Appstr for Indies managing lots of apps

Hacker News

I have 11 apps in the Apple App store and 3 in Google Play store. Every time I submit a new app, I have to provide a privacy policy, a support URL, and some sort of product description page. Even more challenging, I had to keep track or customer reviews and submitted feedback because in the very early days of app launches, those first few reviews are the most critical and carry the most weight. So I built Appstr just as mini platform to quickly auto-generate multiple privacy policies (based on the app-specific data collection), host feedback forms to capture user suggestions and support issues, and a monitor to keep tabs on the reviews as they come in. It also sends me daily and weekly summary updates across all of my apps and also automatically groups the feedback and complaints so that I can prioritize the fixes and new features. I have made Appstr available to everyone because this seems like a pretty common problem that many app developers should be facing. While this could be done by stringing a lot of other solutions together (like Zendesk) the cost adds up really fast and blows past most Indie developer budgets. Since early reviews matter, I'm turning to HN for brutally honest feedback.

Visit product View on Hacker NewsJun 23, 2026

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3points

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Launch Intel predictions

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

84%84% 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

65%65% predicted probability of success on Indie Hackers, 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

46%46% 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: platform, reviews, host · Missing: plus, intuitive, exclusive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, google · 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 · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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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