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AppReviewAI Analyze App Store Reviews Locally with Apple's On-Device AI

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AppReviewAI Analyze App Store Reviews Locally with Apple's On-Device AI

Hi HN, I’ve been experimenting with Apple’s new on-device Foundation Models (iOS 18 / macOS Sequoia) and built a small tool around them: AppReview AI, a Mac + iPad app to analyze App Store reviews privately and offline. The motivation was simple: as an indie developer, reading hundreds of reviews from competitor apps was slow, noisy, and hard to extract signal from. Existing tools rely on cloud processing, API keys, or external servers. I wanted something lightweight and private that used Apple’s new local AI instead. What it does - Summarizes reviews using Apple’s on-device models - Extracts sentiment, recurring issues, bugs, and feature requests - Shows per-country ratings to detect market differences - Displays basic estimated downloads and revenue (SensorTower public data) - Syncs selected apps and analyses through iCloud All AI processing stays on-device. No external servers, no accounts, no OpenAI key. Why I built it I wrote a recent article on Apple Foundation Models and was surprised how far the local models can go with the right prompts. This project was a way to test how practical on-device analysis could be in a real use case for developers. Free tier The app has a small free tier (1 app + 3 AI analyses) so anyone can try it without registration. If you’re curious, here’s the link: https://apps.apple.com/lu/app/appreview-ai-review-analyzer/i... I’d love feedback, criticism, or ideas for what to analyze next (keywords, rankings, crashes, changelogs, etc.). Happy to answer technical questions about the on-device AI integration as well.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor
92%92% 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: ios · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs
29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, recurring · Missing: arr, mrr, 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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