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I wrote a crowd-source supported "aisle finder"

Hacker News

I wrote a crowd-source supported "aisle finder"

http://cartcombine.com I dislike floundering in grocery stores wandering around looking for things. My goals were to get in and out of stores as quickly as possible. I've looked into products like aisle finder but it's behind a paywall and the reviews were bad for the product. For right now you can make searches, create products, create stores, tie them to individual stores and give them attributes. I wanted to concentrate on making it easy to use with a clean interface. This isn't limited to grocery stores either. If there's an aisle with a price, it can go into this site. I am the only developer, it took me six months to write from start to MVP and I'm really excited to show it off. I'm hoping I can make this grow into a self-governing grocery delivery service too, but that's phase 2. I don't want to have to wait for instacart to come to my city, and really it should be publicly supported anyway. This is completely free except when creating shopping lists, which is more of a convenience, and swapping from store to store will update the aisles that the products are in. That feature is $5/month, but really, I feel the amount of time you're saving using something like this will be worth it. Also by charging money I can make phase 2 become a reality. You can also use it to find where a product might be before driving around looking for it. Don't set a current store and do a search for it to discover it's location and price. The data is sparse, but that's because only I am updating it right now. Jump on board! Any questions please ask! It's been a long way coming but I feel pretty accomplished, and personally I enjoy using the site myself as well.

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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: using · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: reviews, interface · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
52%52% 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
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.

Incorrect prediction on native model

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