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Fathom is a free, open-source (AGPL) Jellyfin client for Linux, Windows and Android, with YouTube, Seerr requests and internet radio built in.

Thanks to feedback here, 0.13.1 adds an Interface Size setting on Linux and Windows (75% to 200%). On Linux it also sizes up on its own on a dense 4K screen when the desktop’s scaling is left at 100%.

Also new:

  • Android YouTube downloads are saved to Movies/Fathom and Music/Fathom, where your Files app, gallery and other players see them
  • Windows includes ffmpeg, so YouTube downloads above 360p work with nothing else to install
  • Quick Connect works on Jellyfin 12
  • Android notification, lock screen and earbud controls no longer stop responding
  • Downloaded movies and episodes get readable names with their real file extension
  • Radio reconnects within seconds after a dropped connection

Release and downloads: github.com/Fathom-Media/fathom/…/v0.13.1 Docs: fathom-media.github.io/fathom/

AI Disclosure

Per Rule 7 / [AIP] disclosure requirements AI was used during development as a coding assistant. Level per category:

  • Design (architecture, system design): Hint: I make the architectural calls; AI suggests trade-offs and edge cases I might have missed.
  • Implementation (production code): Pair: roughly 50/50. AI drafts, I review, adjust, test on real hardware, and only commit what I’ve verified. Every commit is manually reviewed before it goes to my dev repo.
  • Testing (writing tests, test plans, QA): Assisted: real-device testing is manual (I test on my own PC and mobile devices before every release). AI helps draft test plans and think through edge cases.
  • Documentation (docs, comments, README, CHANGELOG): Pair: release notes and changelog entries are drafted with AI then edited for tone; comments and code docs are mostly Pair as well.
  • Review (code review, PR feedback): Assisted: I’m the reviewer; AI helps with security sweeps, audit passes on complex changes, and consistency checks.
  • Deployment (CI/CD config): Hint: Docker/GitHub Actions/release pipeline is largely conventional; AI-suggested improvements only.