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Show HN: HomeBrew HN – Generate personal context for content ranking

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122 points azath92 | 1 comments | 03 Jul 25 12:27 UTC | HN request time: 0s | source

TLDR: Build a quick HN profile to see how little context LLMs need to personalise your feed. Rate 30 posts once, get a permanent ranked homepage you can return to.

Our goal was to build a tool that allowed us to test a range of "personal contexts" on a very focused everyday use case for us, reading HN!

We are exploring use of personal context with LLMs, specifically what kind of data, how much, and with how much additional effort on the user’s part was needed to get decent results. The test tool was a bit of fun on its own so we re-skinned it and decided to post it here.

First time posting anything on HN but folks at work encouraged me to drop a link. Keen on feedback or other interesting projects thinking about bootstrapping personal context for LLM workflows!

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mebazaa ◴[03 Jul 25 19:42 UTC] No.44458518[source]▶
Did you consider using more “traditional” recommendation systems? (and maybe using LLMs to create synthetic preferences…)
replies(1): >>44459471 #
1. azath92 ◴[03 Jul 25 21:51 UTC] No.44459471[source]▶
we came to this by looking at how a "user profile" in plain english could be both used and generated by LLMs, but once we were looking at this we did discuss traditional recsys. Two things against it for this usecase: bootstrapping preferences with a low number of data points, and no "unified" storage of all users preferences or pre-existing dataset is difficult with trad ML or statistical methods. Also having your preferences or "model" if you will in plain english gives a sense of agency, transparency and individuality to your recomendataions that are at least difficult, if not impossible to communicate with other types of models.

Id love to have those assumptions challenged though, if there are examples you could point me towards.