OurStars – Custom Star Maps for Special Moments (Join the Waitlist)

Hacker News

Hi HN, I'm Aditya, creator of OurStarse. I created this tool to make it easy for anyone to turn meaningful moments into custom star maps. Whether it’s a special event, a gift, or just for fun, OurStars captures the night sky and enhances it with: - Personalized captions (even zodiac-themed) - Social media-ready formats for quick sharing - Life narratives tied to your special moments I wanted to build something that helps people connect with their memories in a fun and creative way. I’d love to hear your thoughts, feedback, and suggestions! We're currently in pre-launch mode, building out features and adding finishing touches. Join the waitlist to be the first to access exclusive features, early perks, and some free credits when we go live! Thank you for checking it out!

Visit product View on Hacker NewsOct 17, 2024

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Actual performance

2points

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

86%86% 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.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, 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

17%17% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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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