As so many, I'd already landed on a pattern: using one model for development support, a different model family for code review. Just because the reviews were genuinely sharper. No grand theory, just vibes and better results. Turns out GitHub had the same instinct and decided to build it into the tool. Rubber Duck is a new experimental feature in Copilot CLI that pairs your primary coding agent with a reviewer from a completely different model family. For example, a Claude model orchestrates, and a GPT-model critiques... And crucially, it does this before anything gets built, not after you're already committed to a direction. Here's where it gets a bit uncomfortable though: Sonnet paired with Rubber Duck closed 74.7% of the performance gap between Sonnet and Opus on SWE-Bench Pro. Opus on a budget, basically. Great news, obviously, but if a second opinion from a different model family moves the needle that much, it's worth asking what that means for how we should be architecting agentic pipelines in the first place. Because the failure mode here isn't hallucination. It's compounding confidence... One assumption nobody questioned at step 2 quietly becoming a structural problem by step 47. Rubber Duck just asks the awkward questions early, before the damage is already baked in. Essentially a code reviewer who hasn't met you yet and has no reason to be nice. It turns out the duck needed a nemesis. 🐥🔪 I'm curious whether anyone else has stumbled onto patterns like this before the tools caught up? Drop them below, GitHub's clearly taking notes. 📋 Available now via /𝑒𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 in Copilot CLI. #GitHubCopilot #AIEngineering #AgenticAI

This article discusses how GitHub Copilot CLI utilizes the Rubber Duck model to provide developers with a second opinion on code suggestions. I found it interesting that this approach leverages different model families to enhance coding efficiency and reduce errors. What strategies are you using to ensure code quality and accuracy in your projects?

Is your workflow ready for Agentic Mode? 🚀💻 The era of simple autocomplete is over. We’re moving into the age of Vibe Coding, where the focus shifts from writing syntax to directing intent. I’m excited to share a major new resource for the developer community: "Vibe Coding with GitHub Copilot" by Fransesco Malila. This book is a deep dive into the full GitHub ecosystem, showing you how to move beyond basic chat and master Agent Mode for autonomous task execution. What makes this guide stand out: ✅ Mastering multi-file Edits and Agent Mode. ✅ Using MCP (Model Context Protocol) to extend Copilot to your databases. ✅ Deep integration with GitHub Actions, Security, and Codespaces. ✅ Honest comparisons with tools like Cursor and Claude Code. Whether you are a seasoned engineer or just starting out, this is the manual for the next generation of software development. Check it out here: https://a.co/d/051OOQFG #VibeCoding #GitHubCopilot #AIPairProgramming #SoftwareEngineering #DevOps #GitHub #TechInnovation #FransescoMalila

🔥🚀 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

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 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

🚀 55k GitHub stars. One file. Zero overhead. While frameworks like Superpowers and Spec Kit build out massive methodologies, multi-step workflows, and rigid TDD enforcement gates... andrej-karpathy-skills takes the path of "Surgical Minimalism." 🔪 Instead of adding more tools, it adds more discipline. It’s a single CLAUDE.md file that forces a Senior Engineer mindset onto the agent. 🧠 The "Karpathy Stack" in 4 lines: 1️⃣ Think First: Stop the agent from "vibe-coding." Force it to surface assumptions and ask questions before writing a single line. ❓ 2️⃣ Simplify: No abstractions. No speculative code. No bloat. Keep the logic flat and readable. 📉 3️⃣ Surgical: Touch only what’s needed. No more annoying "drive-by" refactors or accidental style changes. 🎯 4️⃣ Goal-Loop: Define success early and work until the specific criteria is met. 🔄 Frameworks are great for teams that need rigid rails. 🛤️ But if you want "senior-level" output without the heavy configuration tax? This is the ultimate shortcut. ⚡ Repo: https://lnkd.in/d_tMhV6D #AI #SoftwareEngineering #ClaudeCode #Programming #Minimalism #CleanCode

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

🚨 You're using GitHub Copilot wrong — and it's costing you hours every week. Most developers just open Copilot and start chatting. But without context, Copilot is just guessing about your stack, your conventions, and your project structure. The fix? Repository Custom Instructions. One file. Permanent memory. A smarter AI assistant for your entire team. Here's what you can do with it 👇 🟢 Create a .github/copilot-instructions.md file to give Copilot a permanent project brief — your stack, build commands, coding rules, and folder structure 🟢 Add path-specific instruction files in .github/instructions/ to apply different rules to different parts of your codebase (frontend vs backend vs tests) 🟢 Use an AGENTS.md file to guide the Copilot cloud agent so it can write PRs that actually pass your CI on the first try 🟢 Control scope with glob patterns — target only TypeScript files, only Python files in a specific folder, or your entire repo 🟢 Use excludeAgent in your frontmatter to restrict certain instructions to either code review or the cloud agent — not both 🟢 Create prompt files (.github/prompts/) for repeatable tasks like "generate a new API endpoint" or "write a unit test" — invoke them in one command 🟢 Custom instructions work across VS Code, Visual Studio, JetBrains, Xcode, and the GitHub web UI 🟢 All instruction types stack together — personal, repository, and organization instructions all apply, with personal taking highest priority The result? Copilot stops suggesting the wrong test framework. The cloud agent stops breaking your build. Code reviews align with your actual standards. One markdown file → a permanently smarter AI that knows your project like a teammate. I wrote a full step-by-step guide on Medium covering everything from setup to pro tips: https://lnkd.in/g-QuhhnF If this helped, drop a ♻️ to share it with your team. #GitHubCopilot #AITools #DeveloperProductivity #SoftwareEngineering #Coding #AIAssistant #GenerativeAI #DevTools #TechTips #DevCommunity #FutureOfWork #VSCode #CodeQuality #ProgrammingTips #Automation

With over thirty years of writing code. I started when a megabyte of RAM was something you bragged about and version control meant making a copy of your floppy disk before you did something wildly wrong. When AI started making noise in this industry, a lot of people got nervous. Honestly, it just made me curious. The way I see it, AI is like fire. It’s not good or bad, it just is. The only question that ever interested me was: how do I get my hands around it and make it work for me? GitHub Copilot answered that question in a way I didn’t expect. Most of my career I’ve worked alongside good people who just didn’t speak the language. No fault of theirs. But there was always a certain loneliness to the work, knowing that what you were doing and why it mattered was mostly something you carried by yourself. For the first time in over thirty years, I feel like I’m working with someone who actually understands. That’s not a small thing. That feeling alone has put the joy back into what I do. The GitHub Copilot product handles the busywork, the boilerplate, the stuff that chips away at your momentum, and frees you up to do the thinking that actually matters. It’s a power tool in the hands of someone who knows how to use it. If you’ve got decades under your belt, don’t fear it. Figure out how to control it. The fire doesn’t care either way, but you’ll sure notice the warmth.