Snap a photo of your fridge or handwritten recipe card — RecipeSnap AI suggests meals or digitizes recipes using AI. Add to your cookbook and filter by diet or allergies. Perfect for busy kitchens and food waste warriors.
Turn fridge photos into recipes with AI
Snap a photo of your fridge or handwritten recipe card — RecipeSnap AI suggests meals or digitizes recipes using AI. Add to your cookbook and filter by diet or allergies. Perfect for busy kitchens and food waste warriors.
Hey Product Hunt! 👋 I’m Mark, an indie dev who’s tired of wasting food and losing handwritten recipes. RecipeSnap AI lets you take a photo of what’s in your fridge/pantry (or a handwritten recipe), then it uses AI to generate recipes or digitize and save them. You can add recipes to your personal cookbook and filter suggestions based on meal type, diet, or allergies. I’d love feedback from food lovers, home cooks, and anyone trying to save time or reduce waste.
Cool! I think this concept is good for partnering with some calorie apps or allergen apps (a few weeks ago, one guy launched such an app). Wish you good luck! :)
RecipeSnap AI makes cooking more intuitive and reduces waste in such a clever way. Love the blend of convenience, personalization, and sustainability — definitely a must-have for modern home cooks.
Love seeing indie projects that solve everyday problems in such a thoughtful way.
Food tech meets computer vision 🍳👁️. The real magic is in: - Ingredient recognition 🥕 → CNN models trained on 100k+ food items - Recipe generation 📝 -> LLMs that understand flavor pairings - Portion estimation ⚖️ -> Depth-aware quantity detection
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.