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Credit card rewards for passive income

Hacker News

Credit card rewards for passive income

Hey everyone, Whether you believe that credit card rewards can be considered passive income or not, figured I’d share this here. I’ve been working on a project called The Card Caddie, it is essentially a tool to know when to use the right credit card. I love to travel and have gotten so many flights and hotels at insane rates, timing when to use points. But the actual process of navigating credit card rewards is not passive, like at all. I built this chrome extension and app to make it passive for me, whenever I go to purchase something, I instantly get informed with the card to use at that merchant to maximize my reward returns. I posted here a few months ago with the chrome extension part of this project, but just got my iOS app live on the App Store. The chrome extension works on website domains, the app works with your location to estimate nearby stores and calculates the card to use. I added a live activity widget so you can know what card to use before you can even open your Apple Wallet. I realize credit card info is very confidential, so you don’t input any personal info to this tool. I scraped all the publicly available data for the reward rates for popular cards and handle it for you. Assume you spend 10k a year. Average card reward rate sits somewhere between 1-2% (assuming cash-back $100 to $200), but if you can get that more like 2-3+% you could be earning hundreds more a year. It’s not life changing money, but might as well earn as much as you can per purchase, especially if it is minimal/no effort. Tool is completely free, give it a try if you’d like!

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, maximize · Missing: supports, reddit linkedin, podcasting
93%93% 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, activity, open · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% 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
47%47% 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: ios, personal, month · Missing: mobile apps, entrepreneurs, apps
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · 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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