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

🚨 Thrilled to share that Anthropic has officially launched its inaugural suite of four professional certifications-real, proctored, role-based credentials designed to empower our partner ecosystem! 🚀

Exams go live July 13, marking an exciting new chapter for professional development within the AI space.

This 10-page roadmap transforms uncertainty into clarity, guiding you from "which path aligns with my goals?" to "I’m fully prepared to excel."

What’s inside:
→ A complete strategic roadmap: all four certifications, pricing, exam codes, and a decision matrix to align with your career track
→ Domain-weight breakdowns for every exam, ensuring you focus your energy where it matters most
→ A tailored 4-week study plan for each role
→ Consolidated concept notes: mastering the patterns and judgment calls that drive exam success

The four tracks available:
• Associate ($99) for consultants and sellers
• Developer ($125) for engineers working with the API, Claude Code, and MCP
• Architect Foundations ($125) for solution architects
• Architect Professional ($175) for senior architects driving production success

Here’s the truth: being among the first wave of certified professionals is a powerful way to get noticed. You want to be part of this movement.

#Claude #Anthropic #AICertification #AIEngineering #ClaudeAI #GenAI #ArtificialIntelligence #TechCertification #AIDevelopers #SolutionArchitecture

Pay for GPUs, not tokens 👾 10 open source ways to run your own AI platform 🦀

Tokens became a metered utility. Prices are set by a handful of providers, and one pricing change can flip a product's unit economics overnight. Self-hosting is the obvious hedge, and open engines (vLLM, SGLang) are already fast enough.

The gap is the layer above the engine - what turns a model into a scalable endpoint with routing, caching and GPU scheduling. Two tiers here: a focused inference control plane, or a full platform where serving is one workload among many.

TIER 1 ⚙️ - inference control planes. One job: model in, endpoint out.
1️⃣ KServe (~5.7k ⭐️, CNCF) - the standard. One InferenceService CRD for classic ML + LLMs: scale-to-zero, canary, model caching. Bloomberg and IBM run it; Red Hat OpenShift AI is built on top of it.
2️⃣ AIBrix (~5k ⭐️, vLLM project) - pluggable parts, not a monolith: LLM-aware gateway, distributed KV cache, LoRA management. Born at ByteDance, battle-tested on their inference fleet.
3️⃣ llm-d (~3.8k ⭐️) - disaggregated serving, KV-cache-aware routing on Gateway API. Founded by Red Hat, Google, IBM, CoreWeave and NVIDIA; powers the inference layer of Red Hat AI.
4️⃣ vLLM production-stack (~2.5k ⭐️) - the paved road for cluster-wide vLLM: router, Helm, observability, LMCache under the hood.
5️⃣ KubeAI (~1.2k ⭐️) - easiest start: OpenAI API, scale-from-zero, no Istio/Knative needed. Telescope runs multi-region batch inference on it. Caveat: seeking new maintainers.
6️⃣ Kaito (~1k ⭐️, Microsoft) - Workspace CRD provisions GPU nodes itself, ships model presets, covers fine-tuning. Available as a managed AKS add-on.

TIER 2 🔩 - full platforms: build entire AI services.
7️⃣ Ray + Ray Serve (~43k ⭐️) - an AI compute engine: serving, batch, data and training share one runtime; KubeRay puts it on K8s. ChatGPT was trained on Ray; Uber, Spotify and Pinterest run it too.
8️⃣ Kubeflow (~15.8k ⭐️, CNCF) - the whole lifecycle: pipelines, notebooks, training, tuning, with KServe as the serving layer. Born at Google; Spotify and CERN built ML platforms on it.
9️⃣ OpenLLM (~12.4k ⭐️, BentoML) - one command turns any open LLM into an OpenAI-compatible API. DX-first: fast for app teams, a clean artifact for platform.
🔟 NVIDIA Dynamo (~7.5k ⭐️) - datacenter-scale distributed inference: disaggregated prefill/decode, KV cache manager. Adopted by Perplexity, Cursor, Pinterest and PayPal.

Rule of thumb: need an endpoint - tier 1. Building an ML/AI org - tier 2. Mixing is normal: Kubeflow ships KServe, Ray runs on K8s.

On tier 2, Ray Serve composes RAG and multi-model pipelines, BentoML packs a model + business logic into one deployable. For AI agents this tier is the substrate, not the brain - agents call these endpoints, while orchestration (LangGraph, kagent) stays a separate layer.

#PlatformEngineering #DevOps #Kubernetes #LLM #MLOps #AI

Sharpening your SRE instincts through self-learning 🧠

SRE isn't something you learn from a textbook. The fastest way to grow is reading how real companies keep production alive under load - how they build observability, break down incidents, make architecture calls, and where they get burned. 🔥

Here are the sources I read regularly. Not motivation - actual engineering practice you can learn from every day 👇
Engineering blogs 📡
• Netflix Tech Blog → https://netflixtechblog.com
• Cloudflare Blog → https://blog.cloudflare.com
• Stripe Engineering → https://stripe.com/blog/engineering
• Shopify Engineering → https://shopify.engineering
• Uber Engineering → https://www.uber.com/blog/engineering/
• Honeycomb Blog → https://www.honeycomb.io/blog
Foundations 📚
• Google SRE Books (free, fully online) → https://sre.google/books/
Learning from other people's failures 💥
• Cloudflare Status History (real incident timeline) → https://www.cloudflarestatus.com/history
• Dan Luu - curated collection of public postmortems → https://github.com/danluu/post-mortems

#SRE #DevOps #Observability #IncidentResponse #SelfLearning