Draichi (Lucas Draichi)

Posts 2

Post

402

🏁 Now it is possible to chat with telemetry data from real Formula 1 races!

This is an AI-powered solution for analyzing and generating detailed reports on Formula 1 racing sessions. This project combines the power of ReAct agents from LangChain with a RAG approach to pull data from a SQL database.

At the core of this system is a text-to-SQL capability that allows users to ask natural language questions about various aspects of F1 races, such as driver performance, weather impact, race strategies, and more. The AI agent then queries the database, processes the information, and generates comprehensive reports tailored to the user's needs.

The reports can be exported in various formats, making it easy to share insights with team members, race fans, or the broader motorsports community.

(The project is in beta, some erros may occur)

Check it out:

- Draichi/Formula1-race-debriefing
- https://github.com/Draichi/formula1-AI

Post

2258

Hey Hugging Face Community 🤗

I'm excited to share my latest project that combines my passion for deep learning and racing cars. I recently created a simple method to predict Formula 1 lap times using machine learning . This is the first solution of its kind in the open-source community, and I'm thrilled to present it to you all.

🏎️ The project leverages historical telemetry data to predict lap times, providing a new tool for race strategy and performance analysis. You can check out the notebook on Kaggle here https://www.kaggle.com/code/lucasdraichi/hamilton-lap-time-prediction and see the detailed breakdown of the model and its predictions.

I invite you all to take a look at the lap time predictor, provide feedback, and join the discussion. Your insights and participation would be invaluable as we continue to develop and enhance these tools.

Let's push the boundaries of what's possible with AI in motorsports together!