Visualizing How Books Reference Each Other Across 3k Years

What it does

An atlas of literary influence — who cites whom, across five millennia of books.

Every circle is an author , holding their books. Height is time — antiquity below, the present above. Color marks the author's region of originfrom thiagolira.github.io

In the maker’s words, at launch

There are two parts for this project: 1) The LLM-powered pipeline to extract citations (books + authors) from books and resolve them using both Wikipedia and Goodreads with offline copies I have. The result is data associating Books/Authors to other Books/Authors with accurate bibliographical information…

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  I used Claude Code to discover connections between 100 booksJan 2026 · trails.pieterma.es · ▲524

  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…

• IS
  I scraped 3B Goodreads reviews to train a better recommendation modelNov 2025 · book.sv · ▲606

  Hi everyone, For the past couple months I've been working on a website with two main features: - https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews - https://book.sv/intersect - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://book.sv/remove-my-data) Technical info available here: https://book.sv/how-it-works Note 1: If you only provide one or two…

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  I mapped HN's favorite books with GPT-4o2024 · hnbooks.pieterma.es · ▲285

  Hey HN! I love finding new books to read on here. I wanted to gather the most mentioned books and recreate the serendipity of physical browsing. I scraped 20k comments from HN threads related to reading, extracted the references and opinions using GPT-4o mini, and visualised their embeddings as a map. - OpenAI's embeddings were processed using UMAP and HDBSCAN. A direct 2D projection from the text embeddings didn't yield visually interesting results. Instead, HDBSCAN is first applied on a high-dimensional projection. Those clusters tend to correspond to different genres. The genre…

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  Every great read I've come across, compiled into a knowledge graph2022 · prashantbarahi.com.np · ▲153

  The network graph that holds all the wonderful reads I've ever found on the internet.

• KG
  Knowledge graph of restaurants and chefs, built using LLMs2025 · theophilecantelob.re · ▲201

  Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…

• HL
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