Milestone update: 3000+ indie makers joined Awesome Indie

Hacker News

Hello HN! Even tho I have not given much love to the project in the last year (even letting the products queue reach 0 for like 4 months - shame on me) the users kept signing up. Recently I have decided to upgrade, refactor, and fix most of the issues and automate a bunch, so this post is to celebrate the milestone crossing of 3000 user signups on https://awesomeindie.com ! Feel free to submit your projects there! Your feedback is appreciated, thanks!

Visit product View on Hacker NewsAug 24, 2024

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Actual performance

2points

Did not reach leaderboard

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

67%67% 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.

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

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

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000 · 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.

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

35%35% 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

12%12% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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