GitHub CLI Telemetry Defaults Impact DevOps

Open-Source Governance Turmoil Affects Ruby Central Finances and AI Tool Limits Weekly DevOps insight: RubyGems governance turmoil hits Ruby Central finances, GitHub Copilot sign-ups pause, Grafana ships free AI assistant. 📅 Coverage period: Apr 21 - Apr 27, 2026 Read the full analysis 👇 #TechNews #TechnologyTrends #DeveloperToolsAndSoftwareEngineering #DevOps #SoftwareDevelopment #Programming https://lnkd.in/g6Zgbd_i

GitHub's Copilot CLI just got smarter — and the logic behind it is worth understanding. A new experimental feature called Rubber Duck adds a second AI model from a different model family to review your coding agent's work at key checkpoints: after planning, after complex implementations, and after writing tests. The idea? A model from a different AI family catches blind spots that the primary model — trained differently — might consistently miss. Early results on SWE-Bench Pro show Claude Sonnet 4.6 + Rubber Duck closing 74.7% of the performance gap between Sonnet and Opus. And it costs less than running Opus solo. The bigger takeaway: the question for development teams may no longer be "which model is best?" It may be "which two models work best together?" Worth a look if your team is evaluating AI tooling for complex, multi-file development work. https://lnkd.in/giSrfXjj #GitHub #GitHubCopilot #DevOps #CodingAgents #AITools #SoftwareDevelopment #DeveloperProductivity

A few weeks ago I had highlighted (https://lnkd.in/dT6uTsS7) some recent non-AI development in the DevOps space and eBPF (kernel level observability) was one of them. Here is GitHub using it effectively to hook into DNS lookups and network connections to help with identifying circular dependencies in deployments and providing better, more observable deployments. I think it is still in a nascent phase and probably lacks a layer or two of abastraction for wider adoption but what a great demostration of the capability. Worth a read.

GitHub Actions has been completely rebuilt from the ground up—and the implications for enterprise DevOps are significant. The numbers speak for themselves: 71 million job executions now running on reimagined infrastructure. But the technical improvements are what matter most for teams at scale: → YAML anchors to eliminate repetition in complex workflows → 10-level reusable workflow nesting (up from 4) → Federated credentials for secure cross-repository automation → Removal of the 10GB cache limit for dependency-heavy builds → 25 workflow_dispatch inputs (up from 10) What's driving this transformation? Agentic development. The rise of AI-powered development workflows is pushing every tech stack to be reimagined. GitHub is positioning Actions at the center of what they call "Continuous AI"—the systematic integration of AI agents into the software development lifecycle. With GitHub Agentic Workflows now in technical preview, we're seeing the beginning of a fundamental shift: AI agents that can automatically triage issues, investigate CI failures, update documentation, and improve test coverage—all running within the guardrails of GitHub Actions. For engineering leaders, this represents both an opportunity and a mandate to rethink CI/CD strategy. Full technical details: https://lnkd.in/guMUcv8U #GitHubActions #DevOps #AgenticAI

Continuous AI: The Next Evolution of DevOps The software development landscape is shifting. GitHub Agentic Workflows represent a fundamental change in how we approach CI/CD—automating the intellectual toil that has historically consumed developer time, while maintaining human oversight through security-first architecture. In our latest GitHub Copilot session, we explored the Seven Pillars of Continuous AI, demonstrating how natural language can now define complex workflows, how MCP server configuration enables intelligent automation, and how safe outputs keep your pipelines secure. The key insight? This isn't about replacing developers—it's about amplifying their capabilities. Waking up to documentation fixes, new unit tests, and refactoring suggestions is becoming reality for teams who embrace agentic development. For a deeper dive into the security architecture behind these workflows, I recommend this excellent piece from the GitHub Blog: https://lnkd.in/gzKK6kwP What aspects of your CI/CD pipeline would benefit most from intelligent automation? #ContinuousAI #DevOps #GitHubCopilot

📝 Claude Code Source Leaked via npm Source Maps: Lessons for Every DevOps Team Anthropic accidentally shipped source maps in their npm package, exposing 512,000 lines of Claude Code source. Here is what went wrong and how to prevent it in your own CI/CD pipeline. Read it here: https://lnkd.in/dUB_8YCy #DevOps #DevOps #Learning

🚀 DevOps Journey — Day 5: The GitOps Leap 🐙 Yesterday I mastered the HPA control loop. Today, I removed myself from the deployment equation. I moved my laboratory from traditional Push-based CI/CD to a Declarative GitOps model using ArgoCD. 🔬 What changed? Until today, my GitHub Actions pipeline was responsible for "shouting" orders to the cluster (helm upgrade --install). If the connection failed or the runner had issues, the deployment broke. Now, the cluster has its own "brain". 🧠 How it works now CI Phase: GitHub Actions only builds the Docker image and pushes it to GHCR (versioned by SHA). CD Phase (The GitOps way): ArgoCD monitors my Helm charts in Git. Reconciliation: If I change a single line in Git (like increasing replicas), ArgoCD detects the "drift" and pulls the changes into the cluster. 🛡️ The "Self-Healing" Test I decided to play "Chaos Engineering": I manually deleted a Pod and a Service using kubectl. The result? In less than 5 seconds, ArgoCD detected the state didn't match Git and recreated everything automatically. The cluster is now "self-healing". It doesn't care what I do manually; it only obeys the Source of Truth: Git. 🛠️ The "WSL2 vs Networking" Battle It wasn't all easy. Running ArgoCD inside a k3d cluster on WSL2 brought some real-world troubleshooting: MTU issues: Network packets were too large for the WSL tunnel, causing timeouts with GitHub. Liveness Probes: In a local environment, ArgoCD's repo-server needed more "patience" (timeouts increased from 1s to 10s) to handle the load. Lesson: In production, networking and resource constraints are your real enemies. If you don't tune your probes and MTU, your "automated" system becomes a "restarting" system. 🧪 What the lab now demonstrates: ✔ GitOps Workflow: Decoupled CI and CD. ✔ Drift Detection: Absolute consistency between Git and Production. ✔ Manual Override Protection: The cluster reverts unauthorized changes. ✔ Infrastructure as Code (IaC): Everything, from the HPA to the ArgoCD app, is defined as code. This isn't just a deployment anymore. It's an Operating Model. 🧭 Next stop: Making the entire cluster reproducible with Terraform. Building production-style systems in public. From "Push" to "Pull". One reconciliation loop at a time. https://lnkd.in/dPdqK99h #DevOps #Kubernetes #GitOps #ArgoCD #CloudEngineering #PlatformEngineering #SRE #BuildingInPublic #WSL2

IaC - Terraform vs Pulumi vs Crossplane They all provision infrastructure, but with different approaches. If you're managing infrastructure today, understanding these three tools is essential. Also, the workflows, abstractions, and control mechanisms vary significantly. Let's break down how each one differs 👇 Terraform → Declarative IaC using HCL (HashiCorp Configuration Language) → Plan → Apply → Manage state workflow → Mature ecosystem with extensive provider support → State management (local or remote) is critical Pulumi → Infrastructure as actual code ~ use TypeScript, Python, Go, C# → Brings software engineering practices to infrastructure → Type safety and compile-time checks built in → Manages stacks with encryption for secrets Crossplane → Kubernetes-native control plane for infrastructure → Treat infrastructure as Custom Resources (CRs) → GitOps-first approach with continuous reconciliation → Multi-cluster and multi-cloud orchestration at the platform level From declarative configs → to real programming languages → to Kubernetes-native control planes IaC is evolving ~ from scripts to code to control plane abstractions. Now, which tool should you pick? It depends on your context. - Most teams start with Terraform for its maturity and ecosystem. - But if you're already deep in Kubernetes, Crossplane makes sense. - If you prefer real code over config, Pulumi shine. Master one first ~ the concepts transfer. _________________________________________ 𝐄𝐧𝐫𝐨𝐥 𝐧𝐨𝐰! 𝐃𝐞𝐯𝐎𝐩𝐬 𝐂𝐨𝐡𝐨𝐫𝐭 𝟒 𝐢𝐬 𝐧𝐨𝐰 𝐨𝐩𝐞𝐧. If you're serious about becoming a world-class DevOps engineer in 2026, this is your path. This isn't another bootcamp. This isn't a tutorial hell with a certificate at the end. This is systems-based training for engineers ready to go from good to exceptional. WHAT YOU'LL BUILD Not toy projects. Not "hello world" apps. Real production-grade systems: → Multi-environment CI/CD pipelines with DevSecOps → Infrastructure as Code that scales across 3+ environments → Production observability with Prometheus, Grafana, and OpenTelemetry Join today 👉 https://lnkd.in/eS3t5NwE

I recently finished a project integrating AWS Amplify, GitHub Projects, and OpenSpec into a solid SDLC for automated builds and releases. The kicker? I kept a Human-in-the-Loop (HITL) requirement for all production deployments. The biggest takeaway? You don’t need a massive, bloated framework. I did this using a bash script, the Anthropic CLI, and some sharp Claude prompting. Key Lessons Learned: Control > Complexity: Control should live on top of agent harnesses, not at the agent level. I’ve found it’s better to manage agents from the outside rather than integrating them deeply at the code level with tools like LangGraph. Be a Manager, Not Just a Coder: Work with the agents. Ask questions. When a task fails, tell it exactly why. Timing is Everything: Don't let your agents get ahead of themselves. For example, don't let the agent create a PR until integration tests have actually passed, not just started running. The Architecture My pattern is very similar to Anthropic’s Managed Agents (https://lnkd.in/g8T5CEZ5) approach, with the main difference being that mine runs entirely on a local environment. If you’re exploring this space, I highly recommend checking out Multica (https://lnkd.in/gQFZRWMp) and gstack (https://lnkd.in/ggztt-Hm) for similar work in multi-agent orchestration. What does this mean for you? You Don’t Need to Be a LangChain/Python Expert: Building multi-agent systems is getting easier. You don’t need to commit to specific programming languages or hard-code complex orchestration anymore. Bash is enough. The Power of Component-Driven AI: Treat agent harnesses as components. You don’t necessarily need to integrate LLMs directly into your code. Instead, use the harnesses themselves as pre-built components, deploying multiple harnesses in the specific domains where they excel. Iterate and Direct: Shift your mindset. Instead of trying to code the perfect flow, talk to your agents and train them. Be opinionated about what you want the agents to do, but leave the implementation details to them. Iterative improvement is better than perfectionism.

The "Python for DevOps" Hook: Stop trying to force your YAML to think. In the DevOps world, we spend 90% of our time in YAML. It’s great for configuration, but the moment you need complex logic, conditional loops, or custom API integrations, YAML starts to feel like a straightjacket. Recently, I noticed our cloud costs creeping up due to "zombie" resources - unattached storage volumes and old snapshots that were no longer linked to any active instances. Instead of manually auditing every region or writing a massive, brittle bash script, I used Python and the Boto3 library. I wrote a script that: >>Scanned all regions for unattached EBS volumes. >>Filtered them by "Age" (older than 30 days). >>Sent a summary report to Slack for approval before triggering a bulk deletion. Why Python is still a DevOps superpower in 2026: -> Bespoke Automation: Handling complex "if/then" logic for resource lifecycle management that standard tools miss. -> Data Processing: Quickly parsing through thousands of lines of cloud metadata. -> Safety Nets: Building in custom dry-run modes and Slack notifications to ensure we don't delete something critical. The Result: We cut our monthly storage waste by nearly 20% and removed the manual overhead of "cloud cleaning" forever. DevOps isn't just about knowing the tools; it's about knowing when to build your own. #DevOps #Python #Automation #AWS #CloudCostOptimization #SRE