Post and Search HN via iOS Shortcut

Hacker News

For my personal use I made two simply shortcuts for iOS that speed up my HN experience using iOS share sheet. Search HN: get the current highlighted text and search the terms using hn.algolia.com, sorted by date, in order to avoid post a dupe. Can be triggered from the iOS pop-up from every text. Download: https://www.icloud.com/shortcuts/d2cc17aa823f4ed9b1d1563aac2ab4b0 Post HN: get the current webpage title/url and fill the submit form on the ‘Submit’ form. Download: https://www.icloud.com/shortcuts/9086897719df4f83a32660520b2ed580

Visit product View on Hacker NewsJun 10, 2021

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

57%57% 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

14%14% 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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