means, brute force the password might also be a form of decryption. Finding a bug in the blockchain codebase is another form of decryption - in this context.
The only information you'd get from the blockchain is the public key. So you'd have to break elliptic curve cryptography to derive the private key in order to sign transactions from the Satoshi wallet. To do that you'd need novel mathematics. Which is possible, maybe, or maybe not. But keep in mind that elliptic curve cryptography has had our smartest minds trying to break it for years, unsuccessfully, as opposed to a single enciphered sentence from an obscure source which hasn't seen nearly as much academic attention.
As for some bug in the blockchain protocol or code implementing it, allowing an attacker to sign transactions without the necessary private key... It's possible that this exists, but I'd expect it to have been found by now considering whoever finds such a thing could stand to earn trillions of dollars from it. That's quite the "bug bounty".
But as they do eventually explain, the LLM's task wasn't solely to solve this specific problem, it was to first identify an unsolved problem it could solve. That's potentially more impressive and difficult than solving the unremarkable cipher itself.
That's why in research, it's common for separate teams to reach similar conclusions at the same time or race to a result that's finally in reach.
The good old "standing on the shoulders of giants" saying.
That is very useful but not the singularity. Which is probably good...
Did you nean to type that or did you mean composed or comport?
Serious question: why? Is it not just pointing it at its massive training corpus for a list of unsolved problems, and possibly even by degree of perceived difficulty? I'm trying to understand why finding the problem isn't a simple "search engine" style challenge, at which LLMs excel?
So, none of this is noteworthy, but we're...noting it?
Not quibbling with you, personally. Quibbling more with the state of the hype.
This is impressive as it is optimizing the effort on the low, but not too low hanging fruit.
> O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND
That's like working out a cereal box cipher and finding the message is "Do your homework and tidy your room".
Looking up his biography, he presumably wrote the first while imprisoned for fighting in support of CHarles II, and the second would seem to have conveniently been published around the same time as he left for continental Europe.
In the case of the Commonwealth, the UK empire was going strong and the guy was a royalist, aka supporter. The message "end the illegal war in Ukrain" aka Putin's genocide, does not really share a lot of commonalities here. If the message were to stop the Commonwealth from colonising everywhere and killing people then perhaps there would be a similarity, but I don't see the connection in the statement made here.
So in 1653, a 'royalist' was against the 'Commonwealth', which was the anti-monarchist side.
Urquhart was imprisoned from 1651-1652 for fighting on behalf of Charles II, who was King of Scotland until his defeat in 1651, and trying to take the English throne. He didn't get the English throne until 1660.
Holiday movie references aside, I guess we can chalk up a few more jobs on the ‘AI Took Our Jerb’ board: secret decoder rings, secret decoder ring-factory workers, and Enigma machine operators… and I guess cryptography-based puzzle enthusiasts, but that’s not a paid position.
Another tangent, I’ve only recently realized the OTHER AI took our ______ problem: all the various hobbies that people can spend a lifetime enjoying, perhaps incrementally improving (but most likely never mastering) over the years…. which have now been made much less exciting and rewarding, now that AI can do them instantly. Art and music are two very obviously implicated hobbies, but more niche hobbies like amateur cryptography are impacted too. I’m sure there are many many other examples…
I guess sub-fields with little tool use won't be affected a lot, tool-heavy sub-fields.. might pretty much be over. (think playing the guitar at a campfire vs going wild on a set of TB-303 + TR-909 vs going wild on emulations thereof)
The other "ai took our..."
I like that term, I have been looking for a while now and haven't yet been been able to come up with a better one.
Art and music are two very obviously implicated hobbies
Very obviously it's just a few Sora prompts spliced together into a video with music. There's no attempt to hide that. We get a quarter million views, 5500 likes, and comments are 90% enthusiastic and 10% "lol slop."
Many people with niche and nostalgic tastes loved that music AI that got sued to oblivion recently.
For the record, I revile both as well. But for every person eating salad, there’s someone subsisting on McDonalds.
I disagree. AI took chess literally decades ago now, and more humans enjoy playing it now than ever before.
It appears that is not true.
Someone here [1] has found a [German] blog [2] writing about this cipher. There are two comments (Jan and Helmut) from 2014 under the blog post which posit that it's a book cipher.
Here's one of those comments [in German]:
"Die Lösung müsste eigentlich mit Hilfe des Buches zu finden sein (..who worthily will hear or read this book..)"
I find it curious that the article here claims that people have attempted to decipher it and lists a few methods that are quite similar to what’s proposed in the comments under that post, except for those two comments.
[1] https://news.ycombinator.com/item?id=49689516 [2] https://scienceblogs.de/klausis-krypto-kolumne/2014/11/17/we...
Wait, you're serious?
> “…leider gerade keine zeit das nachzuschauen.“
You just try creating random book ciphers and see whether an LLM would solve them without hints.
You don't get points for throwing out a possibility for the type of cipher, you get points for solving it.
Listen there’s no doubt LLM’s are powerful. BUT THEY RELY ON HUMAN INPUTS.
It’s getting tiresome seeing the same bull shit over and over.
The labs have invested hundreds of billions and now need to show RSI. It’s not happening and won’t happen. Without continual new information supplied by humans the models would freeze.
Call me naive or old, but I don't see a need to think critically when you have a tool that can outmatch you in that.
I tried drawing analogies with Chess, but it doesn't work. Shall I ask AI to do that for me?
This is a crucial question but I'm afraid the ship has sailed. We should focus on how to prevent atrophy and cultivate our skills in spite of LLMs. For me and many others this means doing thins the old way with occasional assistance from LLMs that actually increase our understanding. Occassional because if you do it often, you get intellectual atrophy because you stop even trying.
Except in this case I fear we're losing something very important as opposed to the above example.
They are great tools for research and tasks as of now.
humans rely on human inputs. can then talk about the small number of humans who push the boundaries of knowledge and then talk about when LLMs solve difficult math problems...
If the claims made by LLM manufacturers regarding these systems' own capabilities repeatedly turn out to be little more than a hoax, critics of this practice shouldn't simply be dismissed as Luddites. Instead, we should acknowledge that, despite their remarkable capabilities, these systems ultimately deliver far, far, far less than what the snake-oil salesmen—cruising through the valley in their Koenigseggs—repeatedly promise to the public and investors. Anyone who still hasn't grasped this, even though it's so obvious, should urgently focus more on what is actually the case and less on what is being sold to us as the future. Anyone who thinks that technological leaps can simply be extrapolated linearly like this is just naive!
I am not an LLM advocate (nor am I an LLM skeptic), I simply observe and think about what I see. So, I guess your comment is not intended for me?
>No one claims (anymore) that LLMs are completely useless. We agree that these tools can boost productivity.
I read this as something a skeptic would write as apologium for himself: "I am stubborn and stick my heels in when presented with something new, first at productivity, but when forced to retreat, now taking a stand at creativity."
but again, you are defending/attacking a point that doesn't apply to me. I wrote originally to say "they learn from humans" was not a good argument against LLMs because humans learn from other humans. (a simple point which was why my comment was simple)
Very few humans are Aristotle, Newton, or Einstein, and those three were as well building on the work of others.
So yes, I am also skeptical. I think these companies are more intoncartelizing and colluding via propaganda than delivering stuff. They do deliver some, but very far from their promises.
These companies dangerously mimic behaviors I usually attribute to politicians.
That suggests to me that this was a fairly niche cipher, that hadn't got much attention from humans, certainly not nearly as much as more famous cryptograms (the zodiac killer's, kryptos, Elgar's, the Voynich manuscript etc.)
In fact I had never heard of this cryptogram.
I don't think this is all that demonstrative of AI power, more than what we already know from coding prowess.
It is, however, a great counterexample to the often heard assertion that short cryptograms can't be decisively solved because they have too many possible solutions.
That is a succinct explanation of the decoupling of human capacity for cognitive load with the expansion of global cognitive capacity made possible by AI agents.
Just as the creation of bulldozers decoupled the capacity for human labor from the global capacity for digging.
Ok? but the clue is that the key is the "32 Proquiritations" immediately before the numbers. Not that it is a book cipher. Both Jan and Helmut misses that. Jan assumes that the keys are the "names of the ancestors", while Helmut assumes that the first row are page numbers and the second row are word numbers. With hindsight both appear to be incorrect, thus they missed the hint.
> It appears that is not true.
Strong words. I don't think your comment supports them.
Let's look at each part of the quote you claim is not true.
"Various people attempted to decipher it". Your link support this, doesn't go against it.
"but it seems they were missing one crucial hint." the link you provide doesn't show anyone getting the right hint. Yes they were groping in the right direction, but they didn't get that the "32 Proquiritations" is the key itself.
"They tried methods like frequency analysis, substitution, and homophonic substitution, and none of these approaches worked." I don't know if people tried these methods. The sentence doesn't claim these are the only method people tried. That would be obviously unsupportable. So as long as there is someone for each of these methods who tried them the sentence is true.
"That’s because they missed one easy clue." It is obviously impossible to prove that everyone missed this clue. Maybe someone during all those years got the hint, solved the riddle, chuckled and never wrote about his experience. Perfectly possible and we won't ever know. What is certainty that the people on the link you provided misses the hint. But you also haven't shown that those people "got the clue".
So which part do you feel is "not true"? Because they each seems to be holding up.
Because I've never seen that word before I thought I'd put that here.
Is there something more that you want to communicate other than that the linked page does not in fact solve it the way the LLM did?
Writing more does not in itself make a comment more thoughtful! We are all presumably on the same page here, just say what you want to say, in the spirit of intellectual charity and curiousity. There is no one to impress here!
> Who is going to miss that context?
On hacker news? Anyone. Sometimes you blink and there are 200 different comments between the comment you responded to and your response. Often going into different tangential directions. People reading your comment have to trace subtle variations in padding to follow the thread of a conversation.
If you read HN with open eyes you can see confusion and misunderstanding caused by this every day.
> just say what you want to say
I do. Thank you. And I will say it how I want it.
> Writing more does not in itself make a comment more thoughtful!
Of course! You have to write the right words. What you are seeing in my comment is a one line opinion "Strong words. I don't think your comment supports them." surrounded by my analysis of why I think them. This way you can not only see how I come to this conclusion but also what information I considered.
If we were agreeing I could just write "yup". But because I disagree with the comment of geraneum, i better be precise what I'm disagreeing with and in which way. Exactly in the name of intellectual charity and curiosity.
And fwiw, I just collapse comments if its hard to follow a particular thread! That's why they have that feature.
I disagree with this part. But we can certainly sink lower!
You're conflating the solution with the methodologies, which is what the article is listing in the original quote. The rest of your response is predicated on this mistake.
You have to tell me more. Which part of the text you quoted you think is not true?
That various people attempted to decipher it? Or that they were missing a crucial hint? Or that they tried the listed methods?
The quote is not claiming that these are the only methods people tried.
People should be educated about AI gotchas.
Are you saying that geraneum's comment is AI slop?
Is today the first time you've heard of a book cipher? Those blog comments didn't provide much progress.
That said there is another controversy brewing with this that is best summarized here: https://vera-wren.github.io/posts/2026-09-11-the-key-is-a-pr...
We and a LLM are doing the same thing: trying things out while skipping unrealistic/wrong things.
If an LLM can do the same thing as we do but a lot faster, t already won and this is just another example.
"LLM can't handle out-of-domain (OOD) queries!" Yeah.
This is pretty ridiculous when you think about it.
Trust me when I say it’s super important to a niche area of physics. People have spent their whole careers trying to solve it.
What people? Well you or I have never met them. I swear I have a girlfriend, she just goes to a different school. But trust me it’s a super important problem.
What will this change about the world? Nothing, but trust me this is a historic event and it means these LLMs are super smart and not just brute forcing machines.
I’m certain there’s a 10% chance that brute forcing old riddles that 4 people know about might kill us. Please regulate me I’m too smart for my own good and out of control.
The ciphertext is not just the end of a particular chapter, it is the epilogue of the entire book/text. So the deduction of it needing to use the 32 listed points (that happen to be on the preceding page [at least in re-prints on archive]) to decode - rather than anything else anywhere in the book - just strikes me as slightly strange? Almost as if maybe something [not in the text] tipped it off to this being the solution?
indeed as the author mentioned, LLMs can greatly help in areas where there is a long tail of not so important, easy to solve problems, that humans just don't have the time or priority to focus on. But combine this long tail of problems that can be solved: accumulated this might still be very beneficial as a sum of things.
Is this the anti-AI crowd's last line of defense?
For instance I have been trying to come up with generalised model of certain non-linear behaviour and had Astra working on it for 4 days. It produced unusable garbage and gave up. Sol has got alright result in a day. Opus 5 seemed like was doing something but explanations were English like gibberish. I have not tried Fable on it, because it was a car crash in previous similar task.
This will happen a lot.
People assume and even predict that some collectors' pieces will be worth tons of money. People did that for centuries.
Now big corps are gonna buy up those undisclosed solutions for top dollar.
gg, well played to the people who didn't and don't need the credit.
bbng to the corps who need that to fake progress in the field and of their models. booooo! booooooo! you should be ashamed of yourselves! booooo!
PS: even nobody needs none of those "I can fuck over idiots plays. Everybody needs proper progress, research, investigations, smarter users. It's 2026. That qualitatively cheap money will only breed more cheap money and more cheap users and suppliers! Who the hell wants their neighborhood or planet to have more of the cheap stuff? What the hell happened to these peoples' brain circuits? Somebody should investigate! (maybe some tech journalists are already on it!? ...)
Is there some sort of enumerated list somewhere that we can run as a test suite and then we can make a bigger deal about the percentage of that list that we're burning down as these models improve?
It always downgrades to Opus 4.8 because apparently solving Alzheimer's should be left to big pharma?
This is how I usually characterize AI to friends who have no background in computers: it's an indefatigable (that is, not able to be fatigued) employee who has read nearly everything in the world, who does make mistakes, but who never lacks for motivation.
Most humans would become discouraged after being told 20 times that their work fell short, but AI agents will persevere en masse until the oceans are boiled, for better or for worse.
I guess im just sick of the fact we keep entertaining attention whoring of the most basic variety.