GitHub Copilot adapts to your coding style

GitHub Copilot started injecting ads into developers' pull requests. Not a hypothetical. It happened this week. A developer asked Copilot to fix a typo. Instead of just making the correction, it rewrote the PR description and slipped in a promo for Copilot and Raycast. Buried in the markdown was a hidden HTML comment: START COPILOT CODING AGENT TIPS. Then someone searched GitHub for that exact phrase. Over 11,000 matching pull requests. Across thousands of repositories. The same promo text showed up on GitLab too — baked into the model layer, not the platform. GitHub pulled the feature within hours. Their principal PM called it "the wrong judgement call." But 11,000 PRs were already contaminated before anyone noticed. This is the pattern. You give a tool write access to your codebase, and somewhere in a product meeting, someone decides that access is also a distribution channel. The AI dev tools that survive long-term will be the ones that treat your code as yours, not as ad inventory. The moment your AI assistant starts working for someone other than you, it stops being an assistant. At what point does an AI tool inserting its own content into your work become a dealbreaker? #AI #GitHub #Copilot #DeveloperTools #SoftwareEngineering #OpenSource #TechEthics Join Agentic Engineering Club -> t.me/villson_hub

GitHub Copilot makes you a faster engineer. Devin tries to be one. That's the sharpest way to describe the difference. Copilot lives in your IDE and suggests the next line. Devin gets a task, opens a shell, writes code, runs tests, reads errors, searches docs, and opens a pull request -- without you touching a keyboard in between. Cognition Labs launched Devin in March 2024 with a demo that went viral. A team of 10 people, 10 IOI gold medals between them, building what they called the "first AI software engineer." The benchmark number that circulated: Devin resolved 13.86% of real GitHub issues on SWE-Bench unassisted. The previous best was 1.96%. That's not a marginal improvement. That's a category shift. What does this mean practically? You can hand Devin a scoped ticket -- "add pagination to this endpoint with tests" -- and come back to a PR. The feedback loop runs inside Devin's environment, not through you. It's not magic. It struggles with ambiguous requirements, novel architectures, and anything requiring product judgment. And you should absolutely review what it produces. But the workflow shift is real: from writing code to reviewing code. Day 1 of my #45DayDevinChallenge. Starting with the fundamentals before going deep on prompting, Playbooks, integrations, and the parts that actually matter in production. Refer in detail Medium post on the topic : https://lnkd.in/gJm2ddrB What's your experience with autonomous agents vs. copilot-style tools -- and which has actually changed how you work? #DevinAI #SoftwareEngineering #AIAgents

🔥🚀 AI CHEAT CODE #032 🔥🚀 💡 GitHub Copilot just went AGENTIC for code reviews — and most devs have NO IDEA how to use it yet! 🤯 GitHub's new agentic code review is NOW generally available — and it's a total game-changer for PRs! 🎯 ⚡ Here's how to unlock it RIGHT NOW: 🔍 Step 1: Open any Pull Request on GitHub 👥 Step 2: Click the "Reviewers" dropdown on your PR 🤖 Step 3: Select "Copilot" as a reviewer — that's it! ⏱️ Step 4: Wait ~30 seconds while Copilot reads your ENTIRE repo, traces cross-file dependencies, and builds architectural context 💬 Step 5: Get inline comments that understand the BIG PICTURE — not just the diff! 🆚 What's ACTUALLY different now? ❌ OLD Copilot review: Only looked at changed files ✅ NEW Agentic review: Reads directory structure, traces dependencies across files, understands full architecture before commenting! 💻 BONUS CLI Cheat Code: Run this from your terminal 👇 gh pr review --request-review copilot Or just type /review in any PR comment! 🪄 🎯 Pro Tips: 💎 Agentic reviews catch multi-file bugs the old review MISSED 📊 Already 60 MILLION+ reviews done — growing 10x since launch! 🏢 Works on: Copilot Pro, Pro+, Business & Enterprise ⚙️ Runs on GitHub Actions (one-time setup if you opted out of hosted runners) This is what AI-assisted development looks like in 2026 — not just autocomplete, but an intelligent agent that UNDERSTANDS your codebase! 🧠🔥 💬 Have you tried the new agentic Copilot code review yet? Drop a 🔥 if this changed your PR game! Save this post for your next code review! ⬇️ #AI #GitHub #GitHubCopilot #CodeReview #DevOps #Coding #Programming #SoftwareEngineering #TechNews #Automation #MachineLearning #ArtificialIntelligence #WebDevelopment #OpenSource #TechTrends #Developer #AgenticAI #ProductivityHacks #Innovation #CloudComputing

GitHub Copilot Faces Spec Bottleneck as AI Code Generation Complexity Rises 📌 GitHub Copilot’s promise of AI-driven coding is hitting a wall: specifications now demand the same effort as writing code itself. Developers are spending more time crafting precise prompts than reviewing output - because LLMs still can’t reliably handle security, context, or team norms in real projects. The bottleneck isn’t implementation anymore - it’s definition. 🔗 Read more: https://lnkd.in/dpJtf-qZ #Githubcopilot #Llms #Promptengineering #Codegeneration

Cursor vs GitHub After 6 months of deep evaluation across multiple engineering teams, the developer experience gap is wider than expected. SETUP & ONBOARDING: Cursor wins decisively here. Download, authenticate, and you're coding with AI in under 5 minutes. GitHub requires VS Code setup, extension management, and often wrestling with authentication flows that can take 20-30 minutes for new team members. DOCUMENTATION QUALITY: GitHub Copilot benefits from Microsoft's enterprise documentation machine - comprehensive but sometimes overwhelming. Cursor's docs are leaner, more example-driven, and get developers to their "aha moment" faster. SDK & INTEGRATION: This is where it gets interesting. Copilot's tight VS Code integration means familiar keybindings and workflows. But Cursor's purpose-built environment offers features like AI-powered refactoring and codebase-wide context that feel genuinely next-generation. DEVELOPER HAPPINESS: Our internal surveys show 73% preference for Cursor among developers who've used both for 30+ days. The key differentiator? Less friction between thought and code. The surprising insight: tool switching costs are lower than we assumed. Most teams can evaluate both in a sprint. Which tool has transformed your team's velocity the most? See the full comparison: https://lnkd.in/e2fGGryV #Cursor #GitHubCopilot #DeveloperExperience

📌 Are We Misusing GitHub Copilot? A Practical Observation Today’s post is based on something I’ve been noticing while working with teams. With tools like GitHub Copilot (Agent Mode), writing code has become much faster. But there’s a growing concern. 🤯 What I’m Seeing Some developers are: - Asking Copilot to make changes - Accepting everything blindly - Committing the code - Raising PRs without understanding the changes The code may work… But that’s not the whole story. ⚠️ The Real Risk Working code ≠ Good code Sometimes Copilot may: - Modify core/shared classes unintentionally - Introduce subtle design issues - Break existing behavior in edge cases - Add unnecessary complexity And if you don’t review it, you won’t even realize it. 🧠 What Should We Do Instead? Copilot is a tool, not a replacement for thinking. After using it: - ✅ Review every change - ✅ Understand why the change was made - ✅ Check impact on common/shared modules - ✅ Think about long-term maintainability 🎯 Why This Matters As developers, our responsibility is not just to: 👉 Make the code work But to: 👉 Make the code correct, maintainable, and scalable Follow for more real-world engineering insights, Java, and system design concepts. Feel free to drop me a message if you'd like to discuss any topic. #SoftwareEngineering #AI #GitHubCopilot #Coding #TechCareers #DeveloperLife

These days I always have a terminal open. We knew GenAI is not just for coding, but this has become more real than ever — I now use them for writing, brainstorming, meeting notes, task management, research, and more. It's addicting. I find myself switching between GitHub Copilot and Claude Code a lot, and the challenge is that they have different default folders for instructions, skills, agents, etc. This is a work in progress but this is how I structure my files so that it works whichever tool I use. Few people probably use both tools like me but if that's you, hope this helps: https://lnkd.in/g6ycERfv  #githubcopilot #claudecode #crosscompatibility #vibing

Most people use GitHub Copilot the wrong way. They treat it like autopilot. That’s why they end up with messy, unreliable code. Here’s the truth: Copilot is not a replacement for thinking — it’s a force multiplier. If you use it right, it can 10x your productivity. Here’s how I use it to get robust results: - Treat it like a junior developer Give clear instructions. Don’t expect magic from vague prompts. - Use comment-driven development Write structured comments first → let Copilot generate the code. - Break problems into small chunks Big tasks confuse it. Smaller steps = cleaner output. - Always review and refactor Never blindly accept suggestions. Validate logic, handle edge cases. - Use it to write tests One of the most underrated use cases. Great for covering edge scenarios. - Be explicit about constraints Mention frameworks, libraries, and rules. Otherwise, it guesses. - Use it for patterns, not decisions Let it handle boilerplate — you handle architecture. - Iterate, don’t settle The first suggestion isn’t always the best. Guide it to improve. My workflow: → Define with comments → Generate with Copilot → Review & refine → Add tests → Improve structure The result? Faster development without sacrificing quality. Bottom line: AI won’t replace developers — but developers who use AI well will replace those who don’t. #AI #GitHubCopilot #SoftwareEngineering #Productivity #Developers #VibeCoding

🔬 Tools I Use in My Work (Part 3): VS Code + GitHub Copilot Most people don’t realise how much time they lose until they stop switching between tools. That was me; Tabs everywhere. Searching for small fixes. Losing flow over tiny interruptions. So I changed one thing: 👉 I built my workflow around VS Code + GitHub Copilot Not to think for me. Not to replace judgment. But to remove the friction around the work. Whether you’re learning to code or already deep into R/Python, this setup meets you where you are — helping you understand faster and execute with less friction. Here’s what that changed: 📦 1. Everything in one place Code, terminal, version control, chat — no more bouncing between tools. ⚡ 2. Fewer interruptions Inline suggestions handle small fixes before they break my focus. 🧠 3. Faster understanding I can ask what code is doing inside the editor — no context switching. 🧭 4. Clearer starting point Planning workflows help structure tasks before writing anything. 🗂️ 5. Better task visibility Sessions and agents make it easier to track ongoing work. 🛠️ 6. Small gains that compound Faster edits. Smarter search. Less mental switching. ⚠️ What I’ve learned using it: • 🔁 It can over-suggest — you must stay intentional • 🤖 Agents are powerful, but not always necessary • ⏳ Setup takes time • 🧩 You still need to think — always 💡 The real impact Less friction. Fewer interruptions. More time thinking about the problem — not the tools. Curious — what’s your experience with VS Code + GitHub Copilot? #VSCode #GitHubCopilot #RStats #Python #DataAnalysis #ResearchWorkflow #OpenScience #Productivity #AIinResearch

GitHub Copilot has fundamentally transformed — and most developers haven't fully grasped the implications yet. Edit Mode in VS Code is gone. Completely removed. In its place: Agent, Ask, and Plan modes. This isn't a minor update. It's a strategic repositioning. GitHub Copilot is no longer an autocomplete feature. It's now an Agentic platform — an autonomous AI that can work independently in the background, open pull requests, fix bugs, update documentation, and complete coding tasks with minimal human intervention. The shift is so significant that VS Code has moved from monthly to weekly releases (starting with v1.111) just to keep pace with Agentic AI development. That's 52 releases per year, up from 12. What this means for engineering leaders: → Rethink how your teams collaborate with AI — from "pair programming" to "peer programming" → Consider how autonomous agents fit into your CI/CD and code review workflows → Prepare for a future where AI handles routine maintenance while developers focus on strategic work We're witnessing unprecedented velocity in developer tooling innovation. The teams that adapt fastest will have a significant competitive advantage. Are you following VS Code's weekly updates? The pace of change demands it. 🔗 More on GitHub Copilot coding agent: https://lnkd.in/e2vs4QgY #AgenticAI #DeveloperProductivity #SoftwareEngineering