Tunemark – Bookmark moments in songs on Android

Hacker News

I've been practising dancing for a few years now and while practising a choreography I found myself constantly jumping back to certains parts of a song. While there are DJ-type apps available that can be used to add cue points to tracks, those require you to have the song file. Since I couldn't find an app where I could do this with a streaming service, I decided to build one myself. Tunemark works by reading and controlling the currently playing media from Android's media notification. This allows it to work with most music apps as long as they show the currently playing media notification. This is my first ever Android application and it has been fun learning some native app development. I have some features planned for future releases such as sharing bookmarks via a link and perhaps a companion app for smart watches. Hopefully you find this interesting and if you or someone you know could benefit from it, please give it a try or let them know! I'm happy to answer any question and hear any feedback you have on the app or the product page etc.

Visit product View on Hacker NewsApr 27, 2026

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

75%75% 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: apps · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

37%37% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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