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I built a tool for my podcast listeners to send me voice messages

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I built a tool for my podcast listeners to send me voice messages

Say you have a podcast and you want your listeners to send you voice messages. The only available options are: - listeners call you via phone (costs them money, very high prices for international listeners); - or, users record themselves on an app, attach the file to an email and send you the file (very cumbersome, high risk of failure due to file size, format, ...). I didn't like these "solutions": they both introduce a very high frictional process to listeners and, for me, are a huge PIA to manage. To fix this whole situation, I built https://mono.tn (read: monotone). It's a simple tool to allow listeners, wherever they are, to easily send me voice messages. Now, it only takes three taps (or clicks) on their phone (or computer) to complete the whole process. And I have a single place to manage all the messages I receive. I'm curious to know if people other than me would find this tool useful?

Visit productView on Hacker NewsJul 19, 2022
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: user, computer, email · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, 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
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
36%36% 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 · Strong signals: introduce · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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