Si

Simple Link Sharing Website

Hacker News

Simple Link Sharing Website

Hi everybody. We made a super simple link sharing website called blnk. https://blnk.so/ You share links to stuff you find interesting and you follow people who share stuff you find interesting. No bloat, no recommended topics, no ads, no nothing. If you have a couple of minutes and would like to give it a try, we'd be very grateful for feedback of any kind — we want to see if people other than us find it interesting and/or useful. Here's the secret link to the secret signup: https://blnk.so/super-secret-signup Many thanks.

Visit product View on Hacker NewsOct 4, 2022

Actual performance

2points

9comments

Did not reach leaderboard

Launch Intel predictions

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Indie Hackers, 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

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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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