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GroceryTrip – Turn receipts into nutritional information

Hacker News

GroceryTrip – Turn receipts into nutritional information

Hey HN, I've been working on a Flutter app for the past few months that turns receipts into useful nutrition information. The aim of this project is to make it easier to understand the nutrition and ingredients in our groceries simply by scanning a receipt. How GroceryTrip works (more detail in github repo): 1. Take a photo of your receipt (remains local, there's a demo receipt in the app if you don't have one on-hand) 2. Crop photo to relevant barcodes/names of products 3. View receipt Summary/Details 4. Optional: Contribute missing barcodes It's a super simple app currently, but I think it has potential to provide more insights into the food we purchase every week (viewing grocery trends over time may be useful). Any feedback or suggestions are appreciated! https://github.com/zanovis/GroceryTrip

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
68%68% 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
62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
47%47% 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
43%43% 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
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.

Incorrect prediction on native model

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