[fingerthieff]

Interesting, I've never used Trakt before but that looks pretty cool. I could see adding support for that. I'll definitely be looking into it.

As for the largest library, I only really know of my own which is around 250 series and 250 movies. Not small but not huge. Passing all of that info is fine enough, but I'm also curious how truly massive libraries or watch histories are handled.

I imagine you would hit the LLM token input limit first if you had thousands of series and movies. Definitely need some further testing in those cases.

[nickthegreek]

Ahh ya. Big libraries can have over 30k movies. Emby, jellyfin, and plex can also integrate into Trakt. So it’s already being used in these apps for many users.

[fingerthieff]

That's good to know, there are ways around the limit of course by breaking up the prompt into multiple messages and then you're at the mercy of the models context window which can be anywhere from 4k to millions.

At some point though like you say, it's going to become ineffective and you'd probably want to use the "Sampling" mode that is available to only send a random subset of your library to model as a general representation. Though how well this works on massive library remains to be seen.

[shellfishgene]

Not sure how useful recommendations based on 30k movies would be, you could basically recommend anything...

[nickthegreek]

Indeed. I would use watch history over library contents.