Spotify and Apple Health data as art

Hacker News

For the longest time, I've been interested in the idea that the personal data we have logged across platforms can "paint" a pretty clear picture of who we are. And, as individuals, this will reveal fun and maybe sharable stories about us. So I made this app, Day by Data, to connect with and visualize your personal data like step count, music listening habits, and more into tiny visualizations and data art right on your iPhone. The launch version has a few year-in-steps breakdowns and a few Spotify data-visualizations. Over time, the goal is to make more data sources available to connect and empower users to learn from their personal data stored across these platforms. Think data about the food we eat, places we travel, or shows we watch. Data accessibility and cleanliness is the hardest part, but I'm always open to different sources and visualization types. Let me know what you think and if there are certain data sets you'd like to see visualized!

Visit product View on Hacker NewsDec 20, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apple, user, visual · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

TrustMRRFits verified-revenue profile · Strong signals: personal, visualize, users · Missing: mobile apps, ios, entrepreneurs

67%67% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% 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 · Strong signals: platform, users · Missing: plus, intuitive, reviews

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

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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