GitHub’s Post

I only started moving fast with AI once I installed guardrails. Early on, I let Copilot “go wild.” It shipped code quickly but also shipped duplicates: two versions of the same screen, overlapping hooks, parallel styles. I kept finding the same problems: merge pain, regressions, and hours fixing things that never should’ve been created. So I started paying attention to the guidelines and wrote a small Guardrails Pack inside the copilot instructions: an agent charter (how the assistant must behave), a knowledge base (vocabulary, page map, no-touch areas), and a strict Copilot zero-duplication rule. Now before writing code, it plans. It searches first, shows me what already exists, proposes a reuse plan, and asks for approval before big diffs. That alone has saved me countless hours and corrected a lot of previous “creative” mistakes. It now follows: 1. Exhaustive Search Scan the repo (semantic + text + file patterns) for anything related to the target domain. 2. Findings Summary What already exists (data schema, screens, components, hooks, server/edge logic, tests). What’s missing or inconsistent (migrations, security, idempotency, triggers, end-to-end coverage). 3. Reuse Plan (no duplication) Reuse/extend: list concrete files to extend. Improve: list minimal changes (e.g., add security to existing logic, wire missing triggers). Do not create: explicitly state which “tempting” new files/pages/hooks will not be created and why. 4. Approval Gate If change > N lines or new files are needed, ask first. Then implement incrementally: refactor → migrate → retire legacy. Validate: navigation/refresh OK, guards/SEO updated, no console warnings, tests green. That resulted in smaller diffs, consistent UX, fewer regressions and AI that reinforces the architecture. If you vibecode (Copilot/Cursor, etc.), install these guardrails once. I’m sharing my instruction below; drop it into your repo and adapt to your stack. If you want to try it yourself, I left my Copilot Instructions template in the first comment 👇 #AIProductivity #AIDevelopment #Vibecoding #GitHubCopilot #LovableDev #CursorIDE #VSCode #BoltNew #BuildWithAI Lovable GitHub Cursor bolt.new

💡 Did you know you can teach Copilot how to work your way across an entire repo? The secret: ⚙️ .github/copilot-instructions.md Think of it as a guidebook for your AI pair programmer. Inside, you can spell out: • Project Overview: What the project does, in plain language. • Build & Test: Commands to build, run tests, and check changes. • Coding Standards: Naming conventions, preferred styles, and architecture rules (e.g., camelCase for variables, all components functional, DDD principles). • Do Not Touch Zones: Legacy files or fragile code Copilot should avoid. Set it up once, and Copilot will stay aligned with your team’s way of working. #GitHubCopilot #AIinDevelopment #CodingStandards #DevOps #SoftwareEngineering #GenAI

Many developers still don’t really get how Copilot can make them more productive… and that’s the curious part. I have been using GitHub Copilot a lot on "big projects," and honestly, it’s a curious experience. Not everything is perfect, but we need to adapt (or adopt) it. The models are different: Claude “thinks” more globally, while GPT is careful and pays attention to details. The key (for me) — for now — is to document everything. Start with commits and make support files — templates, rules, endpoint structures, modal conventions — that can be used as reference points whenever you continue development. Without this context, the creative freedom of the models can turn into a mess. Another thing: working with agents needs a human who knows how to guide. You need to know how to ask questions. Otherwise, the model makes things up — sometimes very confidently 😅 But there is also a fascinating side: you learn a lot. We are not always fully up to date, and it’s surprising to see how the models show new libraries, frameworks, or ways to do things we didn’t know. It’s a continuous cycle of discovery. And then there is the speed — the pace at which code is written and produced is very high. We probably spend time later on troubleshooting and checking… and in the end, maybe it’s worth it. Or maybe not. I’m still figuring it out. All this, of course, is “for now”. The speed at which these tools evolve means that next week, half of these notes could already be out of date.

Just read GitHub’s guide on prompt engineering for Copilot, and it hit me: we’ve been training for this our whole careers without knowing it. “Start general, then get specific.” “Give examples.” “Break complex tasks into simpler ones.” “Avoid ambiguity.” Sound familiar? It’s exactly how you’d explain a tricky requirement to a junior developer, or how you’d document that gnarly legacy system that nobody wants to touch. 🤔 The best part? When Copilot doesn’t understand what you want, the solution isn’t to get frustrated - it’s to iterate and refine your request. Just like those code review conversations where “this needs to be more robust” gets you nowhere, but “handle the null case when the API returns an empty array” gets you exactly what you need. We’re not just learning to talk to AI. We’re getting better at communicating, period. And honestly? My morning tea tastes a little sweeter knowing that all those years of writing clear commit messages and detailed PR descriptions were actually preparing me for the future of development. The tools change, but good communication remains the superpower. ☕→🍵 https://lnkd.in/ei_vThtZ #SoftwareDevelopment #AI #GitHub #Copilot #Communication #TechTrends

"Remembering context within the same pull request" for GitHub Copilot's agent is such a smart upgrade. That constant re-explaining to the AI was a real friction point. This should make iterating on code within a PR so much smoother and faster. Big win for developer flow! 🚀 #GitHubCopilot #DevProductivity

I've been exploring agentic development through tools like SpecKit. Following insights from a recent article, I began by asking questions to GitHub CoPilot NL. This led me to experiment with Agent Mode, then Agent Tasks, and now SpecKit. The article effectively outlines the necessary structure for the evolving concept of "Agentic Development." It's a fascinating read. https://lnkd.in/eTBB-Nf6

GitHub just dropped Copilot CLI 👀 With it, you can use Copilot directly in your terminal: - Access repos, issues, and PRs with natural language - Build, edit, debug, and refactor code with AI - MCP-powered, fully controllable Install via npm & code smarter ⚡

URL: /api/v1/endpoint?isVisible=true/false While reviewing the PR, I noticed that isVisible=false was being treated the same as true. 🤔 Turned out Copilot (LLM) had interpreted both true and false as true, thanks to the ambiguous “true/false” phrasing in the requirement. 👨💻 Proof that developers aren’t going anywhere ✌️

Just wrapped up a GitHub Copilot Live In Person Workshop today 🙌🏻 an incredibly practical and insightful session! Here's what I explored and practiced: 🔹 Used Ask Copilot and Edit , Agent modes to generate, refactor, and debug code 🔹 Worked on Azure-related tasks, including deployments and scripting with AI support 🔹 Learned how to apply prompt engineering techniques to get more accurate and helpful code suggestions 🔹 Gained a deeper understanding of how AI can assist across the full development workflow Also got hands-on with real examples and use cases that I can directly apply in my own projects. Learned much more than expected — definitely a step forward in building AI assisted development skills.

Introducing Ship Guard the AI-powered github app redefining how teams review and ship code. Every engineering team knows the pain: 1️⃣Manual reviews that drag on. 2️⃣Inconsistent feedback across PRs. 3️⃣Missed edge cases hidden in complex logic. Ship Guard was built to change that. It’s more than just an AI reviewer it’s your automated engineering gatekeeper, designed to help teams: ✅ Enforce team coding standards and policies automatically ✅ Catch mistakes early with context-aware AI reviews ✅ Leverage RAG (Retrieval-Augmented Generation) to review pull requests based on your team’s own rules, documentation, and conventions ✅ Accelerate delivery without sacrificing code quality Every review is grounded in your standards not generic AI guesses. Ship Guard learns from your repo, your docs, and your team’s way of building. Our vision: Make code reviews faster, smarter, and deeply aligned with how your team works. Your team shouldn’t waste time enforcing rules. Let us handle the gatekeeping so you can ship code, not doubts. #AI #GitHub #DevTools #CodeReview #EngineeringExcellence #SoftwareEngineering #ProductLaunch