Track your calories easily by saying what you ate

Hacker News

Hi guys, I created this app to solve one of my own problems: I found calorie tracking to be a real pain. Now, instead of scanning barcodes, scrolling through lists, or taking photos of food, I just speak into my phone (or computer), tell Calorio what I’ve eaten, and it handles the rest. It uses OpenAI’s APIs to process audio and map foods to calories. It’s free to use without an account, but if you want it to tally daily calories and remember everything you’ve eaten, you’ll need to sign up with your Google account and subscribe (you get 7 days for free). I would really appreciate any feedback; please let me know if you have any questions, issues or suggestions. Thank you! Paul.

Visit product View on Hacker NewsMar 19, 2024

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

80%80% 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: google, computer, openai · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

36%36% 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

25%25% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · 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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