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Sober Ringtones – Cringe-free ringtones for people who hate ringtones

Hacker News

Sober Ringtones – Cringe-free ringtones for people who hate ringtones

Hey HN, fed up with cringy stock ringtones on my new phones, a few months ago I decided to "compose" my own ringtone for calls and notifications. It had to be minimal, no bells and whistles, low tones. The result was nothing fancy, but that's exactly what I was looking for. I have been using it ever since and I'm pretty happy with it. So after some time I decided to make a few more variants and share them online for other people who hate ringtones that sound like overblown symphonies, cheap disco hits or overly catchy jingles. Would love to know if anyone is interested in this or if you're still stuck that default Samsung ringtone ( https://www.youtube.com/watch?v=mjzdDqpGt6A ) ;) I've been using 102, btw.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
42%42% 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.

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