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Yogi

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yogicodes

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Reacted to Draichi's post with 👀 27 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

Reacted to Draichi's post with ❤️ 27 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

Reacted to TuringsSolutions's post with 👀 27 days ago

Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video. https://youtu.be/wBRem2p8iPM

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New activity in yo/tagger over 1 year ago

master

#1 opened over 1 year ago by

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master

#1 opened over 1 year ago by

yo