Claire turned to face Zoe, her voice steady despite the emotion behind it. “I was nineteen when he started to have trouble remembering the more difficult equations. Within a few years, he couldn’t draw the face of a clock with the right time. Two years later, he didn’t even recognize my face. Something so fundamental … Continue reading →

If you’ve been hearing the buzz about AI but don’t know if it is for you, or you think your company is too small to use AI, I’ve got just the video for you. Since my recent posts predicting the election and some other price predictions, several people I talk with have been asking me … Continue reading →

I mentioned on linkedin that I thought it would be a fun diversion to build a PyTorch model to forecast the US Presidential Election. This was an interesting project for a few reasons. At the time I did this, it was about two weeks before the presidential election in the United States. As I write … Continue reading →

Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with custom, up-to-date information. Integrating RAG into LLM workflows allows organizations to leverage their proprietary data to generate more accurate, relevant, and contextually appropriate responses. This approach bridges the gap between the LLM’s pre-trained knowledge and specific. This includes confidential information that … Continue reading →