AI in Anger

Foo Café recently hosted an event called “AI in Anger”, focusing on how open-source, pre-trained Local Large Language Models (LLMs) are changing the way organizations use AI. These models provide solutions for businesses that need to follow strict privacy, security, or legal rules but still want to take advantage of AI technologies.

What Are Local LLMs?

The event introduced Local LLMs as a tool to bring AI capabilities directly into businesses without relying on cloud services. These models allow organizations to keep sensitive data secure while using AI to improve their processes and decision making. This approach is especially useful for industries like finance, healthcare, and technology.

The session was led by Salim and Anders, experienced professionals who explained how companies can use Local LLMs to solve problems and grow their business.

A Live Demo: Bitcoin Predictions

One of the highlights of the event was a live demo showing how Local LLMs work in practice. Using a GitHub repository, they demonstrated how to get the current Bitcoin value and predict trends for the next week. This example showed how AI can help with financial forecasting and other practical uses.

Using AI in Software Development

The event also explained how Local LLMs can be used in the software development process. Businesses can add these tools to their systems to improve efficiency and stay competitive, all while ensuring their data stays private and secure.

Why This Matters

The “AI in Anger” event showed how Local LLMs are opening new possibilities for businesses. These models make it easier for organizations to use AI without worrying about breaking privacy or security rules. With tools like the GitHub documentation, companies can start exploring how to use these technologies in their own work.

This event highlighted how AI is becoming more accessible and useful for organizations of all sizes. Local LLMs are not just for experiments, they are real tools that can help solve problems and make businesses more efficient.

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