AppDesk – Native macOS Client for App Store Connect

Hacker News

Every morning I'd open App Store Connect in Safari, enter my password for the millionth time, then wait 8 seconds for the dashboard to load and continue fumbling around for what I needed. Everything took too many clicks and way too long to load. I did this for years and got tired of it. Good thing Apple provided API access for all this data. So I finally built the app I wanted, just the way I liked. Fast, keyboard-friendly navigation, data syncs and gets stored only on the local device. Meet AppDesk — a native macOS client built with SwiftUI for App Store Connect. Everything runs locally — no intermediary servers, no cloud sync, credentials stored in the macOS Keychain. What it does: - Sales & revenue charts with period-over-period comparison - Subscription metrics: MRR, churn, trial conversion, billing retries - Review management with on-device AI-generated responses - Credentials stored in macOS Keychain Would love feedback from other indie devs. https://appdesk.dev

Visit product View on Hacker NewsMar 27, 2026

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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

76%76% 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

41%41% 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: mrr, revenue, subscription · Missing: arr, profit, saas

34%34% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, 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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