Storytel-player – unofficial desktop client for Storytel

Hacker News

Hi HN, I’ve been working on a desktop client for Storytel, as there is currently no official app for PC/Mac. I wanted something that felt more "native" than a browser tab and included features like offline listening and system tray integration. The stack: • Frontend: React 18, Tailwind CSS, Vite. • Backend: Fastify (embedded within the app to handle API logic and streaming). • Desktop: Electron with TypeScript. One of the challenges was managing the streaming API and session persistence in a way that feels seamless across different OSs. It currently supports Windows, macOS (x64/ARM64), and Linux. It is still a work in progress, but the core player and library management are stable. Any feedback on the architecture (especially the Fastify-within-Electron approach) or the app itself would be greatly appreciated. Repo: https://github.com/debba/storytel-player Releases: https://github.com/debba/storytel-player/releases

Visit product View on Hacker NewsDec 26, 2025

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

66%66% predicted probability of success on Indie Hackers, 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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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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