I just pushed v22 of my project : a local AI companion for Radarr, that goes beyond generic genre or TMDb lists.
This isn't "yet another recommender". It's your personal taste explorer that actually gets the vibe you want in natural language and builds recommendations starting from your existing library.
Key highlights from a real recent run:
- Command:
--mood "dystopian films like Idiocracy, Gattaca or In Time" - Output: Metropolis (1927), V for Vendetta, Children of Men, Brazil (1985), Minority Report, Dark City, Equilibrium, Upgrade, The Road... → oppressive/surveillance/inequality/societal critique atmosphere, not just "dark sci-fi".
How it works :
- Starts by sampling random movies from your Radarr collection (or uses your mood/like/saga input).
- Asks a local Ollama LLM (e.g. mistral-small:22b) for 25 thematic suggestions based on atmosphere/vibe.
- Validates each via OMDb (IMDb rating, genres, plot, director, cast...).
- Scores intelligently: IMDb rating + genre match + director/actor bonus + plot embedding similarity (cosine on Ollama embeddings).
- Adds the top ones directly to Radarr (with confirmation: all / one-by-one / no).
- Persistent blacklist to avoid repeats.
Different modes :
--mood "dark psychological thrillers with unreliable narrators", any vibe you describe--like "Parasite" --mood "mind-bending class warfare"(or just--like "Whiplash")--saga(auto-detects incomplete sagas in your library and suggests missing entries) or--saga "Star Wars"--director "Kubrick"/--actor "De Niro"/--cast "Pacino De Niro"(movies where they co-star)--analyze→ full library audit + gaps (e.g. "You're missing Kurosawa classics and French New Wave")--watchlist→ import from Letterboxd/IMDb--auto→ perfect for daily cron / Task Scheduler (wake up to 10 fresh additions)
Standout features:
- 100% local + privacy-first (Ollama + free OMDb API only)
- No cloud AI, no tracking
- colored console output, logs, stats, HTML/CSV exports
- Synopsis preview before adding
- Configurable quality profile, min IMDb, availability filters
- Works on Windows, Linux, Mac
GitHub (clean single-file Python script + detailed README):
https://github.com/nikodindon/radarr-movie-recommender
If you're tired of generic Discover lists, Netflix-style randomness, or manual hunting give it a spin. The vibe/mood mode + auto saga completion really change how you expand your collection.
Let me know what you think, any weird mood examples you'd like to test, or features you'd want added!
Yeah. Maybe it's time to adopt some new rule in the selfhosted community. Mandating disclosure. Because we got several AI coded projects in the last few days or weeks.
I just want some say in what I install on my computer. And not be fooled by someone into using their software.
I mean I know why people deliberately hide it, and say "I built ..." when they didn't. Because otherwise there's an immediate shitstorm coming in. But deceiving people about the nature of the projects isn't a proper solution either. And it doesn't align well with the traditional core values of Free Software. I think a lot of value is lost if honesty (and transparency) isn't held up anymore within our community.
Warning, anecdote:
I was unexpectedly stuck in Asia for the last month (because of the impact of the war), turning an in-person dev conference I was organising into an "in-person except for me" one at a few days notice.
I needed a simple countdown timer/agenda display I could mix into the video with OBS; a simple requirement, so I tried a few from the standard package repos (
apt, snap store, that kind of thing.)None of them worked the way I wanted or at all - one of them written in Python installed about 100 goddamned dependencies (because, Python,) and then crashed because, well, Python.
So I gave up and asked my local hosted LLM model to write it for me in Rust. In less than 10 minutes I had exactly what I wanted, in a few hundred lines of Rust. And yeah, I did tidy it up and publish it to the snap store as well, because it's neat and it might help someone else.
Which is more secure? The couple of hundred lines of Rust written by my LLM, or the Python or node.js app that the developer pinky-promises was written entirely by human hand, and which downloads half the Internet as dependencies that I absolutely am not going to spend time auditing just to display a goddamned countdown clock in a terminal window?
The solution to managing untrusted code isn't asking developers for self-declared purity test results. It's sandboxing, containers, static analysis... All the stuff that you are doing already with all the code/apps you download if you're actually concerned. You are doing those things, right?