Overview
Purpose of the ICooking Application
The application works like a Marmiton-style search engine.
Once the user has chosen a recipe, they can communicate verbally with the AI culinary assistant. The assistant guides them step by step through the recipe, using natural language. It can adapt in real time to the user’s constraints and suggest alternatives or options to maintain the harmony of the recipe.
The user can freely modify recipes and keep a history of their experiments.
How Intuition Could Power the Next Generation of Cooking Apps.
Every human on earth eats, and not just eats food we all love a good meal. “But how are good meals made?” They are made by preparing them, which is what we call cooking. Not everyone can be a chef or be so good with cooking, that is why cooking apps exist everywhere, to help us be able to make delicious meals and make our lives easier. To be honest, most of these cooking apps rarely feel personal, they are just made to prepare a meal but never how we like them, or what we would love to avoid, or how much time we actually have to cook.
Sometimes we ask questions like whether we could trust this recipe or who wrote it? But what about if we have an app that knows you well enough to recommend a recipe that fits your exact needs and taste? One that shows you a cooking guide that you can trust because they’ve been verified by humans who stand behind their knowledge. You can also be rewarded for contributing your own knowledge, irrespective of what it is, maybe a unique family recipe or some kitchen hacks, this vision is possible with Intuition.
What makes Intuition different?
Intuition is a token-curated, semantic knowledge graph that organizes facts and relationships like a living web of reused information. Instead of keeping data, it makes use of incentives, which a native tokens called $TRUST, which are used to vouch for knowledge.
For example; If Maya contributes towards a particular meal, maybe she dropped a recipe that works well for people who are allergic to milk and it turns out to be accurate and valuable. She earns rewards but if the information is wrong or misleading she risks losing her stake. Now, this built-in accountability turns raw data into verifiable community-backed knowledge. When Intuition is applied to a cooking app it becomes the truth and brain of the app, making every recipe, ingredient, or kitchen tip become an ATOM (this is a unit of knowledge )
- “Tomato → is an ingredient in→ Lasagna”
- “ChefA → recommends Technique → Layering Noodles Thinly for Lasagna”
- “Lasagna → pairsWith → Garlic Bread”
When you search for something like, “What vegetarian lasagna variation can I make without ricotta?” the app doesn’t just search for the text that matches your words. It traverses these trusted, staked connections to understanding that tomatoes, spinach, and béchamel are important ingredients to help prepare the meal, and with this, it has surfaced a proven recipe backed by real chefs who have staked their token on its quality. With this structure you don’t just have random results, but a community of verified and reliable answers that align with your preferences. Intuition is the trust engine that could make a cooking app truly personal, reliable, and rewarding, transforming it into a collaborative food of wisdom.
Why Does Trust Matter in Food?
Food isn’t just what we eat to feel satisfied, it is health, memory, and culture. A single bad recipe can ruin a good meal, waste ingredients, or create a health risk. Most cooking apps rely on star ratings or anonymous reviews, but ratings can be faked and comments don’t let you know if the person giving the advice is credible. A five-star lasagna recipe that you trust so well, you can find out that it was upvoted by bots or written by someone who has never tried cooking it. With intuition projects the change is dynamic, every piece of knowledge comes with a provenance, you can see exactly who added the information and how much the community has placed in it by staking tokens called $TRUST. This creates a culture of accountability and transparency, trust isn’t implied but earned, tracked, and visible. Also sharing knowledge becomes rewarding, for example, if you have a trick to keep lasagna noodles from sticking without overcooking them, you add the tip to the app and stake a small amount of $TRUST on it. As other users test and stake on your tip, its credibility grows. By growing the system rewards you with a token because your contribution improves the collective knowledge. The cooking app built on intuition transforms users into active participants rather than passive consumers. Now, you can be a part of a living network where your knowledge has measurable value that benefits both you and everyone else.
Conclusion:
This is not just going to be an upgrade to cooking apps but a direction to how we handle knowledge online, instead of scattered unreliable information, we would have trusted, interconnected insights shaped by the people who care about them the most. Every contributor would be rewarded because there will be a constant incentive to keep the knowledge fresh, accurate, and inclusive. With this vision that would become an impact, intuition-powered platforms would influence more than recipes; they would reshape how we learn, share, and connect through food, from global cultural exchange to smarter AI-driven meal planning, the possibilities go beyond dinner.