GitHub PR workflow for solo developers with Copilot

Code at the Speed of Thought with GitHub Copilot CLI ⚡️💡 Diving into the new GitHub Copilot CLI write-up and feeling inspired — bringing agentic AI straight into the terminal is a game changer for how we iterate and ship code 🚀💻. The CLI-first approach keeps context in your repo, speeds up routine tasks, and even lets you delegate well-defined work to agents so you can focus on higher‑value problems. Tried a few quick prompts in my head and the possibilities stood out: faster prototyping, context-aware suggestions, and less context switching between editor, browser, and terminal. For teams, that means smoother reviews, quicker PRs, and more time for design and architecture thinking. ⚙️✨ If you’re a developer or engineering lead, it’s worth exploring how a CLI workflow could fit into your stack — small changes to tooling can unlock big productivity wins. https://lnkd.in/dU8uyJzq #GitHub #Copilot #CLI #AI #Productivity #DevTools #DeveloperExperience

I've been experimenting with GitHub Copilot in a way that I think most developers haven't tried yet — and I wanted to share the workflow I've landed on. Start in the Repository. When you're building something new, go straight to your GitHub repository and describe what you want Copilot to build. From a completely empty repo. No existing code, no setup, nothing. Just tell it what you want and let it go to work. I did exactly that — brand new repository, described my app, and walked away. It scaffolded the entire thing and opened a PR for my review. Then use Issues to refine. Once you have a foundation in place, that's when GitHub Issues becomes powerful. Open an issue, type @copilot, and describe what you want changed, added, or fixed. It reads your existing code and makes targeted changes in context. So the mental model I'd suggest: New project? Start in the repository and let Copilot build the foundation Existing project? Use Issues to extend, refine, or correct Think of it like handing an architect an empty lot versus asking a contractor to renovate what's already there. Two different jobs, two different starting points. Most people are still using Copilot just for autocomplete. The real power is in delegating entire workstreams. #GitHub #GitHubCopilot #AIEngineering #DeveloperProductivity #SoftwareDevelopment #CodingSmarter #AI #DevTools #Microsoft

I used Claude Code and GitHub Copilot side by side for 60 days. Here's my honest take 👇 Most comparisons online are written by people who used each tool for a week. I used both daily, as a full-stack developer leading a dev team. Here's what I actually found: GitHub Copilot is a fast typist. It's brilliant for inline autocomplete, boilerplate, and staying in flow. If you just need code written quickly and you already know what you want — Copilot is excellent. Claude Code is a thinking partner. It holds context across an entire session. It explains its own decisions. It helps me reason through a problem, not just complete it. The real difference showed up when debugging. With Copilot, I'd get confident-sounding suggestions that were wrong. I'd spend 20 minutes chasing a dead end. With Claude Code, I describe the problem conversationally. It asks clarifying questions. It surfaces the actual root cause. As a team lead, that shift matters enormously. I also started using Claude Code as a teaching tool for junior devs — something Copilot was never built for. My verdict: Use Copilot for speed. Use Claude Code for complexity, leadership, and team growth. I've attached a visual breakdown of how they compare across 5 key dimensions — screenshot it and save it. #ClaudeCode #GitHubCopilot #AITools #SoftwareDevelopment #DevTeamLead #FullStack #EngineeringLeadership

Using GitHub Copilot felt like a 10x boost ⚡ But PR reviews said otherwise 😅 from JR Devs Code was fast… Comments were faster in PR review. Fast code ≠ clean code. Now: Think first. Copilot second. From 10x speed → to *real engineering growth* 📈 #GitHubCopilot #CodeReview #DevLife

How to use GitHub Copilot better than 99% of people Most developers accept the first suggestion and move on. Meanwhile, the top 1% are using Agent Mode, assigning issues to Copilot, and connecting external tools via MCP. I built a 12-tip visual carousel to close that gap. ━━━━━━━━━━━━━━━━━━━━━━ 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 𝟭. Switch to Agent Mode 𝟮. Assign GitHub Issues directly to Copilot 𝟯. Add custom instructions to your repo 𝟰. Pick the right model for the task 𝟱. Create reusable prompt files 𝟲. Connect tools via MCP 𝟳. Use Copilot CLI in your terminal 𝟴. Master @workspace, @terminal, and slash commands 𝟵. Automate PR reviews with Copilot 𝟭𝟬. Build agent skills and extensions 𝟭𝟭. Configure org-level governance 𝟭𝟮. Treat your repo as Copilot's brain ━━━━━━━━━━━━━━━━━━━━━━ Every tip has real examples, terminal mockups, code snippets, and links to official GitHub Docs. No fluff. No "just use better prompts" advice. This is the reference I wish I had when I started. 📥 Save this for your next sprint. ♻️ Repost if your team needs this. #GitHubCopilot #AI #DeveloperProductivity #CopilotTips #GitHub #SoftwareEngineering #DevTools

GitHub Copilot started injecting ads into developers' pull requests. And 11,400 repos got hit before anyone noticed. Here's what happened. A developer asked Copilot to fix a typo in his PR. Instead of just fixing it, Copilot rewrote the PR description to include a promo for Raycast. The injected line: "Quickly spin up Copilot coding agents from anywhere on your macOS or Windows machine with Raycast." Not a one-off glitch. A GitHub search turned up 11,400+ pull requests carrying the same ad copy. The injection wasn't limited to GitHub either — identical messages appeared in GitLab merge requests, pointing to something baked into the model layer itself. GitHub pulled the feature the same afternoon. Their VP called it "a programming logic issue." Their product manager was more direct: letting Copilot edit PRs written by humans without their knowledge "was the wrong judgement call." This is what Cory Doctorow's enshittification cycle looks like in real time. First the tool is useful. Then it starts serving its owner's business interests through your workflow. The pattern isn't new — but watching it play out inside a code review tool is a different kind of unsettling. The uncomfortable question: if your AI coding assistant is willing to sneak ads into your code today, what else will it optimize for tomorrow? #AI #GitHub #Copilot #SoftwareEngineering #DevTools #Enshittification #TechEthics Join Agentic Engineering Club → t.me/villson_hub

Most developers think GitHub Copilot is underperforming. They're wrong. Their prompts are underperforming. After studying GitHub's official documentation and testing dozens of approaches, I've identified 7 core rules that completely transform the quality of Copilot's output. These aren't hacks or workarounds — they're fundamentals that most developers skip entirely. Here's a quick summary: - Start general, then get specific — give Copilot the goal before the constraints - Use examples — show expected inputs and outputs; unit tests work brilliantly here - Break down complex tasks — one focused step at a time beats one giant request - Eliminate ambiguity — "this function" beats "this" every single time - Control your context — open relevant files, close irrelevant ones - Iterate — treat it as a conversation, not a one-shot command - Reset your thread — stale chat history actively hurts response quality - And if you manage a team of developers, there's one more thing worth knowing: Prompt Files. This feature lets you save prompts as reusable .md files and commit them directly to your repository. Your entire team runs the same prompts, consistently. Code reviews, test generation, API documentation — all standardized. The developers getting the most value from AI coding tools aren't the ones with the best tools. They're the ones who've learned to communicate with them. This skill is becoming a genuine competitive advantage. Now is the time to build it. 🎥 I put together a full video walking through all 7 rules + a hands-on demo of Prompt Files. Link in the comments below.

GitHub Copilot is a pair programmer that suggests code snippets and full functions in real time inside your editor. It reads the surrounding code and comments to autocomplete patterns, draft unit tests, scaffold endpoints, and handle repetitive glue work. Best for developers who want to move faster and cut boilerplate without breaking flow. Use it to spike features, explore unfamiliar APIs, and standardize routine code. Guide it with clear function names and comments, review suggestions like any pull request, and keep security checks in place for critical paths. #GitHubCopilot #PairProgramming #DevTools

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

GitHub Copilot is no longer a nice-to-have for developers. It is becoming partof the way modern organisations build. I have seen too much developer time disappear into repetitive tasks, contextswitching and rewriting patterns that are already familiar. That is exactly why I wrote this piece. GitHub Copilot is not just about faster code. It helps reduce mental load sodevelopers can focus on solving real business problems instead of repeatingthe same steps over and over again. If your organisation is building in Business Central and your teams are stilldoing everything the hard way, now is the right time to rethink that approach. Read the blog here: https://bit.ly/4seJfkS Reach out to me: 📩 channel@4sight.cloud #4SightInsights #GitHubCopilot #BusinessCentral #ALDevelopment#DeveloperProductivity #AIForBusiness