my collection of HN plugins

Hacker News

I created this 3 plugins to create a better personal experience with HN, they are not made to create a nice UI, in fact, it might make it more confusing, but they help me to find more interesting things to read and have a general better experience. HNTagger: https://bitbucket.org/Duiker101/hntagger Tag users, there is also a file with some tag ready to be used. I like knowing who am I talking to, like if it's a dev of some company, a marketing person etc.. HNNewest: https://bitbucket.org/Duiker101/hnnewest Highlights on the /newest page the posts with some comment or more than 1 point. Also highlight Ask HN, Show HN and text posts. HNReRead: https://bitbucket.org/Duiker101/hnreread Shows the number of comments/score since you refreshed the page(orange) or when you opened the thread(blue) There is also the HNBox, which is all of them together: https://bitbucket.org/Duiker101/hnbox als avaible on the Chrome store https://chrome.google.com/webstore/detail/hnbox/bphebkcfkkmdemhbbomehjbopofkhmib This plugins were created for me but I hope will be helpful for someone else also. Enjoy.

Visit product View on Hacker NewsOct 27, 2012

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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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

69%69% 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.

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

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, 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

45%45% 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

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

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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