I mapped HN's favorite books with GPT-4o
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…
In plain words
This tool creates an interactive map of books frequently mentioned in Hacker News discussions about reading. It scraped 20,000 comments from reading-related threads, extracted book references using GPT-4o mini, and visualized them as a browseable map organized by genre. The interface mimics the experience of physical book browsing, allowing users to explore interconnected books and their community-sourced descriptions. Books are clustered and spatially arranged to reveal thematic relationships across genres.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 memberships are then embedded using a second round of UMAP (using Hellinger distance) which results in pleasingly dense structures. - The books' descriptions are based on extractions from the comments and GPT's general knowledge. Quality levels vary, and it leads to some oddly specific points, but I haven't found any yet that are straight up wrong. - There are multiple books with the same title. Currently, only the most popular one of those makes it onto the map. - It's surprisingly hard to get high quality book cover images. I tried Google Books and a bunch of open APIs, but they all had their issues. In the end, I got the covers from GoodReads through a hacked together process that combines their autocomplete search with GPT for data linkage. Does anyone know of a reliable source?
Does the same job
all alternatives →- 4H40k HN comments mentioning books, extracted using deep learning2021 · hacker-recommended-books.vercel.app · ▲1,359
- EHExploring HN by mapping and analyzing 40M posts and comments for fun2024 · blog.wilsonl.in · ▲520
- HAHackYourNews – AI summaries of the top HN stories2023 · hackyournews.com · ▲335
Hey there HN! I wanted to share a pet project of mine. I built HackYourNews [1] to scratch a personal itch: Knowing which stories to focus on while browsing aimlessly (though there is a certain joy in that, as well!) HackYourNews uses OpenAI's gpt-3.5-turbo to summarize the destination article as well as the comments section. Summarization of the article is always cached, while summaries of the comments are regenerated if the comments count is >10% (or >10 comments) different. While I styled the homepage to welcome HNers, my preferred view is the Mobile view, accessed from the navbar. This…
- ISI 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…
- TBTop books mentioned in comments on Hacker News2016 · hackernewsbooks.com · ▲463
- TBTurning books into chatbots with GPT-32023 · konjer.xyz · ▲261
So far I've used it to reference ideas from books I've read before. I've also used it to explore books I have not read before by asking the bot questions. Some people have told me they use it like a reading companion. They pause while reading the book if they have a question, and use Konjer to answer it.
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, September 2024
the whole month →
BeforeSunset AI 2.0▲1,267Personalized AI daily planning that suits your life
AI · 2024 · beforesunset.ai
