GitHub Copilot Chat Context and Efficiency

Most developers are using GitHub Copilot wrong. It’s not about better prompts. It’s about better context. Copilot performs based on what you feed into it — not what you ask it. Here’s what actually makes a difference: • Instructions → enforce coding standards • Skills → inject domain knowledge • Agents → simulate specialized roles • MCP → connect external systems I applied this in my project by defining clear backend rules and structuring responses consistently across modules. Result: more predictable, cleaner, and reusable code. Prompt engineering gets attention. Context engineering gets results. #GitHubCopilot #AI #SoftwareEngineering #Java #FullStackDeveloper #ContextEngineering GitHub Microsoft

GitHub Copilot and Claude Code may both use slash commands, but they are not the same thing. Here is the simplest way to think about it: Built-in GitHub Copilot slash commands Controlled by GitHub and Microsoft Available based on your VS Code version, Copilot features, and extensions Designed for built-in actions inside Copilot Chat Examples: /explain, /fix, /tests, /doc, /new, /help Custom slash commands in Claude Code User-defined command patterns Used to shape how Claude responds Helpful for formatting, tone, structure, reasoning, and analysis Examples: /TLDR, /ELI5, /CHECKLIST, /SWOT, /COMPARE, /STEP-BY-STEP Claude Code skills Reusable automations for tasks you do often Great for turning repeated workflows into commands Examples: /review, /security, /optimize, /a11y, /test-plan My takeaway: GitHub Copilot slash commands = built-in product features Claude Code custom commands = flexible response controls Claude Code skills = repeatable workflow automation This distinction matters because many people see “slash commands” and assume they all work the same way. They do not. #GitHubCopilot #ClaudeCode #AI #DeveloperTools #VSCode #SoftwareDevelopment #Productivity #GenAI #Coding

Lately, I’ve been diving into AI in Software Testing and getting hands-on with GitHub Copilot—and it’s been an interesting shift in how I approach development of test automation scripts. To make this exploration more structured, I’ve been following the GH-300 (https://lnkd.in/gC3ucbT4) curriculum, which has helped me go beyond just “using” Copilot to actually understand its: 🔹 Strengths Copilot is great at accelerating boilerplate code, suggesting reusable patterns and exploring pull requests—especially useful when working with frameworks like Playwright. 🔹 Limitations It still requires strong human oversight. Context gaps, incorrect assumptions, and occasional flaky suggestions mean you can’t rely on it blindly—especially in critical test scenarios. 🔹 Real Value in Testing When used thoughtfully, it can significantly speed up: ✔ Test case generation ✔ Locator strategies 🔹 The Mindset Shift It’s less about “AI writing code for you” and more about pair programming with context awareness. The better your prompts, the better the output. This journey is helping me understand how AI can augment test engineers, especially in building more resilient and scalable automation frameworks. Still early days, but definitely an exciting and compelling space to explore🚀. #GitHub #Copilot #AI #SoftwareTesting

I reported a bug to GitHub. They fixed it in 2 days—then revamped their entire extension system. Here's what happened: While using GitHub Copilot CLI's extension system, I discovered a critical issue: creating a hook in an extension would override all global hooks. This broke my hook flows—the system I use to harden security across all my repositories. So I filed an issue. Within one week: • Root cause identified • Fix shipped to production • Complete extension system overhaul released The new capabilities are significant: → Custom slash commands now supported in the SDK → UI elicitation dialogs for structured user input → In-session management via /extensions command → Multi-language SDK support (Node.js, Python, Go, .NET) → Hot reload without full session restart This isn't just a bug fix. It's a signal. GitHub is treating Copilot CLI extensions as a first-class extensibility platform. For teams building internal tooling, security enforcement, or custom workflows—this changes the game. The speed of iteration here is remarkable. From power-user secret to documented, multi-language platform in 9 days. We're entering an era where developer feedback directly shapes the AI tools we use daily. If you're not experimenting with Copilot CLI extensions yet, now is the time. Full story in the video. Link in comments. #GitHubCopilot #DeveloperExperience #DevTools

Claw Code: The "Leaked" Claude Code Rebirth The Gist: Claw Code is an explosive open-source project (March/April 2026) that serves as a high-performance, clean-room rewrite of the architecture behind Anthropic’s Claude Code. It recently became the fastest repository in history to surpass 100,000 stars on GitHub, fueled by the viral "leak" of Claude’s internal agentic workflows. The Highlights: - Clean-Room Logic: After the Claude Code source was allegedly leaked, the Ultraworkers collective claims to have reverse-engineered the core "harness" without using proprietary code. It’s now written primarily in Rust for speed and memory safety. - Autonomous Coordination: Unlike simple chat-based coding assistants, Claw Code uses a three-part system (OmX, OmO, and clawhip) to allow multiple agents to coordinate in parallel. One agent plans, another executes, and a third reviews—all without human "babysitting." - "Clawable" Philosophy: The project focuses on "machine-first" automation. It removes human-centric barriers like fragile terminal prompts and opaque session states, allowing the AI to recover from errors and run test loops entirely on its own. - Discord as an IDE: The project promotes a "set and forget" workflow where a human can give a directive via Discord, and the "claws" (agents) handle the labor, pushing the final code to GitHub only once all tests pass. - The Controversy: The repo’s meteoric rise has sparked fierce debate. Critics on Reddit and GitHub have called the star-count "botted" and raised legal concerns, while fans see it as the "democratization" of elite AI engineering tools. The Bottom Line: Claw Code is more than a tool; it's a demonstration of the "Agentic Era." It proves that when you give AI a structured environment to plan and self-correct, the role of the human shifts from "typing code" to "directing the mission." 👉 Repository: https://lnkd.in/d6TWc4H6

I used GitHub Copilot mostly for autocomplete and quick fixes. Recently, I tried something different — I turned it into a custom agent. By defining how it should think, understand my system, and execute tasks, it started feeling less like a tool and more like a teammate. Especially useful for backend workflows with multiple services and async processing. Wrote a quick guide on how to set this up in VS Code 👇 https://lnkd.in/gQaAn8Kw #AI #Programming #DeveloperTools #SoftwareEngineering

Ever tried rebuilding a GitHub repo without just hitting the fork button? Yeah… me neither. Until I decided to make it harder for myself. So I built this: https://lnkd.in/dnX2F7Q8 -- Paste any GitHub repo -- Click analyze -- Get a full AI-generated prompt to rebuild it from scratch Basically… instead of cloning code, you generate instructions to recreate it. Now I know what you're thinking: “bro just fork it” And you’re 100% right. But where’s the fun in that :) Why I did this: Because actually building something teaches way more than just copying it. What I gained: * Deeper understanding of real-world project structures * Hands-on debugging experience (a LOT of it 😅) * Better intuition for how systems are actually built If you're learning dev/AI, try this once pick a repo and rebuild it yourself. It hits different. #AI #MachineLearning #GenAI #GitHub #BuildInPublic #DeveloperJourney

Source: GitHub Official Documentation — docs.github.com/en/copilot GitHub Copilot shines when you give it the right context. 🤖 A .github/copilot-instructions.md file committed to your repo is all it takes — shared with your whole team, updated as your stack evolves. The framework is simple: WHAT — your stack & project structure WHY — architecture principles and conventions HOW — build, test, and lint commands Copilot follows what you tell it. Think of it as onboarding docs for your AI pair programmer — every contributor gets the same focused suggestions from day one. #GitHubCopilot #DeveloperProductivity #AITool

The Return of the Architect — Why Code Still Matters 🛠️✨ The "End of Coding" was a myth. We are entering the age of the "Architect-Engineer." I’ve been analyzing the latest insights from GitHub’s COO, Kyle Daigle, and the message is clear: It is more important than ever to understand the logic of technology, even as AI does the "heavy lifting." We are shifting from simple AI assistants to Agentic AI—systems that can break down problems and build entire solutions. But this shift creates a new, massive responsibility for leaders and creators: The Reviewer’s Burden: If you cannot "read" the logic of the code, you cannot verify the work of the AI. You become a passenger in a vehicle you can't control. Logic Over Syntax: We no longer need to be "code-monkeys" memorizing every command. We need to be Architects of Logic who understand how systems flow and how they impact the human experience. The Human Spark: AI can generate a million lines of code, but only a human understands the "Why"—the community, the purpose, and the ethical guardrails that make a product successful. The Strategic Reality: Companies that laid off developers are realizing that AI doesn't replace the thinker; it only amplifies the builder. This is why I am pushing through the "friction" of learning Python and Linux. Not to become a developer, but to ensure I remain an Architect of my own future. In an era of mass surveillance and automated content, your ability to understand the "Engine" is what gives you the power to design the "Studio." Are you mastering the logic, or just pushing the buttons? #AI #FutureOfWork #GitHub #Leadership #SoftwareEngineering #TechStrategy #Innovation #AgenticAI #HumanCentricTech

GitHub Copilot CLI brings AI assistance directly to your terminal. Instead of switching to a browser or code editor, you can ask questions, generate full-featured applications, review code, generate tests, and debug issues without leaving your command line. here is the beginner samples https://lnkd.in/g4RMVENQ #GenAI #AI #Github #Copilot