I want to share my experience in the cloud, AI, and agents development areas.
The post explores the integration of Large Language Models (LLMs) in applications, stressing the need for effective production testing. It introduces the AI Operational Complexity Cube concept, emphasizing new testing dimensions for LLMs, including prompt testing and user engagement. A structured testing approach is proposed to ensure reliability and robustness.
This blog post explains how to deploy a fine-tuned model to IBM watsonx on IBM Cloud. It highlights the advantages of using this platform, such as avoiding infrastructure management and ensuring enterprise security, as well as detailed steps for configuration, deployment, and accessing the model from IBM watsonx.
The blog post outlines updates on fine-tuning a model with the InstructLab , detailing tasks like data preparation, validation, synthetic data generation, model training, and testing. It emphasizes the need for extensive and accurate input for effective training, while only minimal changes in the overall process since previous versions, particularly in handling data quality. This blog post contains updates related to my blog post InstructLab and Taxonomy tree: LLM Foundation Model Fine-tuning Guide | Musician Example.
This blog post provides updates on the InstructLab project by IBM and Red Hat, detailing installation and configuration changes. It discusses new default locations for files and troubleshooting steps for model serving, emphasizing an overall installation process that remains largely consistent with prior guidance while noting minor user-friendly adjustments.
This blog post discusses the creation of a custom Bee Agent that operates independently from the Bee Stack and interacts in German. It explores requirements, agent examples, coding in TypeScript, and GitHub references. The author implements an agent using a specific system prompt while addressing the challenges of ensuring consistent output in German.
This blog post describes a web chat application using a large language model on watsonx, with the interface built in Streamlit.io. It focuses on motivation, architecture, code sections, and local setup, featuring basic authentication and options for user interaction. The author highlights Streamlit’s rapid prototyping capabilities and ease of use with Python.
In 2024, I published 35 blog posts focusing on innovation in AI and development, emphasizing the Bee Agent Framework and IBM's watsonx.ai. The highlights including creating cheat sheets, exploring large language models, and providing practical guides. That boost my excitement for future challenges in 2025.
This blog post explains how to integrate a custom Python tool into the Bee Agent using the Bee UI, focusing on real-time weather data retrieval. It outlines the setup process, agent customization, and testing to ensure functionality. Clear descriptions and agent interactions enhance the tool's efficacy and future applications.
The content shows my Bee Agent Framework and Bee Stack development learning journey, focusing on their integration with the watsonx.ai. It covers the setup process for different agent applications, including a weather retrieval agent and a travel assistant. It also provides guidance for contributing to the development of Bee API and UI and configuring Podman.
The content outlines the setup process for a development environment aimed at contributing to the Bee API and Bee UI repositories within the broader Bee Stack. It details the steps of cloning repositories, starting infrastructure, configuring .env files, and launching both the Bee API and UI servers, ensuring readiness for development.
Thomas Suedbroecker's Blog 