Li

Librisomnia – A Bedtime Reading App

Hacker News

Librisomnia – A Bedtime Reading App

I built an app to help people fall asleep by reading classic books. I'd like to share my thought process and reasoning behind this app here. Two challenges I've observed for some people: 1. Difficulty falling asleep, often made worse by scrolling through short-form content while in bed. Even when this eventually makes us sleepy, the time spent feels wasted. 2. A desire to read classic literature (like "In Search of Lost Time" — quite ironic given point #1 :P) that remains perpetually on the to-do list. I noticed these two challenges actually complement each other. Think about school time, when we read something serious, we get sleepy :( So the solution: read classics at bedtime, resulting in either sleep or some meaningful reading time. (A practical advantage: most classics are in the public domain, which solved the copyright issues.) However, simply having access to classics isn't enough. The typical ereading experience presents too many options, which often leads to abandoning reading altogether in favor of more immediately engaging activities. To address this, I implemented several practical design principles: 1. Minimize friction by reducing decision fatigue - The app presents just a few random books each night. One tap to start reading. - Reading begins from a random chapter rather than the beginning, removing the commitment pressure of a full book. You don't even see the preface, which you might otherwise debate whether to skip. - For books you want to read fully, there's a limited "Stack" feature. Once full, random ones disappear, reducing choice paralysis. 2. Visual design considerations - During book loading, the cover is displayed — an attractive cover makes readers anticipate the text they're about to read. I designed each cover individually (over 150 books!), which consumed a significant amount of time. Thankfully, modern AI tools became valuable design partners in this process. - The reading interface is thoughtfully crafted — I carefully selected fonts, colors, and typesetting options to create a comfortable reading experience. 3. Social sharing - I know this sounds ironic given the narrative so far. But it may help many of us stick to reading. Think of posting: "Last night I fell asleep reading this page..." - The app generates beautiful reading cards (images) containing the whole page or an excerpt with book cover and other design elements. I worked hard to simulate a real book resting on a nightstand. - These eye-pleasing cards can be saved to the photo library if social sharing is totally not intended. The app is free with no ads or popups. Part of the book collection and some card styles are premium (top 50 ranked books are free, with at most 1 book per author). I plan to add more books in updates. Extra book slots are also premium (though this might increase decision fatigue, so an option to adjust active slot count is coming in updates soon).

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
98%98% 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.
AppSumoStrong fit for a featured deal · Strong signals: interface, soon · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: visual · 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: para · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
27%27% 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: arr, active · Missing: mrr, revenue, profit
15%15% 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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