静阅

静阅 means "quiet reading" – a calm reading app for bedtime

Hacker News

静阅 means "quiet reading" – a calm reading app for bedtime

Hi HN, I’m an indie developer and I built an app called *静阅 (JingYue)* because I wanted a calmer reading experience before bed. Most reading tools push feeds, recommendations, endless scrolls and notifications. I wanted something that felt closer to flipping through a book — quiet, simple, and relaxing. So I made 静阅: • curated calm reading content — quotes, images, short essays • dark and light adaptive themes optimized for night time • no notifications, no algorithmic feeds, just relaxed reading • designed to help you slow down your mind before sleep The app is live on the App Store here: https://apps.apple.com/us/app/静阅/id6758232981 It’s very early and opinionated; I’d love feedback — especially on UX for night time reading and content pacing. Thanks!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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 HuntUnlikely to reach the leaderboard · Strong signals: apple, apps · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
37%37% 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
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: io · Missing: https docs, excited, just released
32%32% 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 · Missing: arr, mrr, revenue
18%18% 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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