News2reader – Readable articles from HN and Pocket on your e-reader

Hacker News

Hi! I recently became the owner of an e-reader, and wanted an easy way to get web articles from Hacker News and my bookmarks onto the device, on-demand, for easy reading. I'm using KOReader ( https://koreader.rocks/ ) on my device, and like lots of e-book readers it allows browsing online catalogs of books (e.g. from sources like Project Gutenberg) using the OPDS protocol. news2reader is a self-hosted Node application that acts as a "virtual" catalog, connecting to link aggregators instead of a local database. When you select a web article from the list, the application will (try to) fetch the page, generate a "readable" version with images, and serve that up as an EPUB on-the-fly. I've found it works pretty well for my needs, but improvements and enhancements (e.g. support for more aggregator services) are welcomed as Pull Requests. If you want to know more, the README has screenshots and setup instructions (using Docker or pure Node+Yarn). Hope it's useful to some of you!

Visit product View on Hacker NewsAug 7, 2023

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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: hacker news, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

32%32% 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

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.

Incorrect prediction on native model

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