Luca Congiu’s Post

🚀 GitHub Copilot Just Took a Big Leap Forward! The new coding agent inside #GitHub Copilot isn’t just another autocomplete feature — it’s a genuine teammate in your development workflow. Imagine assigning a #GitHub issue to Copilot and having it autonomously work in the background, create a draft pull request, apply changes, and even iterate based on your feedback — all while respecting your security policies and branch protections. This evolution transforms #AI from a suggestion tool into a collaborative partner that takes on low-to-medium complexity work like feature additions, bug fixes, test improvements, and documentation updates — freeing developers to focus on higher-value tasks. 🌟 What excites me most is how this accelerates productivity without compromising governance — keeping humans in control while letting #Copilot handle the repetitive grind. Find Curated blog at: https://lnkd.in/ga5qRaYe Are we finally seeing the future of AI-assisted development workflows? I think so — and I’m excited to explore where this leads next. #GitHub #Copilot #AI #DeveloperTools #Productivity #Git #Coding #SoftwareDevelopment #WebDevelopment #CodingPractices

Discover how Agentic Code can streamline your development workflow by integrating GitHub Copilot SDK with Foundry Local. Enhance your coding efficiency and achieve smarter solutions. #Agentic #GitHub #Copilot https://isaacl.dev/g1g

I created a GitHub Copilot instruction file to migrate Cypress tests to Playwright. It worked great on one project—but failed to produce consistent results on another. Same instructions, different codebase. This highlights a key caveat: Copilot instruction files capture patterns, not full context. Each test suite has its own abstractions, custom commands, and assumptions that instructions alone can’t fully encode. A few lessons learned: • Instruction files accelerate migration, but don’t guarantee correctness • Project-specific abstractions reduce portability of instructions • Smaller, focused instructions work better than one generic file • Human review and iterative refinement are still essential Copilot instructions are a force multiplier—but not a substitute for engineering context. #GitHubCopilot #TestAutomation #SoftwareEngineering #AIforDevelopers #DeveloperTools #EngineeringProductivity

Unlock the Future of Coding with GitHub Copilot CLI 🚀 Ready to revolutionize your terminal experience? GitHub Copilot CLI is now generally available, transforming how developers plan, build, and review code—all from the command line! 🌟 Choose your model, delegate tasks with ease, and boost productivity with plugins and skills 🛠️. Empower your workflow with autonomous coding agents that adapt to your needs. Don't miss out on this game-changing tool for professional devs! 🔥 Explore the full potential here: https://lnkd.in/d3GmzbFw #GitHub #Copilot #CLI #DeveloperTools #AI

❓ What is GitHub Copilot (IDE vs Chat) 💡 GitHub Copilot is not just “ChatGPT for code.” There are two very different ways developers use Copilot: Copilot in IDE → context-aware, real-time coding Copilot Chat → conversational help & explanations For serious development work, IDE Copilot wins because it understands: ✔️ Your project structure ✔️ Your imports & dependencies ✔️ Your coding patterns 🚀 Treat it as a coding partner, not a chatbot. 👉 Follow for daily Gen AI & GitHub Copilot tips. #GitHubCopilot #GenAI #SoftwareDevelopment #Java

Over the last 6-8 months, I've been using GitHub Copilot regularly and have narrowed it down to two ways I rely on it. Not for autocomplete. But as a thinking and execution partner. 1️⃣ Understanding existing code When I need to understand unfamiliar or complex code, I use Ask mode. What I usually do: - Share the code snippet and any references I already know are relevant. - Before asking for an explanation, I ask Copilot what additional references or context it needs to explain the code better. - Once all required context is attached, I ask it to explain the code step by step. This approach reduces guesswork and leads to much more accurate explanations, especially in large codebases. 2️⃣ From requirements to code This is where Copilot helps me the most. Step 1: Clarifying the problem I paste the requirements into Ask mode and start a discussion: - What approach should we take? - What assumptions are we making? - What are the edge cases? There's a lot of back-and-forth here. Sometimes I explain why something won't work. Sometimes Copilot points out gaps in my reasoning. Step 2: Confirming understanding Before any code is written, I ask Copilot to: - Re-list my requirements (to confirm it understood them correctly) - Explain the approach it plans to take - List the files / classes / functions that will be created or modified - Break everything down into numbered tasks Only after this do I move forward. Step 3: Writing the code I then switch to Agent mode and use premium models (Claude Sonnet 4 earlier, Sonnet 4.5 lately) to implement the tasks. Step 4: Review and iteration I first read and validate the generated code myself. Based on that validation, I decide how to proceed: - If the change is small, I make it manually - If it's a logic issue, I point it out and ask Copilot to fix it - If it's a bigger issue, I switch back to Ask mode to discuss the correction or approach, and then either apply the fix myself or switch back to Agent mode to update the code This loop continues until I'm satisfied. What this taught me Copilot works best when you stay in control and treat it like a collaborator, not a replacement. #GitHubCopilot #VSCode #DeveloperProductivity

If you're only using Copilot for autocomplete, you're missing 60% of its power. Back after a short break — and I’ve been exploring deeper capabilities of GitHub Copilot inside VS Code. Most developers only use inline autocomplete. But Copilot has much more to offer when you use it intentionally. Here’s how 👇 🔹 / – Slash Commands (Copilot Chat) Inside Copilot Chat, you can use: /explain → Explains selected code /fix → Suggests bug fixes /tests → Generates unit tests /doc → Creates documentation /clear → Clears chat context Example: Select a complex method → type /explain You get a structured breakdown instantly. 🔹 # – Context Referencing You can reference project files directly in chat: #UserService.java Copilot reads that file and answers with proper context. You can also reference documentation URLs (when supported) to improve response quality. 🔹 Autocomplete Suggestions – Don’t Accept the First One Now coming to inline autocomplete suggestions 👇 Most developers press Tab and move on. But you can view multiple suggestions instead of just one: Open Command Palette: Ctrl + Shift + P (Windows) Cmd + Shift + P (Mac) Search: GitHub Copilot: Open Completions Panel This lets you compare different implementations before choosing the best one. The real productivity boost comes from: Structured prompts + proper context + reviewing multiple suggestions. That’s when Copilot becomes a coding partner — not just autocomplete. How are you using Copilot in your workflow? #GitHubCopilot #AIinTech #SoftwareEngineering #VSCode #Developers #LearningInPublic

Finally, a practical guide that shows how GitHub Copilot CLI actually fits into a developer's workflow! It's not about replacing your brain, but about accelerating tedious tasks right in the terminal. I like the emphasis on moving to the IDE for real decision-making. Smart. 🚀 #DevTools #AIinCoding

GitHub Copilot has changed how many of us write code. It accelerates , suggests patterns instantly and reduces the friction of starting from a blank file. For productivity, it’s a solid force multiplier. However we need to remember that Copilot doesn’t replace developers — it amplifies them. The strongest skill of a developer today isn’t typing code fast. It’s thinking — breaking ambiguous problems into solvable parts, choosing the right approach, and knowing when not to code at all. Copilot can suggest lines of code. Developers decide what should exist, why it should exist, and how it should evolve. Tell me your opinion.

Revolutionize Your Coding with GitHub Copilot SDK! 🚀 Unlock the future of coding productivity with the new GitHub Copilot SDK! ✨ Seamlessly embed powerful AI-assisted workflows into your own apps and supercharge your development process. Imagine instantly identifying code ownership, generating architecture diagrams from meeting transcripts, and comparing implementations against design specs—all without breaking your flow. Say goodbye to endless context switching and hello to smarter coding! Dive into how this innovation brings work context right to your fingertips. 🔥 Explore the full story here: https://lnkd.in/eqNS8SjU #GitHub #Copilot #SDK #Developer #Productivity