Running
536
👁
Lucas Draichi
Draichi
AI & ML interests
AI in motorsports | Reinforcement Learning | Agents
Recent Activity
liked
a Space
1 day ago
PR-Puppets/PR-Puppet-Sora
updated
a dataset
17 days ago
Draichi/f1-brazil-2024-telemetry
Reacted to
their
post
with 🔥
17 days ago
🏁 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:
- https://huggingface.co/spaces/Draichi/Formula1-race-debriefing
- https://github.com/Draichi/formula1-AI
Organizations
Draichi's activity
liked
a
Space
1 day ago
updated
a
dataset
17 days ago
Reacted to
their
post with 🔥
17 days ago
Post
3515
🏁 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
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
- 1 reply
updated
a
Space
22 days ago
Reacted to
their
post with ❤️
22 days ago
Post
3515
🏁 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
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
-
- 1 reply
posted
an
update
22 days ago
Post
3515
🏁 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
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
-
- 1 reply
liked
a
Space
22 days ago
liked
a
dataset
22 days ago
liked
a
model
25 days ago
liked
a
Space
25 days ago
OSError: [Errno 36] File name too long
5
#11 opened almost 2 years ago
by
FloydianSound
FloydianSound
Runtime error
#1 opened about 1 month ago
by
Draichi
Draichi
liked
a
model
4 months ago
upvoted
a
collection
4 months ago
liked
a
Space
4 months ago
features
3
#38 opened 7 months ago
by
not-lain
not-lain
Reacted to
their
post with 🤗
6 months ago
Post
2273
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!
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!
- 3 replies
posted
an
update
6 months ago
Post
2273
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!
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!
-
-
- 3 replies
upvoted
a
paper
6 months ago
upvoted
a
collection
6 months ago