a simple reading time estimator for a webpage

Hacker News

This is a project I did to scratch my own itch. I have this problem of finishing an article I start reading; I can not abandon it however bad it is, don't ask me why. So I developed this extension for chrome where it tells you how much time it would take you to read an article. Sounds odd but for me Knowing the time investment I have to put in upfront would restrain me from mindlessly reading random articles. Works best on article based content. its opensource and you can contribute here, https://github.com/usergit/read-time link to the extension: https://chrome.google.com/webstore/detail/read-time/nccohhimobidpghgpnejnbkpoichbbml

Visit product View on Hacker NewsAug 14, 2016

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3points

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

62%62% 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 HuntUnlikely to reach the leaderboard · Strong signals: google, user, open · Missing: mac, agents, macos

46%46% 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

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

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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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