A tool to easily convert any web page into an eBook

Hacker News

I’ve built a tool that allows you to convert any web content into an ebook with just a few clicks. The idea came from my need to frequently save articles or web pages as ebooks for offline reading and sharing. However, I found existing tools either too complex or lacking flexibility, so I decided to create something simpler, faster, and that supports both EPUB and PDF formats. Key Features: - Quick Conversion: Input a list of URLs, with each URL treated as a chapter, and the tool will automatically generate an ebook in either EPUB or PDF format. - Content Optimization: It automatically optimizes the content for better structure and readability in ebook format. - Multilingual Support: It works with web pages in multiple languages. EPUB or PDF Output: You can choose the format you prefer. - Send Directly to Kindle: You can send the generated ebook directly to your Kindle device from Ebookany. I’d love to get feedback from the community to help improve the tool and add new features. Feel free to try the tool here: https://ebookany.com . Thank you for reading, and I look forward to your thoughts!

Visit product View on Hacker NewsSep 21, 2024

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

90%90% 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: new · Missing: mac, agents, macos

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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

10%10% 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.

Incorrect prediction on native model

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