Reptrack

Company: Reptrack

Self-reported basis: The description is entirely self-reported and unverified; it originates from a hackathon submission on Devpost by two individuals (Ayuna 林元薇, Donny Lunardi). No external verification or historical data exists for this project.

What the company appears to be: A mobile application that uses computer vision to analyze workout form in real time, provide feedback and scoring, and count repetitions — mimicking a personal trainer’s role.

What changed: This is a hackathon project submitted to the OpenAI 2026 hackathon; it has not progressed beyond an initial prototype or product demonstration.

Single most important open question: Is there any evidence of user adoption, revenue, or traction beyond the authors’ own claims?

The description states that Reptrack is a mobile application built for Android using Android Studio, Kotlin, and XML, with support for computer vision. It is described as a personal workout trainer that:

• Detects and analyzes the user's workout form in real time.
• Provides evaluations and scoring.
• Counts repetitions during workouts.

The app uses computer vision to perform these functions, and it is built as a mobile application, not a web or desktop product.

Inference: The app is likely a prototype or proof-of-concept, based on the authors’ own account of building it in under a week with limited experience in mobile development and computer vision.

The author states that Reptrack aims to replace personal trainers by offering an application that provides precise metrics for workout analysis. The tagline is:

“Use your phone as your gym personal trainer that checks your form in real time and tracks your workout progress.”

This positioning implies a self-service, low-cost alternative to personal training, leveraging mobile technology and AI.

Claim: Reptrack positions itself as an affordable, accessible personal trainer.

Not evidenced: There is no evidence of how this compares to existing apps or services in the market, nor whether it has been tested with users.

The description does not define a specific customer segment or ideal customer profile (ICP). It implies that Reptrack targets individuals who:

• Want to train at home.
• Are interested in improving workout form.
• May be unable or unwilling to afford personal trainers.

Inference: The target audience is likely fitness enthusiasts or beginners looking for affordable, tech-assisted workout guidance.

Not evidenced: No customer personas, user research, or segmentation data are provided.

The description does not mention any business model or pricing structure. It is unclear whether Reptrack will be:

• Free with in-app purchases.
• Subscription-based.
• A one-time purchase.
• Monetized through advertising or partnerships.

Not evidenced: No information on monetization, pricing tiers, or revenue streams.

The project was built using:

• Android Studio
• Kotlin
• XML
• Computer vision (as a core feature)

It is described as a mobile app, and the authors note that it was built in under a week with limited experience in mobile development and computer vision.

Inference: The technical stack suggests a basic prototype, not a production-ready product.

Not evidenced: No details on performance, scalability, or robustness of the computer vision system are provided.

The project is described as a hackathon submission and has no evidence of:

• User adoption.
• Revenue.
• Customer base.
• Product maturity beyond prototype stage.
• Any form of testing or feedback from users.

Not evidenced: No traction, usage data, or product development milestones are reported.

The description does not mention any competitors. However, the idea of using mobile apps and computer vision for workout tracking is not novel — similar concepts exist in the market (e.g., apps that use smartphone cameras to analyze form).

Inference: Reptrack likely competes with or overlaps with existing fitness apps or AI-powered workout tools.

Not evidenced: No competitive analysis, market positioning, or differentiation strategy is provided.

• Prototype only: The app is described as a hackathon project, not a product in development.
• No traction or revenue: There is no evidence of users, customers, or monetization.
• Limited technical depth: The authors note they are new to mobile and computer vision — this raises questions about scalability and accuracy.
• Unverified claims: All features and capabilities are self-reported without external validation.

Red flag: The lack of any real-world testing or user feedback makes it difficult to assess the product’s viability or effectiveness.

1. What specific workout types does Reptrack support, and how accurate is its form analysis?
2. Have you tested the app with users? If so, what were the results?
3. How do you plan to monetize the product beyond the initial prototype?
4. What are your plans for scaling or improving the computer vision technology?
5. Are there any partnerships or integrations in place with gyms, fitness centers, or wearable device manufacturers?