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I Built AvcadoAI to Easily Analyze Every Ingredient in My Food

Hacker News

I Built AvcadoAI to Easily Analyze Every Ingredient in My Food

I’m thrilled to introduce Avcado AI – your personal food detective that’s here to make food transparency effortless. The Problem Did you know there are over 10,000 food additives used in the food we eat every day? Most of us have no idea what they are or what they do to our bodies. It’s like staring into a black box of chemicals, while all we want is to protect our health and make informed choices. The truth is, the food industry thrives on this opacity. But we believe everyone deserves to know what they’re eating. The Solution Avcado AI is the bridge between the food industry’s complexity and your right to clarity. Here’s how it works: 1. Scans food packaging to identify every ingredient listed. 2. Explains each ingredient in simple terms, so you know what it is and why it’s used. 3. Flags risky ingredients and explains why they might be harmful. 4. Provides special alerts for sensitive groups, helping them make safer choices. Why it matters No more guesswork, no more squinting at fine print. Avocado AI empowers you to take control of what you eat – in seconds.

Visit productView on Hacker NewsMar 28, 2025
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best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
61%61% 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, 000, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: plain · Missing: mac, agents, macos
38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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