I built a mobile site for sharing Costco .97 clearance deals

Hacker News

Hey Costco lovers! If you’re like me, you’ve probably noticed those legendary clearance deals at Costco ending in .97 — the ones that fly off the shelves and disappear before you know it. So I built a mobile-first website: https://97deal.shop It’s a simple community-powered app where people can: Snap & upload .97 price tags they find in-store (Use AI to auto generate the item number, title and price) Tag the location (Costco warehouse) Search and browse recent deals near you The goal is to make it easy for all of us to share these hidden gems before they’re gone Still early days and open to feedback. Would love your thoughts or ideas!

Visit product View on Hacker NewsJun 20, 2025

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3points

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

70%70% 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: open · Missing: mac, agents, macos

62%62% 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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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

28%28% 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

15%15% 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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