ThoughtfulPost Gift Recommendation Engine for Your Friends and Family

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I’ve started working on a new startup called Thoughtful Post ( https://thoughtfulpost.com ) It’s a useful and fun-to-use app that will help you organize and remember all of your friend’s birthdays/weddings/anniversaries and help you find interesting and new gifts to get them based on their likes and dislikes! There are collaboration features and surveys you can use to farm your friends likes/dislikes and work together with friends to pick out gifts for people. All of the functionality is in the mobile app: iOS: https://apps.apple.com/us/app/thoughtful-post/id1636275228 Android: https://play.google.com/store/apps/details?id=com.thoughtful... Here is a YouTube video demo’ing the product made by my cofounder: https://www.youtube.com/watch?v=VdAwGlv9Lao&t=3s Please try it out and share any feedback with us! I think that with everything going on in the world the people you are close to are more important than ever. You don’t always have to spend a lot of money on people to make them feel loved! Investing time and energy into finding a gift or experience can make people feel really special. Our app can send surveys to the gift recipient and your friend circle to find out about the “giftee” and just receiving these surveys will let the people in your life know you really care about them. Some interesting technical notes: The mobile apps are built in React Native. The backend is in Python/Django. We monetize by being affiliate partners with the products we recommend through our gift giving engine. We are using Natural Language processing to map your friend’s likes/dislikes with our product catalogue to recommend new ideas.

Visit product View on Hacker NewsNov 15, 2022

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

91%91% 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, google, apps · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, 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

48%48% 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: mobile apps, ios, apps · Missing: personal, entrepreneurs, month

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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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