I want to share my experience in the cloud, AI, and agents development areas.
This post shows the importance of clear prompt structure when developing local AI agents with frameworks like LangGraph and Ollama. Smaller models are less tolerant of ambiguities, making it crucial to separate instructions, context, and output formats. This enhances reliability, debugging, and reduces risks from untrusted inputs.
This post compares various AI coding assistants/agents, emphasizing privacy from a developer's perspective. It highlights how modern AI systems function as integral tools in software development, shifting the privacy discourse from mere data training concerns to broader issues like data sovereignty, operational exposure, and pricing implications, particularly for individual developers.
The post discusses automating local testing of IBM watsonx Orchestrate with Ollama models using a Bash script. The script simplifies the setup process, ensuring proper connections and configurations. It initiates services, confirms model accessibility, reducing typical setup errors.
Thomas Suedbroecker's Blog 