iOS Grocery List App Powered by Whisper and GPT4

Hacker News

Hey, I'm an iOS engineer who took on the seemingly mad task of creating a new grocery app in this day and age. Yes, there are plenty out there, but I promise there's a method to my madness. It's born out of a personal need – my wife would rattle off a list of things to buy, and I'd struggle to remember them all. She's not a fan of typing into apps or shared notes, and honestly, neither am I when it's about speed. The epiphany was that all existing grocery apps have an input problem. They're not catering to the way real people communicate about errands. So, I built HoneyDo to solve my own problem, which I suspect might be yours too. Here's how it works: You or your partner can tell HoneyDo what to buy in a natural, conversational manner. "We need eggs, milk, and don't forget the chocolate cake that's near the entrance." Whisper and GPT4 processes this and voila, a neatly organized list appears on both your phones (synced via iCloud). Technically speaking, I've crafted a full-stack solution with Swift (it's a native iOS app), and a Node.js-powered backend on AWS Lambda, DynamoDB, and OpenAI's API. I'd love to get feedback. Whether it's about the UI/UX, the technical stack, or how the app performs in real-life scenarios, I'm all ears. I'm here to learn and improve, so please, be brutally honest.

Visit product View on Hacker NewsJan 11, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, openai · Missing: mac, agents, macos

84%84% 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.

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

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% 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

36%36% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real people · 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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