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Sink – Voice-Enabled Lifelogger for Effortless Data Tracking

Hacker News

Sink – Voice-Enabled Lifelogger for Effortless Data Tracking

Hello HN, I'm excited to introduce Sink, a voice-enabled lifelogger designed for effortless data tracking. With Sink, you can easily collect and sync data about virtually any event or metric in your life, all through simple voice commands or Shortcuts (or by tapping a button, of course). Key Features: - Voice-Activated Tracking: Use Siri to log data without opening the app. - Real-Time Google Sheets Sync: For those who prefer desktop analysis, automagically sync to a Google Sheet in your Google Drive. - Data Export: CSV and JSON formats supported for additional flexibility. - Complete Privacy: Your data is only stored locally unless you choose to sync. Why Sink? As two software engineers with a keen interest in data and personal growth,, we felt that existing solutions didn't offer the level of ease and customization we were looking for. Sink aims to fill that gap, especially for those who are interested in quantified self but find data collection to be a hurdle. Key Goals: - Ensuring seamless Siri integration was a major focus, as was creating a robust syncing mechanism for Google Sheets. - Data privacy was another concern, leading us to a local-first approach to data storage. What's Next: - We're looking into Android support and exploring potential API integrations for even more seamless data collection. We'd love for you to try Sink and share your feedback. Download it here: https://apps.apple.com/us/app/sink-lifelogging-analytics/id6... We have some promo codes to share, so if you'd like one please reach out!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: apple, google, apps · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, google · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
13%13% 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
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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