Sous – App that imports recipes from URLs, and cookbook photos using AI

Hacker News

I built Sous because I was tired of: 1. Scrolling through 2000-word life stories to find a recipe 2. Trying to copy recipes from Instagram captions 3. Squinting at my phone with messy hands while cooking The app uses Google Gemini to parse recipes from any URL (handles messy blog formats), photos of cookbooks/handwritten notes, and even TikTok/Instagram/YouTube videos. How it works: - AI extracts structured ingredients and steps, pre-converts all measurements to both metric and imperial - Live Activities show timers on your Lock Screen - Siri control for hands-free cooking ("Hey Siri, start my pasta timer") - AI chat assistant that knows your recipe context for substitutions and troubleshooting I'd love feedback, especially on the parsing accuracy and cooking experience. Learn more: https://getsousapp.com/ Try it out here: https://apps.apple.com/us/app/sous-smart-recipe-companion/id...

Visit product View on Hacker NewsDec 29, 2025

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

88%88% 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: apple, google, apps · Missing: mac, agents, macos

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

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

48%48% 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

33%33% predicted probability of success on AppSumo, 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

26%26% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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: chat, smart · Missing: web3, crypto, cryptocurrency

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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