Manage Apple (Search) Ads From Your Phone

Hacker News

Hi HN! I’m Remington, a professional developer and indie builder. I created App Ads Manager, an iOS app that lets you fully manage Apple Search Ads campaigns directly on your iPhone or iPad. The problem: Apple’s web UI is slow and frustrating on mobile, and you often need a laptop to quickly pause campaigns, adjust bids, or fix keywords. Delays mean wasted budget. With App Ads Manager, you can create and edit campaigns (Today Tab, Search Tab, Product Pages, Search Results), adjust budgets and bids, edit keywords (including negatives), and track performance data right from your phone. It supports advanced targeting (location, age, gender, device type), bulk keyword editing, and custom product pages. Campaign metrics like impressions, taps, installs, and spend are easy to access and filter. All credentials are encrypted and synced via Apple’s official Search Ads API. No data is logged or stored on our servers. I release updates at least once a week based on feedback, and there is a small Discord community for support and sharing ideas. If you run Apple Search Ads and want to manage them on the go, I’d love for you to try it. You can download it now, connect your Apple Ads account, and start managing. Curious to hear how you handle ad optimization when away from your desk and what features you would like to see next. Thanks for reading! Remington

Visit product View on Hacker NewsJun 29, 2025

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

96%96% 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, created, ios · Missing: reddit linkedin, podcasting, latex

95%95% 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, including · Missing: https docs, excited, just released

38%38% 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: builder · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

18%18% 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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