Random Thoughts

Random Comics

I’ve barely read any comics lately (because, er, I’ve been reading comics), so I think these are the ones I’ve read over the last month or so? Unheard of!

Cathon’s Pineapples of Wrath is very amusing.

I love her cartooning — it’s just extremely appealing.

And the story is loopy in a satisfactory way. Class.

I’ve read most of this stuff before in a couple of small-press books from Adam Szym.

It’s still scary!

The new piece is the most complex one of them all, and it really works — it’s unnerving and mysterious and you don’t quite know where this all is going, even though it relies on common tropes as “nerdy put-upon boy spirals” and “lizard/corn people from space” with some Charles Burns-ey sexual ick dynamics. But that lizard/corn thing… Szym’s art chops aren’t quite up to doing what he wants to do — I found myself confused at points just because I didn’t know which character I was looking at, or whether Szym was trying to tell us that all these people were really made from corn. Or something.

But still, very entertaining.

I think this one landed on all of those Best of 2025 lists?

And I’ve enjoyed his previous books, so I thought I’d like this one, too. It’s quite traditional — we have a wise-ass main character who’s sent back in time to view historical events and Learn A Lesson. And I think those parts mostly work.

But it still comes off as relentlessly didactic, and I lost interest about one third through. Perhaps it’ll do great in high school history class, if there is such a thing still? The current US regime may have banned them for all I know…

I didn’t much care for Mimi Pond’s latest book (also on all the Best of 2025 lists), and for much the same reason. But this is from 2014. And I somehow missed it the first time around, but I definitely read the followup.

And it’s just great — it’s funny, it’s touching, and it really feels like you’re getting an insightful account of an interesting time and place.

I really like the artwork and the storytelling, too. I just smiled the entire time I was reading this.

Yes! I got caught up on about two or three months worth of serial comics.

I quite like The Mortal Thor, even though it seems to be getting lost in the weeds by issue four already — the series started off without much context, with this Norwegian guy with amnesia, but we’re starting to get infodump upon infodump of what’s been going on over 60 years of Thor continuity, and that shit’s deadly.

The Black Cat is pretty amusing, too, even though the artwork is so Marvel standard. It’s hard to work up all that much enthusiasm, because it’s Marvel, so of course it’s going to get cancelled in a couple issues. That’s it with Marvel: If they’ve got something good going, it’s either going to be cancelled or they’re going to run it into the ground with some lame crossover event or other. All these perfect jumping-off-points…

And I got the final batches of the Age of Revelation issues — that I bought by mistake because the web interface on G-Mart is so confusing.

But I continue to be surprised at how non-awful these books are. And they all have kinda-sorta proper endings, too.

But of course, there’s duds, too.

Finally, I got caught up on my Spirou abonnement. I’m trying to trick myself into learning French by having this weekly deadline of books that I have to read unless I want them to stack up.

This batch, though, has some really good pieces, like the Seccotine thing.

The biggest news is the Attila serial, which is just hilarious. It’s about Attila the Hun, but as he’s running a camp and taking care of various annoying things that happen there.

And also lots of other funny stuff.

And that’s it? That’s not a lot for a month. I must step up my comics reading!

C’mon, Jimmy: Yet another tale of LLM disappointment

Yesterday, there was an intriguing announcement about an inference card that’s, er, basically 100x faster than ChatGPT. As a hackernews said:

I often remind people two orders of quantitative change is a qualitative change.

Which is true — speed matters. So I went to check out chat Jimmy, and gave it my standard LLM question:

And started looking for those books… which mostly were hallucinated. Very 2023. When I finally scrolled down to ask “er, what?”, I saw:

Oh.

OK, I get it — it’s llama 8B, and it has no particular knowledge of anything, so all it can do is output fantasies. You need to hook it up to something that knows something about the area you’re interested in to get it to do something useful. And it is indeed very fast:

So this is not really interesting in and of itself — this is the expected result, but it just lines up with, basically, every single time I’ve tried to use an LLM for something useful after reading some hype. My previous attempt was yesterday, when I hooked up an LLM to a RAG that had ingested 1 million fanzine pages about comics, and the results were pretty unimpressive — it’s no more than a really bad search engine that’s able to form English-sounding sentences.

It’s just… I don’t think I’ve ever seen such a disconnect between the relentless hype — not just on Twitter, but in the press, and from every CEO of every major company: LLMs aren’t just going to be transformative in the future, they’re absolutely essential to use now, right now, at this very minute… and then I try to use them for something, and what I end up with is basically an unreliable toy. And one that’s very expensive to use.

(I had one LLM success story I used to tell so that I wouldn’t sound like a luddite fighting against windmills: “I had a 200 line Javascript package that used jQuery, and I asked ChatGPT to rewrite it to use standard Javascript, and it worked on the first try!” But then, after being in production for some weeks, I noticed that I wasn’t getting one particular (rarer) event… and looked at the code, and saw that it mostly worked (for the main event) by accident, and not at all for the side case.)

(OK, OK, LLMs are really good for OCR, I’ll give them that…)

There is pushback like the above, but it’s still mind-blowing how people are letting hucksters like this set the agenda:

People are buying Mac Minis so that they can run something that queries the Anthropic API? Whyyye!? (And if they’re running local models, they need a beefier GPU.) But it makes total sense that it’s the people who lost their money on NFTs that are spearheading this revolution.

At least we get some comedy out of all of this.

It’s not unlikely that these technologies will be actually useful and more reliable at some point in the future. Q1 2026 is not that time, and there is still no hurry to jump on any bandwagon: Experimenting with it now is a waste of time and money.

There’s no hurry. You’re not missing out on anything. Relax.

If (or when) this technology matures, you can start using it then. Isn’t that the hook here? This stuff is going to be so intelligent that you don’t have to know anything about anything? Right? So you don’t have to start learning now.

The Further Adventures of KwakkBot

Yesterday, I started messing around with a chatbot based on magazines about comics, and today I’m experimenting a bit more to see whether anything interesting… er… happens?

So I’m using Qwen/Qwen2.5-7B-Instruct, and as expected, the results are of the LLM quality we’ve all come to expect: “Never been married…” “They annulled Superman and Lois Lane’s Marriage”…

This query was done after just incorporating the English-language (and American-language) magazines. After doing the rest of the world (which increases the corpus by 80%):

That’s more useful. (Keep in mind that the “knowledge cut off” in the collection of comics magazines around 1999 — it has few new magazines.)

With 14B, there’s not much difference, really…

And, of course, the most burning question on everybody’s mind…

Well, that’s better than the response yesterday.

OK, it’s a leading question…

So that’s all bullshit. Nice that LLMs still are kinda stupid, but I guess it’s all going to be fixed in the next version, right?

Meanwhile, the ChatGPT window that has been guiding me through the LLM installation and corpus ingestion has gotten so slow that it’s not usable any more.

So this has been a problem for many years, and OpenAI’s brilliant engineers haven’t fixed it. The problem is all client side — it updates too much of the DOM tree when inserting new text.

So how do brilliant prompt engineers deal with this problem?

…

Asking a question and then reloading the page seems to work, but man.

Mistral-Nemo-Instruct-2407.

Llama-3.1-8B-Instruct seems to be the best of them?

But very chatty.

So there you go… or rather, you don’t, because I’m not making this publicly available.

Well, we’ll see whether this’ll actually be useful when blogging about comics, which is my use case here.

KwakkBot

I know that LLM usage is controversial, especially in comics circles. And of course it is — generative AI is a plagiarism machine, and people trying to use it for art are ridiculous.

But on the other hand, LLMs are somewhat useful for trying to zoom in on non-clear data. Of course, most of what you get out is nonsense, still, but for data that has never been systematised, you can use it to find where you’re supposed to read, at least.

So here I am with kwakk.info, which has about a million pages from fanzines and magazines about comics. And the search index is indeed very useful, and what I use all the time to see what people thought about various comics. But it’s pretty much useless for tasks like “I want a list of comics-related fanzines from the 60s, ranked by popularity”.

I wondered whether I could point an LLM at my data set and make it cough up interesting things like that, and so…

… I asked an LLM what to do.

GOOD!? Anyway, I spent an hour or so being the go-between, and:

Now it’s gonna be working for 17 hours, ingesting all those pages.

I can really understand now why LLM Agents have gotten so popular — it feels pretty stupid, sitting here cut and pasting between the LLM and the console. “Why can’t it just do that itself!?” And then you end up with your root file system gone and a very contrite LLM.

100% GPU utilisation! That’s what we like to see.

So after letting it cook away (it’s cold here, so I need the heat), and I then asked it: “What’s Superman’s height?”, and:

Wow! It works!

(For the technically minded: This is Qwen/Qwen2.5-7B-Instruct with Qdrant as the vector DB, the embedding model is BAAI/bge-m3, and the GPU is an RTX 5000 Ada.)

So I spent half an hour and made a web interface, and:

Opinions vary about who’s stronger of Hulk and Superman.

The LLM is set up to answer questions only based on what’s in this magazine/fanzine data set, to post references to its claims, and not give an answer if it doesn’t know.

But here’s the real test — something you can’t just search for, but need actual analysis to give an answer to: “What’s the beef between Harlan Ellison and Gary Groth”. Behold!

That answer is, of course, 100% nonsense, proving that this LLM works just as well as all other LLMs:

Additionally, [6] provides insight into the tension between Ellison and Groth, where Groth is criticized for editing the “Harlan Ellison Letters” section in a manner that Ellison found offensive, using derogatory terms like “gimp” and “feep.” Ellison felt that these terms were inappropriate and that Groth was unfairly targeting him.

Where’s that from?

Oh, it’s from a page that talks both about Harlan Ellison and John Byrne, who sounds like an asshole.

So there you go — an LLM trained on magazines and fanzines about comics. But I don’t think I’ll make a public interface for this — it takes about ten seconds to give an answer, and as you can see, the answers aren’t very good. I mean, it’s fine for simple questions: