fingerthieff

4 ms·

1.
by fingerthieff 2y ago
As an update for others who read this, a server component has been added.
2.
by fingerthieff 2y ago
For my projects I typically lean towards avoiding server components at all costs just because I prefer the ease of setup and it is one less thing to break for users. Though it has some annoyances like you're encountering. Maybe at some
3.
by fingerthieff 2y ago
I toyed with embedding, and still have an implementation laying around in a branch locally, but that would require a readily available dataset of embedded movies and shows to do comparisons against right? I'm pretty new to embedding so
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by fingerthieff 2y ago
Jellyfin support has already been added earlier today.
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by fingerthieff 2y ago
yeah I mentioned in another comment I've never found recommendation systems to work very well for me. I've gone through many of them and the reason I decided to start using LLMs was because I was out of options...and after I tried
6.
by fingerthieff 2y ago
I haven't looked into it too deep if I'm honest, I built this app solely because I find LLM TV and Movie recommendations to be leaps and bounds better than any other recommendation service I've ever used, I find most of them
7.
by fingerthieff 2y ago
That is a likely true to an extent, though it's hard to say at what point it cuts off. If a model was trained 6 months ago for example it will likely have some info on shows that came out this month due to various data points talking a
8.
by fingerthieff 2y ago
An LLM Prompt, not ChatGPT specific. But yeah pretty much that is the core of everything.
9.
by fingerthieff 2y ago
I'm definitely not opposed to adding Lidarr support, I've never personally used it so I may tinker around with it here soon and think on how to structure prompts for that etc...
10.
by fingerthieff 2y ago
It's hard to really explain how the LLM decides what to recommend if I'm honest. The general idea is I generate a prompt to feed into the LLM with specific instructions to use the Sonarr (for example) library info which is passed
11.
by fingerthieff 2y ago
Hopefully it works well for you! I just pushed up changes for adding Jellyfin integration as well, so that should be available through the docker hub here soon as well.
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by fingerthieff 2y ago
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 tho
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by fingerthieff 2y ago
Tautulli proved troublesome in my first attempt at integrating it and got put on the back-burner unfortunately. I would really like to get it integrated properly so per user recommendations could become a thing. I haven't looked at int
14.
by fingerthieff 2y ago
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
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by fingerthieff 2y ago
Thanks! Jellyfin is definitely on the list to be added, it's probably next in line actually. If it's as simple as Plex integration I'll be very happy. Edit - Support Added
16.
Show HN: Recommendarr – AI Driven Recommendations Based on Sonarr/Radarr Media(github.com)
88 points by fingerthieff 2y ago|46 comments
17.
Show HN: MinimalChat Is a Full-Featured and Self-Contained Chat Application(github.com)
1 points by fingerthieff 2y ago|0 comments
18.
Show HN: MinimalChat – A Simple and Customizable LLM Chat Application(github.com)
6 points by fingerthieff 2y ago|0 comments