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Hello Hackernews! I've been building startups for 3 years and finding my initial customers has always been a big struggle. I recently got into the "build in public" trend and love how it helps founders like us get noticed without big connections. I saw many of us sharing our ARR (Annual Recurring Revenue) on IndieHackers, Twitter, and other places. But there wasn't a simple place to see how we're all doing compared to each other. So, I built StartupLeaderboard.com, a site where we can see each other's ARR and learn from it. It's straightforward: you share your ARR, see others, and we all get a better idea of how we're doing. I'd love your feedback. If you have time, check it out and tell me what you think. Does it help? What can be better? Thanks for the help and for sharing your own stories!

Visit product View on Hacker NewsDec 18, 2023

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Actual performance

5points

4comments

Did not reach leaderboard

Launch Intel predictions

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

90%90% 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 HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

57%57% 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 · Strong signals: arr, revenue, recurring · Missing: mrr, profit, saas

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

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

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

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

27%27% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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