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I made a directory of Mac apps and tools and made $126 after 24 hours

Hacker News

I made a directory of Mac apps and tools and made $126 after 24 hours

One week ago, I launched a new directory, which is a curated list of useful and cool Mac apps and tools that are not available on the app store. Initially, I wasn't sure if it was a good idea, but I managed to create it in just one day. I started promoting it on Twitter and some communities, and although I got some views, I didn't make any sales. Then the next day, I wrote a post on a subreddit related to Mac apps about the directory I launched. People loved it so much, and I got 39k views on that post, leading to around 2.5k visitors on my site. Thanks to this exposure, I made my first 4 sales. I hope to get more sales soon. I'm also working on the SEO to attract organic traffic to the site. Any feedback on the site will be great from your side.

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
89%89% 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: mac, apps, new · Missing: agents, macos, agent
67%67% 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 · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
41%41% 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
22%22% 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
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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