Subscription Magician – Subscription tracker built with RSC

Hacker News

Hi HN! I've built an app that I really needed. Maybe it can help you too. Subscription Magician is a free subscription tracker I've built using Next.js 14 with React Server Components. I got tired of paying random fees every month for services I was barely using. A few months ago, I realized that I was spending over $40 a month on dev tools I never actually used, streaming services I forgot about, apps I downloaded once - this sort of stuff. How much are you unknowingly spending on forgotten subscriptions? I figured others might be facing the same problem, so I decided to build a solution. But, to be honest, I mainly viewed this project as an opportunity to get hands-on experience with the major changes introduced last year in Next.js 13. We all know it's not a good idea to replace tools that work perfectly for you, just for the sake of experimenting with the newest, shiniest framework. Since this was a personal side project, I was able to afford the luxury of transforming this app, that solves a real problem for me, into a learning opportunity. Subscription Magician is 100% free to use, and while there's still a lot to improve, the core functionality is there. I would love to hear your feedback as I continue to work on it.

Visit product View on Hacker NewsAug 23, 2024

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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: apps, new, using · Missing: mac, agents, macos

54%54% 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

50%50% 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

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.

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

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

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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