Heark – Passivley record audio and transcribe spoken word on Android

Hacker News

Hi HN. I wrote an Android app that records audio in a background service and transcribes it to text. The idea is to have a personal stenographer and second memory bank of recorded audio. Use the app to search and replay specific conversations, meetings, lectures, interviews, or other events from a limitless audio history. Send, delete, and listen from the app, or use the web UI for bulk export and delete operations. There is a 1 week free trial, and a paid subscription thats roughly 11 USD/month, both offer unlimited storage and transcription. The web UI is available without a subscription to maintain access to your data if you cancel. It only supports English for now. Let me know if you have questions or feedback, thanks!

Visit product View on Hacker NewsJan 27, 2023

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88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

34%34% 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: subscription · 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 · Strong signals: paid, audio · Missing: web3, chat, crypto

10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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