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?