Stop paying for subscriptions you don't use (solo built)

Hacker News

Ever realize you're still paying for subscriptions you completely forgot about? I was frustrated by forgotten subscriptions quietly draining money each month, so I built Subskrybd. It helps you quickly identify unused subscriptions, provides insights, forecasts spending, gives personalized recommendations, organizes upcoming payments into a simple calendar, and sends you email reminders before payments are due. It's lightweight, mobile, and web-friendly, and doesn't bombard you with complicated finance jargon, just quick clarity. There's no ironic subscription fee, just a simple one-time payment if you need more than the free version. I'm solo-building this, so your feedback would mean a lot! As a genuine thank-you to early supporters from HN, use EARLY25 for 25% off if you decide to upgrade. https://subskrybd.com/

Visit product View on Hacker NewsApr 16, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

23%23% predicted probability of success on Acquire.com, 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

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.

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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