I want to share my experience in the cloud, AI, and agents development areas.
This post outlines the process of setting up a local watsonx Orchestrate server and invoking a simple agent via REST API using Python. It covers environment setup, Bearer token retrieval, agent ID listing, and code execution.
This post guides users through building an AI agent locally using the watsonx Orchestrate Agent Development Kit (ADK), exporting it from their local setup, and importing it into a remote instance on IBM Cloud. The process enhances local development while ensuring efficient production deployment.
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 provides a step-by-step guide to setting up InstructLab CLI on an Apple Laptop with an Apple M3 chip, including an overview of InstructLab and its benefits. It also mentions supported models and detailed setup instructions. Additionally, it refers to a Red Hat YouTube demonstration and highlights the project's potential impact.
This blog post focuses on the importance of Prompt Engineering in AI models, particularly Large Language Models (LLMs), for reducing manual effort and automating validation processes. It emphasizes the need for automation to handle increasing test data and variable combinations, and discusses the use of the Watsonx.ai Prompt Lab for manual and initial automation processes. The post also highlights the significance of integrating automation with version control for consistency and reproducibility.
This blog post outlines a bash automation for setting up and testing Text Generation Inference (TGI) using a container. It provides instructions for creating a Python test client, starting the TGI server, and troubleshooting common issues. The post emphasizes the benefits of using containers and references the Hugging Face and Nvidia technologies.
This blog post is a short cheat sheet about how to save Elasticsearch query results with paging in a bash script automation. The automation does download all the query results in JSON files as long the paging returns results in the hits of the return JSON. The files are numbered 01.json to XX.json.
In IBM Cloud Code Engine you also can use kubectl commands to get information about your running application in addition to the IBM Cloud Code Engine CLI.
This extract is from a bash automation script in the question-answering GitHub project. The bash script automates the deployment to IBM Cloud Code Engine. The extraction is about the building and pushing a container to the IBM Cloud Container Registry.
In this blog post we use an existing template file, that we created for a custom resource to insert the needed value for an URL to point to a container image in a container registry, to create custom resource yaml file we use to deploy that custom resource . The template file contains a string which we will replace with the content of URL to the container image. We haven't used helm, kustomize to do that in this situation.
Thomas Suedbroecker's Blog 