Browse Hacker News on your Kindle via browser for free

Hacker News

tl;dr: Go to kindlehnbeta.pythonanywhere.com on your Kindle’s browser. I wrote a wrapper over hackernews that allows me to browse it on my Kindle. Simply visit kindlehnbeta.pythonanywhere.com on your Kindle’s browser. You’ll be able to view top stories, the comments within them and even the article content that the stories link to. The articles are parsed by a library that makes its best attempt to extract the main content. I only own a Kindle Oasis (10th Generation) and thus have only tested this on it. I’d imagine it looks fine on other Kindles, but you’ll have to tell me. Link to screenshots: https://imgur.com/a/8kKneH0 Really, I made this for myself as a quick solution for a personal need. But it seems like there are a few of you out there that would enjoy this as well. So I am making this public for others to try out. Let me know if this is something you actually want to use. If so, and the reception is positive, then I will continue to build it out to be better and more feature complete. In addition to this, would you enjoy a generic rss feed reader as well? — one that allows you to read any rss feed right from your Kindle’s browser? I can build this if that’s something you guys really want.

Visit product View on Hacker NewsAug 27, 2022

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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

69%69% 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 · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% 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.

Correct prediction on native model

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