The story on how I reverse engineered CalAI using the most popular full-stack framework - Next.js. Utilizing Vercel's AI SDK, we can recreate the original premise of CalAI, which is processing food through an AI and adding it to your daily calories.
Imagine taking a picture of your meal, and in seconds, an app estimates its calorie content and adds it to your daily counter. This is the idea behind CalAI—a unique app that’s reportedly bringing in $500k/month by using AI to simplify calorie tracking. Inspired by this innovative approach, I set out to reverse-engineer a similar tool using open-source technologies and the powerful AI tools available to developers today. Here’s a look into how I approached this project, my progress so far, and the potential for an open-source alternative.
Why CalAI Caught My Attention
Calorie tracking is a hassle, and CalAI’s solution of using AI to estimate calories based on a food image is ingenious. This not only saves time but also makes calorie tracking more accessible. As a developer, I wanted to see if I could recreate a similar experience—one that doesn’t require a paid subscription. So, I decided to reverse-engineer CalAI’s concept and build a tool that could do the same, powered by Next.js and Vercel’s AI SDK.
Why Next.js and Vercel’s AI SDK?
For this project, I needed a stack that could handle real-time processing, dynamic routing, and a smooth user experience. Next.js is perfect for handling complex applications, especially when combined with Vercel’s AI SDK, which provides easy-to-integrate AI tools. With these, I could set up an endpoint that processes food images and uses AI to estimate the caloric content—exactly what CalAI does.
The Core Process: Building the Calorie Counter
The key feature of my version is that users can upload an image of their food, and the app estimates the calorie count using AI. Here’s a breakdown of how it works:
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Image Upload: Users upload a photo of their meal through a simple interface.
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AI-Powered Caloric Estimation: Vercel’s AI SDK processes the image to recognize the food items and estimate their caloric values. This part took careful tuning to ensure accurate results, as food recognition and calorie estimation can vary widely.
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Daily Calorie Counter: Once the AI provides a calorie estimate, it adds this to the user’s daily calorie total. The goal is to create an easy-to-use log that’s intuitive for anyone who wants to track their intake without manual input.
Challenges and Learning Along the Way
Building this functionality has been a challenge. Food images can vary in lighting, angle, and presentation, which affects how accurately the AI can estimate calories. Additionally, designing an efficient backend to handle image processing and data storage took careful planning. However, using Vercel’s SDK has been a game-changer, making it easy to work with AI functionalities that would otherwise take extensive setup.
What’s Next?
While the app isn’t open-source just yet, I’m working toward releasing it as an accessible, free alternative for calorie tracking. Once finalized, the project will be open-sourced, allowing developers to experiment with and build upon it. My vision is to create a base that others can adapt—whether that means integrating it into health apps or using it as a standalone calorie counter.
Final Thoughts
Reverse-engineering a high-revenue business model has been both a technical and creative challenge, and it’s opened my eyes to the potential of modern AI in real-world applications. I look forward to completing this project and sharing it with the community. With the right tools, we can democratize technology, bringing sophisticated functionalities like calorie tracking to everyone.
Stay tuned for updates as I continue refining this project!