I'm Jonathan and I built Hacker Search (https://hackersearch.net), a semantic search engine for Hacker News. Type a keyword or a description of what you're interested in, and you'll get top links from HN surfaced to you along with brief summaries.
Unlike HN's otherwise very valuable search feature, Hacker Search doesn't require you to get your keywords exactly right. That's achieved by leveraging OpenAI's latest embedding models alongside more traditional indexes extracted from the scraped and cleaned up contents of the links.
I think there are many more interesting things one could build atop the HN dataset in the age of LLMs (e.g. more explicitly searching for technical opinions, recommending stories to you based on your interests, and making the core search feature more useful). If any of those sound interesting to you, head over to https://hackersearch.net/signup to get notified when I launch them!
Note: at least one person has built something similar before (https://news.ycombinator.com/item?id=36391655). Funnily enough, I only found out about this through my own implementation, and I based on my testing, I think Hacker Search generally performs better when doing keyword/sentence searches (vs. whole document similarity lookup), thanks to the way the data is indexed.