I built my own Jellyfin macOS client with the help of LLM. It has unique features, like: - A seek b
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| Source: Mastodon | Original article
A developer has released a home‑grown macOS client for Jellyfin, the open‑source media server, after leaning on a large language model to flesh out the codebase. The new app, built on Qt and libmpv, replaces the default web interface with a native player that adds a DJ‑style seek bar, real‑time track analysis, a VU meter and colour‑coded quality indicators for each song file.
The seek bar is the most eye‑catching feature: it moves in sync with the music’s tempo, letting users jump to beats or bars rather than arbitrary timestamps. Behind the scenes, the LLM was prompted to generate the rhythm‑detection algorithm and to map audio‑analysis data onto the UI, cutting development time from weeks to days. Track analysis highlights verses, choruses and bridges directly on the bar, while the VU meter offers visual feedback on loudness, a rarity in typical media players. Colour cues—green for lossless FLAC, amber for high‑bitrate MP3, red for low‑quality streams—give instant insight into file fidelity without opening a properties dialog.
For Jellyfin users, especially those with extensive music libraries, the client addresses long‑standing pain points. The official web client struggles with albums exceeding a few hundred tracks, and existing desktop builds lack granular visualisation tools. By integrating AI‑generated components, the project demonstrates how LLMs can accelerate niche feature development in open‑source ecosystems.
The next steps will determine whether the client gains traction. The developer has opened the repository for community contributions and plans to add support for Apple Silicon, automatic playlist generation based on mood detection, and optional integration with third‑party lyric services. If the project garners enough interest, Jellyfin’s core team might consider upstreaming the UI enhancements, potentially reshaping how the community approaches media playback on macOS. Watch for a GitHub release announcement and any subsequent pull‑request discussions in the coming weeks.
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