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LifeHabitsAPI – Get daily habits aligned with core life values

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LifeHabitsAPI – Get daily habits aligned with core life values

I developed LifeHabitsAPI.com – an API that provide value based habits. this mapping helps apps to focus on habits that express one's values and goals in life. It’s designed for: - Coaching platforms - Journaling/self-improvement apps - Personal growth and behavior tools Including scores between a habit and a personal value and effected faculty Live demo: https://lifehabitsapi.com Would love feedback from devs or coaches in the self-improvement and personal growth space!

Visit productView on Hacker NewsJun 19, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
56%56% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
35%35% 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, including · Missing: https docs, excited, just released
33%33% 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: growth · Missing: arr, mrr, revenue
22%22% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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