I’ve been building Fathom, a desktop Jellyfin client, for quite some time and just put out the first public release (0.9.0) for Linux and Windows.

The real draw is having everything in one place. Your Jellyfin movies, shows, music, and Live TV; most of Jellyfin’s important server-side management (users, libraries, scheduled tasks, transcoding, Live TV and DVR, and more); and optional Seerr requests plus a full YouTube client, all without leaving the app. No bouncing between the web dashboard, a requests page, and a browser tab. One window does the lot.

What’s in it:

• Server management, built in. Handle users, libraries, scheduled tasks, transcoding, active sessions, Live TV and DVR, and more, without opening the Jellyfin web dashboard.
• Optional Seerr requests right from the detail page, with Jellyfin, Seerr, or API-key sign-in.
• Optional YouTube client. SponsorBlock, DeArrow, dislike counts, downloads, no ads and no account, off unless you turn it on.
• One shared player. Jellyfin and YouTube use the same controls, seek bar, and shortcuts. Picture-in-picture, or pop a video out to a floating, always-on-top window on your desktop.
• Plus: SyncPlay watch-together (works with other Jellyfin clients too), ratings (Rotten Tomatoes, IMDb, and more), in-app updates with a stable or beta channel, light/dark/AMOLED themes, and searchable settings. Everything plays through mpv, so you get direct play, hardware decoding, and proper subtitle and audio track control.

It’s Linux-first (built and tested on Arch/KDE) with a self-contained AppImage, plus a portable Windows build. Android and more features are on the roadmap.

Download + source: https://github.com/Fathom-Media/fathom/releases/tag/v0.9.0 Repo (AGPL-3.0): https://github.com/Fathom-Media/fathom

This is the first public release, so expect a few rough edges. All feedback is genuinely wanted: bug reports, feature requests, and ideas. The goal is to grow Fathom into one of the best Jellyfin clients out there, so open an issue or start a discussion and it’ll get read.

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 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 — GitHub Actions/release pipeline is largely conventional; AI-suggested improvements only.