I fed 24 years of my blog posts to a Markov model

https://susam.net/fed-24-years-of-posts-to-markov-model.html

by zdw • 7 months ago

303 123 7 months ago

swyx• 7 months ago

now i wonder if you can compare vs feeding into a GPT style transformer of a similar Order of Magnitude in param count..

0_____0• 7 months ago

I thought for a moment your comment was the output of a Markov chain trained on HN

bitwize2• 7 months ago

No mention of Rust or gut bacteria. Definitely not.

fragmede• 7 months ago

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.

atum474• 7 months ago

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

pavel_lishin3• 7 months ago

I can share if you like

Respectfully, absolutely nobody wants to read a copy-and-paste of a chat session with ChatGPT.

atum474• 7 months ago

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.

matusp4• 7 months ago

LLMs are indeed Markov chains. The breakthrough is that we are able to efficiently compute well performing probabilities for many states using ML.

famouswaffles7• 7 months ago

LLMs are not Markov Chains unless you contort the meaning of a Markov Model State so much you could even include the human brain.

cwyers• 7 months ago

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.

arboles3• 7 months ago

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.

srean8• 7 months ago

They are definitely not Markov Chains they may, however, be Markov Models. There's a difference between MC and MM.

roarcher2• 7 months ago

...are you under the impression that you have an exclusive relationship with "him"? Everyone else has access to ChatGPT too.

atum474• 7 months ago

Yes. Yes I was. Thank you for the wake up call. I was under the impression that he was talking only to me.

atum474• 7 months ago

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.

roarcher2• 7 months ago

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.

pavel_lishin3• 7 months ago

Nobody was being nasty. roarcher explained why people reacted the way they did.

lacunary2• 7 months ago

I recall a Markov chain bot on IRC in the mid 2000s. I didn't see anything better until gpt came along!

nurettin• 7 months ago

Yes, I made one using bitlbee back in the 2000s, good times!

pavel_lishin3• 7 months ago

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.

frumiousirc• 7 months ago

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/

lacunary2• 7 months ago

ah, probably so, looks like there were eggdrop scripts for megahal, thanks!

vunderba4• 7 months ago

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.

bitwize2• 7 months ago

Terry Davis, pbuh, did something very similar!

davely• 7 months ago

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

idiotsecant• 7 months ago

Did it work?

vunderba4• 7 months ago

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.

boznz• 7 months ago

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.

echelon2• 7 months ago

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?

ipaddr• 7 months ago

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.

Tallain2• 7 months ago

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/

vunderba4• 7 months ago

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.

wrp• 7 months ago

There was an MS-DOS tool by James Korenthal called Babble[0], which did something similar. It apparently worked according to a set of grammatical transformers rather than by generating n-grams, so it was more akin to the "cut-up" technique[1]. He reported that he got better output from smaller, more focused corpora. Its output was surprisingly interesting.

[0] https://archive.org/details/Babble_1020, https://vetusware.com/download/Babble%21%202.0/?id=11924

[1] https://en.wikipedia.org/wiki/Cut-up_technique

anthk3• 7 months ago

Megahal/Hailo (cpanm -n hailo for Perl users) can still be fun too.

Usage:

      hailo -t corpus.txt -b brain.brn

Where "corpus.txt" should be a file with one sentence per line. Easy to do under sed/awk/perl.

      hailo -b brain.brn

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.

hilti• 7 months ago

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.

hexnuts• 7 months ago

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.)

jacquesm• 7 months ago

"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."

sebastianmestre3• 7 months ago

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!

countWSS• 7 months ago

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.

Tallain2• 7 months ago

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.

andai2• 7 months ago

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.

vunderba4• 7 months ago

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.

yard2010• 7 months ago

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.

travisjungroth• 7 months ago

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.

sebastianmestre3• 7 months ago

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.

samus• 7 months ago

It would make sense to repeat the experiment with the tokenizer of an LLM.

sebastianmestre3• 7 months ago

Pretty sure that OpenAI uses BPE in their GPT models

Aperocky• 7 months ago

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

anthk3• 7 months ago

Hailo from CPAN (Perl) it's much lighter than any NPM solution.

ikhatri• 7 months ago

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

monoidl• 7 months ago

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

Legend2440• 7 months ago

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...

tqian• 7 months ago

I have this DVD set in my basement. Technically, there are still methods for estimating the probability of unseen ngrams. Backoff (interpolating with lower grams) is an option. You can also impose prior distributions like a Bayesian so that you can make "rational" guesses.

Ngrams are surprisingly powerful for how little computation they require. They can be trained in seconds even with tons of data.

manthangupta109• 7 months ago

Damn interesting!

OuterVale• 7 months ago

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

kazinator• 7 months ago

When the order argument is cranked up to 4, it looks to the average LLMvangelist like it is thinking.

andai2• 7 months ago

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

Tepix• 7 months ago

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.

GarnetFloride• 7 months ago

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.

LanceH• 7 months ago

I'm not sure how we'd know. My wife sometimes buys and rereads a novel she's already finished.

user_78322• 7 months ago

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

mattacular• 7 months ago

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.

grahamnorton39• 7 months ago

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!

user_78322• 7 months ago

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.

cluckindan• 7 months ago

That reads as coherently as a pile of Zen koans.

Peteragain3• 7 months ago

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..

microtonal2• 7 months ago

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

Peteragain3• 7 months ago

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?

lelanthran2• 7 months ago

Markov models usually only predict the next token given the two preceding tokens (trigram model) because the data gets so exceptionally sparse beyond that

Of course, that's because it is a probability along a single dimension with a chain-length along that one dimension while LLMs and NNs use multiple dimensions (They are meshed, not chained).

I really want to know what the result would look like with a few more dimensions resulting in a markov mesh type structure rather than a chain structure.

srean8• 7 months ago

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.

NuclearPM• 7 months ago

Learnable? What does that mean?

srean8• 7 months ago

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.

canjobear• 7 months ago

This is right in terms of the rigorous statistical sense of “Markov model”. But in practice in the world of NLP and chatbots through the 90s and 2000s, “Markov model” was usually used to refer to Markov chains (ie you only condition on the previous k words). Hence the term “Hidden Markov Model” to refer to what you’re calling a Markov model.

srean8• 7 months ago

I covered hidden Markov Models briefly in my comment.

It depends on whether the state is visible in the observations or not. Hidden or not is an orthogonal axis of variation compared to the other variations mentioned in the comment.

In a non-hidden model there is no ambiguity or uncertainty about what the current state is.

frizlab• 7 months ago

Also, these days, one hardly needs a Markov model to generate gibberish; social media provides an ample supply.

anthk3• 7 months ago

Quick test for Perl users (so anyone there with a Unix-like). Run these as a NON root user:

       cpanm -n local::lib

       cpanm -n Hailo

       ~/perl5/bin/hailo -E Scored -t corpus.txt -b brain.brn

       ~/perl5/bin/hailo -b brain.brn

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.

msapaydin• 7 months ago

https://cdn.cs50.net/ai/2023/x/lectures/6/src6/markov/# This is a nice Markov text generator.

litver• 7 months ago

"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.

pessimizer• 7 months ago

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...

delfugal• 7 months ago

Should call it Trump Speech Generator. Loads of gibberish.

rumgewieselt• 7 months ago

I love the design of the website more than the Markov model. Good Job!

keithalewis• 7 months ago

https://math.uchicago.edu/~shmuel/Network-course-readings/MC...

effnorwood• 7 months ago

It laughed and gave him a kiss

I fed 24 years of my blog posts to a Markov model

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