Warp, the top terminal app with AI support, has introduced new features in its GA release, including MCP support and Refine for AI responses. The Preview release now allows image uploads and the saving of reusable AI prompts. I also found a few new features that weren’t mentioned in the Launch Log 3 post.

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Creating an AI agent in Amazon Bedrock involves a relatively complex process. The example in this post has been simplified for clarity. Once you have your first Agent setup working, exploring all the features that AI agents in AWS offer becomes much easier. The Bedrock Agent I discuss uses a Lambda function, which can be triggered by the AI model when the user prompt indicates its usefulness. An AWS Lambda function is a serverless compute service that runs your code in response to events without provisioning or managing servers. These functions may involve complex logic, enabling the AI agent to perform sophisticated tasks autonomously.

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OpenAI recently introduced the Agents SDK, a Python-based framework that enables developers and DevOps engineers to build AI agents. The example in this post showcases a script that uses the OpenAI Agents SDK to connect an AI agent to Anthropic’s filesystem MCP server. This integration enables the AI agent to analyze the contents of a monitoring log.

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The leader in coding models has rolled out Claude Code to the public. Anthropic's terminal-based AI coding assistant automates editing, testing, and managing git workflows using natural language commands. Although you cannot install Claude Code as a VS Code extension via the marketplace, its integration into the open-source IDE is straightforward, and the user experience closely resembles GitHub Copilot, Cline, or Roo Code. My review of the preview version was somewhat critical because I dislike coding in the terminal. However, Claude Code now integrates well with VS Code and JetBrains, so I changed my original verdict.

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OpenAI's Sam Altman envisions a future where countless AI agents operate autonomously across the internet, managing tasks on behalf of users and interacting with each other to perform complex operations without human intervention. Realizing this vision necessitates a new internet protocol that supports the sophisticated communication required for AI systems. Google’s new Agent2Agent (A2A) Protocol addresses this need by providing an open standard that enables autonomous AI agents to communicate across diverse systems. This post delves into the necessity of A2A, its current supporters, distinctions from Anthropic’s Model Context Protocol (MCP), the types of data it handles, and offers a straightforward example to illustrate its application.

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In a remarkably short period, MCP has established itself as the standard for secure and efficient connections between AI models and external data sources and tools. Microsoft has integrated MCP into VS Code, Windows will get it, OpenAI plans to implement it for ChatGPT, and the top terminal application, Warp, has introduced MCP support in its Preview version. I installed the official Perplexity MCP server for my test and incorporated it into Warp.

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The relatively new VS Code technical terms chat participants, chat variables, and slash commands can be accessed with the prefixes @, #, and / in GitHub Copilot. This post explains the differences and gives usage examples.

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While GitHub Copilot prompt files are related to the instruction files I discussed in my previous post, they serve a different purpose. Prompt files are custom Markdown templates that allow you to automate coding tasks through custom AI-driven slash commands in GitHub Copilot for VS Code. Prompt templates are powerful, as they can utilize MCP server commands as tools. In this article, I will explain how to create prompt files and provide a simple usage example.

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In Visual Studio Code version 1.100, Microsoft introduced GitHub Copilot instruction files, which mirror the Rules feature in AI IDEs like Cursor and Windsurf. Instructions allow you to guide the AI assistant’s behavior by providing specific, context-aware directives tailored to your coding standards and project requirements.

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Perplexity is an AI search engine that answers questions by combining real-time web results with conversational, natural language responses. Installing Perplexity's official MCP server on your Mac or Windows PC allows querying Perplexity from any AI model in VS Code GitHub Copilot. Enabling your AI to search the web for the latest updates is a must-have feature for scripting or working in the terminal.

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OpenAI Codex CLI is a new open-source command-line tool that integrates AI models into any terminal. It enables you to generate, edit, and execute code using natural language prompts. I tested Codex CLI on Windows and Mac and concluded that it is no match for advanced AI CLIs such as Warp, Cline, or Roo Code. Do not mix up Codex CLI with OpenAI's Codex model, which was discontinued in March 2023. Perhaps Sam Altman should seek advice from ChatGPT before naming future products.

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I compared six frontier AI models—DeepSeek V3, Anthropic Claude Sonnet 3.7, Google Gemini 2.5 Pro, Meta Llama 4 Maverick, OpenAI GPT 4o (GPT 4.1), and Mistral Large 2—in terms of their capabilities in interacting with MCP servers. Surprisingly, the performance in terms of accuracy, token usage, and API costs are quite different..

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Roo Code and Cline are popular AI-driven coding extensions for VS Code. Both assistants indicate whether they support Images, Computer Use, and Prompt Caching when selecting a model. In this post, I explain these model features and whether you need them when coding with AI assistance.

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LLMs' knowledge cutoff date is a significant issue, often resulting in outdated responses. Some frontier models can browse the web, but they frequently skip this ability because they think they can handle things on their own. Bringing in an MCP server that can search the web helps the AI stay updated and assist it when struggling with a problem. Tavily is a research search engine designed to summarize online content for AI models. In this post, I will guide you through installing the Tavily MCP server on Windows. Installing on a Mac follows a similar process.

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Playwright is an open-source framework by Microsoft for automating web browsers to perform end-to-end testing, scraping, and UI interactions across Chromium, Firefox, and WebKit. The Playwright MCP server lets AI agents like GitHub Copilot control a web browser using Playwright’s automation capabilities. Microsoft introduced support for MCP in Visual Studio Code 1.99 (March 2025), enabling tools like the Copilot Agent to connect with external services. In this post, I explain how to install the Microsoft Playwright MCP server in VS Code for the GitHub Copilot Agent.

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In March 2025, the release of VS Code version 1.99 introduced support for Anthropic's Model Context Protocol (MCP) in GitHub Copilot's Agent Mode. This open standard is now backed by leading AI companies such as Google, Amazon, Microsoft, and OpenAI. MCP provides a standardized framework that allows AI models to interact with external tools and data sources. In this post, I will guide you on how to install, start, stop, activate, and deactivate MCP servers. You will also learn to configure settings in mcp.json and settings.json, as well as import MCP servers from Claude Desktop, Cline, and Roo Code.

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This article presents a PowerShell script leveraging the Azure OpenAI API, enabling you to execute PowerShell commands using natural language. A key benefit compared to comparable AI tools like ShellGPT is the ability to customize the script.

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In Visual Studio Code release 1.99, Microsoft introduced GitHub Copilot’s Agent mode, a feature that closely resembles functionalities previously offered by Cursor, Cline, and Roo Code. Agent mode lets Copilot examine the codebase, find relevant files, make edits, and autonomously run and debug code. In this post, I’ll compare Agent mode with Ask and Edit mode and outline how Agent mode facilitates vibe coding—the automation of writing code.

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The AI tools for the Command Line Interface (CLI) we discussed on 4sysops connect the terminal to AI models. For example, the Warp terminal includes AI tools designed to help admins working on a CLI. The Windows CLI MCP Server does the opposite. If you missed it, the Model Context Protocol (MCP) is a new open standard for AI agent communication with software services created by Anthropic and backed by OpenAI, Amazon, and Microsoft. With MCP, your primary interface is the chatbot, and for the Windows CLI MCP Server, shell access is an additional tool the AI can utilize. The Windows CLI MCP Server supports PowerShell, CMD, Git Bash shells, and remote SSH.

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