I built a visual gift-finding app after struggling with Christmas gifts

Hacker News

Hi HN! During Christmas, I found myself struggling to come up with gift ideas for my family. I’m not the best at these kinds of things (classic nerd problems). I even tried ChatGPT, and while it gave me some decent suggestions, I felt like a more visual and interactive approach would work better. As a backend engineer, I’ve always been on the lookout for a side project that aligns with my skills and interests. This idea felt like a good fit. I ended up building the app using Next.js (still learning the frontend concepts). It is still in progress but I would love your thoughts on it — feedback and ideas for improvement are more than welcome!

Visit product View on Hacker NewsJan 22, 2025

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Actual performance

2points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, chatgpt, using · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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.

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.

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

34%34% predicted probability of success on TrustMRR, 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

34%34% 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: active · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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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