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Take back control of your time – Inspired by Nat Friedman

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Take back control of your time – Inspired by Nat Friedman

I developed the "Year Progress App," inspired by the idea that a week is 2% of a year, to make time more tangible using relative scales. Disappointed by the high costs and limited benefits of existing apps, I opted to create a free alternative that aids in effective time management. I chose not to charge for the app to avoid the hassle and to freely contribute to improving productivity. I've open-sourced the app, believing in the community's ability to enhance and expand it. The positive feedback on LinkedIn affirmed my belief in the app's potential and value.

Visit productView on Hacker NewsMar 9, 2024
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using, open · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
51%51% 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
25%25% 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
14%14% 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.

Incorrect prediction on native model

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