SHA1 Clock

Hacker News

SHA1 Clock

@brudgers encouraged me to do a Show HN (in https://news.ycombinator.com/item?id=12670797) Thanks also to @Artemix for the validation! It's my first time sharing something I've made on HN. Demonstration: http://www.verticalsysadmin.com/sha1clock/ Source code: https://gitlab.com/atsaloli/sha1-clock/tree/master It was a fun little project for me as I'm a sysadmin and haven't done much with JavaScript. I quickly found a SHA1 implementation through Google and then after realizing there really is no sleep() in JavaScript and to use setInterval instead, I was pretty much set. :-) There is no practical value, it's just for amusement and for me to learn just enough JavaScript to make it work. :)

Visit product View on Hacker NewsOct 12, 2016

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Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

84%84% 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: io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

47%47% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% 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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