Real-time emotional subtitles for Apple Watch

Hacker News

I made a tonal analysis app with the goal of translating emotions across neurotypes and accents to improve emotional communication. Our ML model runs on edge on the Apple Watch and was trained on representative datasets of the vocal diversity of North American speakers, taking into account race, gender, geography, age, and neurotype. More about VIBES: Introducing Vibes for Apple Watch! Vibes reads emotions during conversation to help improve emotional communication and shared understanding. Vibes is the first emotional subtitle app for Apple Watch, buzzing emotions with real-time haptic and visual feedback. Vibes was created by Valence Vibrations to improve emotional connection and friendship across diverse communities of people, while supporting the power of each person’s unique voice. You may even understand your own emotions better via Vibes. Vibes only uses on-device processing, so all vocal data and emotional classifications cannot be accessed by Vibes or a third party. Vibes currently only supports North American English speakers and Apple Watch Series 4 or later. Other languages and devices are coming soon. We have a free 1 week trial active and I'm happy to extend it for folks in this community if there's interest.

Visit product View on Hacker NewsFeb 21, 2023

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

77%77% 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: model, apple, visual · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

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.

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

19%19% 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

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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