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 + great 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!
Saw it was already commented about CO2, so I thought I’d counter-point your environment claim regarding water usage (since that is something I’ve seen a lot of too).
The ISSA had a call to action due to the AI water use “crisis”: https://www.issa.com/industry-news/ai-data-center-water-consumption-is-creating-an-unprecedented-crisis-in-the-united-states/
68 billion gallons of water by 2028! That’s a lot…right? Well, what I found is that this is somewhat of a bad faith argument. 68 billion gallons annually is a lot for one town, but those are numbers from a national level and it isn’t compared to usage from anything else. So, lets look at US agriculture (that’s something that’s tracked very well by the USDA): https://www.nass.usda.gov/Publications/Highlights/2024/Census22_HL_Irrigation_4.pdf
That’s 26.4 trillion gallons of water annually. So, AI datacenter represents 0.26% of agriculture consumption. If AI datacenter consumption is a crisis, why is agriculture consumption not a crisis? You could argue that agriculture produces “something useful”, but usefulness doesn’t factor into the scarcity of a resource. So, either its not a crisis, or you are cherry picking something that has no meaningful outcome to solving the problem.
yeah, I think the whole “water” argument really dilutes the case against data centers.
On a serious note, the argument works for areas that already struggle to supply enough water for consumers. Otherwise, we should be focusing more on the power stress to the grid, and the domino effect on supply chain of hardware cost increases that it’s happening across many industries. It started with GPUs, now it’s CPU, storage, networking equipment, and other components.
If these prices are too high for a couple of years, we’ll start seeing generalized price increases as companies need to pass along the costs to consumers.
I think the supply chain issue is probably the most pressing out of all of them. The other points people have are either non-issues or a result of dropping usage hogs into existing electrical infrastructure. Infrastructure can be updated, though.
Supply chain is different. There isn’t a supply shortage of chips, its that profitability dictates you should sell them to datacenters or adjacent industry. Unlike infrastructure where you can just build out more, adding more supply for chips just means you have more to sell to datacenters. Since the demand is there, end of day profits will always win.