> 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.
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?
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.
A tech blog is going to have more than it's fair share of enthusiasts using small self-hosted and similar models; it is entirely possible that that completely accounts for the behaviour described in the article.
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.
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.
No, the sense had nothing to do with the content and everything to do with the context. Perfect grammar and writing style were never enough to get me to trust an anonymous forum post or unknown bloggers post for certain topics like health advice. Sloppy grammar and writing style were never a deterrent for me believing them for other kinds of topics like where to check on the HVAC system to find the sticker. I think the line could more accurately be described as the level of risk if it's wrong.
Before we would find an intriguing post on the internet from years ago, and you have to verify it with additional research--it's easy to skip that additional research.
With a LLM when you're skeptical you can interogate it. One thing we know for sure is LLMs are quick to admit mistakes were made when interrogated, comically so. A LLM might not always recognize its own mistake, but at least it is available for easy interogation, unlike the forum posts of old.
Manual research from reputable sources remains an option.
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.
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).
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.
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.
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.
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
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.
I’m guessing GP’s point is that Google also has the locations of all the people not in cars, so it could take the hint if it wanted. But thus far nobody appears to care enough to fix it. (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 experiences would lead us to believe.)
> 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.
> 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).
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.
> 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.
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.
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.
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.
> 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.
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.
> 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.
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.
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.
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.
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?
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.
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.
> 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.
> 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.
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?
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.
>Do you all think its regularly hallucinating links still?
When did that stop? May 7th, 2026?
A tech blog is going to have more than it's fair share of enthusiasts using small self-hosted and similar models; it is entirely possible that that completely accounts for the behaviour described in the article.
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.
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 :(
No, the sense had nothing to do with the content and everything to do with the context. Perfect grammar and writing style were never enough to get me to trust an anonymous forum post or unknown bloggers post for certain topics like health advice. Sloppy grammar and writing style were never a deterrent for me believing them for other kinds of topics like where to check on the HVAC system to find the sticker. I think the line could more accurately be described as the level of risk if it's wrong.
We have other signals now.
Before we would find an intriguing post on the internet from years ago, and you have to verify it with additional research--it's easy to skip that additional research.
With a LLM when you're skeptical you can interogate it. One thing we know for sure is LLMs are quick to admit mistakes were made when interrogated, comically so. A LLM might not always recognize its own mistake, but at least it is available for easy interogation, unlike the forum posts of old.
Manual research from reputable sources remains an option.
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)
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.
It's vector search that's eaten everything that used to have at least a smidgen of parametric search.
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).
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.
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.
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.
> 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
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.
I’m guessing GP’s point is that Google also has the locations of all the people not in cars, so it could take the hint if it wanted. But thus far nobody appears to care enough to fix it. (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 experiences would lead us to believe.)
> 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.
> 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).
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?
> 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."
I'd like to point out that even if one added an URL-validation step, that doesn't do a dang thing for problems of:
1. False-negatives, where relevant posts that exist are not being imagined-up by the chaos-parrot
2. Mischaracterized posts
3. "Relevance" being determined by unpredictable factors that aren't desirable
> 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.
"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.
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...
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
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.
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.
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.
> 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.
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.
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.
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.
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.
Fully 50% of HN front page posts and comments are this now.
Why is it strange? It's still true.
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.
It's not even clear what claim you're trying to make about AI here. "Dangerous", I guess? What does that have to do with its parrotude?
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.
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?
That's a pretty silly thing to say the day after Claude disproved the Jacobian Conjecture.
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.
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.
> 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]