• May 26, 2025
• in Tech, AI, News
• 3 min read

Tech News - Week 2025.05.26

Welcome to this week's tech overview. This post summarizes interesting topics I've come across and would like to share.

Topics may include:

• Software Development
• Cloud Computing / Cloud Native
• Artificial Intelligence (AI)
• *Ops

Weekly topics by group:

AI

AI Projects

1/ More about the llm-d project

The llm-d project was launched last week at Red Hat Summit 2025, and there has already been significant discussion and a variety of use cases shared across the community.

References:

• Quick overview of the llm-d project
• llm-d in action on OpenShift AI
• Deploying llm-d (demo)

AI MCP

2/ AI - Model Context Protocol (MCP):

• Creating an MCP Server using Go
• GitHub Project - MCP Servers
• MCP Servers - Examples
• Setting up MCP Servers on IDEs:
• Cursor: Setting up MCP Server

• VS Code: Use MCP Servers in VS Code (Preview)

• Postman MCP Server Generator: this is a cool postman tool which will navigate through the apispec of any API endpoint and generate an MCP server to you.

3/ AI Courses and Learning:

Highlighted courses and learning materials:

• The Hugging Face Model Context Protocol (MCP) Course

AI Agents

Enhancing the MCP, there are couple of interesting projects and protocols to leverage the Agentic workflow ecosystem.

The A2A protocl is one example which allow creating a system with many agent talking with each other communicating and discoverying the tool (MCP) each one supports.

The framework kagent is one interesting to quickly ramp agent deployments/oprations.

Here are some knobs I got from the project voice-mcp-agent (credits to it):

A2A Agent Integration

The agent supports connecting to A2A (Agent-to-Agent) servers, allowing you to use skills from other AI agents as tools. This is useful for integrating with external AI services or custom agents that expose their own skills.

To add an A2A agent, use the type: a2a field in your mcp_servers.yaml:

servers:
  - name: my-a2a-agent
    type: a2a
    url: https://my-a2a-agent.example.com
    allowed_tools: [*]  # (optional) restrict which skills are available
    headers:
      Authorization: Bearer <token>  # (optional) custom headers for auth
type: a2a tells the agent to treat this server as an A2A agent, not a standard MCP server.

The agent will automatically discover available skills from the A2A agent's /.well-known/agent.json endpoint. Each skill is exposed as a callable tool. You can invoke these skills by natural language or by specifying the tool name. You can use allowed_tools to restrict which skills are available to the agent.

Here is a quick start guide for kagent project.

AI Robotics

4/ NVidia Robotics

The article "Wandercraft Begins Clinical Trials for Physical AI-Powered Personal Exoskeleton" caught my attention. I have a personal interest in this area, as my mom was injured by a stroke, and this company is making a reality for millions of people needing assistance in their daily tasks. Here’s what they are doing:

"Wandercraft builds mobility solutions for individuals with spinal cord injuries, stroke, and other neuromuscular disorders."

"The company’s Personal Exoskeleton, currently in clinical trials, enables users to stand and walk with the support of AI-powered mechanisms for stability and movement. Users can control the robotic system with a joystick."

Red Hat

OpenShift

• The road to quantum-safe cryptography in Red Hat OpenShift
• Post-quantum cryptography in Red Hat Enterprise Linux 10

Experimenting MCP

I was also experimenting MCP (Model Context Protocol) to get more familiar with the tooling and how LLM iteracts with that.

I I've developed a MCP server which fetchs AWS news, with optional fielter by category, and return some content. That content can be analysed by the MCP client to answer the user's question.

Furthermore, I was thinking whynot runnint that MCP server in a free serverless application, such as Vercel, and deploy a browser-based chat application to call it?

So I created the MCP server, it's working when calling http://mtulio.dev/api/news/aws

And now I am trying to use the web-llm project to build a chat application to host it in the Github pages, so I will have a completely serverless chatbot app to fetch relevant information from news of cloud providers.

Well, yeah, we can use RSS feeds, no? yeah! But I am playing with the hype tools, specially running inference in the browser.

Web-llm reference: - https://github.com/mlc-ai/web-llm - https://github.com/mlc-ai/web-llm/tree/main/examples - https://chat.webllm.ai/ - https://mlc.ai/models