This is a praise for the paid community because they 100% deserve it. @Mischa van den Burg , @Sammy van den Burg and everyone else involved in building the community should really get the recognition. I am writing this post after getting my first DevOps position. It included a long journey of over a year to facilitate this transition from end-user support/system administration. I could not have done it without joining KubeCraft last October. I was advancing pretty well before but joining KubeCraft was a game-changer, it has paved me the way to compete for positions and being relevant in the DevOps/cloud job market. It is a safe haven, the ultimate place for self growth. In so many aspects, not only in relation to DevOps. I will also highlight the respectful discussions, technical insights, career and professional tips, inclusion, and acceptance. Non of what I have mentioned is taken for granted in our current Internet landscape. I would also like to mention all the wonderful courses in the classroom that improved my soft and hard skills alike and were/are still instrumental in my journey: - The Kubernetes related courses (fundamentals, homelab, and advanced) - get your hands dirty and start building, breaking, and fixing systems over and over again - The Arch Linux course - strong Linux foundations are non negotiable and digging into an Arch installation and transforming it into your daily driver is so beneficial - The devcontainers, Neovim, and config management course - think encapsulation, portability, and allowing collaboration between teams - The DevOps masterclass course - a real gem by @Mischa van den Burg . It helped me understand better modern release management practices and CI/CD pipelines - The interview preparation course - very well constructed. This helped me change my perception and become better during interviews Always remember that hard work pays off! (in the photo - an Arnie and John (Savill) super combo for some inspiration)

GPT-5 launched this week with the usual fanfare and “revolutionary breakthrough” claims. ​ The reality? “Overdue, overhyped and underwhelming” – that’s how AI researcher Gary Marcus described it just hours after launch. ​ THE BRUTAL X REALITY CHECK Within hours of the GPT-5 livestream, prediction markets told the real story. OpenAI’s chances of having the best AI model dropped from 75% to 14% in one hour. Users on X flooded the platform with harsh reactions. They called it a “huge letdown,” “horrible,” and “underwhelming.” GPT-5 even gave wrong answers when asked to count letters in “blueberry.” ​ WHY AI AGENTS WILL NOT REPLACE YOUR DEVOPS WORK, FOR NOW Gary Marcus predicted exactly what we’re seeing. His analysis shows why AI agents pose zero threat to DevOps professionals. The key issue is that current AI systems work by copying patterns, not real understanding. Marcus calls this “mimicry vs. deep understanding.” AI can copy the words people use to complete tasks. But it has “no concept of what it means to delete a database.” This matters for DevOps work. When you debug a networking issue between services, you don’t just run commands. You form ideas about how systems behave under load. ​ An AI might know kubectl get pods syntax. But it doesn’t understand why pod networking fails. It doesn’t grasp what this means for other services in your environment. ​ Marcus notes that complex tasks involve multiple steps. DevOps work has many steps: deploy, monitor, check results, maybe rollback. And this is why AI agents are not going to replace us anytime soon. Large Language Models (LLM's) are relatively simple input-output systems. They are useful, but the problem is that their output is unreliable. So far it is nearly impossible to make an LLM reliably give the same output in the same format, especially when the input can vary wildly. Since DevOps work at its core always has complex tasks with multiple steps, one mistake in the chain could cause a system-wide outage.

You're failing because you're doing the same thing that everybody else does. Even though you have 2-8 years of experience, you're still just a junior in the CV pile. What does everybody else do? 1. Watch random free video tutorials without a plan 2. Learn two cloud providers at once 3. Going straight for containers & Kubernetes 4. Consuming instead of building. I've helped hundreds of people land DevOps jobs within months. I went from nurse to Senior DevOps in 4 years and created a system to repeat these results in others. If you're serious about landing a DevOps job, >> CLICK HERE << to book a call with my team to see if you qualify for my mentorship program. Only a few spots left for this week, so get it while there are still slots available. Here's what you should do: First, be intentional. Create a clear plan for yourself and study accordingly. Having a clear plan starts with having a clear goal. The main tech stack you should focus on: - Linux - Containers - Kubernetes - Automation (CI/CD) Stick to one cloud provider. Research the market you wish to work in. Find which one has the most jobs. Master that one. Everything becomes easier when you master the fundamentals. For DevOps, Linux is the root of everything. If you don't understand Linux on a deep level, everything else becomes extremely difficult. Don't skip the foundations if you want to build a strong building. Instead of consuming video tutorials, you should start building. Start with a desired outcome. Identify what you need to learn for that outcome. Learn ONLY what you need to achieve that outcome. Create a new desired outcome. This is how you should learn. Literally all of my students land their jobs because I teach them to build a Kubernetes homelab. The interviewing process changes from a technical interrogation to a conversation about what you've built. If you take away only one thing from this post, it should be this: Build a home lab.

Apologies for yet another post! I got thinking lately about my setup and wondered what is most popular here (and what I may be missing/gaining) and why you like it over others. So please do share your setups! Personally, I started with a hypervisor to mess around with VMs, but quickly transitioned to bare-metal Talos. I’m currently modifying it, but it got me wondering about doing a second setup thats more inline with enterprise-standard setups since I don’t yet have real experience with RHEL or Ubuntu servers, nor the commonly used kubernetes distros.

Hey everyone 👋 I’m a 2nd-year CS student. I know intermediate ML, basic frontend (HTML, CSS, JS), and I’m pretty solid in backend (Node + Express). I’m aiming for a career in DevOps — do I really need to go deep into full-stack development, or is my current knowledge enough? Should I keep learning full stack or just focus fully on DevOps? Would love your thoughts 🙏