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
I think the problem is a cyclical one. Some devs are afraid to admit that they used AI to help them code because there’s so much hatred towards using AI to code. But the hatred only grows because some devs are not disclosing that they’ve had help from AI to code and it seems like they’re hiding something which then builds distrust. And of course, that’s not helped by the influx of slop too where an AI has been used and the code has not been reviewed and understood before its released.
I don’t mind more foss projects, even if they’re vibe coded, but please PLEASE understand your code IN FULL before releasing it, if at least so you can help troubleshoot the bugs people experience when they happen!