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Readink – Stories & Books

Readink – Stories & Books

Hong Kong Begin Wealth Limited

Book免费v3.2.7
App Store
评分

4.6

6,051 条评分

星级

★★★★★

最近更新

2026年5月7日

发布日期

2024年6月12日

更新内容

v3.2.7

What's New - Fixed various bugs - Improved app stability

应用信息

开发者
Hong Kong Begin Wealth Limited
分类
Book
价格
免费
版本
3.2.7
App ID
1620546464

简介

Readink – Escape Into Romance, Fantasy, and Worlds Full of Emotion Readink is where addictive storytelling meets endless imagination. Step into a library filled with heart-pounding romance, supernatural fantasy, dark mafia tension, billionaire obsession, fated-mate werewolves, and every kind of page-turner you crave. Whether you want emotional rollercoasters, high-stakes drama, steamy chemistry, or magical realms full of danger and desire—Readink delivers stories that keep you hooked day and night. What Makes Readink Stand Out 100,000+ serialized stories across today’s most-loved genres: Romance, Werewolf, Billionaire, Fantasy, Mafia, Paranormal, Dark Romance, Urban Fiction, and more. Fresh chapters updated every day so you never have to wait. Smart, personalized book recommendations based on your reading mood. Auto-synced personal library across all devices. Smooth, customizable reading modes for long, comfortable reading sessions. Explore the Worlds Readers Love Most Werewolf, Shifter & Fated-Mate Bonds Billionaire Romance & Power Games Paranormal Fantasy & Witchcraft Mafia, Crime & Dark Obsession Contemporary, Urban & New Adult Fiction LGBTQ+ Romance & Diverse Storylines Western, Action & Supernatural Drama From slow-burn romance to high-voltage fantasy, Readink gives you immersive universes where every chapter leaves you wanting more. Download Readink today and fall into your next obsession. Need help? Contact us anytime: support@Readink.app Privacy Policy: https://www.Readink.app/privacy-policy.html Terms & Conditions: https://www.Readink.app/terms-of-service.html

下载量预测

专业 · 预览

预估总下载量

448K303K864K
保守估计乐观估计

13K

低 / 月

19K

预估 / 月

38K

高 / 月

基于6,051 条评分
假设评分率1.4%
应用年龄23 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。