In the discussion thread there seems to be a somewhat contentious argument about what is a Markov Model, whether LLMs are one, RNNs are one and so on.
Markov Models are anything that has state and emit tokens based only on its current state and undergoes a state transition. The token emission and state transitions are usually probabilistic -- a statistical/probabilistic analogue of a state machine. The deterministic state machine is a special case where the transition probabilities are degenerate (concentrated at an unique point).
For a Markov Model to be non-vacuous, non-vapid discussion point, however, one needs to specify very precisely the relationships allowed between state and tokens/observations, whether it's hidden or visible, discrete or continuous, fixed context length or variable context length, causal or non causal ...
The simplest such model is one where the state is a specified, computable function of the last k observations. One such simple function is the identity function -- the state then is the last k tokens. This is called a k order Markov Chain and is a restriction of the bigger class -- Markov Models.
One can make the state a specified, computable function of (k) previous states and k most recent tokens/observations. (Equivalently RNNs)
The functions may be specified only upto a class of computable functions, finite or infinite in size. They may be stochastic in the sense they define only the state transition probabilities.
You can make the context length a computable function of the k most recent observations (therefore they can be of varying length), but you have to ensure that the contexts are always full for this model to be well defined.
Context length can be a computable function of both the (el) most recent states and k most recent observations.
Crazy ones emit more than one token based on current state.
Another term used is identifiable (although learnable and identifiable are not synonyms, I think identifiability is one precondition for learnability).
Identifiability means that out of all possible models, you can learn the correct one given enough samples.causal identifiability has some other connotations
See here https://causalai.net/r80.pdf as a good start (a nose in a causal graph is Markov given its parents, and a k-step Markov chain is a k-layer causal dag)
Cool article, it got me to play around with Markov models, too! I first did a Markov model over plain characters.
> Itheve whe oiv v f vidleared ods alat akn atr. s m w bl po ar 20
Using pairs of consecutive characters (order-2 Markov model) helps, but not much:
> I hateregratics.pyth fwd-i-sed wor is wors.py < smach. I worgene arkov ment by compt the fecompultiny of 5, ithe dons
Triplets (order 3) are a bit better:
> I Fed tooks of the say, I just train. All can beconsist answer efferessiblementate
> how examples, on 13 Debian is the more M-x: Execute testeration
LLMs usually do some sort of tokenization step prior to learning parameters. So I decided to try out order-1 Markov models over text tokenized with byte pair encoding (BPE).
Trained on TFA I got this:
> I Fed by the used few 200,000 words. All comments were executabove. This value large portive comment then onstring takended to enciece of base for the see marked fewer words in the...
Then I bumped up the order to 2
> I Fed 24 Years of My Blog Posts to a Markov Model
> By Susam Pal on 13 Dec 2025
>
> Yesterday I shared a little program calle...
It just reproduced the entire article verbatim. This makes sense as BPE removes any pair of repeated tokens, making order-2 Markov transitions fully deterministic.
I've heard that in NLP applications, it's very common to run BPE only up to a certain number of different tokens, so I tried that out next.
Before limiting, BPE was generating 894 tokens. Even adding a slight limit (800) stops it from being deterministic.
> I Fed 24 years of My Blog Postly coherent. We need to be careful about not increasing the order too much. In fact, if we increase the order of the model to 5, the generated text becomes very dry and factual
It's hard to judge how coherent the text is vs the author's trigram approach because the text I'm using to initialize my model has incoherent phrases in it anyways.
Nice :) I did something similar a few days ago. What I ended up with was a 50/50 blend of hilarious nonsense, and verbatim snippets.There seemed to be a lot of chains where there was only one possible next token.
I'm considering just deleting all tokens that have only one possible descendant, from the db. I think that would solve that problem. Could increase that threshold to, e.g. a token needs to have at least 3 possible outputs.
However that's too heavy handed: there's a lot of phrases or grammatical structures that would get deleted by that. What I'm actually trying to avoid is long chains where there's only one next token. I haven't figured out how to solve that though.
That's where a dynamic n-gram comes into play. Train the markov model from 1 to 5 n-grams, and then scale according to the number of potential paths available.
You'll also need a "sort of traversal stack" so you can rewind if you get stuck several plies in.
I have a pet tool I use for conlang work for writing/worldbuilding that is built on Markov chains and I am smacking my forehead right now at how obvious this seems in hindsight. This is great advice, thank you.
Reading this I get this weird feeling that something there is trying to communicate, which is equally horrifying as the alternative - we are alone, our minds are trying to find order in chaos, there is no meaning except what we create.
I had the same feeling while testing the code. It might be caused by seeing the increasingly coherent output of the different models, makes you feel like it's getting smarter.
The alternative to something trying to communicate through a Markov model isn’t that we’re alone. Just because there’s no life on Mars doesn’t mean there’s no other life in the universe.
I did something similar many years ago. I fed about half a million words (two decades of mostly fantasy and science fiction writing) into a Markov model that could generate text using a “gram slider” ranging from 2-grams to 5-grams.
I used it as a kind of “dream well” whenever I wanted to draw some muse from the same deep spring. It felt like a spiritual successor to what I used to do as a kid: flipping to a random page in an old 1950s Funk & Wagnalls dictionary and using whatever I found there as a writing seed.
Curious if you've heard of or participated in NaNoGenMo[0] before. With such a corpus at your fingertips could be a fun little project; obviously, pure Markov generation wouldn't be quite sufficient but a good starting point maybe.
Hey that's neat! I hadn't heard of it. It says you need to publish the novel and the source at the end - so I guess as part of the submission you'd include the RNG seed.
The only thing I'm a bit wary of is the submission size - a minimum of 50,000 words. At that length, It'd be really difficult to maintain a cohesive story without manual oversight.
I gave a talk in 2015 that did the same thing with my tweet history (about 20K at the time) and how I used it as source material for a Twitter bot that could reply to users. [1]
What a fantastic idea, I have about 30 years of writing, mostly chapters and plots for novels that did not coalesce. Love to know how it turns out too.
I think you're absolutely right about the easiest approach. I hope you don't mind me asking for a bit more difficulty.
Wouldn't fine tuning produce better results so long as you don't catastrophically forget? You'd preserve more context window space, too, right? Especially if you wanted it to memorize years of facts?
So that's the key difference. A lot of people train these Markov models with the expectation that they're going to be able to use the generated output in isolation.
The problem with that is either your n-gram level is too low in which case it can't maintain any kind of cohesion, or your n-gram level is too high and it's basically just spitting out your existing corpus verbatim.
For me, I was more interested in something that could potentially combine two or three highly disparate concepts found in my previous works into a single outputted sentence - and then I would ideate upon it.
So I haven't opened the program in a long time so I just spun it up and generated a few outputs:
A giant baby is navel corked which if removed causes a vacuum.
I'm not sure what the original pieces of text were based on that particular sentence but it starts making me think about a kind of strange void harkonnen with heart plugs that lead to weird negatively pressurized areas. That's the idea behind the dream well.
I can’t believe no one’s mentioned the Harry Potter fanfic written by a Markov Chain. If you’re familiar with HP, I highly recommend reading Harry Potter and the Portrait of What Looked Like a Large Pile of Ash.
While hollow, it is also bad (and absurd) enough to be quite entertaining. It’s from an era where this wasn’t far off the state of the art for coming up with machine-generated text—context that makes it quite a bit funnier than if it were generated by an LLM today.
That said, it’s obviously not to everyone’s tastes!
It's purely meant to be an absurdist read. It obviously makes no sense, yet is close enough to actual language patterns for (some, at least) people to find it hilarious. I had tears in my eyes from laughing way too hard when I first read it.
The one author that I think we have a good chance of recreating would be Barbara Cartwright. She wrote 700+ romance novels all pretty much the same. It should be possible to generate another of her novels given that large a corpus.
I made one for Hipchat at a company. I can't remember if it could emulate specific users, or just channels, but both were definitely on my roadmap at the time.
I'm hoping someone can find it so I can bookmark it but I once read a story about a company that let multiple markov chain bots loose in a Slack channel. A few days later production went down because one of them ran a Slack command that deployed or destroyed their infrastructure.
I think this is more correctly described as a trigram model than a Markov model, if it would naturally expand to 4-grams when they were available, etc, the text would look more coherent
Iirc there was some research on "infini-gram", that is a very large ngram model, that allegedly got performance close to LLMs in some domains a couple years back
Google made some very large ngram models around twenty years ago. This being before the era of ultra-high-speed internet, it was distributed as a set of 6 DVDs.
It achieved state-of-the-art performance at tasks like spelling correction at the time. However, unlike an LLM, it can't generalize at all; if an n-gram isn't in the training corpus it has no idea how to handle it.
I think it took 2-3 hours on my friend's Nvidia something.
The result was absolutely hilarious. It was halfway between a markov chain and what you'd expect from a very small LLM these days. Completely absurd nonsense, yet eerily coherent.
Also, it picked up enough of our personality and speech patterns to shine a very low resolution mirror on our souls...
###
Andy: So here's how you get a girlfriend:
1. Start making silly faces
2. Hold out your hand for guys to swipe
3. Walk past them
4. Ask them if they can take their shirt off
5. Get them to take their shirt off
6. Keep walking until they drop their shirt
Andy: Can I state explicitly this is the optimal strategy
That‘s funny! Now imagine you‘re using Signal on iOS instead of WhatsApp. You cannot do this with your chat history because Signal won‘t let you access your own data outside of their app.
You could literally buy this at Egghead software for $3 from the bargain bin in 1992. I know, because I did. I fed it 5 years worth of my juvenile rants, and laughed at how pompous I sounded through a blender.
I just realized, one of the things that people might start doing is making a gamma model of their personality. I won't even approach who they were as a person, but it will give their Descendants (or bored researchers) a 60% approximation of who they were and their views. (60% is pulled from nowhere to justify my gamma designation, since there isn't a good scale for personality mirror quality for LLMs as far as I'm aware.)
That's the question today. Turns out transformers really are a leap forwards in terms of AI, whereas Markov chains, scaled up to today's level of resources and capacity, will still output gibberish.
So, are current LLMs better because artificial neural networks are better predictors than Markov models, or because of the scale of the training data? Just putting it out there..
Markov models usually only predict the next token given the two preceding tokens (trigram model) because the data gets so exceptionally sparse beyond that, that it becomes impossible to make probability estimations (despite back-off, smoothing, etc.).
I recommend you to read Bengio et al.’s 2003 paper which describes this issue in more detail and introduces distributional representations (embeddings) in an RNN to avoid this sparsity.
While we are using transformers and sentence pieces now, this paper aptly describes the motivation underpinning modern models.
Thanks for the reference and I stand corrected. And yes I had looked at it a long time ago and will give it another read. But I think it is saying that RNNs are a means of approximating a statistical property of a collection of text. That property is what we today think of as "completion"? That is, glorified auto complete, and not "distributed representations" of the world. Would you agree?
When I was in college my friends and I did something similar with all of Donald Trump’s tweets as a funny hackathon project for PennApps. The site isn’t up anymore (RIP free heroku hosting) but the code is still up on GitHub: https://github.com/ikhatri/trumpitter
I usually have this technical hypothetical discussions with ChatGpt, I can share if you like, me asking him this: aren't LLMs just huge Markov Chains?! And now I see your project... Funny
When you say nobody you mean you, right? You can't possible be answering for every single person in the world.
I was having a discussion about similarities between Markov Chains and LLMs and short after I found this topic on HN, when I wrote "I can share if you like" was as a proof about the coincidence.
Not sure why that's contorting, a markov model is anything where you know the probability of going from state A to state B. The state can be anything. When it's text generation the state is previous text to text with an extra character, which is true for both LLMs and oldschool n-gram markov models.
Yes, technically you can frame an LLM as a Markov chain by defining the "state" as the entire sequence of previous tokens. But this is a vacuous observation under that definition, literally any deterministic or stochastic process becomes a Markov chain if you make the state space flexible enough. A chess game is a "Markov chain" if the state includes the full board position and move history. The weather is a "Markov chain" if the state includes all relevant atmospheric variables.
The problem is that this definition strips away what makes Markov models useful and interesting as a modeling framework. A “Markov text model” is a low-order Markov model (e.g., n-grams) with a fixed, tractable state and transitions based only on the last k tokens. LLMs aren’t that: they model using un-fixed long-range context (up to the window). For Markov chains, k is non-negotiable. It's a constant, not a variable. Once you make it a variable, near any process can be described as markovian, and the word is useless.
Sure many things can be modelled as Markov chains, which is why they're useful. But it's a mathematical model so there's no bound on how big the state is allowed to be. The only requirement is that all you need is the current state to determine the probabilities of the next state, which is exactly how LLMs work. They don't remember anything beyond the last thing they generated. They just have big context windows.
The etymology of the "markov property" is that the current state does not depend on history.
And in classes, the very first trick you learn to skirt around history is to add Boolean variables to your "memory state". Your systems now model, "did it rain The previous N days?" The issue obviously being that this is exponential if you're not careful. Maybe you can get clever by just making your state a "sliding window history", then it's linear in the number of days you remember. Maybe mix the both. Maybe add even more information .Tradeoffs, tradeoffs.
I don't think LLMs embody the markov property at all, even if you can make everything eventually follow the markov property by just "considering every single possible state". Of which there are (size of token set)^(length) states at minimum because of the KV cache.
The KV cache doesn't affect it because it's just an optimization. LLMs are stateless and don't take any other input than a fixed block of text. They don't have memory, which is the requirement for a Markov chain.
Have you ever actually worked with a basic markov problem?
The markov property states that your state is a transition of probabilities entirely from the previous state.
These states, inhabit a state space. The way you encode "memory" if you need it, e.g. say you need to remember if it rained the last 3 days, is by expanding said state space. In that case, you'd go from 1 state to 3 states, 2^3 states if you needed the precise binary information for each day. Being "clever", maybe you assume only the # of days it rained, in the past 3 days mattered, you can get a 'linear' amount of memory.
Sure, a LLM is a "markov chain" of state space size (# tokens)^(context length), at minimum. That's not a helpful abstraction and defeats the original purpose of the markov observation. The entire point of the markov observation is that you can represent a seemingly huge predictive model with just a couple of variables in a discrete state space, and ideally you're the clever programmer/researcher and can significantly collapse said space by being, well, clever.
>Sure many things can be modelled as Markov chains
Again, no they can't, unless you break the definition. K is not a variable. It's as simple as that. The state cannot be flexible.
1. The markov text model uses k tokens, not k tokens sometimes, n tokens other times and whatever you want it to be the rest of the time.
2. A markov model is explcitly described as 'assuming that future states depend only on the current state, not on the events that occurred before it'. Defining your 'state' such that every event imaginable can be captured inside it is a 'clever' workaround, but is ultimately describing something that is decidedly not a markov model.
It's not n sometimes, k tokens some other times. LLMs have fixed context windows, you just sometimes have less text so it's not full. They're pure functions from a fixed size block of text to a probability distribution of the next character, same as the classic lookup table n gram Markov chain model.
1. A context limit is not a Markov order.
An n-gram model’s defining constraint is: there exists a small constant k such that the next-token distribution depends only on the last k tokens, full stop. You can't use a k-trained markov model on anything but k tokens, and each token has the same relationship with each other regardless. An LLM’s defining behavior is the opposite: within its window it can condition on any earlier token, and which tokens matter can change drastically with the prompt (attention is content-dependent). “Window size = 8k/128k” is not “order k” in the Markov sense; it’s just a hard truncation boundary.
2. “Fixed-size block” is a padding detail, not a modeling assumption.
Yes, implementations batch/pad to a maximum length. But the model is fundamentally conditioned on a variable-length prefix (up to the cap), and it treats position 37 differently from position 3,700 because the computation explicitly uses positional information. That means the conditional distribution is not a simple stationary “transition table” the way the n-gram picture suggests.
3. “Same as a lookup table” is exactly the part that breaks.
A classic n-gram Markov model is literally a table (or smoothed table) from discrete contexts to next-token probabilities. A transformer is a learned function that computes a representation of the entire prefix and uses that to produce a distribution. Two contexts that were never seen verbatim in training can still yield sensible outputs because the model generalizes via shared parameters; that is categorically unlike n-gram lookup behavior.
I don't know how many times I have to spell this out for you. Calling LLMs markov chains is less than useless. They don't resemble them in any way unless you understand neither.
I think you're confusing Markov chains and "Markov chain text generators". A Markov chain is a mathematical structure where the probabilities of going to the next state only depend on the current state and not the previous path taken. That's it. It doesn't say anything about whether the probabilities are computed by a transformer or stored in a lookup table, it just exists. How the probabilities are determined in a program doesn't matter mathematically.
Just a heads-up: this is not the first time somebody has to explain Markov chains to famouswaffles on HN, and I'm pretty sure it won't be the last. Engaging further might not be worth it.
I did not even remember you and had to dig to find out what you were on about. Just a heads up, if you've had a previous argument and you want to bring that up later then just speak plainly. Why act like "somebody" is anyone but you?
My response to both of you is the same.
LLMs do depend on previous events, but you say they don't because you've redefined state to include previous events. It's a circular argument. In a Markov chain, state is well defined, not something you can insert any property you want to or redefine as you wish.
It's not my fault neither of you understand what the Markov property is.
By that definition n-gram Markov chain text generators also include previous state because you always put the last n grams. :) It's exactly the same situation as LLMs, just with higher, but still fixed n.
We've been through this. The context of a LLM is not fixed. Context windows =/ n gram orders.
They don't because n gram orders are too small and rigid to include the history in the general case.
I think srean's comment up the thread is spot on. This current situation where the state can be anything you want it to be just does not make a productive conversation.
'A Markov chain is a mathematical structure where the probabilities of going to the next state only depend on the current state and not the previous path taken.'
My point, which seems so hard to grasp for whatever reason is that In a Markov chain, state is a well defined thing. It's not a variable you can assign any property to.
LLMs do depend on the previous path taken. That's the entire reason they're so useful! And the only reason you say they don't is because you've redefined 'state' to include that previous path! It's nonsense. Can you not see the circular argument?
The state is required to be a fixed, well-defined element of a structured state space. Redefining the state as an arbitrarily large, continuously valued encoding of the entire history is a redefinition that trivializes the Markov property, which a Markov chain should satisfy. Under your definition, any sequential system can be called Markov, which means the term no longer distinguishes anything.
A GPT model would be modelled as an n-gram Markov model where n is the size of the context window. This is slightly useful for getting some crude bounds on the behaviour of GPT models in general, but is not a very efficient way to store a GPT model.
I'm not saying it's an n-gram Markov model or that you should store them as a lookup table. Markov models are just a mathematical concept that don't say anything about storage, just that the state change probabilities are a pure function of the current state.
QM and GR can be written as matrix algebra, atoms and electrons are QM, chemistry is atoms and electrons, biology is chemistry, brains are biology.
An LLM could be implemented with a Markov chain, but the naïve matrix is ((vocab size)^(context length))^2, which is far too big to fit in this universe.
Like, the Bekenstein bound means writing the transition matrix for an LLM with just 4k context (and 50k vocabulary) at just one bit resolution, the first row (out of a bit more than 10^18795 rows) ends up with a black hole >10^9800 times larger than the observable universe.
Markov models with more than 3 words as "context window" produce very unoriginal text in my experience (corpus used had almost 200k sentences, almost 3 million words), matching the OP's experience. These are by no means large corpuses, but I know it isn't going away with a larger corpus.[1] The Markov chain will wander into "valleys" of reproducing paragraphs of its corpus one for one because it will stumble upon 4-word sequences that it has only seen once. This is because 4 words form a token, not a context window. Markov chains don't have what LLMs have.
If you use a syllable-level token in Markov models the model can't form real words much beyond the second syllable, and you have no way of making it make more sense other than increasing the token size, which exponentially decreases originality. This is the simplest way I can explain it, though I had to address why scaling doesn't work.
[1] There are 4^400000 possible 4-word sequences in English (barring grammar) meaning only a corpus with 8 times that amount of words and with no repetition could offer two ways to chain each possible 4 word sequence.
What do you mean? The states are fully observable (current array of tokens), and using an LLM we calculate the probabilities of moving between them. What is not MC about this?
I suggest getting familiar with or brushing up on the differences between a Markov Chain and a Markov Model. The former is a substantial restriction of the latter. The classic by Kemeny and Snell is a good readable reference.
MC have constant and finite context length, their state is the most recent k tuple of emitted alphabets and transition probabilities are invariant (to time and tokens emitted)
LLMs definitely also have finite context length. And if we consider padding, it is also constant. The k is huge compared to most Markov chains used historically, but it doesn't make it less finite.
What do you mean? I can only input k tokens into my LLM to calculate the probs. That is the definition of my state. In the exact way that N-gram LMs use N tokens, but instead of using ML models, they calculate the probabilities based on observed frequencies. There is no unbounded context anywhere.
You can certainly feed k-grams one at a time to, estimate the the probability distribution over next token and use that to simulate a Markov Chain and reinitialize the LLM (drop context). In this process the LLM is just a look up table to simulate your MC.
But an LLM on its own doesn't drop context to generate, it's transition probabilities change depending on the tokens.
Don't know what happened. I stumbled onto a funny coincidence - me talking to a LLM about its similarities with MC - decided to share on a post about using MC to generate text. Got some nasty comments and a lot of down votes. Even though my comment sparked a pretty interesting discussion.
Hate to be that guy, but I remember this place being nicer.
Ever since LLMS became popular, there's been an epidemic of people pasting ChatGPT output onto forums (or in your case, offering to). These posts are always received similarly to yours, so I'm skeptical that you're genuinely surprised by the reaction.
Everyone has access to ChatGPT. If we wanted its "opinion" we could ask it ourselves. Your offer is akin to "Hey everyone, want me to Google this and paste the results page here?". You would never offer to do that. Ask yourself why.
These posts are low-effort and add nothing to the conversation, yet the people who write them seem to expect everyone to be impressed by their contribution. If you can't understand why people find this irritating, I'm not sure what to tell you.
In the discussion thread there seems to be a somewhat contentious argument about what is a Markov Model, whether LLMs are one, RNNs are one and so on.
Markov Models are anything that has state and emit tokens based only on its current state and undergoes a state transition. The token emission and state transitions are usually probabilistic -- a statistical/probabilistic analogue of a state machine. The deterministic state machine is a special case where the transition probabilities are degenerate (concentrated at an unique point).
For a Markov Model to be non-vacuous, non-vapid discussion point, however, one needs to specify very precisely the relationships allowed between state and tokens/observations, whether it's hidden or visible, discrete or continuous, fixed context length or variable context length, causal or non causal ...
The simplest such model is one where the state is a specified, computable function of the last k observations. One such simple function is the identity function -- the state then is the last k tokens. This is called a k order Markov Chain and is a restriction of the bigger class -- Markov Models.
One can make the state a specified, computable function of (k) previous states and k most recent tokens/observations. (Equivalently RNNs)
The functions may be specified only upto a class of computable functions, finite or infinite in size. They may be stochastic in the sense they define only the state transition probabilities.
You can make the context length a computable function of the k most recent observations (therefore they can be of varying length), but you have to ensure that the contexts are always full for this model to be well defined.
Context length can be a computable function of both the (el) most recent states and k most recent observations.
Crazy ones emit more than one token based on current state.
On and on.
Not all Markov Models are learnable.
Learnable? What does that mean?
There are various notions of this.
The most basic/naive one is where one can estimate the unknown parameters of the model given example token streams generated by the model.
So learnable, in this context, rhymes with reverse-engineerable?
Another term used is identifiable (although learnable and identifiable are not synonyms, I think identifiability is one precondition for learnability).
Identifiability means that out of all possible models, you can learn the correct one given enough samples.causal identifiability has some other connotations
See here https://causalai.net/r80.pdf as a good start (a nose in a causal graph is Markov given its parents, and a k-step Markov chain is a k-layer causal dag)
Cool article, it got me to play around with Markov models, too! I first did a Markov model over plain characters.
> Itheve whe oiv v f vidleared ods alat akn atr. s m w bl po ar 20
Using pairs of consecutive characters (order-2 Markov model) helps, but not much:
> I hateregratics.pyth fwd-i-sed wor is wors.py < smach. I worgene arkov ment by compt the fecompultiny of 5, ithe dons
Triplets (order 3) are a bit better:
> I Fed tooks of the say, I just train. All can beconsist answer efferessiblementate
> how examples, on 13 Debian is the more M-x: Execute testeration
LLMs usually do some sort of tokenization step prior to learning parameters. So I decided to try out order-1 Markov models over text tokenized with byte pair encoding (BPE).
Trained on TFA I got this:
> I Fed by the used few 200,000 words. All comments were executabove. This value large portive comment then onstring takended to enciece of base for the see marked fewer words in the...
Then I bumped up the order to 2
> I Fed 24 Years of My Blog Posts to a Markov Model
> By Susam Pal on 13 Dec 2025
>
> Yesterday I shared a little program calle...
It just reproduced the entire article verbatim. This makes sense as BPE removes any pair of repeated tokens, making order-2 Markov transitions fully deterministic.
I've heard that in NLP applications, it's very common to run BPE only up to a certain number of different tokens, so I tried that out next.
Before limiting, BPE was generating 894 tokens. Even adding a slight limit (800) stops it from being deterministic.
> I Fed 24 years of My Blog Postly coherent. We need to be careful about not increasing the order too much. In fact, if we increase the order of the model to 5, the generated text becomes very dry and factual
It's hard to judge how coherent the text is vs the author's trigram approach because the text I'm using to initialize my model has incoherent phrases in it anyways.
Anyways, Markov models are a lot of fun!
Nice :) I did something similar a few days ago. What I ended up with was a 50/50 blend of hilarious nonsense, and verbatim snippets.There seemed to be a lot of chains where there was only one possible next token.
I'm considering just deleting all tokens that have only one possible descendant, from the db. I think that would solve that problem. Could increase that threshold to, e.g. a token needs to have at least 3 possible outputs.
However that's too heavy handed: there's a lot of phrases or grammatical structures that would get deleted by that. What I'm actually trying to avoid is long chains where there's only one next token. I haven't figured out how to solve that though.
That's where a dynamic n-gram comes into play. Train the markov model from 1 to 5 n-grams, and then scale according to the number of potential paths available.
You'll also need a "sort of traversal stack" so you can rewind if you get stuck several plies in.
It would make sense to repeat the experiment with the tokenizer of an LLM.
the trick to prevent 'dry' output that quotes verbatim is to make the 5 words limit flexible: if there is only one path, reduce it to 4.
I have a pet tool I use for conlang work for writing/worldbuilding that is built on Markov chains and I am smacking my forehead right now at how obvious this seems in hindsight. This is great advice, thank you.
Reading this I get this weird feeling that something there is trying to communicate, which is equally horrifying as the alternative - we are alone, our minds are trying to find order in chaos, there is no meaning except what we create.
I had the same feeling while testing the code. It might be caused by seeing the increasingly coherent output of the different models, makes you feel like it's getting smarter.
The alternative to something trying to communicate through a Markov model isn’t that we’re alone. Just because there’s no life on Mars doesn’t mean there’s no other life in the universe.
I did something similar many years ago. I fed about half a million words (two decades of mostly fantasy and science fiction writing) into a Markov model that could generate text using a “gram slider” ranging from 2-grams to 5-grams.
I used it as a kind of “dream well” whenever I wanted to draw some muse from the same deep spring. It felt like a spiritual successor to what I used to do as a kid: flipping to a random page in an old 1950s Funk & Wagnalls dictionary and using whatever I found there as a writing seed.
Curious if you've heard of or participated in NaNoGenMo[0] before. With such a corpus at your fingertips could be a fun little project; obviously, pure Markov generation wouldn't be quite sufficient but a good starting point maybe.
[0]: https://nanogenmo.github.io/
Hey that's neat! I hadn't heard of it. It says you need to publish the novel and the source at the end - so I guess as part of the submission you'd include the RNG seed.
The only thing I'm a bit wary of is the submission size - a minimum of 50,000 words. At that length, It'd be really difficult to maintain a cohesive story without manual oversight.
I gave a talk in 2015 that did the same thing with my tweet history (about 20K at the time) and how I used it as source material for a Twitter bot that could reply to users. [1]
It was pretty fun!
[1] https://youtu.be/rMmXdiUGsr4
What a fantastic idea, I have about 30 years of writing, mostly chapters and plots for novels that did not coalesce. Love to know how it turns out too.
Terry Davis, pbuh, did something very similar!
What would the equivalent be with LLMs?
I spend all of my time with image and video models and have very thin knowledge when it comes to running, fine tuning, etc. with language models.
How would one start with training an LLM on the entire corpus of one's writings? What model would you use? What scripts and tools?
Has anyone had good results with this?
Do you need to subsequently add system prompts, or does it just write like you out of the box?
How could you make it answer your phone, for instance? Or discord messages? Would that sound natural, or is that too far out of domain?
Simplest way pack all text into a prompt.
You could use a vector database.
You could train a model from scratch.
Probably easiest to use OpenAI tools. Upload documents. Make custom model.
How do you make it answer your phone? You could use twillio api + script + llm + voice model. Want natural use a service.
I think you're absolutely right about the easiest approach. I hope you don't mind me asking for a bit more difficulty.
Wouldn't fine tuning produce better results so long as you don't catastrophically forget? You'd preserve more context window space, too, right? Especially if you wanted it to memorize years of facts?
Are LoRAs a thing with LLMs?
Could you train certain layers of the model?
A good place to start with your journey is this guide from Unsloth:
https://docs.unsloth.ai/get-started/fine-tuning-llms-guide
Did it work?
So that's the key difference. A lot of people train these Markov models with the expectation that they're going to be able to use the generated output in isolation.
The problem with that is either your n-gram level is too low in which case it can't maintain any kind of cohesion, or your n-gram level is too high and it's basically just spitting out your existing corpus verbatim.
For me, I was more interested in something that could potentially combine two or three highly disparate concepts found in my previous works into a single outputted sentence - and then I would ideate upon it.
So I haven't opened the program in a long time so I just spun it up and generated a few outputs:
I'm not sure what the original pieces of text were based on that particular sentence but it starts making me think about a kind of strange void harkonnen with heart plugs that lead to weird negatively pressurized areas. That's the idea behind the dream well.> A giant baby is navel corked which if removed causes a vacuum.
Very The Age of Wire and String.
I can’t believe no one’s mentioned the Harry Potter fanfic written by a Markov Chain. If you’re familiar with HP, I highly recommend reading Harry Potter and the Portrait of What Looked Like a Large Pile of Ash.
Here’s a link: https://botnik.org/content/harry-potter.html
Genuine question: Why would anyone want to read that? I glanced at the first sentence and decided not to go any further.
It is hollow text. It has no properties of what I'd want to get out of even the worst book produced by human minds.
Even more sophisticated models have a ceiling of pablum.
While hollow, it is also bad (and absurd) enough to be quite entertaining. It’s from an era where this wasn’t far off the state of the art for coming up with machine-generated text—context that makes it quite a bit funnier than if it were generated by an LLM today.
That said, it’s obviously not to everyone’s tastes!
It's purely meant to be an absurdist read. It obviously makes no sense, yet is close enough to actual language patterns for (some, at least) people to find it hilarious. I had tears in my eyes from laughing way too hard when I first read it.
I love the design of the website more than the Markov model. Good Job!
The one author that I think we have a good chance of recreating would be Barbara Cartwright. She wrote 700+ romance novels all pretty much the same. It should be possible to generate another of her novels given that large a corpus.
I'm not sure how we'd know. My wife sometimes buys and rereads a novel she's already finished.
I recall a Markov chain bot on IRC in the mid 2000s. I didn't see anything better until gpt came along!
Perhaps you are thinking of megahal https://homepage.kranzky.com/megahal/Index.html or if a bit later in the millennium, cobe https://teichman.org/blog/
ah, probably so, looks like there were eggdrop scripts for megahal, thanks!
Yes, I made one using bitlbee back in the 2000s, good times!
I made one for Hipchat at a company. I can't remember if it could emulate specific users, or just channels, but both were definitely on my roadmap at the time.
I'm hoping someone can find it so I can bookmark it but I once read a story about a company that let multiple markov chain bots loose in a Slack channel. A few days later production went down because one of them ran a Slack command that deployed or destroyed their infrastructure.
I think this is more correctly described as a trigram model than a Markov model, if it would naturally expand to 4-grams when they were available, etc, the text would look more coherent
Iirc there was some research on "infini-gram", that is a very large ngram model, that allegedly got performance close to LLMs in some domains a couple years back
Google made some very large ngram models around twenty years ago. This being before the era of ultra-high-speed internet, it was distributed as a set of 6 DVDs.
It achieved state-of-the-art performance at tasks like spelling correction at the time. However, unlike an LLM, it can't generalize at all; if an n-gram isn't in the training corpus it has no idea how to handle it.
https://research.google/blog/all-our-n-gram-are-belong-to-yo...
"I Fed 24 Years of My Blog Posts to a Markov Model" you're not the first who did it. Already dozens of LLMs did it.
First of all: Thank you for giving.
Giving 24 years of your experience, thoughts and life time to us.
This is special in these times of wondering, baiting and consuming only.
Here's a quick custom markov page you can have fun with, (all in client) https://aperocky.com/markov/
npm package of the markov model if you just want to play with it on localhost/somewhere else: https://github.com/Aperocky/weighted-markov-generator
Hailo from CPAN (Perl) it's much lighter than any NPM solution.
When the order argument is cranked up to 4, it looks to the average LLMvangelist like it is thinking.
Really fascinating how you can get such intriguing output from such a simple system. Prompted me to give it a whirl with the content on my own site.
https://vale.rocks/micros/20251214-0503
> Also, these days, one hardly needs a Markov model to generate gibberish; social media provides an ample supply.
In 2020, a friend and I did this with our mutual WhatsApp chat history.
Except instead we fine-tuned GPT-2 instead. (As was the fashion at the time!)
We used this one, I think https://github.com/minimaxir/gpt-2-simple
I think it took 2-3 hours on my friend's Nvidia something.
The result was absolutely hilarious. It was halfway between a markov chain and what you'd expect from a very small LLM these days. Completely absurd nonsense, yet eerily coherent.
Also, it picked up enough of our personality and speech patterns to shine a very low resolution mirror on our souls...
###
Andy: So here's how you get a girlfriend:
1. Start making silly faces
2. Hold out your hand for guys to swipe
3. Walk past them
4. Ask them if they can take their shirt off
5. Get them to take their shirt off
6. Keep walking until they drop their shirt
Andy: Can I state explicitly this is the optimal strategy
That‘s funny! Now imagine you‘re using Signal on iOS instead of WhatsApp. You cannot do this with your chat history because Signal won‘t let you access your own data outside of their app.
https://cdn.cs50.net/ai/2023/x/lectures/6/src6/markov/# This is a nice Markov text generator.
Should call it Trump Speech Generator. Loads of gibberish.
You could literally buy this at Egghead software for $3 from the bargain bin in 1992. I know, because I did. I fed it 5 years worth of my juvenile rants, and laughed at how pompous I sounded through a blender.
https://archive.org/details/Babble_1020
A fairly prescient example of how long ago 4 years was:
https://forum.winworldpc.com/discussion/12953/software-spotl...
I just realized, one of the things that people might start doing is making a gamma model of their personality. I won't even approach who they were as a person, but it will give their Descendants (or bored researchers) a 60% approximation of who they were and their views. (60% is pulled from nowhere to justify my gamma designation, since there isn't a good scale for personality mirror quality for LLMs as far as I'm aware.)
"Dixie can't meaningfully grow as a person. All that he ever will be is burned onto that cart;"
"Do me a favor, boy. This scam of yours, when it's over, you erase this god-damned thing."
now i wonder if you can compare vs feeding into a GPT style transformer of a similar Order of Magnitude in param count..
I thought for a moment your comment was the output of a Markov chain trained on HN
No mention of Rust or gut bacteria. Definitely not.
That's the question today. Turns out transformers really are a leap forwards in terms of AI, whereas Markov chains, scaled up to today's level of resources and capacity, will still output gibberish.
So, are current LLMs better because artificial neural networks are better predictors than Markov models, or because of the scale of the training data? Just putting it out there..
Markov models usually only predict the next token given the two preceding tokens (trigram model) because the data gets so exceptionally sparse beyond that, that it becomes impossible to make probability estimations (despite back-off, smoothing, etc.).
I recommend you to read Bengio et al.’s 2003 paper which describes this issue in more detail and introduces distributional representations (embeddings) in an RNN to avoid this sparsity.
While we are using transformers and sentence pieces now, this paper aptly describes the motivation underpinning modern models.
https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf
Thanks for the reference and I stand corrected. And yes I had looked at it a long time ago and will give it another read. But I think it is saying that RNNs are a means of approximating a statistical property of a collection of text. That property is what we today think of as "completion"? That is, glorified auto complete, and not "distributed representations" of the world. Would you agree?
distributed representations
Distributional representations, not distributed.
https://en.wikipedia.org/wiki/Distributional_semantics#Distr...
When I was in college my friends and I did something similar with all of Donald Trump’s tweets as a funny hackathon project for PennApps. The site isn’t up anymore (RIP free heroku hosting) but the code is still up on GitHub: https://github.com/ikhatri/trumpitter
Megahal/Hailo (cpanm -n hailo for Perl users) can still be fun too.
Usage:
Where "corpus.txt" should be a file with one sentence per line. Easy to do under sed/awk/perl. This spawns the chatbot with your trained brain.By default Hailo chooses the easy engine. If you want something more "realistic", pick the advanced one mentioned at 'perldoc hailo' with the -e flag.
Damn interesting!
Quick test for Perl users (so anyone there with a Unix-like). Run these as a NON root user:
As corpus.txt, you can use a Perl/sed command for instance with book from Gutenberg.I forgot to put the '-E' flag in my previous comments, so here it is. It's to select a more 'complex' engine, so the text output looks less gibberish.
I usually have this technical hypothetical discussions with ChatGpt, I can share if you like, me asking him this: aren't LLMs just huge Markov Chains?! And now I see your project... Funny
> I can share if you like
Respectfully, absolutely nobody wants to read a copy-and-paste of a chat session with ChatGPT.
When you say nobody you mean you, right? You can't possible be answering for every single person in the world.
I was having a discussion about similarities between Markov Chains and LLMs and short after I found this topic on HN, when I wrote "I can share if you like" was as a proof about the coincidence.
LLMs are indeed Markov chains. The breakthrough is that we are able to efficiently compute well performing probabilities for many states using ML.
LLMs are not Markov Chains unless you contort the meaning of a Markov Model State so much you could even include the human brain.
Not sure why that's contorting, a markov model is anything where you know the probability of going from state A to state B. The state can be anything. When it's text generation the state is previous text to text with an extra character, which is true for both LLMs and oldschool n-gram markov models.
Yes, technically you can frame an LLM as a Markov chain by defining the "state" as the entire sequence of previous tokens. But this is a vacuous observation under that definition, literally any deterministic or stochastic process becomes a Markov chain if you make the state space flexible enough. A chess game is a "Markov chain" if the state includes the full board position and move history. The weather is a "Markov chain" if the state includes all relevant atmospheric variables.
The problem is that this definition strips away what makes Markov models useful and interesting as a modeling framework. A “Markov text model” is a low-order Markov model (e.g., n-grams) with a fixed, tractable state and transitions based only on the last k tokens. LLMs aren’t that: they model using un-fixed long-range context (up to the window). For Markov chains, k is non-negotiable. It's a constant, not a variable. Once you make it a variable, near any process can be described as markovian, and the word is useless.
Sure many things can be modelled as Markov chains, which is why they're useful. But it's a mathematical model so there's no bound on how big the state is allowed to be. The only requirement is that all you need is the current state to determine the probabilities of the next state, which is exactly how LLMs work. They don't remember anything beyond the last thing they generated. They just have big context windows.
The etymology of the "markov property" is that the current state does not depend on history.
And in classes, the very first trick you learn to skirt around history is to add Boolean variables to your "memory state". Your systems now model, "did it rain The previous N days?" The issue obviously being that this is exponential if you're not careful. Maybe you can get clever by just making your state a "sliding window history", then it's linear in the number of days you remember. Maybe mix the both. Maybe add even more information .Tradeoffs, tradeoffs.
I don't think LLMs embody the markov property at all, even if you can make everything eventually follow the markov property by just "considering every single possible state". Of which there are (size of token set)^(length) states at minimum because of the KV cache.
The KV cache doesn't affect it because it's just an optimization. LLMs are stateless and don't take any other input than a fixed block of text. They don't have memory, which is the requirement for a Markov chain.
Have you ever actually worked with a basic markov problem?
The markov property states that your state is a transition of probabilities entirely from the previous state.
These states, inhabit a state space. The way you encode "memory" if you need it, e.g. say you need to remember if it rained the last 3 days, is by expanding said state space. In that case, you'd go from 1 state to 3 states, 2^3 states if you needed the precise binary information for each day. Being "clever", maybe you assume only the # of days it rained, in the past 3 days mattered, you can get a 'linear' amount of memory.
Sure, a LLM is a "markov chain" of state space size (# tokens)^(context length), at minimum. That's not a helpful abstraction and defeats the original purpose of the markov observation. The entire point of the markov observation is that you can represent a seemingly huge predictive model with just a couple of variables in a discrete state space, and ideally you're the clever programmer/researcher and can significantly collapse said space by being, well, clever.
Are you deliberately missing the point or what?
> Sure, a LLM is a "markov chain" of state space size (# tokens)^(context length), at minimum.
Okay, so we're agreed.
>Sure many things can be modelled as Markov chains
Again, no they can't, unless you break the definition. K is not a variable. It's as simple as that. The state cannot be flexible.
1. The markov text model uses k tokens, not k tokens sometimes, n tokens other times and whatever you want it to be the rest of the time.
2. A markov model is explcitly described as 'assuming that future states depend only on the current state, not on the events that occurred before it'. Defining your 'state' such that every event imaginable can be captured inside it is a 'clever' workaround, but is ultimately describing something that is decidedly not a markov model.
It's not n sometimes, k tokens some other times. LLMs have fixed context windows, you just sometimes have less text so it's not full. They're pure functions from a fixed size block of text to a probability distribution of the next character, same as the classic lookup table n gram Markov chain model.
1. A context limit is not a Markov order. An n-gram model’s defining constraint is: there exists a small constant k such that the next-token distribution depends only on the last k tokens, full stop. You can't use a k-trained markov model on anything but k tokens, and each token has the same relationship with each other regardless. An LLM’s defining behavior is the opposite: within its window it can condition on any earlier token, and which tokens matter can change drastically with the prompt (attention is content-dependent). “Window size = 8k/128k” is not “order k” in the Markov sense; it’s just a hard truncation boundary.
2. “Fixed-size block” is a padding detail, not a modeling assumption. Yes, implementations batch/pad to a maximum length. But the model is fundamentally conditioned on a variable-length prefix (up to the cap), and it treats position 37 differently from position 3,700 because the computation explicitly uses positional information. That means the conditional distribution is not a simple stationary “transition table” the way the n-gram picture suggests.
3. “Same as a lookup table” is exactly the part that breaks. A classic n-gram Markov model is literally a table (or smoothed table) from discrete contexts to next-token probabilities. A transformer is a learned function that computes a representation of the entire prefix and uses that to produce a distribution. Two contexts that were never seen verbatim in training can still yield sensible outputs because the model generalizes via shared parameters; that is categorically unlike n-gram lookup behavior.
I don't know how many times I have to spell this out for you. Calling LLMs markov chains is less than useless. They don't resemble them in any way unless you understand neither.
I think you're confusing Markov chains and "Markov chain text generators". A Markov chain is a mathematical structure where the probabilities of going to the next state only depend on the current state and not the previous path taken. That's it. It doesn't say anything about whether the probabilities are computed by a transformer or stored in a lookup table, it just exists. How the probabilities are determined in a program doesn't matter mathematically.
Just a heads-up: this is not the first time somebody has to explain Markov chains to famouswaffles on HN, and I'm pretty sure it won't be the last. Engaging further might not be worth it.
I did not even remember you and had to dig to find out what you were on about. Just a heads up, if you've had a previous argument and you want to bring that up later then just speak plainly. Why act like "somebody" is anyone but you?
My response to both of you is the same.
LLMs do depend on previous events, but you say they don't because you've redefined state to include previous events. It's a circular argument. In a Markov chain, state is well defined, not something you can insert any property you want to or redefine as you wish.
It's not my fault neither of you understand what the Markov property is.
By that definition n-gram Markov chain text generators also include previous state because you always put the last n grams. :) It's exactly the same situation as LLMs, just with higher, but still fixed n.
We've been through this. The context of a LLM is not fixed. Context windows =/ n gram orders.
They don't because n gram orders are too small and rigid to include the history in the general case.
I think srean's comment up the thread is spot on. This current situation where the state can be anything you want it to be just does not make a productive conversation.
'A Markov chain is a mathematical structure where the probabilities of going to the next state only depend on the current state and not the previous path taken.'
My point, which seems so hard to grasp for whatever reason is that In a Markov chain, state is a well defined thing. It's not a variable you can assign any property to.
LLMs do depend on the previous path taken. That's the entire reason they're so useful! And the only reason you say they don't is because you've redefined 'state' to include that previous path! It's nonsense. Can you not see the circular argument?
The state is required to be a fixed, well-defined element of a structured state space. Redefining the state as an arbitrarily large, continuously valued encoding of the entire history is a redefinition that trivializes the Markov property, which a Markov chain should satisfy. Under your definition, any sequential system can be called Markov, which means the term no longer distinguishes anything.
A GPT model would be modelled as an n-gram Markov model where n is the size of the context window. This is slightly useful for getting some crude bounds on the behaviour of GPT models in general, but is not a very efficient way to store a GPT model.
I'm not saying it's an n-gram Markov model or that you should store them as a lookup table. Markov models are just a mathematical concept that don't say anything about storage, just that the state change probabilities are a pure function of the current state.
You say state can be anything, no restrictions at all. Let me sell you a perfect predictor then :) The state is the next token.
Well LLMs aren't human brains, unless you contort the definition of matrix algebra so much you could even include them.
QM and GR can be written as matrix algebra, atoms and electrons are QM, chemistry is atoms and electrons, biology is chemistry, brains are biology.
An LLM could be implemented with a Markov chain, but the naïve matrix is ((vocab size)^(context length))^2, which is far too big to fit in this universe.
Like, the Bekenstein bound means writing the transition matrix for an LLM with just 4k context (and 50k vocabulary) at just one bit resolution, the first row (out of a bit more than 10^18795 rows) ends up with a black hole >10^9800 times larger than the observable universe.
Markov models with more than 3 words as "context window" produce very unoriginal text in my experience (corpus used had almost 200k sentences, almost 3 million words), matching the OP's experience. These are by no means large corpuses, but I know it isn't going away with a larger corpus.[1] The Markov chain will wander into "valleys" of reproducing paragraphs of its corpus one for one because it will stumble upon 4-word sequences that it has only seen once. This is because 4 words form a token, not a context window. Markov chains don't have what LLMs have.
If you use a syllable-level token in Markov models the model can't form real words much beyond the second syllable, and you have no way of making it make more sense other than increasing the token size, which exponentially decreases originality. This is the simplest way I can explain it, though I had to address why scaling doesn't work.
[1] There are 4^400000 possible 4-word sequences in English (barring grammar) meaning only a corpus with 8 times that amount of words and with no repetition could offer two ways to chain each possible 4 word sequence.
Yeah, there's only two differences between using Markov chains to predict words and LLMs:
* LLMs don't use Markov chains, * LLMs don't predict words.
* Markov chains have been used to predict syllables or letters since the beginning, and an LLMs tokenizer could be used for Markov chains
* The R package markovchain[1] may look like it's using Markov chains, but it's actually using the R programming language, zeros and ones.
[1] https://cran.r-project.org/web/packages/markovchain/index.ht...
They are definitely not Markov Chains they may, however, be Markov Models. There's a difference between MC and MM.
What do you mean? The states are fully observable (current array of tokens), and using an LLM we calculate the probabilities of moving between them. What is not MC about this?
I suggest getting familiar with or brushing up on the differences between a Markov Chain and a Markov Model. The former is a substantial restriction of the latter. The classic by Kemeny and Snell is a good readable reference.
MC have constant and finite context length, their state is the most recent k tuple of emitted alphabets and transition probabilities are invariant (to time and tokens emitted)
LLMs definitely also have finite context length. And if we consider padding, it is also constant. The k is huge compared to most Markov chains used historically, but it doesn't make it less finite.
That's not correct. Even a toy like an exponential weighted moving averaging produces unbounded context (of diminishing influence).
What do you mean? I can only input k tokens into my LLM to calculate the probs. That is the definition of my state. In the exact way that N-gram LMs use N tokens, but instead of using ML models, they calculate the probabilities based on observed frequencies. There is no unbounded context anywhere.
That's different.
You can certainly feed k-grams one at a time to, estimate the the probability distribution over next token and use that to simulate a Markov Chain and reinitialize the LLM (drop context). In this process the LLM is just a look up table to simulate your MC.
But an LLM on its own doesn't drop context to generate, it's transition probabilities change depending on the tokens.
Don't know what happened. I stumbled onto a funny coincidence - me talking to a LLM about its similarities with MC - decided to share on a post about using MC to generate text. Got some nasty comments and a lot of down votes. Even though my comment sparked a pretty interesting discussion.
Hate to be that guy, but I remember this place being nicer.
Ever since LLMS became popular, there's been an epidemic of people pasting ChatGPT output onto forums (or in your case, offering to). These posts are always received similarly to yours, so I'm skeptical that you're genuinely surprised by the reaction.
Everyone has access to ChatGPT. If we wanted its "opinion" we could ask it ourselves. Your offer is akin to "Hey everyone, want me to Google this and paste the results page here?". You would never offer to do that. Ask yourself why.
These posts are low-effort and add nothing to the conversation, yet the people who write them seem to expect everyone to be impressed by their contribution. If you can't understand why people find this irritating, I'm not sure what to tell you.
Nobody was being nasty. roarcher explained why people reacted the way they did.
...are you under the impression that you have an exclusive relationship with "him"? Everyone else has access to ChatGPT too.
Yes. Yes I was. Thank you for the wake up call. I was under the impression that he was talking only to me.