Blen – A Minimalist EReader to Help You Read More Books

Hacker News

Hi HN, Most of our attention today is hijacked by short-form content on apps like TikTok and X. There's growing evidence this contributes to cognitive decline ( https://pmc.ncbi.nlm.nih.gov/articles/PMC11236742/ ) and is only going to affect more people as video generation models advance. One of the best ways to combat this is by reading more books. I built blen to help myself do this, and figured other people might want to join me. Blen is a minimalistic PDF e-reader with a clean interface, bookmark and search features, and automatic progress tracking. Just put blen.ink/ in front of a url to a PDF of your favorite book (eg: blen.ink/ https://www.sausd.us/cms/lib/CA01000471/Centricity/Domain/24... ) or add the link within our interface. There are no sign-ups, no paywall, no collection of your personal data, and blen will always be free. I hope it helps you read more. I’d love any thoughts, feedback, or feature requests!

Visit product View on Hacker NewsJul 31, 2025

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Actual performance

3points

3comments

Did not reach leaderboard

Launch Intel predictions

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

69%69% 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, apps, models · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released

50%50% 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 · Strong signals: interface · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, apps, video · Missing: mobile apps, ios, entrepreneurs

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% 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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