🚀 Looking to sharpen your GitOps skills with Argo CD?

🎯 Learn what it takes to become an Argo CD Project Champion 👉 https://www.youtube.com/watch?v=LSejrYaYcBI

⚙️ And go deeper with advanced application configuration patterns for more efficient and reliable deployments 👉 https://www.youtube.com/watch?v=aRKrDmqYLCE

#GitOps #ArgoCD #DevOps 🖥✨

Looking to break into Linux System Administration but currently only know how to ls and pray?
Don’t worry — we’ve all been there. 🙃

Here’s a completely free course to get you started: https://training.linuxfoundation.org/training/introduction-to-linux/

💻 60+ hours of content
🧪 Hands-on labs (because we learn by breaking things)
🏅 Completion badge (so you can flex on LinkedIn)
♾️ Lifetime access (for when you forget a command and Google betrays you)
💰 $0 (finally something in tech that doesn’t require a credit card)

Fun fact: Linux runs over 90% of servers and cloud infrastructure.
Translation: If you want to be in DevOps, CyberSec, or a SysAdmin, Linux is like oxygen… you kinda need it.

Also, once you understand Linux, your AWS bill will still be high — but at least you’ll know why. 😅

#DevOps #Linux #SysAdmin #ITCareer #CareerGrowth #SRE

🚀 Kubernetes at Massive Scale – Lessons for Real Production

This experiment shows that Kubernetes can be pushed all the way to 1,000,000 nodes. While it’s not production-ready, the project gives powerful insights:

✅ Think about network design early (IPv6 becomes a must at scale)

✅ etcd writes and API load are the real bottlenecks — optimize them

✅ Sharding + horizontal scaling of control plane components is the key

✅ Every “small overhead” becomes huge at scale — design clean & simple

Even if your cluster is 100 or 1,000 nodes - these patterns help you build reliable, efficient, and future-proof production systems.

🔗 https://bchess.github.io/k8s-1m/

#kubernetes #k8s #production #devops #sre #cloudnative
#scalability #infrastructure #etcd #clusters #platformengineering

#DevOps #kubernetes #humour

#DevOps #linux #humour

🧰 Tech Vault - A great collection of tech interview questions

A clean, open-source repo with real interview questions for DevOps, software engineering, algorithms, networking, AWS, Docker/K8s, and more.

Perfect for interview prep or sharpening your skills.

📎 Link: https://github.com/moabukar/tech-vault

#CareerGrowth #DevOps #learning

🚀 Want to level-up your Kubernetes deployments? This article explains how to build a fully automated GitOps pipeline using GitHub Actions and ArgoCD - from PR-based preview environments to safe and controlled production rollouts. Perfect for improving CI/CD efficiency and release reliability.

Read it here 👉 https://sheraziqbal.medium.com/from-pr-preview-production-with-github-actions-argocd-83ec64e57ec0

#DevOps #GitOps #ArgoCD #GitHubActions #Kubernetes #CICD #CloudNative #Automation

#humour #devops

This video shares a real-life experience of receiving three DevOps job offers in the UAE, including practical strategies for job searching, interview preparation, and insights into the current IT job market.

In this video, you’ll learn:
✅ What DevOps is and what DevOps engineers actually do
✅ Salary ranges and career levels in DevOps
✅ How to start a DevOps career from scratch
✅ Useful tips for applying, interviewing, and standing out to employers

📌 This video is perfect for anyone looking to build a career in DevOps or improve their job-hunting results in tech.

👉 Watch the full video here:
https://www.youtube.com/watch?v=uWHPaAEXRC4

#DevOps #DevOpsEngineer #ITJobs #TechCareer #JobOffers
#CareerTips #CloudEngineering #InterviewTips #TechCareers

📚 New Book Alert: Cilium: Up and Running

Connectivity, security, and observability - all powered by eBPF. If you are working with Kubernetes, Cilium is the platform you need to know.

This O'Reilly guide simplifies the daunting task of learning Cilium. Whether you're a beginner or an experienced architect, you'll find deep insights into how packets move around and how to protect your production workloads.

#Learning #books #Cilium #SRE #K8s

🏗 Scaling GitOps: Argo CD + Kargo for 500+ Microservices

Running GitOps for a few services is easy.
Running GitOps for 500+ microservices across multiple environments is a completely different challenge.

At that scale, the classic "commit → PR → merge → sync" workflow quickly becomes a bottleneck:
- endless PRs
- environment drift
- fragile promotion pipelines
- manual verification steps
To keep delivery fast and reliable, GitOps needs automation, orchestration, and abstraction.

This deep dive into Argo CD and Kargo explains how modern platform teams handle GitOps at scale.
Key Technical Takeaways —
🚦 The Promotion Problem
Standard GitOps struggles with environment promotion (Dev → Staging → Prod).
Kargo introduces promotion pipelines that automate artifact movement between stages while maintaining GitOps integrity.
🧩 Abstraction at Scale
Using ApplicationSets and Generators in Argo CD allows platform teams to manage hundreds of applications from a small set of templates, avoiding massive repo duplication.
🔄 Decoupling Environments
Separating application definitions from environment configuration keeps deployments flexible and prevents the dreaded monolithic GitOps repository.
✅ Automated Verification
Promotion pipelines can run tests, health checks, and validations before advancing deployments—removing manual approvals and reducing production risk.

For teams building platform engineering capabilities or operating high-density Kubernetes environments, this is a solid blueprint for scaling GitOps beyond the basics.

🔗 https://akuity.io/blog/gitops-at-scale-500-microservices-argo-cd-kargo

#GitOps #ArgoCD #Kargo #Kubernetes #CloudNative #PlatformEngineering #DevOps #ContinuousDelivery

🚀 If you're working in Kubernetes, Platform Engineering, DevOps, or Cloud Infrastructure, understanding controllers is becoming one of the most valuable skills you can have.

The controller pattern is the foundation behind Kubernetes, Crossplane, Argo CD, Karpenter, Operators, and many of the platforms shaping the future of infrastructure automation.

As AI and vibe coding continue to evolve, generating code, manifests, Terraform, Helm charts, and automation workflows is becoming easier than ever. But understanding why systems behave the way they do, how reconciliation works, how desired state converges with actual state, and how large-scale platforms operate remains a fundamentally human skill.

The future belongs to engineers who understand the underlying abstractions, not just the tools built on top of them.

If you want to remain relevant in the age of AI-assisted engineering, learning how controllers work is a great investment.

This course is an excellent introduction to one of the most important concepts in modern cloud-native architecture.

🎥 https://www.youtube.com/watch?v=odP153inZUo

#Kubernetes #PlatformEngineering #DevOps #CloudNative #CloudComputing #GitOps #Crossplane #Karpenter #InfrastructureAsCode #SRE #SoftwareEngineering #AI #VibeCoding #PlatformOps #EngineeringLeadership #DistributedSystems #Containers #OpenSource

🤖 5-Day AI Agents Intensive - Free Course by Google & Kaggle
One of the most popular AI courses ever made. Over 1.5 million developers joined the live run. Now available as a self-paced guide - free, no deadlines.
What's inside:
📌 Day 1 - Intro to Agents: how agentic architectures differ from classic LLM apps
🔧 Day 2 - Tools & MCP: how agents act — APIs, functions, Model Context Protocol
🧠 Day 3 - Context Engineering: short & long-term memory, multi-turn tasks
📊 Day 4 - Agent Quality: logs, metrics, reliability, security & AgentOps
🚀 Day 5 - Prototype → Production: deployment, scaling, multi-agent systems
Stack: Gemini + Agent Development Kit (ADK) + hands-on codelabs in Kaggle Notebooks
🆕 Bonus: A new edition — AI Agents Vibe Coding — runs June 15–19, 2026. Build agents using natural language. Registration is open now.
👉 kaggle.com/learn-guide/5-day-agents
#AI #agents #LLM #Google #Kaggle #free #machinelearning

⚙️ formae is live.
An agentic, 100% code-based Infrastructure-as-Code tool, built from scratch for the modern age. The whole idea: code is the one medium every engineer understands - so formae keeps your infrastructure entirely as code, in and out, at any granularity.

What you get:
🔹 Code is the single source of truth
- every resource and every change, fully versioned. No state files to babysit, lock, or corrupt.
🔹 Always in sync with reality - it detects changes made outside the tool and merges them back into your code instead of ignoring or reverting them. The 3 a.m. on-call fix is never lost.
🔹 Prevents avoidable mistakes - built around a strict, enforced schema. Bad config fails before it ships.
🔹 Built for co-existence - no migration, no imports. It auto-discovers existing resources and works happily alongside Terraform, other IaC, and even ClickOps.
🔹 Perfect for Day 0 and beyond - great for standing up new systems and for small, safe changes with minimal blast radius.

Why it's built for Platform Engineering:
🏗️ Core platform engineers work at the low level of detail and own system-wide changes. Developers consume reusable, schema-safe services by setting just a few predefined properties - big power for the few, small blast radius for the many. Security and cost teams ship targeted patches across the system without touching the core.

Ops, DevOps, SRE, Platform - whatever your level, formae meets you there.
⭐ Star the repo → github.com/platform-engineering-labs/formae
📚 Docs → docs.formae.io
💬 Discord → discord.gg/hr6dHaW76k

Open source AI SRE projects:

3 AM. Checkout down. 17 services alerting. Dashboards open. Logs dig for 20 mins. Pods restarting, fragmented logs. Metrics mismatch found in dependent service; Kafka consumers shifted offsets. Fix: 5-10 mins with runbook. Investigation: 4x longer.

AI SRE agents target this gap. Not chatbots explaining CrashLoopBackOff. Agents pull logs, query Prometheus, check deploys, verify hypotheses, and drop RCAs in Slack with evidence.

Why now? LLMs excel at tool calling; MCP standardized integrations.

6 Open Source Projects:
1. OpenSRE (Python, Apache 2.0, ~8k ⭐️): Investigation agent, 60+ integrations, synthetic incident benchmark. "SWE-bench for incident response."
2. HolmesGPT (Python, CNCF Sandbox, ~3k ⭐️): Production-ready. Reads Alertmanager/PagerDuty alerts (read-only, RBAC), follows YOUR runbooks.
3. K8sGPT (Go, CNCF Sandbox, ~7.8k ⭐️): Kubernetes-only, single binary. Masks object names before LLM.
4. Kagent (Go, CNCF Sandbox, ~3k ⭐️): Framework. Agents/tools as CRDs. Knowledge lives in Git next to ArgoCD.
5. OpenObserve (Rust, ~19.7k ⭐️): Logs+metrics+traces, Parquet on S3. O2 SRE Agent for RCA. Platform open source; Agent Enterprise.
6. Keep (Python/TS, ~12k ⭐️): Enriches 50 raw alerts into one incident before agent starts. Underrated pipeline part.

Key takeaway: Extensible. Internal tool? Write MCP server, agent picks it up. Not a black box; a framework. Runbooks and senior engineer knowledge become agent inputs.

#DevOps #ai #AIOps