I asked my AI agent to audit my Kubernetes namespace security using this prompt: Analyze the security posture of my namespace. Identify risks, explain them, and suggest fixes. It found: - No NetworkPolicies - Public API server open to 0.0.0.0/0 - Pods using nginx:latest - Default service account - No IAM roles (IRSA) And gave me the exact fixes. Watch the video 👇👇👇👇

DevOps Job Switch Reality In 2026: Clearing all the technical rounds for a DevOps role with 3 years of experience is incredibly hard. Honestly, there’s a 90% chance you won’t make it because if you mess up even one question, you're often out of the process. I’ve reached the final rounds multiple times and have even been rejected in CEO rounds. The entire process is exhausting. The funny part? The actual job is often much easier than the interview itself. If I prepare Kubernetes thoroughly, they ask me to write Terraform code to provision an EKS cluster with a custom VPC. If I prepare Kubernetes and Terraform, they start digging deep into AWS networking. If I prepare all of that, they move on to Linux troubleshooting, Docker internals, monitoring, or CI/CD questions. At this point, you’re expected to be an expert in: - AWS - Terraform - Kubernetes - Docker - Linux - ELK - Monitoring & Observability - Jenkins - GitHub Actions - ArgoCD - Production Troubleshooting Dear hiring teams, for a ₹10 LPA DevOps role with 3 years of experience, you are not hiring a DevOps Engineer you are trying to hire an entire IT team in one person. One interview process was especially frustrating. I cleared the assessment round, a technical round, and then an in-person practical round. The task? In just 1.5 hours, I had to: - Set up an EKS cluster with a custom VPC - Create public and private subnets - Configure an Ingress Controller - Build a GitHub Actions CI/CD pipeline - Build and push a Docker image - Deploy and expose the application All of this had to be done using my own AWS account and my own laptop while sharing my screen the entire time. After that, they would decide whether I was good enough for the role. I’m not sure who designed these interview processes, but they often feel far more difficult than the actual job itself.

Answer: B. kubelet The kubelet watches the Pods running on the node and checks their container status. If a container crashes, the kubelet detects it and restarts it based on the Pod restartPolicy.

Day 4/30 Kubernetes Learning Series What is a Pod?

If you work with Azure networking, read this carefully.👇👇

Day 3/30 Kubernetes Learning Series Kubernetes Architecture👇👇👇

Answer: C Kubernetes can run many types of applications. But the application must be packaged as a container image first. Kubernetes deploys only containers.

A Pod is running on a Kubernetes node. Suddenly, the container inside the Pod crashes. Which component detects the failure?

If you cannot manage one container properly, Kubernetes will make the confusion bigger.👇

Answer: B Kubernetes does not replace Docker. Docker is used to build and run containers. Kubernetes is used to manage containers at scale.

Day 2/30 Kubernetes Learning Series Why Kubernetes?

Day 2/30 Kubernetes Interview Question Can we deploy any type of application on Kubernetes?

Don’t miss this serie👇👇 Observability from Zero to Hero

Day 1/30 Kubernetes Interview Question Can Kubernetes replace Docker?

Day 1/30 Kubernetes Learning Series What is Kubernetes? Kubernetes is a platform used to run and manage containers. When you have one container, Docker is enough. But when you have many containers running in production, you need something to manage them. Kubernetes helps you: - start containers - restart failed containers - scale applications - update applications with less downtime - manage networking between services

Hey guys, I was off for one week but I am back I am starting two Kubernetes series for beginners. 1⃣Series 1 30 Days of Kubernetes Learning Every day, I will explain one Kubernetes concept in simple English. 2⃣Series 2 30 Days of Kubernetes Interview Questions Every day, I will post one simple interview question. The answer will come 24 hours later with a short explanation. Day 1 starts tomorrow. Stay tuned

How do you declare your Terraform variables? There is the traditional way we see in every tutorial: - One variable for instance_type - One variable for ami_id - One variable for key_name - etc. It works fine. But once you join real-world projects, you start seeing another approach: - Group related variables into one object. It is cleaner and more professional. - It keeps related values together. - It makes the module easier to read. - It scales better when your infrastructure grows.

Terraform users: Learn the object data type. Here is why: - It keeps related values together - It makes your variables file cleaner - It avoids too many separate variables - It makes your modules easier to reuse - It scales better when your infrastructure grows

Most engineers miss jobs because they don’t prepare enough for interviews. They spend months building, fixing, and solving real problems. But during the interview, they struggle to explain what they did. It’s sad when you realize you failed an interview because you could not explain what you did.

Why the upcoming IPOs of OpenAI & Anthropic will be the final nail in the coffin for the global markets. Everyone wants a piece of the next OpenAI, SpaceX, or Anthropic. The internet is full of people claiming that if you can somehow get access to these private companies before an IPO, you'll become a millionaire. Maybe they're right. But before putting money into any opportunity, it's worth understanding something most people aren't discussing: AI companies don't necessarily have the same economics as the software companies that dominated the last two decades. Traditional software is an incredible business. Once you build the product, the cost of serving the next customer is almost zero. Whether you have 1,000 users or 1,000,000 users, the infrastructure costs don't grow at the same pace as revenue. That's why software companies often command premium valuations and generate extraordinary profit margins. AI changes that equation. Every new user consumes compute. Every prompt requires GPUs. Every improvement in model capability requires more training, more infrastructure, more power, and more cooling. Unlike traditional SaaS products, the cost of serving customers doesn't disappear. In many cases, success itself creates additional infrastructure requirements. This is why I think many retail investors are looking at AI companies through the wrong lens. They see software and assume software economics. In reality, frontier AI companies increasingly resemble a combination of software business, cloud provider, infrastructure company, and energy consumer. That distinction matters because valuation ultimately comes down to cash flows and profitability. A company can grow revenue at an incredible pace and still struggle to justify an extreme valuation if the cost of sustaining that growth continues to rise alongside it. History gives us plenty of examples. The internet changed the world, but thousands of internet companies disappeared. Cloud computing transformed enterprise software, but not every cloud company became Amazon. Revolutionary technology and successful investments are related, but they are not the same thing. The biggest risk today isn't that AI fails. AI is already proving useful across industries. The bigger risk is that investors convince themselves that every company associated with AI deserves a trillion-dollar valuation. When expectations become disconnected from business fundamentals, markets eventually correct that mismatch. As engineers, we're trained to think about constraints. We ask where systems break, what resources are limited, and what assumptions may not hold at scale. I think investors should approach AI with the same mindset. Before investing, ask a few simple questions: • How much revenue does the company generate today? • How much does it cost to serve that revenue? • Does each new customer improve profitability or increase operating costs? • How much additional capital is required to sustain growth? The answers to those questions matter far more than headlines, hype, or promises about the future. I've learned one lesson from both technology and money: great technology doesn't automatically create great investments. Sometimes the technology wins and the investors lose. Knowing the difference is where the real opportunity lies.