I Built a Python script that uses a local Ollama LLM to automatically find and add movies to Radarr.
It picks random films from your library, asks Ollama for similar suggestions based on theme and atmosphere, validates against OMDb, scores with plot embeddings, then adds the top results to Radarr automatically.
Examples:
- Whiplash → La La Land, Birdman, All That Jazz
- The Thing → In the Mouth of Madness, It Follows, The Descent
- In Bruges → Seven Psychopaths, Dead Man’s Shoes
Features:
- 100% local, no external AI API
- –auto mode for daily cron/Task Scheduler
- –genre “Horror” for themed movie nights
- Persistent blacklist, configurable quality profile
- Works on Windows, Linux, Mac
GitHub: https://github.com/nikodindon/radarr-movie-recommender
Yes, LLMs are different from human beings, what kind of question is this?
When you go to a shelf of recommendations, you’re not picking from a human; you’re picking from a shelf.
Ah, yes, those magic recommendations shelves that assemble themselves with no input from human hands or minds. I’d forgotten about those.