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Show HN: I used Claude Code to discover connections between 100 books (trails.pieterma.es)

524 points by pmaze ↗ HN
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.

76 comments

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Aurornis
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.

wormpilled
>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! ;)

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joe_the_user
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.

drakeballew
Don't believe Claude, let's put it that way.
theturtletalks
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

smusamashah
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.

hecanjog
Yes, they look really good but they're being connected by an LLM.
pxc
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

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sidrag22
> gets at something fundamental.

  :D
hising
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.
lkbm
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...

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amadeuswoo
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?
timoth3y
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.

pmaze
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.

Aurornis
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

8organicbits
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.

Terretta
Thanos is the comic book villain snapping his fingers.

Theranos is the fraud mentioned in the piece.

urbandw311er
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.

bonkusbingus
"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
drakeballew
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.
tolerance
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.

sciences44
Love the originality here - makes you curious to explore more.

Solid technical execution too. Well done!

jereees
now do this for research papers! fun stuff :)
miracoli
wow I hope the bubble pops soon.. now that you discovered books with AI that was illegally trained on them, how about reading them?
johnwatson11218
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.

johnwatson11218
Thanks for the supportive comments. I'm definitely thinking I should release sooner rather than later. I have been using LLM for specific tasks and here is some sample stored procedure I had an LLM write for me.

-- -- Name: refresh_topic_tables(); Type: PROCEDURE; Schema: public; Owner: postgres --

CREATE PROCEDURE public.refresh_topic_tables() LANGUAGE plpgsql AS $$ BEGIN -- Drop tables in reverse dependency order DROP TABLE IF EXISTS topic_top_terms; DROP TABLE IF EXISTS topic_term_tfidf; DROP TABLE IF EXISTS term_df; DROP TABLE IF EXISTS term_tf; DROP TABLE IF EXISTS topic_terms;

    -- Recreate tables in correct dependency order
    CREATE TABLE topic_terms AS
    SELECT
        dt.term_id,
        dot.topic_id,
        COUNT(DISTINCT dt.document_id) as document_count,
        SUM(frequency) as total_frequency
    FROM document_terms dt
    JOIN document_topics dot ON dt.document_id = dot.document_id
    GROUP BY dt.term_id, dot.topic_id;

    CREATE TABLE term_tf AS
    SELECT
        topic_id,
        term_id,
        SUM(total_frequency) as term_frequency
    FROM topic_terms
    GROUP BY topic_id, term_id;

    CREATE TABLE term_df AS
    SELECT
        term_id,
        COUNT(DISTINCT topic_id) as document_frequency
    FROM topic_terms
    GROUP BY term_id;

    CREATE TABLE topic_term_tfidf AS
    SELECT
        tt.topic_id,
        tt.term_id,
        tt.term_frequency as tf,
        tdf.document_frequency as df,
        tt.term_frequency * LN( (SELECT COUNT(id) FROM topics) / GREATEST(tdf.document_frequency, 1)) as tf_idf
    FROM term_tf tt
    JOIN term_df tdf ON tt.term_id = tdf.term_id;

    CREATE TABLE topic_top_terms AS
    WITH ranked_terms AS (
        SELECT
            ttf.topic_id,
            t.term_text,
            ttf.tf_idf,
            ROW_NUMBER() OVER (PARTITION BY ttf.topic_id ORDER BY ttf.tf_idf DESC) as rank
        FROM topic_term_tfidf ttf
        JOIN terms t ON ttf.term_id = t.id
    )
    SELECT
        topic_id,
        term_text,
        tf_idf,
        rank
    FROM ranked_terms
    WHERE rank <= 5
    ORDER BY topic_id, rank;

    RAISE NOTICE 'All topic tables refreshed successfully';
EXCEPTION WHEN OTHERS THEN RAISE EXCEPTION 'Error refreshing topic tables: %', SQLERRM; END; $$;
mannanj
Seems like a lot of successful leaders have a history of or normalize deception and lying for some benefit.