Cursor vs GitHub: Developer Experience Gap

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

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

🚀 I’ve completed GitHub Copilot Fundamentals – Part 2 of 2 by GitHub & Microsoft 🎉 🔗 Explore the learning path: https://lnkd.in/du9jJChK This comprehensive program (3+ hours, 6 modules) provided a deep dive into how AI-assisted development is reshaping the way we build, review, and maintain software. It goes far beyond basic autocomplete—focusing on real-world implementation, scalability, and responsible usage within teams and organizations. 🔍 What I Learned: 🧠 Advanced GitHub Copilot Capabilities Explored powerful features like Agent Mode, where Copilot can iteratively plan, generate, refactor, and improve code across an entire codebase—not just suggest snippets. ☁️ Copilot Cloud Agent Learned how to delegate development tasks to AI in a structured way, combining automation with human expertise to accelerate delivery while maintaining quality. 🔗 MCP Server Integration Gained hands-on understanding of GitHub MCP Server—enabling secure, scalable integration of GitHub features into AI tools like Copilot Chat, especially within environments like Visual Studio Code. 🔍 Smarter Code Reviews & PRs Discovered how Copilot enhances pull requests by identifying issues, suggesting improvements, and helping enforce coding standards—leading to faster and more reliable review cycles. 💻 Language-Specific Productivity (JavaScript & Python) Applied Copilot in real coding scenarios using JavaScript and Python, leveraging AI suggestions to write cleaner, faster, and more efficient code. 🔐 Responsible & Secure AI Usage Understood best practices for using AI tools in development environments—especially important for organizations adopting Copilot at scale. 🏢 Copilot for Individuals, Business & Enterprise Clarified the differences between various Copilot offerings and how they can be implemented effectively depending on team size and organizational needs. 🎯 Why This Matters: AI is no longer just an assistant—it’s becoming an integral part of the development lifecycle. This learning path strengthened my ability to: ✔️ Collaborate more effectively with AI tools ✔️ Increase development speed without compromising quality ✔️ Apply modern DevOps and AI-driven workflows ✔️ Build smarter, more scalable solutions 🎓 Proud to earn this certification from Microsoft and add it to my continuous learning journey! 🔗 Certificate: https://lnkd.in/d2-eR2DD #GitHub #GitHubCopilot #Microsoft #AI #DevOps #SoftwareEngineering #MachineLearning #Python #JavaScript #ContinuousLearning #Innovation

Most of us use GitHub Copilot like autocomplete… I felt the same while building a full-stack system. It kept giving: generic code inefficient business logic - giving the universal logics instead of Architecture oriented zero awareness of system architecture So I tried something different 👇 👉 Instead of writing better prompts, I designed a system around Copilot. Custom agents (like roles for AI) Global instructions Domain skills + repo context Result? What has been the Outcome. Copilot stopped guessing… and started behaving like a context-aware engineer. I wrote a full breakdown + case study here: 👉 https://lnkd.in/guzTgCEY Big takeaway: AI doesn’t get better with prompts. It gets better with structure. Curious — how are you using Copilot today? Still prompting… or building systems around it? 👀 #AI #GitHubCopilot #SoftwareEngineering #DeveloperTools #BuildInPublic #MachineLearning

GitHub has paused new Copilot sign-ups and tightened usage limits for existing users because AI coding demand is overwhelming its compute capacity. The pause affects individual Copilot plans and reflects the raw infrastructure cost of running AI-assisted development at scale. GitHub Copilot has become one of the most widely adopted AI tools in software engineering, and the fact that Microsoft-backed GitHub cannot keep up with demand is a telling signal about where the AI compute bottleneck really sits. This is not just a supply issue. It is a strategic vulnerability for every engineering organization that has built Copilot into its development workflow. When your productivity tool becomes capacity-constrained, your team's velocity drops with it. For engineering leaders, this should prompt a serious conversation about single-tool dependency for AI-assisted coding. If the platform you rely on can pause sign-ups without warning, your development pipeline is more fragile than you thought. #GitHubCopilot ♻️ Repost if you think someone in your network should see this. 🌤️ Follow for daily enterprise IT news.

GitHub Copilot Launches New AI-Generated Software Framework for Developers 📌 GitHub Copilot unleashes a new AI-generated software framework, transforming dev workflows from snippets to full ecosystems - think encrypted vaults and remote shells. Vibe coding is no longer fantasy; it’s powering 41% of 2025 code, with giants like Snap using AI for over 65%. DevOps teams now wield agentic tools, GPU-accelerated SDKs, and context-rich models to rebuild systems faster - and smarter. 🔗 Read more: https://lnkd.in/djMtQtKC #Githubcopilot #Llm #Vibecoding #Softwareframework #Developertool

🔥🚀 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

Lately, I’ve been diving into AI in Software Testing and getting hands-on with GitHub Copilot—and it’s been an interesting shift in how I approach development of test automation scripts. To make this exploration more structured, I’ve been following the GH-300 (https://lnkd.in/gC3ucbT4) curriculum, which has helped me go beyond just “using” Copilot to actually understand its: 🔹 Strengths Copilot is great at accelerating boilerplate code, suggesting reusable patterns and exploring pull requests—especially useful when working with frameworks like Playwright. 🔹 Limitations It still requires strong human oversight. Context gaps, incorrect assumptions, and occasional flaky suggestions mean you can’t rely on it blindly—especially in critical test scenarios. 🔹 Real Value in Testing When used thoughtfully, it can significantly speed up: ✔ Test case generation ✔ Locator strategies 🔹 The Mindset Shift It’s less about “AI writing code for you” and more about pair programming with context awareness. The better your prompts, the better the output. This journey is helping me understand how AI can augment test engineers, especially in building more resilient and scalable automation frameworks. Still early days, but definitely an exciting and compelling space to explore🚀. #GitHub #Copilot #AI #SoftwareTesting

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