Yes us to! I spend heaps of time playing with the link between preferences and the profile.

We aren't really sure yet how best to surface _why_ the model predicts what it does. You can hover over the skim label and there is a bit of reasoning text, which might shed some light on why for now. We will think more about how to make these relationships more clear in the process of tightening them up and generally improving them.

Once the relationships are a bit more clear theres probably the 80/20 rule of work to tighten up those predictions.

Are you referring to what's in the textarea on the edit profile text dialog? Seems to be a simple concatenation of the titles according to how I tagged them. If it's indeed this what you're referring to, what did you find interesting about this?

thats grand, thank you! we really want to look at both using comments as a source for preference, and applying your preferences to comments to perhaps flag/sort/filter once you are on a page. Will have to think about the user flow, and the way in which preferences differ for comments vs posts

hahaha thats a great test case, gona remember that for evals. Did it notice anything? or did it just get confused. we found if you have random preferences it often just says to skim half and skim half, which i suspect it might do in your case.

It _in theory_ should try and pick up a content style (funny stuff??) even if the tech is seemingly random, but i wouldn't be surprised if it just failed.

Sadly not! It lives in our FE monorepo at work, it just kind of jumped out at us as something to split out and demo once we started using it to test some of our ideas. Its something wed consider for sure, but for now the lack of an open repo is kind of a bit of tech debt in a way. Easier to dev in our monorepo to get something out fast.

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.

I genuinely find that interesting to hear. what about the 30 examples felt different to say the frontpage? (assuming it did)

On a meta level i was suuuuper conscious of writing every word of this post/comments myself, as my prior is that HN's community is very intollerant of and highly sensitive to low effort content, whether via AI or not. This is despite using AI tools for lots of other parts of work (drafting, coding, summarising, brainstorming etc).

Do you think HN has become more accepting of AI slop, the slop is becoming harder to detect, or isnt as discerning as i assume?

hackernews is such a rich source of "non trivial" content, and we are really seeing this when using it for a project compared with just consuming it as an individual.

We are focusing right now on how comments could be used to build up a better user context, and your comment has made me think about how we can feed comments in (instead of just titles and urls) for your selected preferences to make a better profile, without needing to scrape anything (expensive and slow).

But i think that would only work because of their quality and relevance generally, which would for sure make it an interesting knowledge source for pure LLM search. Feels like something someone should build or maybe already has! keen to see if anyone knows of projects like that.