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A programmer's approach to finding gifts

Hacker News

A programmer's approach to finding gifts

Hey Folks, I’ve been really annoyed with the search part of finding gifts. It’s hard to use keyword search for something when it’s an “I’ll know it when I see it” kind of deal. So I thought, what if we scraped/indexed TONS of products and then just focused on removing the things we don’t want and then see what’s left to see if there’s anything cool? I built it in Flutter so it's both iOS and Android but it could be web too. I’ve only just started the scraping/tagging of products and if anyone has suggestions for bulk tagging images/content I would love to hear them. That’s the current bottleneck. Any feedback would be welcome! cvanvlack AT gmail DOT com if you want to discuss 1-on-1.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
80%80% 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 · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
41%41% 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
13%13% 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.

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