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GimmeGimme – Finding gifts for hard-to-shop-for people

Hacker News

GimmeGimme – Finding gifts for hard-to-shop-for people

Hi y'all, it's that time of year -- you're going to visit the in-laws and you have no idea what to get Uncle Bobby this holiday but you don't want to show up with something boring or generic. You want to find a gift that fits. I scoured through hundreds of 2025 Holiday Gift Guides, and built a little workflow that combines some scraping, prompt chains, and embeddings that matches the description of a person with a set of gifts that might be a good fit for them. The lists persist, and you can remove items from them to get new suggestions and iterate on the lists until you find some options that make sense for you. This was built mostly for fun, but full disclosure, there's Amazon Affiliate tags for the items presented. I'm open to feedback. Hope this helps you find something unique for that person that's hard shop for. Happy Thanksgiving, Tyler

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
77%77% 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: new, open · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
37%37% 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
34%34% 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
29%29% 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 · Missing: arr, mrr, revenue
17%17% 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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