Show HN: I used Claude Code to discover connections between 100 books
1 day ago (trails.pieterma.es)
I think LLMs are overused to summarise and underused to help us read deeper.
I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them.
I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising.
On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration in comparison.
One of my favourite trail of excerpts goes from Jobs’ reality distortion field to Theranos’ fake demos, to Thiel on startup cults, to Hoffer on mass movement charlatans (https://trails.pieterma.es/trail/useful-lies/). A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.
Details:
* The books are picked from HN’s favourites (which I collected before: https://hnbooks.pieterma.es/).
* Chunks are indexed by topic using Gemini Flash Lite. The whole library cost about £10.
* Topics are organised into a tree structure using recursive Leiden partitioning and LLM labels. This gives a high-level sense of the themes.
* There are several ways to browse. The most useful are embedding similarity, topic tree siblings, and topics cooccurring within a chunk window.
* Everything is stored in SQLite and manipulated using a set of CLI tools.
I wrote more about the process here: https://pieterma.es/syntopic-reading-claude/
I’m curious if this way of reading resonates for anyone else - LLM-mediated or not.
This is a beautiful piece of work. The actual data or outputs seem to be more or less...trash? Maybe too strong a word. But perhaps you are outsourcing too much critical thought to a statistical model. We are all guilty of it. But some of these are egregious, obviously referential LLM dog. The world has more going on than whatever these models seem to believe.
Edit/update: if you are looking for the phantom thread between texts, believe me that an LLM cannot achieve it. I have interrogated the most advanced models for hours, and they cannot do the task to any sort of satisfactory end that a smoked-out half-asleep college freshman could. The models don't have sufficient capacity...yet.
When I saw that the trail goes through just one word like "Us/Them", "fictions" I thought it might be more useful if the trail went through concepts.
The links drawn between the books are “weaker than weak” (to quote Little Richard). This is akin to just thumbing the a book and saying, “oh, look, they used the word fracture and this other book used the word crumble, let’s assign a theme.” It’s a cool idea, but fails in the execution.
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give it a more thorough look maybe?
https://trails.pieterma.es/trail/collective-brain/ is great
It’s any interesting thread for sure, but while reading through this I couldn’t help but think that the point of these ideas are for a person to read and consider deeply. What is the point of having a machine do this “thinking” for us? The thinking is the point.
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This is a software engineering forum. Most of the engineer types here lack the critical education needed to appreciate this sort of thing. I have a literary education and I’m actually shocked at how good most of these threads are.
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I checked 2-3 trails and have to agree.
Take for example the OODA loop. How are the connections made here of any use? Seems like the words are semantically related but the concept are not. And even if they are, so what?
I am missing the so what.
Now imagine a human had read all these books. It would have come up with something new, I’m pretty sure about that.
https://trails.pieterma.es/trail/tempo-gradient/
Build a rag with significant amount of text, extract it by key word topic, place, date, name, etc.
… realize that it’s nonsense and the LLM is not smart enough to figure out much without a reranker and a ton of technology that tells it what to do with the data.
You can run any vector query against a rag and you are guaranteed a response. With chunks that are unrelated any way.
unrelated in any way? that's not normal. have you tested the model to make sure you have sane output? unless you're using sentence-transformers (which is pretty foolproof) you have to be careful about how you pool the raw output vectors
Cool stuff. Thanks for conceptualizing, implementing and sharing
What I'm taking from this post and the responses to it is that LLMs are used most enthusiastically by functionally illiterate people.
What the LLM eats doesn't make you shit.
Can someone break this down for me?
I'm seeing "Thanos committing fraud" in a section about "useful lies". Given that the founder is currently in prison, it seems odd to consider the lie useful instead of harmful. It kinda seems like the AI found a bunch of loosely related things and mislabeled the group.
If you've read these books I'm not seeing what value this adds.
I guess the lies were useful until she got caught?
Why lie if it isn’t useful? Lying is generally bad, why do a generally bad thing if there isn’t at least a justification, a “use” if you will.
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Thanos is the comic book villain snapping his fingers.
Theranos is the fraud mentioned in the piece.
Wow! Amazing!
Have you read the Syntopicon by Mortimer J Adler?
It's right up your alley on this one. It's essentially this, but in 1965, by hand, with Isaac Asimov and William F Buckley Jr, among others.
Where did you get the books from? I've been trying to do something like this myself, but haven't been able to get good access to books under copyright.
Yeah, thinking a bit more here, you've created a Syntopicon. I've always wanted to make a modern one too! You can do the old school late night Wikipedia reading session with the trails idea of yours. Brilliant!
Really though, how can I help you make this bigger?
I did something similar whereby I used pdfplumber to extract text from my pdf book collection. I dumped it into postgresql, then chunked the text into 100 char chunks w/ a 10 char overlap. These chunks were directly embedded into a 384D space using python sentence_transformers. Then I simply averaged all chunks for a doc and wrote that single vector back to postgresql. Then I used UMAP + HDBScan to perform dimensionality reduction and clustering. I ended up with a 2D data set that I can plot with plotly to see my clusters. It is very cool to play with this. It takes hours to import 100 pdf files but I can take one folder that contains a mix of programming titles, self-help, math, science fiction etc. After the fully automated analysis you can clearly see the different topic clusters.
I just spent time getting it all running on docker compose and moved my web ui from express js to flask. I want to get the code cleaned up and open source it at some point.
Yes. Please publish. Sounds very interesting
This sounds amazing, totally interested in seeing the approach and repo.
Sounds a lot like Bertopic. Great library to use.
In a similar vein, I've been using Claude Code to "read" Github projects I have no business understanding. I found this one trending on Github with everything in Russian and went down the rabbit hole of deep packet inspection[0].
0. https://github.com/ValdikSS/GoodbyeDPI
ValdikSS is the guy behind the SBC XQ patches for Android (that alas were never merged by G). I didn’t expect to see him here with another project!
https://habr.com/en/articles/456476/
https://android-review.googlesource.com/c/platform/system/bt...
That's a cool idea. There are so many interesting projects on GitHub that are incomprehensible without a ton of domain context.
I got the idea from an old post on here called Story of Mel[0] where OP talks about the beauty of Mel's intricate machine code on a RPC-4000.
This is the part that always stuck with me:
I have often felt that programming is an art form, whose real value can only be appreciated by another versed in the same arcane art; there are lovely gems and brilliant coups hidden from human view and admiration, sometimes forever, by the very nature of the process. You can learn a lot about an individual just by reading through his code, even in hexadecimal. Mel was, I think, an unsung genius.
0. http://catb.org/esr/jargon/html/story-of-mel.html
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Really great work but have to agree with others that I don’t see the threads.
The one I found most connected that the LLm didn’t was a connection between Jobs and the The Elephant in the Brain
The Elephant in the Brain: The less we know of our own ugly motives, the easier it is to hide them from others. Self-deception is therefore strategic, a ploy our brains use to look good while behaving badly.
Jobs: “He can deceive himself,” said Bill Atkinson. “It allowed him to con people into believing his vision, because he has personally embraced and internalized it.”
I dont understand the lines connecting two pieces of text. In most cases, the connected words have absolutely zero connection with each other.
In "Father wound" the words "abandoned at birth" are connected to "did not". Which makes it look like those visual connections are just a stylistic choice and don't carry any meaning at all.
I had the exact same impression.
Yes, they look really good but they're being connected by an LLM.
I read a book maybe a decade ago on the "digital humanities". I wish now I could remember the title and author. :(
Anyway, it introduced me to the idea of using computational methods in the humanities, including literature. I found it really interesting at the time!
One of the the terms it introduced me to is "distant reading", whose name mirrors that of a technique you may have studied in your gen eds if you went to university ('close reading"). The idea is that rather than zooming in on some tiny piece of text to examine very subtle or nuanced meanings, you zoom out to hundreds or thousands of texts, using computers to search them for insights that only emerge from large bodies of work as wholes. The book argued that there are likely some questions that it is only feasible to ask this way.
An old friend of mine used techniques like this for dissertation in rhetoric, learning enough Python along the way to write the code needed for the analyses she wanted to do. I thought it was pretty cool!
I imagine LLMs are probably positioned now to push distant reading forward in an number of ways: enabling new techniques, allowing old techniques to be used without writing code, and helping novices get started with writing some code. (A lot of the maintainability issues that come with LLM code generation happily don't apply to research projects like this.)
Anyway, if you're interested in other computational techniques you can use to enrich this kind of reading, you might enjoy looking into "distant reading": https://en.wikipedia.org/wiki/Distant_reading
> I wish now I could remember the title and author.
LLMs are great at finding media by vague descriptions. ;)
According to Claude (easy guess from the wikipedia link?):
The book is almost certainly by *Franco Moretti*, who coined the term "distant reading." Given the timeframe ("maybe a decade ago") and the description, it's most likely one of these two:
1. *"Distant Reading"* (2013) — A collection of Moretti's essays that directly takes the concept as its title. This would fit well with "about a decade ago."
2. *"Graphs, Maps, Trees: Abstract Models for Literary History"* (2005) — His earlier and very influential work that laid out the quantitative, computational approach to literary analysis, even if it didn't use "distant reading" as prominently in the title.
Moretti, who founded the Stanford Literary Lab, was the major proponent of the idea that we should analyze literature not just through careful reading of individual canonical texts, but through large-scale computational analysis of hundreds or thousands of works—looking at trends in genre evolution, plot structures, title lengths, and other patterns that only emerge at scale.
Given that the commenter specifically remembers learning the term "distant reading" from the book, my best guess is *"Distant Reading" (2013)*, though "Graphs, Maps, Trees" is also a strong possibility if their memory of "a decade" is approximate.
After some digging, I think it was likely this one: https://direct.mit.edu/books/book/5346/Digital-Humanities
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I’m not surprised that it found connections when you told it to find connections. Most of those connections seem rather dubious to me. I think you’d have been better off coming up with these yourself.
This feels like a nice idea but the connection between the theme and the overarching arc of each book seems tenuous at best. In some cases it just seems to have found one paragraph from thousands and extrapolated a theme that doesn’t really thread through the greater piece.
I do like the idea though — perhaps there is a way to refine the prompting to do a second pass or even multiple passes to iteratively extract themes before the linking step.
Do you have details of the tech stack? Really loved it..
There's a useful write-up of that here: https://pieterma.es/syntopic-reading-claude/#how-its-impleme...
I don’t like this product as a service to readers (i.e., people who read as a cognitive/philosophical exploit) but I do think that somewhere embedded in its backend there are things of benefit.
I think that this sucks the discreet joy out of reading and learning. Having the ways that the topics within a certain book can cross over in lead into another book of a different topic externalized is hollowing and I don’t find it useful.
On the other hand I feel like seeing this process externalized gives us a glimpse at how “the algorithms” (read: recommender systems) suggest seemingly disjunctive content to users. So as a technical achievement I can’t knock what you’ve done and I’m satisfied to see that you’re the guy behind the HN Book map that I thought was nice too.
At its core this looks like a representation of the advantages that LLMs can afford to the humanities. Most of us know how Rob Pike feels about them. I wonder if his senior former colleague feels the same: https://www.cs.princeton.edu/~bwk/hum307/index.html. That’s a digression, but I’d like to see some people think in public about how to reasonably use these tools in that domain.
> Having the ways that the topics within a certain book can cross over in lead into another book of a different topic externalized is hollowing and I don’t find it useful.
Intuitively, I agree. This feels like the different between being a creator (of your own thoughts as inspired by another person's) and a consumer (although in a somewhat educational sense). There would need to be a big advantage to being taught those initial thoughts, analogous to why we teach folks algebra/calculus via formulas rather than having every student figure out proofs for themselves.
"There are, you see, two ways of reading a book: you either see it as a box with something inside and start looking for what it signifies, and then if you're even more perverse or depraved you set off after signifiers. And you treat the next book like a box contained in the first or containing it. And you annotate and interpret and question, and write a book about the book, and so on and on. Or there's the other way: you see the book as a little non-signifying machine, and the only question is "Does it work, and how does it work?" How does it work for you? If it doesn't work, if nothing comes through, you try another book. This second way of reading's intensive: something comes through or it doesn't. There's nothing to explain, nothing to understand, nothing to interpret." — Gilles Deleuze
I am not familiar with the source of this quote, but I don't disagree, it is just incredibly reductive. Gilles Deleuze him-/her-self was not born and did not live in a vacuum. They were influenced and mimetically reproduced ideas they were exposed to, like we all do. I don't find the point of this project meaningless myself. The opposite in fact. But the results are not accurate for anyone who has actually read any of these texts.
retard
You might enjoy my tool deciduous. It is for building knowledge trees and reference stuff exactly like this. The website tells a bit more http://notactuallytreyanastasio.github.io/deciduous/
This is really, really, good. Ignore the commenters in this thread who don’t see the connections. It takes a very high degree of artistic creativity and linguistic imagination to see these types of connections, and many of the “engineer types” on this forum are unfamiliar with that mode of thinking. Ignore them. Every one of these connected threads are really good.
It’s the opposite. The connections are so trivial/obvious as to be uninteresting.
The feedback loop you describe—watching Claude's logs, then just asking it what functionality it wished it had—feels like an underexplored pattern. Did you find its suggestions converged toward a stable toolset, or did it keep wanting new capabilities as the trails got more sophisticated?
I do this all the time in my Claude code workflow: - Claude will stumble a few times before figuring out how to do part of a complex task - I will ask it to explain what it was trying to do, how it eventually solved it, and what was missing from its environment. - Trivial pointers go into the CLAUDE.md. Complex tasks go into a new project skill or a helper script
This is the best way to re-enforce a copilot because models are pretty smart most of the time and I can correct the cases where it stumbles with minimal cognitive effort. Over time I find more and more tasks are solved by agent intelligence or these happy path hints. As primitive as it is, CLAUDE.md is the best we have for long-term adaptive memory.
I ended up judging where to draw the line. Its initial suggestions were genuinely useful and focused on making the basic tool use more efficient. e.g. complaining about a missing CLI parameter that I'd neglected to add for a specific command, requesting to let it navigate the topic tree in ways I hadn't considered, or new definitions for related topics. After a couple iterations the low hanging fruit was exhausted, and its suggestions started spiralling out beyond what I thought would pay off (like training custom embeddings). As long as I kept asking it for new ideas, it would come up with something, but with rapidly diminishing returns.
Earlier today, I was thinking about doing something somewhat similar to this.
I was recently trying to remember a portal fantasy I read as a kid. Goodreads has some impressive lists, not just "Portal Fantasies"[0], but "Portal Fantasies where the portal is on water[1], and a seven more "where/what's the portal" categories like that.
But the portal fantasy I was seeking is on the water and not on the list.
LLMs have failed me so far, as has browsing the larger portal fantasy list. So, I thought, what if I had an LLM look through a list of kids books published in the 1990s and categorize "is this a portal fantasy?" and "which category is the portal?"
I would 1. possibly find my book and 2. possibly find dozens of books I could add to the lists. (And potentially help augment other Goodread-like sites.)
Haven't done it, but I still might.
Anyway, thanks for making this. It's a really cool project!
[0] https://www.goodreads.com/list/show/103552.Portal_Fantasy_Bo...
[1] https://www.goodreads.com/list/show/172393.Fiction_Portal_is...
Given the common goals of every book (fame and sales by grabbing user attention), the general themes and styles would have high similarity. It's like flowers with bright colors and nice shapes.
Orwelliian motives (sheer egoism, aesthetic enthusiasm, historical impulse and political purposes) are somewhat dated.
I had the same idea. I think this is very useful. As it is it does look like a proof-of-concept, and that's OK. I'd develop this as a book recommendation site and simply link to the books on Amazon or your preferred book source. Collect cash on referrals. Good stuff!
You really know what a good interface should be like, this is really inspiring. So is the design of everything I've seen on your website!
I won't pile on to what everyone else has said about the book connections / AI part of this (though I agree that part is not the really interesting or useful thing about your project) but I think a walk-through of how you approach UI design would be very interesting!
LLMs are for people who judge books by their covers.
I assume that high-frequency LLM users are functionally illiterate to the extent that the design of a book's cover is genuinely the only criterion they can use to evaluate said book.
What meaningful connections did it uncover?
You have an interesting idea here, but looking over the LLM output, it's not clear what these "connections" actually mean, or if they mean anything at all.
Feeding a dataset into an LLM and getting it to output something is rather trivial. How is this particular output insightful or helpful? What specific connections gave you, the author, new insight into these works?
You correctly, and importantly point out that "LLMs are overused to summarise and underused to help us read deeper", but you published the LLM summary without explaining how the LLM helped you read deeper.
The connections are meaningful to me in so far as they get me thinking about the topics, another lens to look at these books through. It's a fine balance between being trivial and being so out there that it seems arbitrary.
A trail that hits that balance well IMO is https://trails.pieterma.es/trail/pacemaker-principle/. I find the system theory topics the most interesting. In this one, I like how it pulled in a section from Kitchen Confidential in between oil trade bottlenecks and software team constraints to illustrate the general principle.
Can you walk me though some of the insights you gained? I've read several of those books, including Kitchen Confidential and Confessions of an Economic Hit Man, and I don't see the connection that the LLM (or you) is trying to draw. What is the deeper insight into these works that I am missing?
I'm not familiar with he term "Pacemaker Principle" and Google search was unhelpful. What does it mean in this context? What else does this general principle apply to?
I'm perfectly willing to believe that I am missing something here. But reading thought many of the supportive comments, it seems more likely that this is an LLM Rorschach test where we are given random connections and asked to do the mental work of inventing meaning in them.
I love reading. These are great books. I would be excited if this tool actually helps point out connections that have been overlooked. However, it does not seem to do so.
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I like design that highlights words in one summary and links them to highlights in the next. It's a cool idea
But so many of the links just don't make sense, as several comments have pointed out. Are these actually supposed to represent connections between books, or is it just a random visual effect that's suppose to imply they're connected?
I clicked on one category and it has "Us/Them" linked to "fictions" in the next summary. I get that it's supposed to imply some relationship but I can't parse the relationships
100 books is too small a datasize - particularly given it's a set of HN recommendations (i.e. a very narrow and specific subset of books). A larger set would probably draw more surprising and interesting groupings.
> 100 books is too small a datasize
this to me sounds off. I read the same 8, to 10 books over and over and with every read discover new things. the idea of more books being more useful stands against the same books on repeat. and while I'm not religious, how about dudes only reading 1 book (the Bible, or Koran), and claiming that they're getting all their wisdom from these for a 1000 years?
If I have a library of 100+ books and they are not enough then the quality of these books are the problem and not the number of books in the library?
First thank you for coming up with this idea. Love it. In the spirit of "tools for thought" I think this scratches an important itch.
Someone called the output "egregious, obviously referential LLM dog". And someone else called this trail an example of that: https://trails.pieterma.es/trail/tempo-gradient/
What struck me when reading it was that I parachuted into that trail with zero context. So I asked an LLM to generate some surrounding context for me: this is the verbatim prompt that I just gave gpt-5.2/medium running in my own agentic harness:
> read this: https://trails.pieterma.es/trail/tempo-gradient/. then i want you to write a short essay that shows the relationship between the different quotes making sure to use each quote in the process. the narrative should help me understand the links between the different quotes in each book in context. feel free to use your own knowledge of the books and your general knowledge to make this an approachable thing. what another llm did here is cool - it found relationships between this set of quotes. now i want you to materialize them into a compelling essay that invites curiosity and teaches me something. write the essay to tempo-gradient.md and respond in chat with how you thought about the problem and whether you thought these relationships were meaningful.
This was my agent's response:
Wrote the essay to tempo-gradient.md.
How I thought about it: I treated “tempo gradient” as the shared mechanism behind all eight excerpts—mismatch in loop speed (notice → interpret → decide → act) caused by attention, infrastructure latency, organizational rigidity, system delays, stealthy manipulation, or runaway acceleration. Then I ordered the quotes from “inner time” (tennis/attention) → engineered milliseconds (HFT) → explicit doctrine (OODA) → industrial schedules (mobilization) → structural delays (Beer Game/economy) → slow covert tempo (cyber) → extreme fast tempo (AI takeoff), so each quote sets up the next.
Whether the relationships felt meaningful: yes, mostly. They’re not “the same idea” so much as the same shape appearing in different contexts—feedback delay and decision cadence determining who gets to set the terms of reality for everyone else. The only relationship that feels a bit more interpretive than direct is tennis ↔ geopolitics, but even there the bridge (“how many nows” as a controllable input to decision tempo) is surprisingly sturdy.
And this is the output: https://gist.github.com/jflam/49753b7da64a74f07e35f6e2465f1a...
This is so cool!
On a long enough timeline, we will be using Claude Code for .. any.. type of work?
I did something similar with occult books and famous programming resources.
Conclusion: you find wisdom in everything if you look for it.
we discordians refer to this as the Law of Fives:
>The Law of Fives states simply that: ALL THINGS HAPPEN IN FIVES, OR ARE DIVISIBLE BY OR ARE MULTIPLES OF FIVE, OR ARE SOMEHOW DIRECTLY OR INDIRECTLY APPROPRIATE TO 5.
>The Law of Fives is never wrong.
>In the Erisian Archives is an old memo from Omar to Mal-2: "I find the Law of Fives to be more and more manifest the harder I look."
yes exactly. Replace five with shit and it still holds.
Yeah, I had a similar idea, I used Open AI API to break down movies into the 3 act structure, narrative, pacing, character arcs etc and then trying to find movies that are similar using PostgreSQL with pgvector. The idea was to have another way to find movies I would like to watch next based on more than "similar movies" in IMDb. Threw some hours at it, but I guess it is a system that needs a lot of data, a lot of tokens and enormous amount of tweaking to be useful. I love your idea! I agree with you on that we could use LLM:s for this kind of stuff that we as humans are quite bad at.
Nice! I've been using Claude Code and ChatGPT for something similar. My inspiration is Adler's concept of The Great Conversation and Adler's Propædia. I've been able to jump between books to read about the same concept from different author's perspectives.
This is his Syntopicon for modern works, and automated. It's amazing, I've been wanting to do this for a while but haven't had the time.
I really think we all should sync up and talk more. I want to make this bigger.
It’s interesting how many of the descriptions have a distinct LLM-style voice. Even if you hadn’t posted how it was generated I would have immediately recognized many of the motifs and patterns as LLM writing style.
The visual style of linking phrases from one section to the next looks neat, but the connections don’t seem correct. There’s a link from “fictions” to “internal motives” near the top of the first link and several other links are not really obviously correct.
The names & descriptions definitely have that distinct LLM flavour to them, regardless of which model I used. I decided to keep them, but as short as possible. In general, I find the recombination of human-written text to be the main interest.
There's two stages to the linking: first juxtaposing the excerpts, then finding and linking key phrases within them. I find the excerpts themselves often have interesting connections between them, but the key phrases can be a bit out there. The "fictions" to "internal motives" one does gel for me, given the theme of deceiving ourselves about our own motivations.
Well even the post itself reads to me as AI generated
Finally, Schizophrenia as a Service (SaaS).
surprised to that "seeing like a state" didn't get included in the "legibility tax" category
Like this initial step and its findings.
#1: would a larger dataset increase the depth and breadth of insight ( go to #2) #2: with the initial top 100, are there key ‘super node’ books that stand out as ones to read due the breadth they offer. Would a larger dataset identify further ‘super node’ books.
Interesting - ages ago I used SML to look at the relationships between Shakespears plays.
https://medium.com/gft-engineering/using-text-embeddings-and...
Validation is a problem here - you find relationships, but so what? Is it right.... I can't say. It is interesting though.
Makes me wonder, how well could an LLM-based solution score on the Netflix prize?
https://en.wikipedia.org/wiki/Netflix_Prize
(Are people still trying to improve upon the original winning solution?)
Love the originality here - makes you curious to explore more.
Solid technical execution too. Well done!
Claude code is good for arranging random things into categories, with code, configuration and documentation files it is barely goes into random rabbit holes or hallucinates categories for me.
This is GraphRAG using SQLite.
Wouldn't it be good if recursive Leiden and cypher was built into an embedded DB?
That's what I'm looking into with mcp-server-ladybug.
Where did you come across Leiden partitioning? I’m facing a similar use case and wonder what you’re reading.
Pretty new graph clustering algorithm (published in 2019). Original publication which is actually fairly readable: https://www.nature.com/articles/s41598-019-41695-z
this is amazingly cool, great work!
> A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.
I really appreciate you mentioning this. I think this is the nature of LLMs in general. Any symbol it processes can affect its reasoning capabilities.
The UI animations are so fun
What did it say about who wrote To Kill a Mockingbird?
>A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems
Interesting... seems like it wants the keys on your system! ;)
The website design and content are much nicer than the "ideas" here. Just standard LLM slop once if you actually have read some of these books.
This is an IQ test lol
to be clear: LLMs are for retards.
now do this for research papers! fun stuff :)
Seems like a lot of successful leaders have a history of or normalize deception and lying for some benefit.
A fun tendency is that Claude kept getting distracted by topics of secrecy, conspiracy, and hidden systems - as if the task itself summoned a Foucault’s Pendulum mindset.
It's all fun and game 'till someone loses an eye/mind/even-tenuous-connection-to-reality.
Edit: I'd mention that the themes Claude finds qualify as important stuff imo. But they're all pretty grim and it's a bit problematic focusing on them for a long period. Also, they are often the grimmest spin things that are well known.
Don't believe Claude, let's put it that way.
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I'm carrying a thought around for the last few weeks:
A LLM is a transformer. It transforms a prompt into a result.
Or a human idea into a concrete java implementation.
Currently I'm exploring what unexpected or curious transformations LLMs are capable of but haven't found much yet.
At least I myself was surprised that an LLM can transform a description of something into an IMG by transforming it into a SVG.
Format conversions (text → code, description → SVG) are the transformations most reach for first. To me the interesting ones are cognitive: your vague sense → something concrete you can react to → refined understanding. The LLM gives you an artifact to recognize against. That recognition ("yes, more of that" or "no, not quite") is where understanding actually shifts. Each cycle sharpens what you're looking for, a bit like a flywheel, each feeds into the next one.
Ironically your comment is clearly written by an LLM.
Ironic indeed: pattern-matching the prose style instead of engaging the idea is exactly the shallow reading the post is about.
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> gets at something fundamental.
LLMs are generators, and that was the correct way to view them at the start. Agents explore.
Generator vs. explorer is a useful distinction, but it's incomplete. Agents without a recognition loop are just generators with extra steps.
What makes exploration valuable is the cycle: act, observe, recognize whether you're closer to what you wanted, then refine. Without that recognition ("closer" or "drifting"), you're exploring blind.
Context is what lets the loop close. You need enough of it to judge the outcome. I think that real shift isn't generators → agents. It's one-shot output → iterative refinement with judgment in the loop.
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wow I hope the bubble pops soon.. now that you discovered books with AI that was illegally trained on them, how about reading them?
Claude Code users only know how to read well enough to skim the responses fed to them by their chatbots.
The people who use software like Claude Code are overwhelmingly not literate or motivated enough to read books.
I'm not sure I understand what the connections are exactly, or whether they go much deeper than certain words and phrases.
I'm really not trying to be mean, but one of the things we learn in the humanities is that basically any two texts can be connected via extremely broad statements (e.g. "Perfect is the enemy of the good"). This is like the joke on twitter about how every couple of years someone in tech invents the concept of public transportation.
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Please don't give yourself LLM-induced psychosis.
Monetize it!