Hello! I'm Tim, a software architect, photographer, and writer. This is my little corner on the internet where I share my thoughts, projects, and experiences.
I've been fascinated by technology since I was a kid. Coding, designing, and writing are not just my profession but also my passion. When I'm not in front of a computer screen, I'm probably hiking, reading, or experimenting with photography.
Thoughts
Setting up RAG with JupyterLab and Jupyternaut
Retrieval-Augmented Generation (RAG) is revolutionizing AI-driven data analysis. By combining JupyterLab's interactive environment with Jupyternaut's AI capabilities, we can create a powerful RAG system that enhances our data exploration and analysis workflows. This post explores the benefits of this setup, provides step-by-step instructions for implementation, and discusses key considerations such as data privacy and model selection. Whether you're a researcher or data professional, integrating RAG into JupyterLab represents an exciting frontier in AI and data science.
⟶Running your server at home
In the era of digital transformation, running a server at home has become increasingly popular, especially with compact and affordable devices like the Raspberry Pi. This tiny yet powerful tool offers a great way to learn about servers and networking, host your own applications, and control data privacy. However, like any technological endeavor, it comes with its own set of challenges. Let's delve into why running a server at home on a Raspberry Pi is important, and also highlight some of the difficulties you might face.
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