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I made a app after too many "Oh crap, forgot to cancel my subscription"

Hacker News

I made a app after too many "Oh crap, forgot to cancel my subscription"

Hey there, My name is Thomas and I am a solopreneur. One day I realized I was paying for a lot of services I was no longer using, and I kept receiving those "You've been charged..." notifications. Sounds familiar? So I built Ruby, a web application that warns me right before trials are about to end or subscriptions are about to be charged. I've been using it personally and I've noticed 2 big changes in my spending: -37% reduction in monthly recurring costs -I get to explore a lot more SaaS projects without the fear of forgetting to cancel them I hope Ruby is as helpful to you as it is for me, and I would appreciate some feedback as this is my first project I've actually launched. Thank you and keep on building! Thomas Link: https://rubyapp.net?ref=hackernews

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, 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
39%39% 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
31%31% 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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