GitHub Copilot Code Generation Workflow

Six months of GitHub Copilot. Five articles. Here's the honest version of what happened — not the polished retrospective, but the one where I tell you what I actually got wrong. The governance policy I spent two weeks writing? Useless. The metric that mattered most? I hadn't planned to track it. The most uncomfortable moment? A developer saying they feel anxious when Copilot doesn't have a suggestion. This is the last article in the series. And it's the most personal one. Now published in gitconnected 🎉 🔗 https://lnkd.in/dFZ52r6m #GitHubCopilot #SoftwareEngineering #GenerativeAI #EngineeringLeadership #DevSecOps

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

The premise needs a small correction first: GitHub Copilot hasn't actually lost the AI coding war — but it has gone from dominant pioneer to a genuinely threatened incumbent. Here's the full picture: Copilot is struggling, but still standing Copilot holds 42% market share and has reached 20 million cumulative users, deployed across 90% of Fortune 100 companies. Quantumrun Those are not the numbers of a defeated product. But the competitive pressure is very real, and the cracks are showing. Why developers are losing faith: Developer complaints about suggestion quality, latency, and context awareness have grown significantly since late 2025, with model swaps being a primary cause — GitHub cycled through Codex, multiple GPT-4 variants, and GPT-5 series, and each transition introduced regressions. Nxcode In March 2026, Copilot injected promotional "tips" into over 1.5 million pull requests, badly eroding developer trust. Nxcode Copilot's suggestion acceptance rate sits at 35–40%, compared to Cursor's 42–45%. Nxcode Tasks requiring changes across 10+ files with architectural implications produce noticeably more mistakes than competing tools. Nxcode Its characteristic failure mode is the confident wrong answer.

🔥🚀 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

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

Friday reminded me why I’m genuinely excited about where AI tooling is heading. I pointed GitHub Copilot to a separate repo — one that already had auth/API access code built out — and asked it to pull what I needed into my current project and wire up the matching endpoint. It found the repo. Found the code. Dropped it in. Built the scaffolding around it. What would have taken a developer a solid chunk of their day? Done in minutes. But here’s the part that doesn’t get said enough: 🧭 AI is a GPS, not a driver. A GPS gets you there faster. It reroutes when traffic hits. It saves you from guessing at every turn. But if the road is icy, if the bridge is out, if something just feels off — you still need someone with hands on the wheel and the experience to know what to do next. When that generated code doesn’t behave the way it should, when the integration breaks in a way the tool didn’t anticipate, when the edge case shows up at 4pm before a release — that’s not an AI problem to solve. That’s a developer problem. The win isn’t AI replacing the craft. The win is AI eliminating the crawl so skilled developers can spend their time on the parts that actually require them. Know your stuff. Use the tools. Get there faster. #SoftwareDevelopment #GitHubCopilot #AITools #DeveloperProductivity #TechLeadership

💻 Claude Code vs GitHub Copilot in VS Code — A Developer’s Comedy Show 😄 Day 1 with AI coding assistants in VS Code… 🧑💻 Me: “Write a simple function.” 🤖 Copilot: “Sure bro, here’s 20 lines of code… also I added 3 edge cases, 2 optimizations, and a pattern you didn’t ask for.” 🧑💻 Me: “I just wanted a sum function…” ⸻ 🧑💻 Me: “Claude, can you help?” 🤖 Claude Code: “Absolutely. But first… let me explain the philosophy of ‘sum’, its history, and 3 possible approaches. Which one aligns with your long-term architectural vision?” 🧑💻 Me: “Bhai… just add two numbers 😅” ⸻ 🔁 Real-life loop: • Copilot = Fast & Furious 🚀 (sometimes too fast, you don’t know what just happened) • Claude = Guru mode 🧘 (you learn everything… except finishing your task on time) ⸻ 📊 Summary: 👉 Copilot writes code you didn’t think of 👉 Claude makes you think about code you didn’t want to think about ⸻ 🔥 Best strategy? Use Copilot when deadline is in 10 mins ⏳ Use Claude when you want to sound smart in code reviews 😎 ⸻ #AI #Developers #VSCode #GitHubCopilot #Claude #CodingLife #TechHumor #Produvity

Cursor vs GitHub After 6 months of deep evaluation across multiple teams, the developer experience gap is stark. SETUP & ONBOARDING: Cursor wins decisively. Download, authenticate, start coding. 5 minutes max. Copilot requires VS Code extensions, authentication flows, and often troubleshooting. Teams report 30-60 minutes average setup time. DOCUMENTATION QUALITY: Cursor's docs are refreshingly direct. Clear examples, minimal fluff. Copilot's documentation feels scattered across GitHub, Microsoft, and community wikis. Finding authoritative answers takes too long. SDK & INTEGRATION: This is where it gets interesting. Cursor's chat interface feels native - like talking to a pair programming partner who actually understands your codebase. Copilot's autocomplete is solid but the conversation flow breaks when you need deeper architectural discussions. DEVELOPER HAPPINESS: The real metric that matters. Teams using Cursor report higher satisfaction with AI assistance quality. Less context switching, fewer "that's not what I meant" moments. Copilot users appreciate the GitHub ecosystem integration but frequently mention friction in complex scenarios. Both tools are reshaping how we write code, but the DX delta is real. What's been your team's experience with AI coding assistants? Where do you see the biggest productivity gains? #Cursor #GitHubCopilot #DeveloperExperience

GitHub dropping Opus from Copilot Pro is not really a model story. It is a packaging story. Agentic coding is expensive. Flat-rate pricing was always going to break. Users can accept limits. They do not accept surprises. That is why this hit so hard. My take: this is the clearest sign yet that AI dev tools will be priced around capacity, not just model quality. The real question now: cheap monthly price, or reliable agent capacity?

GitHub just changed how Copilot is priced for individuals. New signups paused, tighter limits. Opus models removed from the base Pro tier. The stated reason: agentic workflows now regularly generate compute costs that exceed the plan price. A handful of requests could consume more than a month’s subscription. This is a direct consequence of the agent/subagent era. A lot of people read this as “AI is getting more expensive.” I don’t totally agree. What actually happened is that the unit of AI work changed - and the pricing model hadn’t caught up. Copilot was priced for chat. You send a message, you get a reply, that’s a request. Agents broke that model. A single well-specified session can now do what used to take 40 back-and-forth exchanges. The compute is real and the request count is not the right proxy for it anymore. The shift is straightforward: write the spec first. Give the agent the full picture upfront - what you’re building, the constraints, the acceptance criteria, what done looks like. One well-constructed session replaces 40 back-and-forth exchanges. That’s one request, not forty. This is exactly how Claude Opus 4.7 is designed to be used - and why the 7.5x premium request weight (introductory price) is justified. “More expensive” is the wrong lens. The real question is whether you’re interacting with it correctly for the agent era by using long-horizon, well-specified, context-rich sessions - not rapid-fire back-and-forth that burn requests. The price of getting AI wrong is going up. Expect more restructuring and usage-based pricing and tighter tiers across the industry. #GitHubCopilot #AIProductivity #DeveloperProductivity #SoftwareEngineering #AgenticAI #SpecDrivenDevelopment