Cursor vs GitHub: Developer Experience Gap

GitHub Copilot just went through a change that looks small on the surface, but actually says a lot about where dev tools are heading. They’re moving toward usage-based billing. The plans still look the same. The pricing hasn’t changed. But what you’re really getting is no longer “unlimited assistance.” It’s a fixed amount of credits based on how much you actually use the system. Tokens, agent runs, even code review workflows now tie back to consumption. That shift matters. Until now, tools like Copilot felt lightweight. You didn’t think twice before using them. Generate something, tweak it, retry a few times—it all felt free enough to not care. That mental model doesn’t hold anymore. When usage becomes visible, behavior changes. You start to notice where you’re spending time and compute. You think twice before running multi-step flows for something trivial. You become a bit more deliberate about when to rely on the tool and when to just do it yourself. It also reveals something about the product itself. Copilot isn’t just an editor assistant anymore. It’s moving toward something closer to an execution layer—running longer workflows, touching more of your codebase, consuming actual infrastructure behind the scenes. And infrastructure is never flat-priced for long. This feels less like a pricing update and more like a correction. The earlier phase made AI feel abundant and frictionless. But the reality is that these systems are expensive to run, especially as they get more capable. So the experience becomes a balance again. Speed vs cost. Convenience vs control. Automation vs understanding. In a strange way, this might actually improve how we use these tools. Because when something isn’t “free or restrictive” you pay more attention to how you use it. And in engineering, that usually leads to better decisions. #SoftwareEngineering #AI #GitHubCopilot #DevTools #Engineering #Tech #Backend #Developers #Productivity

🚀 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

I used to treat GitHub Copilot like a magic “fix my bad code” button. It turns out, I was only using about 10% of the magic. I’m officially GitHub Copilot Certified (GH-300). 🏆 Let’s be brutally honest: most of us use Copilot as a glorified typewriter that finishes our sentences. We hit Tab like we’re playing a rhythm game and hope for the best. But after going through the GH-300 rigor, I realized that if you're only using it for autocomplete, you’re essentially using a Ferrari to drive to the grocery store. As a backend developer, my flow in IntelliJ IDEA is usually interrupted by soul-crushing boilerplate or trying to remember the exact syntax for a complex Regex. Here is how my workflow actually looks now: • Orchestration over Autocomplete: I’ve stopped letting it "guess" what I want. I’m providing high-context prompts that respect my architecture rather than just suggesting "generic-function-01." • The Unit Test Cheat Code: I’ve offloaded the repetitive "happy path" testing to the AI so I can spend my brainpower on the edge cases that actually break things at 3:00 AM. • Documentation is no longer a lie: I’m using Copilot to document logic as I write it, ensuring the README actually matches the code for the first time in human history. The certification didn't teach me how to code—it taught me how to orchestrate. AI isn’t taking our jobs, but it is taking away our excuses for writing mediocre, undocumented code. To my fellow devs: Are you actually using Copilot to architect systems, or are you just using it so you don't have to look up syntax on Stack Overflow? Give me your honest (or brutal) take below. 👇 #GitHubCopilot #GH300 #BackendDevelopment #IntelliJ #SoftwareEngineering #AIWorkflows #DeveloperHumor #GenerativeAI #DevEx #microsoft GitHub Microsoft 💻

👉 𝗜𝗳 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗿𝗲𝗽𝗼𝘀, 𝗵𝗲𝗿𝗲'𝘀 𝗮 𝗱𝗮𝘁𝗲 𝘁𝗼 𝗻𝗼𝘁𝗲: 𝗝𝘂𝗻𝗲 𝟭, 𝟮𝟬𝟮𝟲. GitHub announced that Copilot code review will start drawing from your 𝘎𝘪𝘵𝘏𝘶𝘣 𝘈𝘤𝘵𝘪𝘰𝘯𝘴 minutes quota on that date. Right now it runs without touching your Actions budget - that changes in about a month. ● 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗿𝗲𝗽𝗼𝘀 𝗼𝗻𝗹𝘆 - public repositories stay unmetered; this specifically affects private repo PR workflows ● 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 𝗾𝘂𝗼𝘁𝗮 - the same pool your CI pipeline uses; high-volume review teams will need to check if this changes monthly spend ● 𝗣𝗮𝗿𝘁 𝗼𝗳 𝗮 𝗯𝗶𝗴𝗴𝗲𝗿 𝘀𝗵𝗶𝗳𝘁 - GitHub is consolidating Copilot, Actions, Packages, and Codespaces into a unified AI Credits billing model from June 1 If your team runs hundreds of PRs per month on private repos, this is worth a line item review before the change takes effect. Is your team factoring Copilot review into your Actions budget yet? #GitHubCopilot #GitHubActions #DevOps #CodeReview #AIBilling

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

GitHub Copilot + GitHub Actions -The 2026 DevOps Power Combo ⚡ If you're still doing manual code reviews and running deployments by hand, you're leaving speed AND quality on the table 🏎️💨. I've been running 🤖 GitHub Copilot with ⚙️ AI-powered PR review and 🔄 GitHub Actions CI/CD together in my daily workflow - and the results have been transformational. Copilot catches edge cases before I even open a PR. AI review flags security issues in seconds. GitHub Actions ships the whole thing to prod —-with zero human hand-holding. ⚡ Real-Time Benefits I've Experienced: 🤖 GitHub Copilot: ✅ Inline suggestions as you type ✅ Catches logic gaps before commit ✅ Reduces boilerplate by 60%+ ✅ Context-aware across your whole codebase ⚙️ AI PR Review: ✅ Security vulnerabilities flagged automatically ✅ Missing test coverage highlighted ✅ Auto-fix suggestions inline on your diff ✅ Zero waiting on slow human reviewers for trivial checks 🔄 GitHub Actions CI/CD: ✅ Lint, test, build - all in under 35s ✅ 94% test coverage enforced on every push ✅ Auto-rollback if AI monitoring detects anomalies ✅ Slack alerts before users ever notice a problem 🚀 All Three Together: ✅ Ship faster with higher confidence ✅ Sleep through on-call rotation without sweating ✅ The complete AI-native engineering loop The old workflow: write → wait for review → pray CI passes → manually deploy. The 2026 workflow: WRITE → AI REVIEW → CI/CD → DEPLOY — all automated, all intelligent. If your team isn't running this stack yet, this is your sign. The gap between AI-native teams and everyone else is only going to widen. #DevOps #GitHubCopilot #GitHubActions #CICD #AIEngineering #DeveloperProductivity #SoftwareEngineering #2026Stack

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