I made MediaManager, a modern alternative to the *ARR ecosystem

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Hi, I'm currently developing an alternative to Sonarr/Radarr/Jellyseer that I called MediaManager. Github Repo Link: https://github.com/maxdorninger/MediaManager Why you might want to use MediaManager: OAuth/OIDC support for authentication; movie AND tv show management; multiple qualities of the same Show/Movie (i.e. you can have a 720p and a 4K version); you can on a per show/per movie basis select if you want the metadata from TMDB or TVDB; Built-in media requests (kinda like Jellyserr); support for torrents containing multiple seasons of a tv show; Support for multiple users; config file support (.toml); merging of Frontend and Backend container (no more CORS issues!); addition of Scoring Rules, they kinda mimic the functionality of Quality/Release/Custom format profiles; addition of media libraries, i.e. multiple library sources not just /data/tv and /data/movies; addition of Usenet/Sabnzbd support; addition of Transmission support; MediaManager also doesn't completely rely on a central service for metadata, you can self host the MetadataRelay or use the public instance that is hosted by me (the dev).

Visit product View on Hacker NewsJul 20, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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