An app that turns doomscrolling into learning

Hacker News

Hi HN, I made BrainScroller — an app that takes the same scrolling habit we all have and turns it into learning instead of doomscrolling. Why I built it: I noticed I wasn’t addicted to my phone — I was addicted to scrolling. So I wondered: what if the scroll itself fed my curiosity instead of draining it? How it works: You swipe like Instagram/TikTok, but each card is a short summary, idea, or concept from philosophy, history, psychology, tech, and science. It's meant to feel as easy as social media but leave you smarter, not anxious. What I'm looking for: – Feedback on the UX and the “scroll-to-learn” model – Whether the content feels useful or too shallow – Any suggestions before I work on the next version Happy to answer anything. Thanks!

Visit product View on Hacker NewsNov 26, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

79%79% 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 HuntOn track for Day 1 leaderboard · Strong signals: model · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% 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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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