Tuisic – A simple TUI online music streaming application

Hacker News

Hi HN, I built Tuisic , a terminal-based online music player. It’s probably the first of its kind — a minimal CLI app where you can search, stream, and download music from multiple online platforms without ever touching a browser. No ads, no distractions. + Features: - Works with YouTube, SoundCloud, JioSaavn, Last.fm, ForestFM (YouTube Music coming) - Clean terminal UI built with FTXUI - Vim-like controls and keyboard shortcuts - Favorites list, easy sharing, and downloading - Optional MPRIS (DBus) support for `playerctl`, media keys, etc. - Lightweight and doesn't depend on Electron or a GUI There's also a basic "daemon mode" (press `w`) for leaving it running in the background. + GitHub: https://github.com/Dark-Kernel/tuisic + AUR package: `tuisic-git` Would love feedback or testing, especially from CLI lovers and Linux users. Contributions welcome — even better if you help bring Apple Music or Spotify support one day :) – Sumit Patel (Dark-Kernel)

Visit product View on Hacker NewsJun 23, 2025

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

83%83% 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: apple, user · Missing: mac, agents, macos

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

65%65% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, 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

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

nativeThis product was originally launched on this platform.

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

16%16% 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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