Building an F1 Racing Leaderboard with AI: A Complete Solution Created by Code Assistants

F1 Leaderboard System in Shop with 4 Rigs

The Power of AI in Modern Development

What if I told you that a complete, production-ready F1 racing simulator leaderboard system could be built entirely using AI coding assistants? Not just simple scripts or prototypes, but a full-featured application with real-time telemetry processing, administrative controls, timer management, and a polished user interface that's currently running at racing events across Romania.

That's exactly what I accomplished with my F1 24 Leaderboard project – an end-to-end solution built exclusively using AI tools, specifically Gemini 2.5 Pro for architectural planning and Claude Sonnet 3.7 in Cursor for code generation.

The Vision: Professional Racing Experience

The goal was ambitious: create a leaderboard system for a simulator shop with multiple F1 racing rigs that could:

F1 Leaderboard at Moto Event in Brasov

The AI-First Development Approach

Phase 1: Architectural Planning with Gemini 2.5 Pro

Rather than diving straight into code, I started by leveraging Gemini 2.5 Pro to create a comprehensive development plan. I provided the AI with my requirements and asked it to design the entire system architecture, including:

The AI generated a detailed 96-point specification document that became my development bible. This wasn't just high-level guidance – it included specific implementation details, error handling strategies, and even code style conventions.

Phase 2: Cursor Rules and AI-Guided Development

The architectural plan was then converted into Cursor rules (stored in .cursor/rules/development-rules.mdc), creating a persistent context for the AI coding assistant. These rules ensured that every piece of generated code would:

Phase 3: Code Generation with Claude Sonnet 3.7

With the architectural foundation in place, I used Claude Sonnet 3.7 in Cursor to generate the actual implementation. The AI created:

F1 Leaderboard at Tech Days Bucharest

Technical Deep Dive

Real-Time Telemetry Processing

One of the most impressive aspects of the AI-generated solution is how it handles F1 2024 telemetry data. The system:

Sophisticated State Management

The leaderboard display features intelligent state management:

Administrative Control Suite

The admin interface provides comprehensive control over the entire system:

Testing and Refinement: AI-Assisted Debugging

Even the testing and debugging phase leveraged AI assistance. When issues arose during deployment, I could describe problems to Claude Sonnet, and it would:

This AI-assisted debugging was particularly valuable when fine-tuning the telemetry capture logic and ensuring reliable operation across different network configurations.

Real-World Deployment Success

The proof of concept became a production reality. The system has been successfully deployed at:

The system handles real telemetry data, manages concurrent sessions, and provides a professional racing experience that rivals commercial solutions. The flexible architecture allows it to work equally well with single-rig portable setups and multi-rig permanent installations.

What This Means for the Future of Development

Democratization of Complex Software

This project demonstrates that sophisticated, production-grade applications can now be built by developers without deep expertise in every involved technology. The AI assistants handled:

Speed of Development

What would traditionally take weeks or months of development was accomplished in a fraction of the time. The AI-generated solution was functional and worked exactly as intended from the start.

Trust in AI-Generated Solutions

The remarkable aspect of this project is that I never read a single line of the generated code. The AI created a working, production-ready system that I deployed with complete confidence based purely on its functionality. This represents a new paradigm where we can trust AI to handle implementation details while we focus on requirements and outcomes.

Key Takeaways for Developers

1. Start with Architecture, Not Code

Using Gemini 2.5 Pro to design the system architecture before writing any code proved invaluable. The AI helped identify potential issues and design patterns that would have been easy to miss.

2. Invest in Good Prompts and Context

The Cursor rules file acted as a persistent context that ensured consistency across the entire codebase. Good prompting is as important as good coding.

3. AI Excels at Integration

Rather than building everything from scratch, the AI was excellent at integrating existing libraries and tools (like the F1 telemetry parser) into a cohesive solution.

4. Focus on Outcomes, Not Implementation

The AI-assisted development process proved that we can achieve complex functionality without diving into implementation details. The focus shifts from "how to code" to "what should it do."

The Technical Stack

For those interested in the technical details, the complete system includes:

Conclusion: The New Era of Development

This project represents more than just a successful application – it's a glimpse into the future of software development. When AI tools can generate production-ready, complex applications from architectural specifications, it fundamentally changes what's possible for individual developers and small teams.

The F1 leaderboard system continues to operate successfully in production, handling real telemetry data and providing professional racing experiences. It stands as proof that the question is no longer "Can AI build real software?" but rather "What amazing things will we build next with AI as our development partner?"

The age of AI-assisted development isn't coming – it's here, and it's more powerful than we imagined.