The number of GitHub Copilot concepts continues to grow, and their similar names can easily confuse. This post offers an overview of all the concepts, along with a link to find more information. The main confusion stems from the Copilot tools on GitHub.com and GitHub Copilot in VS Code. Although there is some overlap, keeping these two areas separate is essential.

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Google's new Gemini CLI allows up to 60 model requests per minute and 1,000 daily requests. Professional developers might hit these limits quickly, but many IT admins will find the free plan sufficient. Compared to Anthropic Claude Code and OpenAI Codex CLI, I find Google's open-source AI-powered terminal more useful for the AI-augmented admin.

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The brand-new Warp 2.0 no longer wants to be just an AI-powered terminal. The Warp team appears to be taking inspiration from Anthropic’s Claude Code and OpenAI’s Codex CLI. Both AI-driven coding tools target devs who prefer to code in a terminal instead of working in an Integrated Development Environment (IDE). Zach Lloyd, Warp's CEO, is eager to join the AI agent race, believing that features like the Agent Management Panel, diffs, and a built-in editor can transform a CLI tool into a vibe coding platform or what he calls an Agentic Development Environment (ADE).

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Recently, leading AI companies announced support for remote MCP, allowing an MCP client to connect to a remote MCP server via Streamable HTTP, which still supports Server-Sent Events (SSE). The Streamable Data Input/Output (SDIO) protocol remains essential for local integrations and remote services, which are accessible only through conventional APIs. In my opinion, the excitement surrounding new model releases has lost its significance. AI model developers seem to primarily focus on optimizing their models for AI benchmarks. However, the genuine innovations occur in the technologies surrounding AI models—most notably in AI agents and the supporting infrastructure in which remote MCP servers will play an important role.

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Microsoft Edit is a new command-line editor for Windows and Linux. Currently, you cannot install Microsoft Edit using apt-get on Ubuntu, so you must either download the latest version from GitHub or install it via snap. I will also briefly compare Microsoft Edit with GNU nano, the standard text editor in the Linux terminal. It turns out that Microsoft Edit has at least one crucial advantage over nano.

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The VS Code May 2025 update (version 1.101), released a few days ago, introduces a range of new features primarily focused on GitHub Copilot. My favorite addition is the custom chat mode, which lets you create a personalized mode alongside the built-in options: Ask, Edit, and Agent. In this post, I'll also explore the new chat tool sets and the undoing edits feature. While the Simple Browser feature was included in the previous release, it deserves discussion. Furthermore, I will briefly summarize the MCP enhancements.

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The MCP catalog in Docker Desktop has been available for a couple of weeks now. However, MCP support only works properly in the just-released Docker Desktop 4.42. The Docker Desktop MCP server catalog significantly simplifies installing MCP servers for any MCP client. I tested it with Gordon (Docker Desktop chatbot), Anthropic Claude Desktop, and VS Code GitHub Copilot on macOS, but the process should be identical on Windows.

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AWS now allows automatic deletion of associated Amazon EBS snapshots when deregistering EC2 AMIs, reducing storage costs and simplifying cleanup. Previously, snapshots had to be manually deleted, risking orphaned resources. This feature is long overdue. In a previous post, I introduced a Bash script that deleted the snapshot when an AMI was deregistered. The script should still work, but now you can automatically delete snapshots in the AWS Console and the AWS CLI.

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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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Mountpoint for Amazon S3 (mountpoint-s3) now allows you to mount an S3 bucket in AWS at EC2 instance boot time via fstab. However, it remains non-POSIX compliant, so standard filesystem operations like deleting or renaming files still do not work. In contrast, the open-source alternative s3fs-fuse offers substantial POSIX support but comes with its own drawbacks.

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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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The Gmail MCP server covered in this post lets you access Gmail from any AI client that supports MCP. I found the MCP server more capable than Google's integration with Gemini in Gmail. The AI-powered search feature proved to be helpful, but the MCP server also offers several other valuable commands. In this post, I used Claude Desktop. However, once you install the MCP server and set up the Gmail API, you can access Gmail through MCP from any compatible AI application by copying the MCP JSON. I expect that the ChatGPT app and even Windows 11 will add MCP support very soon.

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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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