I built a tool to help you read Hacker News on Kindle

Hacker News

Hi HN, I'm Daniel Nguyen. In June, I quit my job to start indie hacking full-time. The idea of KTool first came to my mind when I was reading "Ask HN: I'm a software engineer going blind, how should I prepare?"[0] I've been wearing glasses since I was 5. My right eye is basically blind. Doctors said there is no chance to cure it. I was genuinely scared. Like holy shit, if my left eye stops working, my life is done. Since then I've been very conscious about time spent on computer screens. That's when I started using Kindle-related products: to offload as many reading materials as possible to the Kindle. I was a happy customer of Push to Kindle. Great product! Then I ran into multiple limitations which led me to build KTool: a tool to send anything online to Kindle. Blog posts, Twitter threads, Hacker News discussions, RSS, newsletters... you name it. But I'm not here to pitch my vision for KTool. I built a specific tool to help you send HN discussions to your Kindle. And in the spirit of Show HN, it doesn't require an account. If you don't own a Kindle, there is the option to download the EPUB. Let me know what you think. Any feedback will be much appreciated. If you're a Kindle owner and you read a lot of online content, give KTool a try. [0]: https://news.ycombinator.com/item?id=22918980

Visit product View on Hacker NewsAug 29, 2022

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393points

78comments

Made the leaderboard

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

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.

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

74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% 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

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

13%13% 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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