Why all the fuss about open-source vs. proprietary AI?
In this blog, Philip D'Souza discusses the pros and cons of open source versus proprietary AI and how striking a balance between the two is important.
Telum II: the wait is nearly over
Find out how the improved power of IBM's Telum II processor will deliver better performance to the mainframe in 2025, in our new blog.
Macro 4: Our Open DevOps Journey (Part 3)
In this third and final blog, Michelle Harris discusses why Macro 4 uses VS Code, GitLab and Docker to develop web interfaces on its mainframe applications.
Exploring AI Code Generation in IBM's Granite 3.0 Models
IBM's Granite series is developing rapidly. Find out in this blog how these large language models (LLM's) may be particularly well-suited to code generation tas...
Macro 4: Our Open DevOps Journey (Part 2)
In this second blog, Michelle Harris explains how moving to a mix of open-source tools and in-house solutions has helped Macro 4 increase efficiency, reduce ris...
Macro 4: Our Open DevOps Journey (Part 1)
In the first of three blogs, Michelle Harris explains how Macro 4 has implemented Open DevOps for its mainframe solutions
AI agents, the next step in the evolution of GenAI
In this blog, Phil D'Souza discusses the emergence of AI agents capable of tackling more complex tasks and problems.
Three approaches to unlocking the potential of GenAI
In this blog, find out how to get more from generative AI by integrating additional tools and resources into your LLM.
What is the flow of processes for building a Generative AI application?
In this blog, read about the steps you need to incorporate into your GenAI application process.
Fine-tuning vs prompt engineering: how to customize your AI LLM
Find out about the differences between fine-tuning and prompt engineering in this blog, and how they can be used to customize an AI large language model (LLM).