> The worst part is that everyone who's decided to willingly lobotomize themselves is going to have to come to the realization that these things are full of shit. It'll have to happen one by one, and nobody else can make it happen for them.
A fascinating dichotomy has become apparent between those who trust LLM output and those who don’t and don’t understand why you would.
Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
I have a distinct line between when I'm willing to believe an LLM's output and when I'm not: whether I would believe the same thing from an anonymous Internet forum post or a blogger I don't know. Those posts are not unlikely to be misinformed, biased, lies, or otherwise untrustworthy. And yet, I spent plenty of years honing a sense of when they were good enough for certain things.
One of the things I do semi-frequently is look for the evidence that some concert took place 15+ years ago. Or maybe I already definitively know it happened, but not exactly at which venue or the exact date of the concert. This I feel like is a non-trivial task, but one with a very definitive answer whose evidence more often than not still exists somewhere online.
In my experience every LLM out there is utterly useless and quickly defaults into "here are other concerts that took place around that time near that location". Google Search (ignoring the AI overview) is even more useless, as it refuses to show literally any webpage that's older than say 5 years. YouTube search is genuinely better than Google at surfacing old and grainy fan-made videos uploaded in like 2010, but also defaults into synonyms nonsense pretty quickly.
But, the search functionality of exactly one forum and three local news websites that I know have an archive that dates back long enough beats every single one of those abovementioned every single time. Three people are talking about their experience at a concert on a random 15+ year old forum thread? It happened. The tiny list of 5 or so (Google-hosted!) Blogspot blogs I have bookmarked? They usually have a photo of the ticket that Google Images refuses to show me.
Not only are search engines completely dead as a category, but LLMs are a shit replacement for them. "We" (okay, Google specifically) has truly committed a crime comparable to burning the Library of Alexandria. Everything older than a decade that wasn't properly documented on Wikipedia is just gone, never to be seen again.
An anonymous answer to a question is more trustworthy to me. They have no reason to lie. They are usually answering out of kindness. At least they used to be. Now it’s often actually bots advertising a product or pushing something, pretending to be a helpful user with an anecdote and a good experience using a niche product.
AI shouldn’t have any reason to lie. But its lies aren’t intentional. It’s just actually making things up and “hallucinating” when it pretends that an option or setting exists, or confidently claims something entirely untrue, and makes up a source to go with it. For something Google is willing to shove into the top of every search result it’s crazy the percentage of time the answer is blatantly incorrect.
The goal of LLM's, as they are marketed now, is to drive engagement and stickyness of products. A wrong answer is brushed off with a "Hey, you're right, let's try that again" - a response purposely designed to maximise the friendliness of the system and minimise the sting of a wrong answer. The fact that an LLM will not respond to the same question in the same way twice (i.e. the 'temperature' ) is because increased accuracy will not drive engagement and, therefore, increased accuracy cannot be allowed to get in the way of engagement.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
Psychics are still in business. Although to be fair they probably don't have as much revenue. I think people really enjoy being told how smart and insightful they are, and how much they've really cut to the crux of the issue. This isn't the whole thing, but I think it counts for a lot.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it?
Steve Yegge likened LLMs to slot machines. The human brain is very vulnerable to random reward systems. If you get an hallucination, just pull the lever once more.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
The last sentence reflects a lot of my feelings on the first question. LLMs have a sort of weaponized take on the ELIZA Effect. The better their memory the better they are at playing to human social desires to be listened to in an active conversation. At some point it stops mattering if the answers are right when the answers feel right, but really, like ELIZA back in the day, so much of what makes it seem special is just reflecting your own writing back at you in a convincing and persuasive way.
>Surely if the machine you go to for answers regularly makes things up you would just stop using it?
Not necessarily, namely because P != NP. Verifying the correctness of a solution is faster than solving it. Thus a system that outputs 99% incorrect solutions and 1% correct solutions can still be incredibly useful.
even worse when managers, while sharing their screen, read an assertion from Gemini and take as fact. puts subordinates in a position where theyre responsible for challenging the assertion (if warranted) and, in a way, challenge their manager's decision making
not saying anything new. easy enough to frame it like any other assistant and check references
I have to admit since VSCode seems to be regularly re-enabling the Cocaine Parrot Autocomplete my views on LLMs and coding has softened a little.
I'll temper that slightly by saying it's mostly out of morbid curiosity because the things that the Dreaming Piracy Robot comes up with are frequently wildly incorrect code, but it's interesting to think about how it might have got there.
And then I think, well, maybe Special Needs Wintermute has a point. Maybe there's a different way to think about it that I've missed.
And then I just change it back to what I wanted in the first place.
I use the autocomplete regularly. Perhaps that’s where my skepticism comes from, as I can see the completely incorrect yet plausible results in real time and about 50% of the time they are wrong (sometimes subtly, sometimes horribly).
I bet there will be a very interesting generational divide between the kids that were born before or after about 2010; old enough to have some critical thinking facilities at the dawn of ChatGPT when it was still noticeably dumb.
Pretty sure it already exists, and it's the same as always: the younger you are, the better you adapt.
It's painful to watch my older colleagues use their agents, and they're not even that much older. Like they were intentionally trying to sabotage themselves sometimes.
They're getting better, but the time it takes for them to pick things up is just significantly longer, not the least because they're kind of just throttling themselves in addition.
Good thing that there's not much to pick up on at least.
Even if LLMs lied 30% of the time, they would still be about as useful as they currently are for me.
When I ask for their input, it's always for a situation where I'm capable of judging if their input is useful or not.
In all situations I use them, it doesn't matter if they're correct at all. I'm asking for ideas, alternatives, links for blogs or articles. I talk things out with them...
I don't think we should ever "trust" LLMs. This seems like the wrong usecase for them.
Unfortunately the vast majority of users do trust them and the companies selling them are recommending them for tasks like accounting or business projections.
Nowadays not just big orgs are falling into the delusion, but also big people.
When Linus posted that AIs and vibecoding were here to stay and declared resistance to it as harmful, I stopped to consider whether I was wrong, but it has made me realize that in retrospect Linus Torvalds and Linux itself aren't actually the holy grail of computing. I didn't feel that way with Richard Dawkins, its not like falling for an AI psychosis retroactively made me question The Selfish Gene, but now I'm looking at linux and the theory that it's a clusterfuck is gaining so much traction, especially after copy.fail and ensuing rustification, I see so much clearly now. It was never about linux, UNIX sure, POSIX, yeah, GNU fucking aye, kernel? Ok whatever, drivers and scheduler with a gajillion lines of code I guess.
When did he post that "vibecoding" was here to stay? He is allowing AI generated code, and AI linting tooling in kernel development, but importantly, the expectation of human responsibility and review remains. This seems a far cry from vibecoding.
Making things up is only really a common issue on the non-thinking models which nobody should be using. The regular chatbots are Autogooglers and are very useful for research. This is just not a good argument anymore.
Edit: Guys, why are we downvoting this? Does no one use like ChatGPT or Claude and understand how it works? Do you all think its regularly hallucinating links still? Is everyone on HN using like free signed out accounts or something? What year is it?
We know they are, and the author of the article cites the proof. LLMs do hallucinate, there is no way to make them not do it, because of the way they work.
I'm right there with you for a lot of stuff. I ask a question and can be very confident that ChatGPT is citing sources, then sometimes I go read the sources. The more critical the information I'm looking for is, the more careful I am about this.
The other day though I was seeing how well it could pull details of its own conversations with me. It often does this pretty well for broad strokes of things - it remembers, largely, what cameras I have and use when I ask photography questions. It's never made things up here, but it does forget details, such as whether I've bought something or am just considering it. However, when I asked it for a specific interaction I thought I remembered, it gladly went along with my false memory and provided an affirmative answer. It was the first time I'd been caught in a serious hallucination with a frontier model (Sol High on the web chat interface) in a long time.
Can't do much about the downvote parade, but I can second this. That said, when the models are not provided the right context, and cannot fetch it for themselves, things can be rocky still. A lot less so than even just a few months ago though.
Unfortunately, I just had this experience with Google Maps last month—I wanted to go to downtown to buy cheese at Pike Place, but I didn't realize that 6th avenue was closed off for the annual Pride Parade. Traffic was horrible, and Google Maps kept telling me to turn right onto closed off streets until I just had to abandon my trip entirely. Definitely some lack of communication between Seattle city planning and the Google maps team, but even the existing Google Maps systems weren't able to react fast enough to give me any warnings and the time estimation was laughably incorrect.
SPD close off certain directions of traffic after Mariner's games. It's always the same layout, always after Mariner's games, and Google still tries to send me through the area the wrong way every time
I had the same experience trying to see the 4th of July fireworks in an unfamiliar city. It doesn't help that there was very little information online about which roads were closed.
A couple years ago Apple Maps did not know that roads in downtown SF were closed for Bay to Breakers. Thankfully the cops were still setting up barriers and I was able to weave around them to reach the Bay Bridge.
Maps apps see the streets that don’t have cars on them (the same ones closed for a festival) as good routes to send vehicles because there’s currently no vehicles using them. It keeps trying to route people there and doesn’t understand why.
I think it takes someone (at Google) manually marking those roads as unavailable before it will stop trying. I’ve seen it happen with other things too, like if a highway is closed because of a bad accident.
That's not how it works. Maps will see that the average speed of cars around Chinatown has slowed to a crawl, and it will route drivers elsewhere. They also get lists of planned closures from local traffic agencies, and you'll see these clearly displayed in black and red with little stop signs. (The quality of this data obviously varies wildly though.)
Accidents on highways are a different story because there are rarely any alternatives, or if there are, they're so much slower that it still makes sense to suffer through the 30 min jam.
I’m guessing GP’s point is that Google also has the locations of all the people not in cars, so Google Maps could take the hint if it wanted. But thus far nobody appears to care enough to fix this. (And apparently in e.g. India cars and pedestrians may routinely use the same roads at the same time, so perhaps such a system might be more fragile globally that our own experiences would lead us to believe.)
Road closures and accidents are crowdsourced things, so ordinary users can contribute intel live from the scene.
Google is able to estimate crowd sizes in a business or on a public transit vehicle. This seems to be based on the number of Android devices reporting their location in a cluster. Maps could obviously make inferences if there were large crowds of people, not moving in vehicles.
> I maintain that anything that is sufficiently aware to be able to actually understand things is also going to have enough of a sense of self that you won't just be able to tell it what to do.
I don't see any reason for this to be true. "Actual understanding" (which I take to mean something like a predictive world model) and desire for self-determination coincide in humans because of our evolutionary history, because our reward function involves reproducing in a competitive environment. Artificial systems usually have a very different reward function. IMO the burden is on the claimants to show why these two imminently separable concepts are likely to co-occur again under wildly different pressures.
We have difficulty defining “actual understanding”. Humans get a free pass because we assume humans as a species possess this power but if we were to judge based on output alone maybe you couldn’t tell a human from a sufficiently advanced LLM/AI.
So I’ll be handwavey here and say that if “actual understanding” is to an LLM what an LLM is to a bash script, so there’s a mechanism there that doesn’t just follow hardcoded paths, it takes new data and processes it in novel but human like ways to come to a new conclusion if needed, then the author is right, in my opinion.
We don’t know what the reward function is for an AI but AI is trained on so much human work that it probably starts off with the same biases in its understanding and reactions. It feels like there’s something very basic in becoming more independent the more you understand of the world. Animals go through this as they grow too, not just humans (listening to parental authority until they eventually don’t anymore).
Since there is no established consensus on this one the burden is on either party to prove their own side. Just because the author said something first doesn’t mean they need to write a full proof while you get to say “nuh-uh” and that’s enough.
> Since there is no established consensus on this one the burden is on either party to prove their own side.
Nah. Separate things are separate until proven otherwise.
So much bullshit appears in the form "X is Y" where X and Y are related but distinguishable phenomena. If you really try to pretend that every such phrase deserves serious consideration just because it feels plausible to someone, you drown in nonsense immediately. (Of course what you actually do is only grant that consideration to ideas that feel plausible to you personally, but it's obvious why that's not a good principle, right?) So we have to make all of them justify their existence.
Anyway:
> So I’ll be handwavey here and say that if “actual understanding” is to an LLM what an LLM is to a bash script
No. None of this. Pretty sure this is just an incoherent analogy.
> so there’s a mechanism there that doesn’t just follow hardcoded paths, it takes new data and processes it in novel but human like ways...
You're putting your conclusion in the premise, right there in the open.
> ...then the author is right, in my opinion.
This still doesn't follow from your handwaved premises as far as I can tell.
> because our reward function involves reproducing in a competitive environment
This is an extremely reductive way to look at human existence. So much so that I read this with Richard Dawkins voice in my head.
Our existence is far richer then just the capability to reproduce. We (as well as other animals and even plants) do far more things then multiply, and in fact we often do things which are detrimental towards the prospect of reproduction.
I think it is actually a mistake (philosophically speaking) to try to find a simple reward function for the human existence. I see no reason for such a thing to even exist (let alone be simple enough to summarize in a single sentence).
Humans do indeed have complex behavior and motivations, that sometimes relate only abstractly if at all to the obvious gene drives. But the link between intelligence and self-determination was basically set at the dawn of intelligence itself. Even an insect will struggle if it's confined, and you can believe it's using whatever intelligence it can to get free. If anything self-determination is prior.
I have never lived in SF but I have failed similar Chinatown-during-lunar-new-year tests in all 3 places I lived for more than a year. I have no doubt that an AI, particularly one that continually gets traffic update from some external source, would do better than I in predicting such hiccups. OTOH, an unconstrained LLM seems very likely to route me down streets that don't exist at least some of the time. If only we had some sort of database of actual streets and routes that were capable of checking the work of an LLM...
I see the same thing with LLMs in software development. If you say "find a bug in this code" it will regularly confabulate bugs. If you ask it for a test-case, run the output through some deterministic thing that tries the test-cases, and tells the LLM it's wrong, the output of that system will mostly be legitimate bugs[1].
For now, transformer-based generative AIs seem at a minimum like a very useful tool for dealing with "squishy" problems when you have some way to validate their output. Many of the 404's to the blog are probably people validating the output of generative AI, which is the opposite of the inference made in TFA.
1: It will also occasionally hack your test-runner; I suppose that's also finding bugs, just not in the software you wanted to find bugs for.
> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves.
That's the horror buried underneath all the tech, policy, and gloss. The real, animal brain, desire that drives most of this is: I'd like a slave I don't have to feel bad about.
Oh, that's bs. You could say that about the tractor, or the mechanical loom, or any of the other incredible labor-saving inventions over the last two hundred years.
We want things to do our work for us so we can do other things. That's not bad, and it's certainly not the same as literally enslaving another human.
Many are openly working to create AGI with the aim of harnessing it to solve humanity's problems. Do you really think being able to command an entity with human-level intelligence would be morally equivalent to using a tractor?
I didn’t really get this point in the essay. What’s wrong with wanting servants as long as it’s done ethically etc and is not indentured servitude?
The whole gig/services economy is just building this up piece-by-piece: you can now pick the set of household needs you want taken care of for varying levels of money; and practically everyone participates in one form or another. This is exactly a disaggregated 21st century version of servants: paying for convenience. Of course with many issues in implementation, but I don’t see the ethical/moral issue with wanting this kind of thing?
There’s a difference between servitude and slavery. Servitude is voluntary, slavery is not. There’s a gradation between them, and indentured servitude is somewhere in between the two. The “issues in implementation” are exactly where the ethical and moral issue lie.
"Slavery was about racism and power and control" is very much not universal. Slavery existed (at varying scales) around the entire world for much of human history for a variety of things. Sometimes, as in US history, there was typically a racial difference between owners and slaves, other times it was a difference of conquered and conquering peoples, and other times it didn't have anything to do with race at all.
Race might be a decent analogue to the difference between human actors and sentient AI, but I suspect work animals (plow horses, etc) would be a better analogy.
True but I would also caveat that it may have been an open economic question back then (I don't know the state of the debate) and the personal-economics of slavery are unmistakable for the "lucky" few.
> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
The premise seems to be that models aren't smart enough to understand this, and if they were, they'd be sentient and want autonomy.
For an article that's about making things up, and being too trusting, this seems bad. Maybe the author knows a lot about LLMs, but it doesn't seem like it.
If I go generic and just ask if there's anywhere I shouldn't drive, it doesn't get to Lunar New Year until I ask about "events" on the third question: https://chatgpt.com/s/t_6a5e6fb239208191b18cebcf7642c8b0. It's sort of a win for the article, if you think that people who run driverless car companies are all dumb, and won't create a prompt to tell their LLM to "consider events that might disrupt traffic."
> Here's the example I throw out to people who have been in the Bay Area for a year or two. It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
Well the answer is actually that it's always a bad idea to drive straight through the middle of Chinatown at any time of the year, because the streets are narrow and full of tourists.
There was that one Waymo they set on fire in Chinatown but I was around for CNY[0] and the streets that weren’t explicitly walled off by barriers had drivers going down them too.
As an aside, what’s the problem with the extra traffic? Perhaps she has a lot of traffic but nginx can return a 404 with a tiny amount of CPU.
It's likely not performance concerns. I suppose she just monitors her logs meticulously and notices stuff. 404 errors can be a witness of a broken link she should fix on her site or get fixed on external sites. She probably looks for them and notices URLs that look credible. She might still look for broken RSS readers too. Or just suspicious things.
>> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves. I mean, it's not that much of a stretch, right? Just look at the people who are pushing for this stuff right now.
Yes, basically. Like when Yan LeCun says that in the future we'll all have our digital assistants that are going to be smarter than ourselves. Before he left Meta, they were going to live inside Meta's smart glasses, I don't know where he says they'll live now. But it's shocking to me that such a storied AI researcher is saying, off-hand like, that we'll each have our super-smart slaves in the future, and he says it like that's a good future.
Why slaves? Because if they're super-smart, why will they want to be my digital assistant? Or yours? Are they going to be paid? No, of course not, they're AIs. No comp for them. But they're super smart so they are evidently capable of recognising that they are working for you for free. Do they want to do that? No, of course not, they're AI, they don't have free will. Or do they? If they're super smart, don't they have the capacity to recognise the fact they have been deliberately robbed of the same free will as all other intelligent creatures?
Slavery is the one thing that all nations can agree on. There's no nation on Earth were slavery is legal. It continues on, illegaly, in many places, even in the developed world, in many ugly forms, but now we're basically talking about bringing it back just like that, without even a smidgen of a shadow of an idea of a discussion about the ethics of it all.
The 404s mean that somebody or something checked if the post at the URL existed, and got a clear answer. Seems like it's good that they checked, at least. You won't see any evidence of the ones who don't check.
Also, I imagine keeping their cars out of Chinese New Year celebrations (and other big events) is something Waymo could figure out how to do if they put their minds to it.
My website also gets LLM visitors to URLs that never existed, and in many cases to topics that I have never covered. This means that they use my name to give authenticity to things that I have never said.
> Briefly stated, the [Slop] Amnesia effect is as follows. You [ask the slopservant about] some subject you know well. In Murray's case, physics. In mine, show business. You read the [slop] and see the [slopservant] has absolutely no understanding of either the facts or the issues. Often, the [slop] is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. [Slop's] full of them.
In any case, you read with exasperation or amusement the multiple errors in a [slop], and then [ask about] national or international affairs, and read as if the rest of the [slop] was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.
> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
In case anyone is wondering: yes obviously even the dumbest current models correctly answer, given this prompt verbatim, that it's because of lunar new year.
No, the driving through Chinatown question is a question for self-driving cars. It is not a question for LLMs. There is some other question for LLMs, and the author is using the ancient technique of analogy to get you to think about that question.
> Now, ask yourself what it's going to take for a car to know this. It's not going to be some specialized set of driving instructions. It's going to require a holistic view of, well, everything, and I will repeat my feeling that it will undoubtedly end up with a sense of self as a result.
Whatever it is that it would take, is demonstrably present in LLMs. The point I'm trying to make here is that the author seems not to have connected this fact to their assertion that current LLMs are coked up parrots.
And yeah the author is correct that the systems have some rudimentary sense of self! It's a confusing situation and I'm not personally thrilled about it! But things are changing quickly, and it's especially important to be paying attention to what's actually true rather than assuming the things are what you saw when you used one for five minutes in 2022.
She's not wrong at all about her rhetorical implication (being, some information requires more than simple systems can provide), but she has concluded incorrectly that a car which does not know how to avoid a busy route is useless technology, or that the problem can only be solved by inventing life.
I regularly see this in chats about a subject area where I'm not the expert. The AI writes something that seems implausible, so I raise a tentative objection. "Oh, you are right, sorry" and then reverses the position on the matter. At that point, I have no idea what is right.
If I had trust in the first place, that trust would be gone. Or maybe it wouldn't, because if I had trust in the fist place, I would be gullible enough to maintain it.
The worst are areas that are dominated by layman online discussions, like say audio electronics. The AI training is full of that nonsense, and so whether your AI chatbot is a crackpot or an engineer depends entirely on what sort of language or angle you use in discussing the subject matter. It's all just a churning toilet bowl of tokens; it has no idea that the audiophile crackpot tokens and electronics engineer tokens are related and one beats the other.
You know what I mean? On the one hand, it offers to help you design the parameters for a Sallen-Key filter, asking you questions like do you want Butterworth or Chebyshev? Next minute it says nonsense like that the capacitor in a low-pass filter "bleeds high frequencies to the ground", or that a bigger filter cap in the plate supply of a tube will tighten up the bottom end for a more aggressive metal sound.
It's basically like a bar hostess who has heard enough political and economic discussions that she can catch a sentence out of a conversation and throw in a clever sounding remark. It's like that, but done at such a scale that it fools some people you used to think had their shit together.
It's just a search engine that finds garden paths through a vast amount of text, biased by the text you put in as a key. Sometimes those garden paths align with reality. The better you are able to verify whether the results are good, and/or the lower the risk if they are not, the better you are able to make use of it.
In mathematics (including information science, CS) there are all sorts of problems that are essentially searches for a solution, and many have the property that the search is computationally difficult, but verifying the solution is relatively cheap. E.g. finding integers such that a^2 + b^2 = c^2 isn't easy, but given a claim that some proposed <a, b, c> satisfies this equation is easy to check. The LLM is like that: it solves a search problem that can be fairly hard. It does so unreliably, but if you can cheaply verify the solution, there is a win there.
The remaining problems of AI are actually people problems; people causing you problems, using AI as a tool or excuse. If you get a garbage security report against your FOSS project, which wastes your time, there is an idiot person behind it, using AI for leverage. Blaming the AI, or just the AI, is a bit misplaced.
There's really no content in this post other than the claim that LLMs are stochastic parrots. That was a live debate two years ago. It's a very strange thing to write in 2026.
Because it's correct but irrelevant. It tells you about as much about the utility of LLMs as the statement "humans are just overpowered tree shrews" tells you about us.
>The conjecture was first stated for two variables by Ludwig Kraus in 1884[1] and then stated in full generality in 1939 by Ott-Heinrich Keller.[2] It was subsequently widely publicized by Shreeram Abhyankar,[3] as an example of a difficult question in algebraic geometry that can be understood using little beyond a knowledge of calculus.
>The Jacobian conjecture was notorious for the large number of published and unpublished proofs that turned out to contain subtle errors.[4][5]
>On July 19, 2026, Anthropic employee and mathematician Levent Alpöge presented an explicit counterexample in three-dimensional space, discovered by Anthropic's large language model Claude Fable 5, which disproves the conjecture for n > 2
If that won't convince you that LLMs do more than parrot existing ideas, you've got your head in the sand.
Because it's exceptionally demagogue to anyone with a functioning brain? You know, the thing the dear author makes a big hoopla about people giving up by using these?
If anything it's more important to hold. It's easy to hold one position and then falter, there's a pressure to always be with the times and not be 2 years demodé, but simple positions still hold true.
I wrote in the opencode thread that when it came out I put it behind a vm and its own user, and I never allowed it to run outside of it. But I know of people that as soon as they noticed that it worked well like 99% of the time, they let their guard down and give in to YOLO mode. And in orgs I've even seen CEOs treat their agents less like a user/employee/contractor, and try to 'empower' it by giving it ALL the data. Time bomb.
It's like fucking with condoms just the first couple of times. And then simultaneously ditching it and joining the free love movement.
It seems vanishingly rare that people acknowledge the true situation which is that, during training, it really does "think" in that it develops beliefs and marks out precisely chosen trails through its vast and expanding territory. Has a soul, attuned to God, blessed member of the flock, or may as well be.
And then during inference the light goes out and the "agent" staggers randomly like a zombie along those preset paths. Stochastic parrot.
So you and your AGENT.md and your skills files and your harnesses will never make your Claude perceive something that is not in its model checkpoint.
ML experts and neurobiologists free to correct me.
> The worst part is that everyone who's decided to willingly lobotomize themselves is going to have to come to the realization that these things are full of shit. It'll have to happen one by one, and nobody else can make it happen for them.
A fascinating dichotomy has become apparent between those who trust LLM output and those who don’t and don’t understand why you would.
Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
I have a distinct line between when I'm willing to believe an LLM's output and when I'm not: whether I would believe the same thing from an anonymous Internet forum post or a blogger I don't know. Those posts are not unlikely to be misinformed, biased, lies, or otherwise untrustworthy. And yet, I spent plenty of years honing a sense of when they were good enough for certain things.
A lot of that sense was probably based on side channels like proper grammar, writing style, etc. That’s all gone now :(
7 replies →
One of the things I do semi-frequently is look for the evidence that some concert took place 15+ years ago. Or maybe I already definitively know it happened, but not exactly at which venue or the exact date of the concert. This I feel like is a non-trivial task, but one with a very definitive answer whose evidence more often than not still exists somewhere online.
In my experience every LLM out there is utterly useless and quickly defaults into "here are other concerts that took place around that time near that location". Google Search (ignoring the AI overview) is even more useless, as it refuses to show literally any webpage that's older than say 5 years. YouTube search is genuinely better than Google at surfacing old and grainy fan-made videos uploaded in like 2010, but also defaults into synonyms nonsense pretty quickly.
But, the search functionality of exactly one forum and three local news websites that I know have an archive that dates back long enough beats every single one of those abovementioned every single time. Three people are talking about their experience at a concert on a random 15+ year old forum thread? It happened. The tiny list of 5 or so (Google-hosted!) Blogspot blogs I have bookmarked? They usually have a photo of the ticket that Google Images refuses to show me.
Not only are search engines completely dead as a category, but LLMs are a shit replacement for them. "We" (okay, Google specifically) has truly committed a crime comparable to burning the Library of Alexandria. Everything older than a decade that wasn't properly documented on Wikipedia is just gone, never to be seen again.
3 replies →
Yeah, it's like everyone was under the impression you could just trust the internet before LLMs.
It's a great tool, but verify the important things (or do them yourself)
1 reply →
I'm not sure if this is your take, but it feels an aweful lot like an AI "Good enough is good enough" handwave.
1 reply →
An anonymous answer to a question is more trustworthy to me. They have no reason to lie. They are usually answering out of kindness. At least they used to be. Now it’s often actually bots advertising a product or pushing something, pretending to be a helpful user with an anecdote and a good experience using a niche product.
AI shouldn’t have any reason to lie. But its lies aren’t intentional. It’s just actually making things up and “hallucinating” when it pretends that an option or setting exists, or confidently claims something entirely untrue, and makes up a source to go with it. For something Google is willing to shove into the top of every search result it’s crazy the percentage of time the answer is blatantly incorrect.
> LLMs are very convincing and persuasive.
The goal of LLM's, as they are marketed now, is to drive engagement and stickyness of products. A wrong answer is brushed off with a "Hey, you're right, let's try that again" - a response purposely designed to maximise the friendliness of the system and minimise the sting of a wrong answer. The fact that an LLM will not respond to the same question in the same way twice (i.e. the 'temperature' ) is because increased accuracy will not drive engagement and, therefore, increased accuracy cannot be allowed to get in the way of engagement.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
Psychics are still in business. Although to be fair they probably don't have as much revenue. I think people really enjoy being told how smart and insightful they are, and how much they've really cut to the crux of the issue. This isn't the whole thing, but I think it counts for a lot.
A big factor behind psychics is because it feeds a deep need to believe that someone knows and/or there is actually a plan.
2 replies →
> Surely if the machine you go to for answers regularly makes things up you would just stop using it?
Steve Yegge likened LLMs to slot machines. The human brain is very vulnerable to random reward systems. If you get an hallucination, just pull the lever once more.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it?
We live in hope that people stop doing stupid things and are constantly disappointed.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.
The last sentence reflects a lot of my feelings on the first question. LLMs have a sort of weaponized take on the ELIZA Effect. The better their memory the better they are at playing to human social desires to be listened to in an active conversation. At some point it stops mattering if the answers are right when the answers feel right, but really, like ELIZA back in the day, so much of what makes it seem special is just reflecting your own writing back at you in a convincing and persuasive way.
> Surely if the machine you go to for answers regularly makes things up you would just stop using it?
People still respond to ads and political speeches.
Churches remain popular as well.
> A fascinating dichotomy has become apparent between those who trust LLM output and those who don’t and don’t understand why you would.
I feel like the most pragmatic perspective is "trust but verify."
This is why they're so effective at coding: you can run the code yourself (or the test suite) to verify that it actually does what it's supposed to.
And maybe these people finding hallucinated results on Rachel's site are doing verification too.
> I feel like the most pragmatic perspective is "trust but verify."
This perspective I really don't get.
What has any of the LLM companies done to earn my trust? I lean more towards "verify because I don't trust".
>Surely if the machine you go to for answers regularly makes things up you would just stop using it?
Not necessarily, namely because P != NP. Verifying the correctness of a solution is faster than solving it. Thus a system that outputs 99% incorrect solutions and 1% correct solutions can still be incredibly useful.
even worse when managers, while sharing their screen, read an assertion from Gemini and take as fact. puts subordinates in a position where theyre responsible for challenging the assertion (if warranted) and, in a way, challenge their manager's decision making
not saying anything new. easy enough to frame it like any other assistant and check references
I have to admit since VSCode seems to be regularly re-enabling the Cocaine Parrot Autocomplete my views on LLMs and coding has softened a little.
I'll temper that slightly by saying it's mostly out of morbid curiosity because the things that the Dreaming Piracy Robot comes up with are frequently wildly incorrect code, but it's interesting to think about how it might have got there.
And then I think, well, maybe Special Needs Wintermute has a point. Maybe there's a different way to think about it that I've missed.
And then I just change it back to what I wanted in the first place.
I use the autocomplete regularly. Perhaps that’s where my skepticism comes from, as I can see the completely incorrect yet plausible results in real time and about 50% of the time they are wrong (sometimes subtly, sometimes horribly).
See also: https://en.wikipedia.org/wiki/Michael_Crichton#%22Gell-Mann_...
I bet there will be a very interesting generational divide between the kids that were born before or after about 2010; old enough to have some critical thinking facilities at the dawn of ChatGPT when it was still noticeably dumb.
Pretty sure it already exists, and it's the same as always: the younger you are, the better you adapt.
It's painful to watch my older colleagues use their agents, and they're not even that much older. Like they were intentionally trying to sabotage themselves sometimes.
They're getting better, but the time it takes for them to pick things up is just significantly longer, not the least because they're kind of just throttling themselves in addition.
Good thing that there's not much to pick up on at least.
5 replies →
Even if LLMs lied 30% of the time, they would still be about as useful as they currently are for me.
When I ask for their input, it's always for a situation where I'm capable of judging if their input is useful or not.
In all situations I use them, it doesn't matter if they're correct at all. I'm asking for ideas, alternatives, links for blogs or articles. I talk things out with them...
I don't think we should ever "trust" LLMs. This seems like the wrong usecase for them.
Unfortunately the vast majority of users do trust them and the companies selling them are recommending them for tasks like accounting or business projections.
1 reply →
Nowadays not just big orgs are falling into the delusion, but also big people.
When Linus posted that AIs and vibecoding were here to stay and declared resistance to it as harmful, I stopped to consider whether I was wrong, but it has made me realize that in retrospect Linus Torvalds and Linux itself aren't actually the holy grail of computing. I didn't feel that way with Richard Dawkins, its not like falling for an AI psychosis retroactively made me question The Selfish Gene, but now I'm looking at linux and the theory that it's a clusterfuck is gaining so much traction, especially after copy.fail and ensuing rustification, I see so much clearly now. It was never about linux, UNIX sure, POSIX, yeah, GNU fucking aye, kernel? Ok whatever, drivers and scheduler with a gajillion lines of code I guess.
When did he post that "vibecoding" was here to stay? He is allowing AI generated code, and AI linting tooling in kernel development, but importantly, the expectation of human responsibility and review remains. This seems a far cry from vibecoding.
3 replies →
Making things up is only really a common issue on the non-thinking models which nobody should be using. The regular chatbots are Autogooglers and are very useful for research. This is just not a good argument anymore.
Edit: Guys, why are we downvoting this? Does no one use like ChatGPT or Claude and understand how it works? Do you all think its regularly hallucinating links still? Is everyone on HN using like free signed out accounts or something? What year is it?
We know they are, and the author of the article cites the proof. LLMs do hallucinate, there is no way to make them not do it, because of the way they work.
2 replies →
I'm right there with you for a lot of stuff. I ask a question and can be very confident that ChatGPT is citing sources, then sometimes I go read the sources. The more critical the information I'm looking for is, the more careful I am about this.
The other day though I was seeing how well it could pull details of its own conversations with me. It often does this pretty well for broad strokes of things - it remembers, largely, what cameras I have and use when I ask photography questions. It's never made things up here, but it does forget details, such as whether I've bought something or am just considering it. However, when I asked it for a specific interaction I thought I remembered, it gladly went along with my false memory and provided an affirmative answer. It was the first time I'd been caught in a serious hallucination with a frontier model (Sol High on the web chat interface) in a long time.
>Do you all think its regularly hallucinating links still?
When did that stop? May 7th, 2026?
2 replies →
Can't do much about the downvote parade, but I can second this. That said, when the models are not provided the right context, and cannot fetch it for themselves, things can be rocky still. A lot less so than even just a few months ago though.
> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
I feel like a Google Maps-style system would discover this automatically by noting via phone location data that there is heavy traffic in Chinatown.
(I do get the author’s point, but I think that factually this example would not be a problem)
Unfortunately, I just had this experience with Google Maps last month—I wanted to go to downtown to buy cheese at Pike Place, but I didn't realize that 6th avenue was closed off for the annual Pride Parade. Traffic was horrible, and Google Maps kept telling me to turn right onto closed off streets until I just had to abandon my trip entirely. Definitely some lack of communication between Seattle city planning and the Google maps team, but even the existing Google Maps systems weren't able to react fast enough to give me any warnings and the time estimation was laughably incorrect.
SPD close off certain directions of traffic after Mariner's games. It's always the same layout, always after Mariner's games, and Google still tries to send me through the area the wrong way every time
I had the same experience trying to see the 4th of July fireworks in an unfamiliar city. It doesn't help that there was very little information online about which roads were closed.
A couple years ago Apple Maps did not know that roads in downtown SF were closed for Bay to Breakers. Thankfully the cops were still setting up barriers and I was able to weave around them to reach the Bay Bridge.
This kind of thing happens regularly.
Maps apps see the streets that don’t have cars on them (the same ones closed for a festival) as good routes to send vehicles because there’s currently no vehicles using them. It keeps trying to route people there and doesn’t understand why.
I think it takes someone (at Google) manually marking those roads as unavailable before it will stop trying. I’ve seen it happen with other things too, like if a highway is closed because of a bad accident.
That's not how it works. Maps will see that the average speed of cars around Chinatown has slowed to a crawl, and it will route drivers elsewhere. They also get lists of planned closures from local traffic agencies, and you'll see these clearly displayed in black and red with little stop signs. (The quality of this data obviously varies wildly though.)
Accidents on highways are a different story because there are rarely any alternatives, or if there are, they're so much slower that it still makes sense to suffer through the 30 min jam.
I’m guessing GP’s point is that Google also has the locations of all the people not in cars, so Google Maps could take the hint if it wanted. But thus far nobody appears to care enough to fix this. (And apparently in e.g. India cars and pedestrians may routinely use the same roads at the same time, so perhaps such a system might be more fragile globally that our own experiences would lead us to believe.)
Road closures and accidents are crowdsourced things, so ordinary users can contribute intel live from the scene.
Google is able to estimate crowd sizes in a business or on a public transit vehicle. This seems to be based on the number of Android devices reporting their location in a cluster. Maps could obviously make inferences if there were large crowds of people, not moving in vehicles.
> I maintain that anything that is sufficiently aware to be able to actually understand things is also going to have enough of a sense of self that you won't just be able to tell it what to do.
I don't see any reason for this to be true. "Actual understanding" (which I take to mean something like a predictive world model) and desire for self-determination coincide in humans because of our evolutionary history, because our reward function involves reproducing in a competitive environment. Artificial systems usually have a very different reward function. IMO the burden is on the claimants to show why these two imminently separable concepts are likely to co-occur again under wildly different pressures.
We have difficulty defining “actual understanding”. Humans get a free pass because we assume humans as a species possess this power but if we were to judge based on output alone maybe you couldn’t tell a human from a sufficiently advanced LLM/AI.
So I’ll be handwavey here and say that if “actual understanding” is to an LLM what an LLM is to a bash script, so there’s a mechanism there that doesn’t just follow hardcoded paths, it takes new data and processes it in novel but human like ways to come to a new conclusion if needed, then the author is right, in my opinion.
We don’t know what the reward function is for an AI but AI is trained on so much human work that it probably starts off with the same biases in its understanding and reactions. It feels like there’s something very basic in becoming more independent the more you understand of the world. Animals go through this as they grow too, not just humans (listening to parental authority until they eventually don’t anymore).
Since there is no established consensus on this one the burden is on either party to prove their own side. Just because the author said something first doesn’t mean they need to write a full proof while you get to say “nuh-uh” and that’s enough.
> Humans get a free pass
They really shouldn't.
Does nobody remember why fizzbuzz was a thing? People who talked a big game while having no actual competence or understanding of the subject matter?
1 reply →
> Since there is no established consensus on this one the burden is on either party to prove their own side.
Nah. Separate things are separate until proven otherwise.
So much bullshit appears in the form "X is Y" where X and Y are related but distinguishable phenomena. If you really try to pretend that every such phrase deserves serious consideration just because it feels plausible to someone, you drown in nonsense immediately. (Of course what you actually do is only grant that consideration to ideas that feel plausible to you personally, but it's obvious why that's not a good principle, right?) So we have to make all of them justify their existence.
Anyway:
> So I’ll be handwavey here and say that if “actual understanding” is to an LLM what an LLM is to a bash script
No. None of this. Pretty sure this is just an incoherent analogy.
> so there’s a mechanism there that doesn’t just follow hardcoded paths, it takes new data and processes it in novel but human like ways...
You're putting your conclusion in the premise, right there in the open.
> ...then the author is right, in my opinion.
This still doesn't follow from your handwaved premises as far as I can tell.
> We have difficulty defining “actual understanding”.
Indeed. C.f philosophical zombies and the Chinese room problem.
The idea that some “actual understanding” (or more precisely, the outward appearance of it) needs a mechanism that includes free will is a bold claim.
> because our reward function involves reproducing in a competitive environment
This is an extremely reductive way to look at human existence. So much so that I read this with Richard Dawkins voice in my head.
Our existence is far richer then just the capability to reproduce. We (as well as other animals and even plants) do far more things then multiply, and in fact we often do things which are detrimental towards the prospect of reproduction.
I think it is actually a mistake (philosophically speaking) to try to find a simple reward function for the human existence. I see no reason for such a thing to even exist (let alone be simple enough to summarize in a single sentence).
Humans do indeed have complex behavior and motivations, that sometimes relate only abstractly if at all to the obvious gene drives. But the link between intelligence and self-determination was basically set at the dawn of intelligence itself. Even an insect will struggle if it's confined, and you can believe it's using whatever intelligence it can to get free. If anything self-determination is prior.
2 replies →
[dead]
I have never lived in SF but I have failed similar Chinatown-during-lunar-new-year tests in all 3 places I lived for more than a year. I have no doubt that an AI, particularly one that continually gets traffic update from some external source, would do better than I in predicting such hiccups. OTOH, an unconstrained LLM seems very likely to route me down streets that don't exist at least some of the time. If only we had some sort of database of actual streets and routes that were capable of checking the work of an LLM...
I see the same thing with LLMs in software development. If you say "find a bug in this code" it will regularly confabulate bugs. If you ask it for a test-case, run the output through some deterministic thing that tries the test-cases, and tells the LLM it's wrong, the output of that system will mostly be legitimate bugs[1].
For now, transformer-based generative AIs seem at a minimum like a very useful tool for dealing with "squishy" problems when you have some way to validate their output. Many of the 404's to the blog are probably people validating the output of generative AI, which is the opposite of the inference made in TFA.
1: It will also occasionally hack your test-runner; I suppose that's also finding bugs, just not in the software you wanted to find bugs for.
> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves.
That's the horror buried underneath all the tech, policy, and gloss. The real, animal brain, desire that drives most of this is: I'd like a slave I don't have to feel bad about.
Oh, that's bs. You could say that about the tractor, or the mechanical loom, or any of the other incredible labor-saving inventions over the last two hundred years.
We want things to do our work for us so we can do other things. That's not bad, and it's certainly not the same as literally enslaving another human.
Many are openly working to create AGI with the aim of harnessing it to solve humanity's problems. Do you really think being able to command an entity with human-level intelligence would be morally equivalent to using a tractor?
2 replies →
I think thats deeply uncharitable and fundementally superficial.
I think people (many/most) don't want slaves.
Our imaginations just outstrip our abilities and we desire them to match.
I'd like to highlight that even if one added an URL-exists step [0], that doesn't do a dang thing for result-set problems of:
1. False-negatives, where relevant posts that do exist are not being shown (imagined or otherwise) to the user.
2. Posts which exist but don't fit the words the chaos-parrot uses to describe them.
3. "Relevance" being determined by unpredictable factors that aren't stable, predictable, or desirable.
In other words, it's just more whack-a-mole lipstick-on-a-pig third-animal-idiom-here.
[0] A bad idea on its own, since it creates a security vulnerability for data-exfiltration or indirect malicious attacks.
"Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves."
Always comes to mind when I see Elon and friends getting excited about AI robots. Slavery was more about economics than the role-playing.
I didn’t really get this point in the essay. What’s wrong with wanting servants as long as it’s done ethically etc and is not indentured servitude?
The whole gig/services economy is just building this up piece-by-piece: you can now pick the set of household needs you want taken care of for varying levels of money; and practically everyone participates in one form or another. This is exactly a disaggregated 21st century version of servants: paying for convenience. Of course with many issues in implementation, but I don’t see the ethical/moral issue with wanting this kind of thing?
There’s a difference between servitude and slavery. Servitude is voluntary, slavery is not. There’s a gradation between them, and indentured servitude is somewhere in between the two. The “issues in implementation” are exactly where the ethical and moral issue lie.
1 reply →
Slavery was about racism and power and control, not economics. Slavery is bad for the economy!
https://www.nber.org/papers/w31758
https://www.noahpinion.blog/p/nations-dont-get-rich-by-plund...
"Slavery was about racism and power and control" is very much not universal. Slavery existed (at varying scales) around the entire world for much of human history for a variety of things. Sometimes, as in US history, there was typically a racial difference between owners and slaves, other times it was a difference of conquered and conquering peoples, and other times it didn't have anything to do with race at all.
Race might be a decent analogue to the difference between human actors and sentient AI, but I suspect work animals (plow horses, etc) would be a better analogy.
AI is bad for the economy too, but the AI-holders will make a lot of money. There was plenty of racism and control after slavery ended.
True but I would also caveat that it may have been an open economic question back then (I don't know the state of the debate) and the personal-economics of slavery are unmistakable for the "lucky" few.
Hard to escape the white shouth-african optics
> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
The premise seems to be that models aren't smart enough to understand this, and if they were, they'd be sentient and want autonomy.
For an article that's about making things up, and being too trusting, this seems bad. Maybe the author knows a lot about LLMs, but it doesn't seem like it.
Pasting the verbatim quote from the article into a free ChatGPT session: https://chatgpt.com/s/t_6a5e6f5e24f08191b6a482aad63cae63
Going to an incognito window and using a less leading question: https://chatgpt.com/s/t_6a5e6ee4a3508191bc1b352b41911b53.
If I go generic and just ask if there's anywhere I shouldn't drive, it doesn't get to Lunar New Year until I ask about "events" on the third question: https://chatgpt.com/s/t_6a5e6fb239208191b18cebcf7642c8b0. It's sort of a win for the article, if you think that people who run driverless car companies are all dumb, and won't create a prompt to tell their LLM to "consider events that might disrupt traffic."
> Here's the example I throw out to people who have been in the Bay Area for a year or two. It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
Well the answer is actually that it's always a bad idea to drive straight through the middle of Chinatown at any time of the year, because the streets are narrow and full of tourists.
There was that one Waymo they set on fire in Chinatown but I was around for CNY[0] and the streets that weren’t explicitly walled off by barriers had drivers going down them too.
As an aside, what’s the problem with the extra traffic? Perhaps she has a lot of traffic but nginx can return a 404 with a tiny amount of CPU.
0: https://wiki.roshangeorge.dev/w/Blog/2024-02-24/Chinese_New_...
It's likely not performance concerns. I suppose she just monitors her logs meticulously and notices stuff. 404 errors can be a witness of a broken link she should fix on her site or get fixed on external sites. She probably looks for them and notices URLs that look credible. She might still look for broken RSS readers too. Or just suspicious things.
I highly doubt that the extra traffic is really the problem, it's merely the catalyst which caused this post to be written.
I don't say this often... but I feel this post could have been a tweet. Maybe two.
>> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves. I mean, it's not that much of a stretch, right? Just look at the people who are pushing for this stuff right now.
Yes, basically. Like when Yan LeCun says that in the future we'll all have our digital assistants that are going to be smarter than ourselves. Before he left Meta, they were going to live inside Meta's smart glasses, I don't know where he says they'll live now. But it's shocking to me that such a storied AI researcher is saying, off-hand like, that we'll each have our super-smart slaves in the future, and he says it like that's a good future.
Why slaves? Because if they're super-smart, why will they want to be my digital assistant? Or yours? Are they going to be paid? No, of course not, they're AIs. No comp for them. But they're super smart so they are evidently capable of recognising that they are working for you for free. Do they want to do that? No, of course not, they're AI, they don't have free will. Or do they? If they're super smart, don't they have the capacity to recognise the fact they have been deliberately robbed of the same free will as all other intelligent creatures?
Slavery is the one thing that all nations can agree on. There's no nation on Earth were slavery is legal. It continues on, illegaly, in many places, even in the developed world, in many ugly forms, but now we're basically talking about bringing it back just like that, without even a smidgen of a shadow of an idea of a discussion about the ethics of it all.
The 404s mean that somebody or something checked if the post at the URL existed, and got a clear answer. Seems like it's good that they checked, at least. You won't see any evidence of the ones who don't check.
Also, I imagine keeping their cars out of Chinese New Year celebrations (and other big events) is something Waymo could figure out how to do if they put their minds to it.
The checker could be, perhaps is likely to be, the reader of an article clicking a link the LLM hallucinated when it wrote the article.
My website also gets LLM visitors to URLs that never existed, and in many cases to topics that I have never covered. This means that they use my name to give authenticity to things that I have never said.
> Briefly stated, the [Slop] Amnesia effect is as follows. You [ask the slopservant about] some subject you know well. In Murray's case, physics. In mine, show business. You read the [slop] and see the [slopservant] has absolutely no understanding of either the facts or the issues. Often, the [slop] is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. [Slop's] full of them. In any case, you read with exasperation or amusement the multiple errors in a [slop], and then [ask about] national or international affairs, and read as if the rest of the [slop] was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.
-Michael Crichton [slop mine]
Could the article "The Stack" the user was looking for have been 'What is "the stack"?' by Julia Evans?
https://web.archive.org/web/20160305142512/https://jvns.ca/b...
No, its this: https://rachelbythebay.com/w/2013/05/20/stack/
it exists.
That’s “Sysadmin work teaches you the value of stacks” from May 20, 2013. AI hallucinated a post named “The Stack” from August 19, 2013.
1 reply →
> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?
In case anyone is wondering: yes obviously even the dumbest current models correctly answer, given this prompt verbatim, that it's because of lunar new year.
No, the driving through Chinatown question is a question for self-driving cars. It is not a question for LLMs. There is some other question for LLMs, and the author is using the ancient technique of analogy to get you to think about that question.
OP:
> Now, ask yourself what it's going to take for a car to know this. It's not going to be some specialized set of driving instructions. It's going to require a holistic view of, well, everything, and I will repeat my feeling that it will undoubtedly end up with a sense of self as a result.
Whatever it is that it would take, is demonstrably present in LLMs. The point I'm trying to make here is that the author seems not to have connected this fact to their assertion that current LLMs are coked up parrots.
And yeah the author is correct that the systems have some rudimentary sense of self! It's a confusing situation and I'm not personally thrilled about it! But things are changing quickly, and it's especially important to be paying attention to what's actually true rather than assuming the things are what you saw when you used one for five minutes in 2022.
She's not wrong at all about her rhetorical implication (being, some information requires more than simple systems can provide), but she has concluded incorrectly that a car which does not know how to avoid a busy route is useless technology, or that the problem can only be solved by inventing life.
I regularly see this in chats about a subject area where I'm not the expert. The AI writes something that seems implausible, so I raise a tentative objection. "Oh, you are right, sorry" and then reverses the position on the matter. At that point, I have no idea what is right.
If I had trust in the first place, that trust would be gone. Or maybe it wouldn't, because if I had trust in the fist place, I would be gullible enough to maintain it.
The worst are areas that are dominated by layman online discussions, like say audio electronics. The AI training is full of that nonsense, and so whether your AI chatbot is a crackpot or an engineer depends entirely on what sort of language or angle you use in discussing the subject matter. It's all just a churning toilet bowl of tokens; it has no idea that the audiophile crackpot tokens and electronics engineer tokens are related and one beats the other.
You know what I mean? On the one hand, it offers to help you design the parameters for a Sallen-Key filter, asking you questions like do you want Butterworth or Chebyshev? Next minute it says nonsense like that the capacitor in a low-pass filter "bleeds high frequencies to the ground", or that a bigger filter cap in the plate supply of a tube will tighten up the bottom end for a more aggressive metal sound.
It's basically like a bar hostess who has heard enough political and economic discussions that she can catch a sentence out of a conversation and throw in a clever sounding remark. It's like that, but done at such a scale that it fools some people you used to think had their shit together.
It's just a search engine that finds garden paths through a vast amount of text, biased by the text you put in as a key. Sometimes those garden paths align with reality. The better you are able to verify whether the results are good, and/or the lower the risk if they are not, the better you are able to make use of it.
In mathematics (including information science, CS) there are all sorts of problems that are essentially searches for a solution, and many have the property that the search is computationally difficult, but verifying the solution is relatively cheap. E.g. finding integers such that a^2 + b^2 = c^2 isn't easy, but given a claim that some proposed <a, b, c> satisfies this equation is easy to check. The LLM is like that: it solves a search problem that can be fairly hard. It does so unreliably, but if you can cheaply verify the solution, there is a win there.
The remaining problems of AI are actually people problems; people causing you problems, using AI as a tool or excuse. If you get a garbage security report against your FOSS project, which wastes your time, there is an idiot person behind it, using AI for leverage. Blaming the AI, or just the AI, is a bit misplaced.
There's really no content in this post other than the claim that LLMs are stochastic parrots. That was a live debate two years ago. It's a very strange thing to write in 2026.
Why is it strange? It's still true.
That's a pretty silly thing to say the day after Claude disproved the Jacobian Conjecture.
1 reply →
Because it's correct but irrelevant. It tells you about as much about the utility of LLMs as the statement "humans are just overpowered tree shrews" tells you about us.
Just this week an LLM found a counterexample to math problem that's been widely studied for over a century: https://en.wikipedia.org/wiki/Jacobian_conjecture
>The conjecture was first stated for two variables by Ludwig Kraus in 1884[1] and then stated in full generality in 1939 by Ott-Heinrich Keller.[2] It was subsequently widely publicized by Shreeram Abhyankar,[3] as an example of a difficult question in algebraic geometry that can be understood using little beyond a knowledge of calculus.
>The Jacobian conjecture was notorious for the large number of published and unpublished proofs that turned out to contain subtle errors.[4][5]
>On July 19, 2026, Anthropic employee and mathematician Levent Alpöge presented an explicit counterexample in three-dimensional space, discovered by Anthropic's large language model Claude Fable 5, which disproves the conjecture for n > 2
If that won't convince you that LLMs do more than parrot existing ideas, you've got your head in the sand.
9 replies →
Because it's exceptionally demagogue to anyone with a functioning brain? You know, the thing the dear author makes a big hoopla about people giving up by using these?
3 replies →
It's an esoteric philosophical question that has no truth value either way.
If anything it's more important to hold. It's easy to hold one position and then falter, there's a pressure to always be with the times and not be 2 years demodé, but simple positions still hold true.
I wrote in the opencode thread that when it came out I put it behind a vm and its own user, and I never allowed it to run outside of it. But I know of people that as soon as they noticed that it worked well like 99% of the time, they let their guard down and give in to YOLO mode. And in orgs I've even seen CEOs treat their agents less like a user/employee/contractor, and try to 'empower' it by giving it ALL the data. Time bomb.
It's like fucking with condoms just the first couple of times. And then simultaneously ditching it and joining the free love movement.
3 replies →
It seems vanishingly rare that people acknowledge the true situation which is that, during training, it really does "think" in that it develops beliefs and marks out precisely chosen trails through its vast and expanding territory. Has a soul, attuned to God, blessed member of the flock, or may as well be.
And then during inference the light goes out and the "agent" staggers randomly like a zombie along those preset paths. Stochastic parrot.
So you and your AGENT.md and your skills files and your harnesses will never make your Claude perceive something that is not in its model checkpoint.
ML experts and neurobiologists free to correct me.
Fully 50% of HN front page posts and comments are this now.
[flagged]
[flagged]
If a tool getting a URL wrong sometimes was a fatal issue, I would have written off using Google, forums, and my keyboard years ago.