Chris, the creator of the Amy calorie tracking app, celebrates reaching $1,000 in monthly recurring revenue but faces a major challenge with low user retention. To address this, he’s added highly requested features like photo-based food logging and micronutrient tracking, while improving app speed and reliability, with the goal of increasing user engagement and retention.
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Chris, the creator of the productivity app Amy, shares an update one month after launch. The app has reached a significant milestone: 100 paying users and $1,000 in monthly recurring revenue, making it his fastest-growing app to date. Amy is a calorie tracking app designed to be as simple as Apple Notes—users type what they ate, and the app automatically calculates the calories. Despite the early success, Chris emphasizes that the journey is just beginning and there’s still a lot of work ahead.
The main challenge Chris faces is user retention. Currently, only about 8% of users stick with the app one week after signing up, which he identifies as the most critical problem to solve. He explains that no amount of marketing will help if users don’t continue using the app. To address this, Chris gathers feedback through a feedback board, social media, and direct emails, prioritizing suggestions that are repeatedly requested or come from users who take the time to reach out directly.
One of the most requested features was the ability to log food by taking a picture. Initially hesitant to add this, Chris eventually built a prototype using Gemini 2.5 Flash Light for image recognition and Perplexity Sonar for calorie calculation. The feature turned out to be surprisingly accurate and even helped Chris realize when he received the wrong order at a restaurant. Users can also edit the AI-generated descriptions and specify the restaurant for more precise results. This new feature aims to reduce friction and improve retention by making food logging easier and more accurate.
Another highly requested addition was micronutrient tracking, such as sugar and fiber, alongside the existing macronutrient tracking (protein, carbs, fats). Chris added this feature, allowing users to enable specific micronutrients and set targets. He also updated the onboarding process to highlight this capability. Personally, Chris found the feature educational, as it revealed his own high sugar intake, and he sees potential for more educational features that build user trust and engagement.
Finally, Chris improved the app’s calorie calculation system. Previously, every edit triggered a full web search via Perplexity Sonar, which was slow, costly, and sometimes inaccurate for simple portion changes. Now, a lightweight AI model first checks if an edit is just a portion adjustment and handles it instantly and cheaply, while more complex edits still use Perplexity Sonar. These changes make the app faster, more reliable, and less expensive to run. Chris’s main focus remains on boosting retention to 30–40% by continuing to listen to users and iterate quickly, while also exploring new growth channels like user-generated content.