I built an opinionated minimalist ePub/pdf online reader

Hacker News

Hey so I am making my own book reader and wanted to share it with y'all on hackernews and I saw a submission talking about DRM free books[0] so I decided to submit this submission on Hackernews to show what I am upto :-D I prefer minimalism so I have created three different flavors of it depending upon the particular use-cases and I have linked the most minimalist one as the submission. If someone is interested in a Epub/pdf reader, I have recently made one: https://epub.mirror.forum/ After making that, I was also feeling the need of having standard book downloads and seeing this project as well. I have added it first using cors.sh: https://guten.mirror.forum I have also added a cf worker native solution which focuses on the search aspect only as well: https://gutencf.mirror.forum Feel free to ask me or give me any feedback or which one you might like more within this somewhat A/B testing experiment of mine and I just hope that it might be useful to anybody. Cheers and take care! [0]: https://news.ycombinator.com/item?id=48709186

Visit product View on Hacker NewsJun 28, 2026

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

71%71% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

65%65% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

22%22% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

11%11% 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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