I made an app so you never forget to cancel a subscription

Hacker News

42% of consumers waste more than $100 per month on unused subscriptions. That's $1,200 a year. That could do a lot for you don't you think? I saw a Twitter (X) post about how someone forgot to cancel a subscription and it cost them almost $50. It's happened to me before and I'm sure it's happened to you. I built Subout to fix that. And so you don't have to pay a subscription to track and cancel unused subscriptions, I have a one-time payment of $5 for lifetime access. The price increases up to $50 for new customers as I add the features below. => Finding you one-time payment deals => SMS or WhatsApp reminders => Analytics on Monthly Subscriptions. => Dealing with difficult subscription cancellation There's also a generous free plan where you get: => To track all your subscriptions in one mobile view => An installable mobile web app => Email notifications on the 2nd to last day of the month about subscriptions to be renewed in the next month.

Visit product View on Hacker NewsJul 17, 2024

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Actual performance

5points

13comments

Did not reach leaderboard

Launch Intel predictions

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

74%74% 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.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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