1. What is GitHub Copilot CLI?

GitHub Copilot CLI is an AI-powered assistant that runs directly inside your terminal.

Instead of manually writing commands, scripts, or debugging issues, you can simply describe what you want — and Copilot executes it.

Here’s the shift:

For Cloud and DevOps engineers, this becomes extremely powerful because most of our work already happens in:

• CLI (Azure CLI, Bash, PowerShell)
• Infrastructure as Code (Terraform)
• Pipelines (CI/CD)

Copilot CLI sits right in the middle of all this.

2. Why This Matters for Cloud & DevOps

Here’s the thing — DevOps work is repetitive and context-heavy.

You constantly:

• Write Azure CLI commands
• Debug IAC (Infrastructure as Code) issues
• Fix pipeline failures
• Check logs and configs

Copilot CLI reduces this friction.

Example

Instead of:

az group create --name my-rg --location eastus

You can say:

Create a resource group in Azure named my-rg in East US

And it generates + executes the command.

Now scale this to:

• Multi-region deployments
• App Gateway configs
• Terraform modules

That’s where it becomes a real productivity multiplier.

3. Setting Up GitHub Copilot CLI

Prerequisites

• GitHub Copilot Subscription:
  Active subscription required, including Individual, Business, or Enterprise plans with proper licensing
• Supported Operating Systems:
  Supports Windows, macOS, and Linux for consistent development across major platforms
• Software Version Requirements:
  Node.js version 22+, npm version 10+, and PowerShell 6+ on Windows necessary for npm-based installation

Installation

• Cross-Platform Installation:
  Installing via npm provides a global Copilot CLI setup working consistently on Windows, macOS, and Linux
• macOS and Linux Installation:
  Homebrew allows easy Copilot CLI installation on macOS and Linux with a single command
• Windows Installation:
  WinGet enables native Windows package management for seamless Copilot CLI installation
• System PATH Integration:
  All installation methods add Copilot CLI to system PATH for immediate terminal access

npm install -g github​/copilot

Login

copilot auth login

Start CLI

copilot

When you start, it will ask permission to trust the directory — this is important because it:

• Reads files
• Modifies code
• Executes commands

 

4. How to Use Copilot CLI (Real DevOps Examples)

Let’s move beyond basics.

🔹 Azure CLI Usage

Create an Azure App Service with Linux runtime and Node.js

Update an Application Gateway backend pool using az cli

Delete all resources in this resource group safely

🔹 Terraform Usage

Create a Terraform module for Azure VNet with 3 subnets

Fix issues in @main.tf

Explain this Terraform code and suggest improvements

🔹 Pipeline Debugging

Analyze this Azure DevOps pipeline YAML and fix errors

Why is this deployment failing?

🔹 File Context Usage

Explain @variables.tf

Optimize azure​-pipelines.yml

🔹 Built-in Commands

/review → Code review /context → Current context /usage → Token usage /compact → Optimize memory

 

     5. Advantages of using Github Copilot CLI
         Less Context Switching

• No need to jump between:
• Docs
• Terminal
• Browser

• Faster Troubleshooting

• It understands:
• Errors
• Logs
• Config files

• Infrastructure Automation

• You can:
• Generate Terraform/Bicep or any IAC code
• Write Azure CLI scripts
• Automate deployments

• Acts Like an Agent

• Not just suggestions — it can:
• Execute commands
• Modify files
• Run workflows

6. MCP (Model Context Protocol) – The Real Power

This is where things get interesting.

MCP allows Copilot to connect with external systems.

👉 Think: APIs, documentation servers, automation tools

🔷 MCP Architecture (Diagram)

 

7. Adding MCP Servers

You can extend Copilot using MCP servers.

Add MCP Server (Interactive)

/mcp add

Provide:

• Name
• Type (local or HTTP)
• Command or URL

Example: Local MCP Server

npx @playwright/mcp@latest

Example: Remote MCP Server

https://mcp.context7.com/mcp

Config File Method

📁 ~/.copilot/mcp-config.json

{
  "mcpServers": {
    "docs": {
      "type": "http",
      "url": "https://mcp.context7.com/mcp",
      "tools": ["*"]
    }
  }
}

Manage MCP Servers

/mcp show
/mcp edit docs
/mcp enable docs
/mcp disable docs

Real DevOps Use Case

You can connect:

• Azure documentation APIs
• Internal tools
• Monitoring systems

Then ask:

Fetch latest Azure App Gateway documentation

8. CLI Workflow (How It Actually Works)

User Prompt
   ↓
Copilot CLI understands intent
   ↓
Reads files / context
   ↓
(Optional) Uses MCP tools
   ↓
Generates + executes commands
   ↓
Returns result

9. Adding Skills (Underrated Feature)

Skills = reusable workflows.

Think of them like:

• Predefined automation
• Standardized instructions
• Tool integrations

Example Skills

• Run security scans
• Validate pipelines
• Analyze logs

How Skills Work

They are defined using:

• Instruction files
• Agent configurations
• Repo-level context

Copilot automatically picks them when relevant.

 

10. Customizing Copilot CLI

Config File

📁 ~/.copilot/config.json

What You Can Control

• Permissions
• Execution behavior
• Tool access
• Logging

Recommended: Custom Instructions

We can create custom instructions which our copilot will follow while taking actions on our promps.

📁 .github/copilot-instructions.md

Example:

- Always use Terraform for infrastructure
- Prefer Azure CLI for automation
- Follow naming convention: env-app-region
- Use managed identity where possible

This ensures:

• Consistency
• Best practices
• Governance

11. Real-World UseCase Scenario
    
    You can find the sample instruction files and skills in my https://github.com/ibrahimbaig12345/GHCP_CLI_DEMO repo.

• A POC for storage account with private endpoints:
  We simply enter a prompt to create the storage account; it will take all the details from its custom instructions file and generate a .sh file to execute the relevant cmds for the resource creation.

• Using Skills to do a routine Security Scan of the Azure Subscription:
  You can give a simple prompt like "Can you help me with a quick security scan of my current azure subscription"
  

  You can check the result as shown above.

Copilot doesn’t just answer — it helps implement.

12.  Reference

• https://docs.github.com/en/copilot/how-tos/copilot-cli/set-up-copilot-cli/install-copilot-cli

• https://docs.github.com/en/copilot/how-tos/copilot-cli/set-up-copilot-cli/authenticate-copilot-cli

• https://docs.github.com/en/copilot/how-tos/copilot-cli/customize-copilot/add-mcp-servers

• https://docs.github.com/en/copilot/how-tos/copilot-cli/customize-copilot/create-skills

For Azure and DevOps engineers, this is a big shift.

 

In Searches: Selfhood in the Digital Age, journalist Vauhini Vara explores how the technologies we use to understand the world—search engines, social platforms, and now AI systems—are also reshaping how we understand ourselves. Drawing from her own experience using chatbots to write about her sister’s death, Vara reflects on what happens when our most human questions, memories, and emotions are filtered through systems designed to analyze and monetize them. Humanities scholar Luca Messarra speaks with Vara about the promises and limits of machine understanding.

Grab your copy of Searches: Selfhood in the Digital Age: https://www.vauhinivara.com/searches

This conversation was recorded on 2/26/2026. Watch the full video recording at: https://archive.org/details/searches-book-talk

Check out all of the Future Knowledge episodes at https://archive.org/details/future-knowledge

Download audio: https://media.transistor.fm/5f42feda/04090356.mp3

.NET 11 Preview 2 introduces a set of targeted updates to .NET Multi-platform App UI (MAUI), focusing on the Map control, binding performance, and API consistency. The changes are incremental but concrete, addressing specific usability and performance issues in XAML, data binding, and control behaviour.

By Edin Kapić

Work perks are taking on new meaning in the AI boom.

Speaking at GeekWire’s Agents of Transformation event in Seattle on Tuesday, Microsoft EVP Charles Lamanna talked about a job candidate who said they would come aboard as long as their team was given a certain dollar amount of AI tokens — the fuel that powers interactions with AI systems.

Lamanna didn’t reveal the exact dollar amount request, but said “you should think of $100 to hundreds of dollars of token cost per day, at the limit.”

The anecdote reflects how access to AI models is becoming as fundamental as salary — and how quickly AI is moving from experimentation to a core part of day-to-day work.

If a “fully loaded” (total cost of an employee to a company) engineer costs $500,000 a year and the employee asks for $100,000 worth of tokens — which makes them three times as efficient — Lamanna said it’s a great deal for everyone involved.

He compared denying engineers sufficient AI resources to stripping away basic workplace tools. Imagine showing up to work with no mouse, no email, no Microsoft Teams — that’s how an engineer accustomed to AI-powered coding agents would feel working under a tight token budget, he said.

“So how you think about what it means to hire, and fully loaded cost, and where we invest is going to change completely as a result of this,” said Lamanna, Microsoft’s executive vice president of Business Applications & Agents. He sees this happening beyond software engineering — to multiple other forms of office and information work, such as financial planning.

“They’ll be like, I’m not going to work there unless I actually get a certain amount of token budget,” he said.

Lamanna isn’t alone in seeing this shift. Nvidia CEO Jensen Huang last week said AI tokens would become “one of the recruiting tools in Silicon Valley,” CNBC reported. In a blog post last month, venture capitalist Tomasz Tunguz described inference costs as a potential fourth pillar of engineer compensation alongside salary, bonuses, and equity. “Will you be paid in tokens?” Tunguz wrote. “In 2026, you likely will start to be.”

The New York Times last week reported on how employees at tech companies are competing on internal leaderboards that track token consumption, creating a new status game called “tokenmaxxing.”

Linux gamers are seeing massive performance gains with Wine's new NTSYNC support, "which is a feature that has been years in the making and rewrites how Wine handles one of the most performance-sensitive operations in modern gaming," reports XDA Developers. Not every game will see a night-and-day difference, but for the games that do benefit from these changes, "the improvements range from noticeable to absurd." Combined with improvements to Wayland, graphics, and compatibility, as well as a major WoW64 architecture overhaul, the release looks less like an incremental update and more like one of Wine's most important upgrades in years. From the report: The numbers are wild. In developer benchmarks, Dirt 3 went from 110.6 FPS to 860.7 FPS, which is an impressive 678% improvement. Resident Evil 2 jumped from 26 FPS to 77 FPS. Call of Juarez went from 99.8 FPS to 224.1 FPS. Tiny Tina's Wonderlands saw gains from 130 FPS to 360 FPS. As well, Call of Duty: Black Ops I is now actually playable on Linux, too. Those benchmarks compare Wine NTSYNC against upstream vanilla Wine, which means there's no fsync or esync either. Gamers who use fsync are not going to see such a leap in performance in most games. The games that benefit most from NTSYNC are the ones that were struggling before, such as titles with heavy multi-threaded workloads where the synchronization overhead was a genuine bottleneck. For those games, the difference is night and day. And unlike fsync, NTSYNC is in the mainline kernel, meaning you don't need any custom patches or out-of-tree modules for it work. Any distro shipping kernel 6.14 or later, which at this point includes Fedora 42, Ubuntu 25.04, and more recent releases, will support it. Valve has already added the NTSYNC kernel driver to SteamOS 3.7.20 beta, loading the module by default, and an unofficial Proton fork, Proton GE, already has it enabled. When Valve's official Proton rebases on Wine 11, every Steam Deck owner gets this for free. All of this is what makes NTSYNC such a big deal, as it's not simply a run-of-the-mill performance patch. Instead, it's something much bigger: this is the first time Wine's synchronization has been correct at the kernel level, implemented in the mainline Linux kernel, and available to everyone without jumping through hoops.

Read more of this story at Slashdot.

Meta willfully violated New Mexico law by misleading users about the safety of its products and engaging in an unconscionable trade practice, a jury found. The company will face a $375 million penalty for the violations, awarding the maximum penalty of $5,000 per violation for 37,500 violations across two counts. The jury decided against Meta on every count, though it declined to award a penalty as high as the state sought, which would have been closer to $2 billion.

It's a landmark verdict delivered just one day after closing arguments. New Mexico argued that Meta had flouted state law by misleading consumers and facilitating child predato …

Read the full story at The Verge.