AI-Powered Food Logger

Hacker News

Hey HN! A few months ago I was dealing with some minor IBS-related things and wanted an easy food tracker to figure out which foods were causing issues. I didn't like the available apps so I took a stab at building my own which uses voice recognition instead of typing as an input method. It seemed like a much simpler and faster way to log meals and whatnot. Fast forward to today, the MVP is ready. There are a bunch of features I could add, but I'd love to hear feedback first from people that want to use this. It's free for now as it's still in beta. Please do log in and start using it. It's a PWA and designed for mobile. Side-note: one UX thing I like is the instant sign-up process. Its the same as login--a phone number input and a verification code. If you don't have an account, it creates one behind the scenes without doing a sign-up process. Not all apps can get away with no KYC but I think it's great for something like this. Curious to hear contrary viewpoints. Thank you for any feedback

Visit product View on Hacker NewsMay 13, 2024

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

72%72% 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: apps, using, code · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: apps, month, way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

41%41% 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

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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