Xreviews: manage App Store reviews

Hacker News

Xreviews is a native Mac app that allows you to view and manage reviews for your Apple App Store apps. Just sign in with your App Store Connect API keys, and we'll import customer reviews for all of your apps. You can search, sort, and filter reviews by keyword, date, rating, and more. Xreviews also lets you respond to App Store reviews directly from the app. You can even filter out reviews that you already responded to. For our power users, Xreviews supports multiple App Store accounts. Have too many apps? You can hide apps for which you don't need to see reviews. And once the initial import completes, you can view all customer reviews offline.

Visit product View on Hacker NewsOct 26, 2022

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

82%82% 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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

36%36% 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: reviews, users · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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