Mindfuse – A voice-only app for anonymous 1-on-1 conversations

Hacker News

Hey HN, I built Mindfuse because I missed having real conversations online. It’s a simple concept: You press a button and get matched with someone, anonymously, for a voice-only conversation. No usernames, no profiles, no feeds. Just two people talking. The goal is to create quiet, meaningful spaces in a noisy, performative internet. It’s live on iOS now. Still early — small user base, no monetization, just people talking. Happy to answer any questions or feedback. I’d especially love to hear from anyone building in the audio/social space or interested in digital minimalism, mindful tech, or mental health. Thanks for reading.

Visit product View on Hacker NewsJun 24, 2025

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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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

74%74% 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 · Missing: mac, agents, macos

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

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% 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

39%39% 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

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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