Sporty — AI Personal Coach and Training Tracker

Inspiration

Sporty was inspired by a problem I faced in my own training.

I have been involved in strength sports and armwrestling for many years, but I was still recording workouts, weights, repetitions, and personal records in a regular notebook. This approach worked, but it was inconvenient, difficult to analyze, and did not provide any meaningful feedback about my progress.

I wanted to create something more than another basic workout log. My goal was to build a mobile application that could act as a personal AI coach: a system that remembers training history, tracks progress, understands recovery, and helps athletes make better training decisions.

Another important motivation was the lack of specialized tools for grip-strength athletes. Most fitness applications focus on running, bodybuilding, or general strength training. They do not provide leaderboards or meaningful comparisons for grip strength, even though grip performance is extremely important in armwrestling, climbing, strongman, and many other sports.

That is how the idea for Sporty was born.

What Sporty Does

Sporty is a mobile fitness application that combines workout tracking, performance analytics, recovery data, and AI-powered coaching.

Instead of writing workouts in a notebook, users can record exercises directly in the application, including:

• weight;
• repetitions;
• sets;
• exercise history;
• personal records;
• training notes.

The application stores previous results and allows users to see how their performance changes over time.

Sporty also includes an AI personal coach that can use training history and recovery information to provide more personalized recommendations. The long-term goal is for the coach to understand not only what the user trained, but also whether the user is ready for another hard session.

Grip-Strength Leaderboards

One of Sporty's most distinctive features is its grip-strength leaderboard.

There are many applications for tracking running speed, powerlifting totals, or bodybuilding workouts, but there is no widely adopted platform for comparing grip-strength results.

Sporty allows athletes to record grip-related achievements and compare their performance with other users. This feature is especially relevant for:

• armwrestlers;
• climbers;
• strongman athletes;
• grip-sport competitors;
• strength-training enthusiasts.

The leaderboard also introduces an additional motivational and community element. Users are not only trying to beat their own records but can also see how their results compare with other athletes.

Oura Ring and Recovery Analytics

Sporty is also designed to work with recovery data from wearable devices such as the Oura Ring.

This part of the project was inspired by my partner, Masha, and by the idea that training recommendations should account for individual recovery and physiological differences.

The application can present analytics based on:

• readiness scores;
• sleep and recovery;
• daily activity;
• menstrual-cycle phases;
• changes in performance across different cycle days;
• personal records achieved under different recovery conditions.

Instead of showing wearable data as isolated numbers, Sporty aims to connect those numbers with actual training performance.

For example, the application can help answer questions such as:

• Does the user perform better on days with a high readiness score?
• How does sleep quality affect strength?
• During which cycle phases is training performance usually higher or lower?
• Should the next workout be heavy, moderate, or recovery-focused?

Conceptually, training readiness can be represented as a function of several signals:

[ R = f(S, H, A, C, T) ]

where:

• (R) is overall readiness;
• (S) is sleep quality;
• (H) is recovery or health data;
• (A) is recent activity and training load;
• (C) is menstrual-cycle context;
• (T) is recent training performance.

The purpose is not to treat the formula as a medical diagnosis, but to transform scattered data into useful training context.

How I Built It

I developed Sporty as a mobile-first application with several connected functional areas:

1. workout logging;
2. personal-record tracking;
3. athlete profiles;
4. grip-strength leaderboards;
5. wearable and recovery analytics;
6. AI coaching.

I started with the core experience: replacing the training notebook.

The first priority was making workout input simple enough that an athlete could use it during a real training session. After that, I expanded the application with historical records, progress analytics, rankings, and recovery information.

The AI component was designed around the idea that recommendations should be based on the user's own history rather than on generic fitness advice.

A useful coaching system needs context such as:

[ \text{Recommendation} = f(\text{training history}, \text{recovery}, \text{goals}, \text{recent performance}) ]

This means that two users performing the same exercise may receive different recommendations depending on their experience, fatigue, recovery, and previous results.

Challenges

One of the biggest challenges was deciding how much information to collect without making workout logging slow or frustrating.

Athletes want detailed analytics, but they do not want to spend several minutes entering every set. The application therefore needs to balance two competing goals:

• collecting enough structured data for useful analytics;
• keeping the interface fast and convenient during training.

Another challenge was combining information from different sources. Workout records, personal records, readiness scores, sleep data, cycle information, and leaderboard results all have different structures and update frequencies.

The AI coach also created an important challenge: recommendations must be personalized without pretending to provide medical certainty. Recovery scores and cycle data should support the user's decisions, not replace professional medical advice or the athlete's own judgment.

The grip-strength leaderboard introduced additional complexity. Different devices, exercise variations, handles, positions, and measurement techniques can produce different results. To make comparisons meaningful, Sporty needs standardized exercise definitions and clear record-validation rules.

What I Learned

Building Sporty taught me that a successful fitness application is not only about storing workout data.

The real value comes from connecting three layers:

1. Recording — what the athlete did.
2. Understanding — how performance and recovery are changing.
3. Acting — what the athlete should consider doing next.

I also learned that personalization requires consistent historical data. An AI coach cannot provide meaningful guidance from one isolated workout. Its recommendations become more valuable as the application learns the user's habits, progress, recovery patterns, and individual response to training.

Another important lesson was that features should come from real user problems. Workout logging came from my own need to replace a notebook. Grip-strength rankings came from my experience in armwrestling. Recovery and cycle analytics came from observing that training readiness is highly individual.

The Future of Sporty

The long-term vision for Sporty is to become a complete AI-powered training companion.

Future development may include:

• more advanced training-load analytics;
• adaptive workout planning;
• automatic detection of progress and plateaus;
• better wearable-device integration;
• standardized grip-sport competitions and rankings;
• AI-generated training summaries;
• personalized recovery recommendations;
• coach and athlete collaboration tools;
• predictions of readiness and performance trends.

Sporty began as a way to stop recording workouts in a notebook, but it is growing into a broader platform that connects strength training, recovery, competition, and artificial intelligence.

The main idea behind the project is simple:

Training data should not only be stored. It should help the athlete make better decisions.

Built With

• typescript