chatgpt-cli: A Simple ChatGPT CLI That Stays Out of Your Way I recently built chatgpt-cli, a minimal command-line interface for interacting with ChatGPT. 👉 Project link: github.com/umbertocicciaa/chatgpt-cli The motivation is straightforward: most existing ChatGPT CLI tools are far more complex than they need to be. The Problem with Most ChatGPT CLIs Search for a ChatGPT CLI today, and you’ll typically find tools that: Require multiple files, folders, and configuration steps Depend on several external libraries Try to do everything instead of doing one thing well Demand more setup time than actual usage time They’re powerful, sure — but often overkill. When all you want is to quickly ask ChatGPT something from the terminal, that complexity becomes friction. ...

🌍 An Inspiring and Hands-On Adventure for SysOps, DevOps, MLOps Enthusiasts This weekend I joined the MLSysOps Hackathon — an intense and rewarding experience for anyone passionate about SysOps, DevOps and MLOps. It was a powerful reminder of how collaboration and teamwork accelerate innovation: working side by side with talented people from all over the world at the Università della Calabria made the journey both exciting and deeply enriching. 📚 A Valuable Step for AI & ML Growth Beyond the challenge itself, this hackathon was an incredible opportunity to deepen my understanding of AI in production — from model lifecycle management to infrastructure orchestration. Experiences like this are essential for anyone who wants to master MLOps and build real-world, production-ready AI systems. ...

I’ve just passed the CKA. 🎉 Here’s exactly how I prepared, what I practiced, how I set up my terminal, and the mindset I used on exam day. No spoilers, no NDA violations—just the process that worked for me. TL;DR Learn by doing: daily labs > passive videos. Automate muscle memory: aliases, completion, templates. Use kubectl explain + --dry-run=client -o yaml to avoid YAML typos. Be ruthless with time: solve, verify, move on, return later. My 4-Week Study Plan Week 1 — Core Workloads & Kubectl Pods, Deployments, DaemonSets, Jobs/CronJobs Services (ClusterIP/NodePort/LoadBalancer), Probes, ConfigMaps, Secrets Hands-on: create → tweak → roll back → scale Week 2 — Cluster Admin Basics Control plane components, kubelet & static Pods, kubeadm basics Node ops: cordon/drain/uncordon, taints/tolerations Backups (etcd snapshot/restore concepts), upgrades flow Week 3 — Networking & Storage CNI basics, NetworkPolicies, Ingress StorageClasses, PV/PVC, CSI, access modes, reclaim policies Week 4 — Security & Troubleshooting RBAC (Roles, ClusterRoles, Bindings), ServiceAccounts Scheduling (affinity/anti-affinity, topology spread), resources/limits Troubleshooting: events/logs, image pulls, CrashLoopBackOff, DNS Daily rhythm: 60–90 min labbing + 15–30 min notes + quick review of mistakes. ...

🚀 Why I Started This Project You know that feeling when you’ve just unboxed a couple of Raspberry Pi boards and your mind races with all the possibilities? That was me. Two Raspberry Pi 4s, a mini rack, and a network switch were staring at me, and the idea hit: “Let’s build a K3s cluster, and on top of it run Jenkins, Grafana, and Prometheus.” Not for production — but for learning, for fun, and for the sheer thrill of watching tiny computers act like a mini data center. ...

Introduction After some tinkering, I decided to build an homebalb with 2-node Raspberry Pi 4 cluster running K3s — one master and one worker — to learn better Kubernetes and host my own services. In this post, I’ll walk you through the process: from hardware setup to K3s installation and homepage deployment with ingress access. Hardware Setup Here’s the base hardware I used: 2× Raspberry Pi 4 (4GB RAM each) Mini rack with dual fans for cooling Gigabit network switch Ethernet cables Personal PC (used as a proxy to manage the cluster) Raspberry Pi OS installed on both Pis (SSH enabled) The Raspberry Pis are connected to the switch, and my DHCP server handles: ...