GitHub Copilot Launches AI-Generated Software Framework

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

🚀 The Rise of GitHub Agent Workflows: Building a Self-Maintaining Repository with AI Modern repositories shouldn’t just store code—they should maintain themselves. I recently explored how to build agent-driven GitHub workflows that can: • Automatically refactor and simplify code via PRs • Keep README and docs in sync with changes • Diagnose and fix CI failures autonomously • Generate structured release notes from commits Instead of treating CI/CD as just validation, this approach turns it into an active contributor—a system of specialized AI agents working together inside your repo. The key shift: 👉 From manual maintenance → to continuous, intelligent automation 👉 From developers fixing issues → to developers reviewing AI-generated improvements In this blog, I’ve broken down: - End-to-end PR-centric architecture - Real GitHub Actions workflows (copy-paste ready) - Node.js agent implementations - Guardrails to make this production-safe - How to extend using multi-agent patterns inspired by Agent Factory If you're a backend or platform engineer exploring AI-native workflows, this might give you a practical starting point. Curious to hear how others are approaching agentic CI/CD and self-healing systems. #AI #GitHub #DevOps #BackendEngineering #Automation #LLM #SoftwareEngineering #OpenSource #AgenticAI

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

Our team uses GitHub Copilot with AGENTS.md files in each repository to give the AI context about our projects. Over time, we noticed the same dependency upgrade patterns being copy-pasted across multiple repositories: Framework migration steps Library compatibility matrices Known breaking changes and their fixes CI configuration patterns Instead of maintaining the same knowledge in 6+ places, we consolidated it into a single GitHub Copilot Agent Skill — a structured knowledge file that Copilot loads on demand when you need it. The result: 1,136 lines removed from scattered documentation files One source of truth, updated as we learn Today: the skill diagnosed a failing dependency upgrade in minutes — it already knew the root cause and the exact fix from the last time we solved a similar problem The real win isn't the line count. It's that next time someone on the team hits a dependency upgrade failure, the AI assistant already knows the solution from the last time we solved it. Knowledge that used to live in someone's head now lives in the toolchain. If you're using GitHub Copilot with AGENTS.md files, Copilot Agent Skills are worth looking into. Curious if others have found similar patterns for sharing AI context within a team. #GitHubCopilot #DeveloperExperience #DevOps #KnowledgeManagement

GitHub Copilot represents a major shift in modern software development. We are moving into a new era where developers are no longer working alone, but alongside AI-powered assistants that can accelerate coding, reduce repetitive effort, and improve overall productivity. The opportunity is significant, but so is the responsibility. Tools like Copilot are most powerful when paired with experienced engineers who understand architecture, security, scalability, and code quality. AI can increase speed, but it cannot replace accountability, technical leadership, or sound judgment. The organizations that benefit most from GitHub Copilot will be the ones that treat it as a force multiplier for strong engineering teams, not as a shortcut around engineering discipline. This is not just a tool trend. It is a shift in how software will be designed and delivered. #GitHubCopilot #AI #EngineeringLeadership #SoftwareEngineering #DigitalTransformation #DeveloperProductivity

10 Best Practices for GitHub Copilot's /fleet Command /fleet lets you run multiple AI agents in parallel. One prompt. Multiple sub-agents. All working simultaneously. Here's how to use it like a pro: 1. Write artifact-specific prompts "Create docs/auth.md, docs/api.md, docs/deploy.md" — not "improve docs." Each sub-agent needs a clear deliverable. 2. Declare dependencies explicitly If index.md depends on other files, say so. The orchestrator respects the order. 3. Pick tasks that are truly independent Separate files, separate modules, batch refactoring. Parallel = no shared state. 4. Avoid monolithic prompts Vague prompts force sequential execution. Specificity unlocks parallelism. 5. Use custom agents for specialized work Point test generation to @test-writer, docs to @docs-agent. Right tool for each sub-task. 6. Review merge conflicts carefully More parallel changes = more potential collisions. Extra oversight is non-negotiable. 7. Use --no-ask-user for CI/CD Running /fleet in pipelines? Add the flag. No human in the loop = no blocking prompts. 8. Monitor with Agent HQ GitHub's Mission Control lets you view, compare, and triage all agent sessions from one dashboard. 9. Set up copilot-instructions.md Global coding conventions + repo-specific context = consistent output across all parallel agents. 10. Track usage and costs More agents = more credits burned. Set budgets per project. Parallel power isn't free. /fleet turns your weekend maintenance backlog into a 20-minute task. The developers who learn to orchestrate agents will outpace those who code alone. #GitHubCopilot #FleetCommand #CopilotCLI #AgenticAI #MultiAgent #DeveloperProductivity

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 is evolving fast and Copilot is no longer just a coding assistant. It’s becoming a DevOps teammate. One of the most exciting recent shifts is how GitHub Copilot integrates into GitHub workflows and Actions. Here’s what stands out: Copilot in GitHub Actions Copilot can now help you: • Generate entire workflow YAML files from simple prompts • Suggest fixes when your pipeline fails • Explain what a workflow is doing (great for debugging complex CI/CD setups) • Optimize pipelines for performance and efficiency Faster CI/CD Development Instead of memorizing syntax or digging through docs, you can: “Create a CI pipeline for a Node.js app with Docker and deploy to AKS” And Copilot builds a working starting point instantly. Smarter Debugging Pipeline failed? Copilot can analyze logs and suggest what went wrong cutting down troubleshooting time significantly. Security and Best Practices Copilot doesn’t just generate code ,it often suggests: • Secure configurations • Proper secrets handling • Improved workflow structures What this means for DevOps Engineers We’re moving from: Writing pipelines manually To: Designing, reviewing, and optimizing AI-generated pipelines Less time on boilerplate. More time on architecture and impact. My take: Copilot in workflows isn’t about replacing engineers ,it’s about amplifying how fast we build, debug, and ship. If you’re in DevOps and not exploring this yet, you’re already behind. #DevOps #GitHub #GitHubCopilot #CICD #Automation #CloudComputing #AI #PlatformEngineering

Ollama and GitHub Copilot. Another important union for privacy and power in the terminal.... The AI development landscape gains another chapter in its evolution. The announcement that Ollama now supports GitHub Copilot CLI reinforces an integration movement seeking the balance between cloud intelligence and local processing security. To date, no one holds an exclusive path to efficiency, but this combination of tools certainly opens new doors for developers. What this integration allows you to do now: - Repository Exploration. You can use Copilot CLI to map codebases and understand complex structures with the support of local processing. - Terminal Automation. This union allows for task planning based on GitHub tickets where AI assists in editing files and installing dependencies more fluidly. - Privacy and Control. By using Ollama as a backend option, developers gain another layer of choice regarding where their sensitive context should be processed. My personal analysis on this movement In my view, what we are witnessing is the consolidation of a hybrid model. Copilot's support for Ollama is another step acknowledging that the future of corporate software will not be centered on a single closed solution. My predictive analysis is that the terminal will remain the primary command center, now powered by agents that respect the security perimeter of each project. True productivity does not stem from a single tool, but from the ability to integrate the best available solutions into your workflow. #Ollama #GitHubCopilot #AI #OpenSource #CTO #SoftwareDevelopment #Privacy #TechTrends #Coding