$2

$2 Tuesday, Mac App Store Apps for $2. Suggestions wanted.

Hacker News

$2 Tuesday, Mac App Store Apps for $2. Suggestions wanted.

I love those Mac daily deal sites, but prefer buying all my Mac Apps from the App Store now. So I made Two Dollar Tuesday to offer discounted apps that are available in the App Store. I'm hoping it will help developers get noticed as well since they move up the ranks a lot. I'm a Mac developer, not a web developer (I developed Compartments, one of the 3 apps offered), so I barely know what I'm doing with web development. I built the app on Wordpress, by modifying a Wordpress theme a bit, because that's about all I really know how to do. I'm confident Wordpress isn't the best thing for something like this, and I'm paranoid that the website will die if I get a lot of visitors. Any feedback on design or functionality or a better route to go than Wordpress is very welcome. Link: twodollartues.com Edit: Formatting

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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: para · Missing: supports, reddit linkedin, podcasting
95%95% 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 · Missing: agents, macos, agent
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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, io · Missing: https docs, excited, just released
35%35% 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: apps, para · Missing: mobile apps, ios, personal
34%34% 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
17%17% 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.

Correct prediction on native model

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