How I Built an AI-Powered Hacker News Book Recommendation Site in 2.5 Hours
Karpathy built HackerTube in about 2 hours with Claude Code. I rebuilt it for books with Cursor. Here's my honest time, cost, and everything that broke.
Signup Write New Story How I Built an AI-Powered Hacker News Book Recommendation Site in 2.5 Hours by Ishank Choudhary by Ishank Choudhary | @ ishankdev Software Architect, AI Engineer & Technical Leader. Building apps, exploring dev tools, sharing insights from a decade in tech.
Software Architect, AI Engineer & Technical Leader. Building apps, exploring dev tools, sharing insights from a decade in tech.
On 1 Nov, Andrej Karpathy shared HackerTube (https://hackertube.org/). He had Claude filter 43M Hacker News comments from 20 years down to 6,454 recommended videos, and he said the site took about 2 hours with Opus 5.5 in Claude Code. He also published the data as a public zip.
As a Lead SWE, my first question was whether I could pull off the same trick with a different topic and a different tool. I picked books, because HN comment threads are basically one long reading list. I picked Cursor, because that's the tool I use every day.
The result is HackerShelf: https://ishankdev.github.io/hackershelf/ (code: https://github.com/IshankDev/hackershelf).
His DATA.md lays out the method, and I copied it:
He did a few steps by hand: a Google Cloud API key for YouTube metadata, a VPS and a domain.
The final shelf has 151 books, 723 mentions and 601 unique recommenders, sorted into 9 categories.
Being honest, my total was longer than his.
From start to finish that's about 2.5 hours of wall clock (7:47 to about 10:17 PM IST). That includes my own manual steps: creating the repo, giving Cursor GitHub access and turning on GitHub Pages.
Cursor's part, writing the actual code, took about 40 minutes. Most of the remaining time went to calling APIs and waiting on rate limits.
I spot-checked 10 books and 83 mentions against the original HN comments. 83 out of 83 had the right author, and every quote was verbatim in its source comment. For example:
The ranking uses a score that weighs strong recommendations above passing mentions, which is why a book with 9 recommenders can rank above one with 11.
All credit for the idea and method goes to Andrej Karpathy and HackerTube. The comment data comes from open-index/hacker-news under ODC-By 1.0. The comments themselves are still their authors' work, and every mention on HackerShelf links back to the original comment.
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