Industry

Fitness

Type

Personal Project

Activity Track App. A lightweight activity tracking app.

Context

ActivityTrack is a personal project born from a simple need: I wanted an effortless way to keep track of my activities and quickly see, for example, which days I had been climbing throughout the month. The challenge was to design a simple and flexible way to log physical activities while giving users a clear, high-level overview of their habits without the friction or rigidity of traditional tracking apps. During the discovery phase, I found that most existing solutions fell into one of two extremes: 1) They were overly complex, requiring too many inputs, settings, and configuration steps before an activity could be logged. 2) They were too restrictive, supporting only predefined activity types with little room for customization or open-ended tracking.

The primary goal was to enable users to quickly log any type of physical activity and easily visualize their consistency over time.

The project started from a very specific personal need: seeing my climbing sessions on a monthly calendar. As I shared early concepts with friends and potential users, the scope naturally expanded to support a broader range of physical activities. These conversations helped shape a set of user stories that revealed how differently people measure progress and define success. This led to a key product decision: instead of relying on predefined activity types and rigid forms, I designed an open-input system that prioritizes flexibility while minimizing friction. To build the MVP, I connected a frontend created with Figma Make to a Supabase database, using ChatGPT as a development partner to help structure technical decisions, explore implementation approaches, and overcome development roadblocks.

Results & Key Learnings

The project validated the initial hypothesis that many users value speed and flexibility over feature-rich activity tracking. By reducing the number of required inputs, the experience became lightweight and adaptable to different types of physical activities. Building the MVP also demonstrated how modern AI-assisted workflows can significantly accelerate product development, allowing ideas to move from concept to a functional prototype in a short period of time. - Solving a real personal problem is often the best starting point for discovering broader user needs. - The simplest solution is not always the one with fewer features, but the one with the least friction. - Open-ended systems can provide greater long-term flexibility than rigid predefined models. - Early conversations with users help uncover assumptions before investing in implementation. - AI tools can dramatically speed up prototyping and development, but product thinking and user validation remain the key drivers of a successful solution. ActivityTrack started as a personal tool and evolved into a product exploration around flexible activity tracking. The MVP is currently being tested with early users, using their feedback to validate assumptions and guide future iterations.

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