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Scriptlet to remove greyed out comments on HN

Hacker News

Scriptlet to remove greyed out comments on HN

Hello everyone, I frequently send the comments from an article on HN to a TTS software I use on my iPhone called Voice Dream Reader. The only problem for me was listening to a greyed out comment and its children. Although valid discussions can be had in these situations, the majority in my opinion could be eliminated, so I created a scriptlet to do just that. I decided to share it here in case someone else finds it useful: javascript:void%20function()%7Bvar%20e=document.getElementsByClassName(%22commtext%22),n=0,t=0,a=!1;for(i%20in%20e)n=e%5Bi%5D.parentElement.parentElement.parentElement.children%5B0%5D.children%5B0%5D.width,1==a%26%26(n%3Et%3Fe%5Bi%5D.parentElement.parentElement.parentElement.parentElement.parentElement.parentElement.parentElement.hidden=!0:a=!1),0==e%5Bi%5D.classList.contains(%22c00%22)%26%260==a%26%26(a=!0,t=n,e%5Bi%5D.parentElement.parentElement.parentElement.parentElement.parentElement.parentElement.parentElement.hidden=!0)%7D();

Visit product View on Hacker NewsMay 25, 2019

Actual performance

2points

4comments

Did not reach leaderboard

Launch Intel predictions

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

64%64% 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 · Missing: mac, agents, macos

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

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

31%31% 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

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

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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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