Submind – Free Subscription Tracker

Hacker News

Hey folks After years of manually tracking Netflix, Spotify, and random SaaS renewals in Excel sheets, I finally decided to build something that actually makes sense — Submind. We’ve spent the past few months designing and polishing it with a focus on ease of use and powerful features that make managing recurring payments simple (and kinda satisfying). It’s completely free for now. More advanced features are planned and early users will be grandfathered — meaning you’ll keep full access even after we introduce paid plans later on. What Submind does Supports hundreds of services — Netflix, Spotify, Adobe, Disney+, Apple iCloud, and more Calendar View — see all your upcoming renewals at a glance Smart Reminders — get notified before payments Detailed Analytics — total cost, categories, and monthly averages Widgets & Filters — added recently based on user feedback Dark Mode + Liquid Glass design for that clean iOS 26 look We’re improving it every week and would love your feedback! What features would you love to see next? Family/shared subscriptions Currency conversion Auto import from bank or email AI insights like “you never use this — cancel?” Something else entirely? Download it here: https://apps.apple.com/us/app/subscription-tracker-submind/i... No sign-up, no ads — just clean subscription tracking. Hope you find it useful, and I’d love to hear what you think!

Visit product View on Hacker NewsOct 21, 2025

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created

96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% predicted probability of success on AppSumo, 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

29%29% 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: saas, recurring, subscription · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, introduce, smart · 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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