GitHub Copilot: A Game-Changer for Developers

🚀 Top GitHub Copilot Features You Should Know! 🧠💻 Hey friends! 👋 If you code or work with developers, you’ve got to check out what’s new with GitHub Copilot: Smarter AI Suggestions: Project-wide help that gets your coding context right, whether you’re building with React, doing backend logic, or just fixing bugs faster. Works in More Places: From VS Code to JetBrains, Eclipse, or Xcode—use Copilot where you do your best work! Built-in AI Agents: Delegate tasks like debugging or writing tests without leaving your IDE—total game changer for focus and productivity. Crystal-Clear Commit Messages: Auto-generated, readable commits make team collaboration a breeze. Personal Touch: Train Copilot to match your team’s style for smoother workflows. Get Answers—Not Just Code: Ask Copilot why it made a suggestion, and understand your code better. ✨ Benefit: Shave hours off repetitive tasks, learn as you go, and boost code quality—no matter your experience level. 🛡️ Trusted by thousands of devs worldwide! 👉 Check out the latest tips here: https://lnkd.in/gSQrYWj6 Don’t miss out—Copilot keeps getting better, and so can your workflow! Let’s level up together! 💬😊

As someone who’s always exploring AI tools to make life a bit easier, I recently decided to dive deeper into GitHub Copilot. Since I use VS Code every day, I came across this great playlist from the VS Code YouTube channel. It goes beyond just the basics — covering things like prompting strategies, Agent Mode, and even what not to do when using Copilot. Here are a few videos I found really helpful 👇 🎯 Get Started with GitHub Copilot in VS Code (2025) Good starting point — covers the basics, different use cases, available features, and extra learning resources. ▶️ https://lnkd.in/dkBEsF5W 💡 Essential AI Prompts for Developers Explains 4 prompting strategies (Role Prompt, Stepwise Chain of Thought, Pros & Cons, and Q&A Strategy) that help get better results from Copilot. ▶️ https://lnkd.in/dsw9rrNV 🤖 VS Code Agent Mode Just Changed Everything This one shows how you can use Agent Mode, MCP servers, and PRD documents to build a complete app — including the database part. ▶️ https://lnkd.in/db_kPGDU 🚫 Copilot Best Practices (What Not To Do) A nice take on best practices — explained through common mistakes people make with Copilot. ▶️ https://lnkd.in/dJ73mt9j 🎥 Full playlist (GitHub Copilot + VS Code): https://lnkd.in/dfaBTEyB) 💡 Copilot has already become an integral part of modern software development. If used right, it’s not just a productivity tool — it’s like having a pair programmer who helps you write, learn, and grow.  👇 I’d love to hear what tools or tricks you use to make your dev workflow smarter. #githubcopilot #vscode #ai #coding #copilot #developer #odoo

Did you know you can give GitHub Copilot custom review instructions? I’ve started using this to automate the kind of feedback I keep repeating in pull requests. With the rise of AI-assisted code, even good contributions tend to reuse patterns or miss small team conventions. Instead of commenting the same things like “use async,” “prefer f-strings,” or “move this into a shared utils module,” I can ask Copilot to look for them automatically. All it takes is a .github/copilot-instructions.md file. You can describe what you want in plain English, such as “flag any code that calls time.sleep in async functions” or “remind authors to add type hints.” Copilot’s reviewer then incorporates those rules into its automated review. It is not perfect and sometimes misses context, but it helps enforce consistency across a codebase. Unlike a linter or pre-commit hook, it can also catch more subjective issues that are part of a team’s culture. If your team uses Copilot reviews, this feature is worth trying. It is a small change that saves a surprising amount of repetitive feedback.

💡 Make GitHub Copilot Work With Your Team Using Prompt Files When working with GitHub Copilot in VS Code, you can create reusable prompt files that live right alongside your source code. 📦 These files can be stored in source control and executed anytime, making them perfect for tasks your team performs repeatedly. For example, my team uses a prompt file to: ✅ Add new properties across multiple layers of our API ✅ Generate consistent release notes for those properties Since creating it, we’ve used the same prompt file to add 20 new properties, saving time and keeping our output consistent across services. ⚙️ You can even define which AI mode and model Copilot uses when running the prompt, giving you predictable, repeatable results every time. If your team uses Copilot regularly, try building a few prompt files for your common workflows. It’s a small step that makes AI collaboration more structured, consistent, and team-friendly. 💬 Have you tried using prompt files with Copilot yet? I’d love to hear how your team’s using them. #GitHubCopilot #VSCode #AIinDevelopment #DeveloperProductivity #SoftwareEngineering #CodingTools

GitHub Copilot CLI: Use custom agents and delegate to Copilot coding agent Two Key Points 1. In addition to defining agents under .github/agents in a repository or in the {org}/.github repository, GitHub Copilot CLI will recognize custom agent configurations in ~/.copilot/agents. You can explicitly invoke an agent interactively with the /agent slash command. Your custom agents are also made available as tools to Copilot, and the model will start a new agentic loop using a relevant custom agent when necessary. See More: https://lnkd.in/eu6FcxJR 2. Copilot coding agent is our asynchronous, autonomous background agent. Running the /delegate TASK-DESCRIPTION slash command from GitHub Copilot CLI will commit any unstaged changes to a new branch. After that, Copilot coding agent will open a draft pull request, make changes in the background, and then request a review from you. Copilot will provide a link to the pull request and its coding agent’s session in your terminal once this process begins. See More: https://lnkd.in/eKN_F5xp Complete Blog: https://lnkd.in/etD8iRjV

## 🜍 Rebuilding the Cathedral — From Confusion to Clarity with Rust, Godot, and Open Tools My project [**https://lnkd.in/gYgxzPkX) has evolved into a living experiment — blending **Rust**, **Godot 4.5**, and **free open deployment** using **Cloudflare** and **GitHub Pages**. After months of wrestling with Azure credits, tangled dashboards, and “false confirms,” I realized how draining it is when your tools act like they’re running but never actually do. Even assistants inside VS Code can fake execution — leaving you in limbo. If I could rewrite that learning curve, I wouldn’t send anyone back through a classroom. I’d hand them the *real commands* that open the door: ```bash # Install the package manager brew install pnpm # Verify everything’s healthy brew doctor brew audit # Set up your workspace pnpm install -g pnpm run build # Push your work live git add . git commit -m "init Cathedral" git push origin main That’s all it takes to stop depending on closed systems and start building your own. Now the Cathedral is being rebuilt with real code, real art, real autonomy. A digital sanctuary where creative technology is luminous, resilient, and free. ⸻ 🜲 Reflection If you ever felt locked out of your own project — remember: You can teach yourself to walk through the gates. Rust, Godot, PNPM, and GitHub Pages are enough to build your own Cathedral.

🚀 Creative Use of GitHub Copilot for Code Review A few months ago, I faced an interesting challenge during a code review. One of my colleagues had pushed 35-40 files - a lot of files, limited time, and everything bundled together in a single Bitbucket branch. Even though we had access to GitHub Copilot, we couldn’t use its built-in code review features directly since the repo was hosted on Bitbucket, not GitHub. So, I decided to get creative. Here’s what I did 👇 1. I pulled the latest code locally and took all the commits that were pushed to Bitbucket, keeping them in a staged state. 2. Then, I removed the files from staging, moving them under the Changes section in my local setup. 3. Once all the modified files appeared there, I used GitHub Copilot locally to review each file one by one, by prompting it with clear instructions for what to check and analyze. 4. I then went through each of Copilot’s suggestions, verified the logic, and made adjustments where needed. This approach turned out to be surprisingly effective - Copilot helped me quickly identify potential logic issues (like an extra for loop in a setup function), while I could focus on validating the reasoning and context. 💡 Key takeaway: Even when your workflow doesn’t perfectly align with your tools, creativity and the right prompts can help you get the most out of AI. #AI #CodeReview #GitHubCopilot #Bitbucket #SoftwareEngineering #DeveloperExperience #Innovation #Productivity #MERNSTACK #REACT

This guide shows how GitHub Copilot now supports multi-step workflows across the editor, terminal, and GitHub. It explains how you set it up, use Agent Mode, generate tests and pull requests, review code and automate tasks to streamline your development https://lnkd.in/dvvV28Mn

How to read Software Code written by others Today, I had a pretty eye-opening experience with GitHub Copilot. While reviewing some code, I decided to let Copilot run in agent mode to help me understand the functionality buried in a pretty large codebase. And honestly, I was pleasantly surprised. We all know how it goes: often the core functionality is just a small slice of the code, maybe 10%, and the rest is all supporting structure. Finding and understanding that 10% can be time-consuming. But with Copilot, I just asked it to describe where the main microservices functionality was and to give me an example of what the code was doing. It pinpointed things beautifully and even suggested a working code snippet for a new feature I had in mind. What does this mean for us as software professionals? It means tools like Copilot are letting us focus more on the business logic and less on the low-level plumbing. We can spend our time understanding the domain, refining requirements, and writing good automated tests, making our engineering process more productive and business-friendly. I'm genuinely excited about how these tools are evolving and how they can help us focus on what really matters. Feel free to tweak that however you like, but I think it captures your experience and insights nicely!

👨💻 Just published a full walkthrough of the new GitHub Copilot Code Review Agent (2025 update). From custom .instructions.md files to CLI workflows and hybrid review logic, it’s built to make code reviews faster and smarter—right from your editor or terminal. If you're working in VS Code or GitHub regularly, this update is worth checking out. ▶️ Watch here: https://lnkd.in/gfJ2sBng #GitHubCopilot #CodeReview #DeveloperExperience #VSCode #DevTools #TheCuriousDev #AIForDevelopers