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TonePush - set the ringtone on someone else's phone

Hacker News

TonePush - set the ringtone on someone else's phone

I've had this concept bashing about in my head for just around a year now. Now - before the haters - ITS NOT FOR SERIOUS PEOPLE. Wear a suit? Don't install it. Present in front of people ? Don't install it. Hate the permissions it needs ? Just don't go there. However, the feedback I've had so far has been very very positive. I even demo'd at Droidcon 2012 UK on Thursday and it worked ! (Very surprising as all the other network dependant demos failed on their ass). Anyway, would love to get some positive feedback about where to go next. Oh yes, and as I said, if you don't like the idea or trust the permissions I need, then please, just don't install it. It's for the "crazy frog" crowd.

Visit productView on Hacker NewsOct 28, 2012
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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
69%69% 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 · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
55%55% 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
46%46% 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
35%35% 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
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.

Incorrect prediction on native model

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