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I made a simple journal app
Hacker NewsI made a simple journal app
Hey HN! I got sick of journaling apps with endless subscriptions and feature bloat, so I built my own. It's super simple: - Web-first - One payment, then it's yours forever (no subscription BS) - Clean interface that gets out of your way - Just the essentials for actually writing Roadmap: - Media upload - Importing Day One's entries Give it a try if you're into journaling. Would love to hear what you think!
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Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: apps · Missing: mac, agents, macos78%78% 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, podcasting70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue33%33% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released20%20% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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