Category Archives: AI

If you want to hold costs down, efficient resource management is paramount. One of the critical resources in AI applications is token usage. Tokens are the basic units of text that models process, and managing them effectively can lead to … Continue reading →

In the previous blog post I mentioned Neo4j. In this post I will provide an overview of this important framework. A Graph Database In the era of big data, the way we store and manage information has evolved significantly. Traditional … Continue reading →

In the realm of software development, managing configuration values and sensitive information is a critical aspect that can significantly impact the security and functionality of applications. Developers often find themselves at a crossroads when deciding how to store configuration values, … Continue reading →

I fed the first half of one of the blog posts generated by my demonstration program to Pangram. Here are the results: Bzzzz Still your turn.

Long-term memory in AI agents refers to the ability to retain information across multiple interactions and sessions. This is essential for creating a more personalized user experience, as it allows agents to recall user preferences, past conversations, and contextual information. … Continue reading →

In the rapidly evolving landscape of artificial intelligence, developers are presented with a myriad of frameworks to build applications that leverage the power of large language models (LLMs) and multi-agent systems. Among these, LangChain, LangGraph, and the Microsoft Agent Framework … Continue reading →

Observability has become a critical component for ensuring the performance and reliability of applications. OpenTelemetry, an open-source observability framework, provides developers with the tools necessary to collect and export telemetry data from their applications. When integrated with the Microsoft Agent … Continue reading →