Hapi with Typescript

Hacker News

Last year I started to develop in hapi.js, a nodejs framework developed at Walmart, for a personal project. Around the same time, I started to use Typescript. I blogged about them[1]. I saw lot of traffic around these posts, so I decided to write a book. I named the book "Hapi with Typescript". I have completed 60% of it. You can read the book for free[2]. It will remain free forever in HTML format. You can purchase ebooks at 50% discount until I complete the book. You will get updates for a year. If you find it good, buy it and refer to others. If you have suggestions to improve, tell me. [1]: https://jjude.com/hapijs/ [2]: https://hapibook.jjude.com

Visit product View on Hacker NewsApr 3, 2017

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2points

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

81%81% 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

50%50% 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

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

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

34%34% 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

28%28% 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

17%17% 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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