Kaptain

Hey there,

Starting today, in every issue, you’ll now find a short career insight built for engineers who want to grow with intention.

These notes blend long-term strategy with specific technical habits, focusing on leverage, timing, and smart positioning. The tone is direct, the kind of guidance a senior engineer would share when they genuinely want someone to move up.

Here is our methodology, or at least what we try to achieve:

We build each note by pulling ideas from well-known books on personal development, career growth, and long-term decision making. Then we translate those ideas into the technical world: the world of systems, codebases, architectures, and real engineering constraints.

The goal, as you can guess, is to turn broad principles into something a developer can actually use in their daily work.

You can find this new section "Growth Notes" at the end of each issue!

We hope you like it! You feedback is welcome!

From a 130,000‑node GKE monster to a 25‑pound, battery‑fed AI rig, this batch swings between planetary scale and pocket power. Sidecarless mTLS, a kubectl sanity guide, hard‑won prod lessons, and a Bash trick that cuts K8s triage by 80% - dive into the details inside.

🔄 CI/CD and Gitops with Microservices: Open Ecosystem vs AWS Native

🐳 Docker Workbook - Your Guide to Containerization

🚀 Google Breaks Kubernetes Limits Again: Inside the 130,000-Node GKE Cluster

⚡ How I Cut Kubernetes Debugging Time by 80% With One Bash Script

🐧 How Kubernetes Became the New Linux

🧭 The guide to kubectl I never had.

🎒 The Most Absurd (and Brilliant) Kubernetes Cluster at KubeCon 2025

🧠 Top 5 hard-earned lessons from the experts on managing Kubernetes

⚙️ Valkey 9.0 Released: Faster Clusters, New TTL Controls, and Big Networking Gains

🔐 Zero Trust with Cilium : Enforcing mTLS in Kubernetes

Less folklore, more leverage—onward.

Have a great week!
FAUN.dev() Team

Valkey 9.0 drops some serious upgrades: atomic slot migrations, hash field expiration, and numbered databases in cluster mode. That trio clears old roadblocks around scale and consistency.

Performance gets a kick, too. Pipeline memory prefetching drives throughput up by 40%. Add zero-copy responses and Multipath TCP, and latency doesn’t stand a chance.

System shift: Valkey’s not just riding Redis’s coattails anymore. It’s building out a faster, leaner in-memory system on its own terms.

AWS just handed over Karpenter and Kubernetes Resource Orchestrator (Kro) to Kubernetes SIGs. Big move. It's less about AWS-first, more about playing nice across the ecosystem.

Kro auto-spins CRDs and microcontrollers for resource orchestration. Karpenter handles just-in-time node provisioning - leaner, faster, cheaper - with a clean API.

Google Cloud just dropped a 130,000-node GKE cluster - twice the old limit - built to wrangle massive AI workloads.

New tricks under the hood: a snapshottable, consistent API server cache, Kueue for job-first orchestration, and MultiKueue to run distributed training across clusters like a pro. The whole setup leans on a Spanner-backed KV store, RDMA for low-latency networking, and serious I/O from GCS FUSE and Cloud Lustre.

At KubeCon 2025, Justin Garrison rolled in with a 25-pound, battery-fed Kubernetes AI cluster running fully offline. Inside: a NVIDIA DGX Spark, a LattePanda control plane, and zero reliance on the cloud.

It pulled off real-time image inference using ComfyUI, held together by a tiny router and a USB-tethered phone for backup networking.

Cilium ditches sidecar proxies and runs mutual TLS (mTLS) right in the K8s data plane using eBPF. Fewer hops. Less mess.

It enforces identity-based policies at the source, handles dynamic cert rotation with SPIFFE and cert-manager, and pipes in real-time visibility through Hubble - even for encrypted traffic.

What matters: No more juggling sidecars. Cilium flips service mesh on its head with a sleek, identity-first approach built into the cluster itself.

A 16-year-old full-stack web developer exploring AWS and DevOps shares insights on CI/CD and GitOps for deploying microservices. Automation tools like GitHub Actions and Argo CD streamline testing, building, and deployment processes, enhancing developer productivity. Utilizing an open ecosystem approach with GitHub Actions and Argo CD or an AWS native approach with CodeBuild and CodePipeline can automate deployment workflows effectively, making deployments faster and more manageable.

Glasskube dropped a thorough guide to kubectl - the commands, the flags (--dry-run, etc.), how to chain stuff together, and how to keep your config sane. Bonus: a solid roundup of kubectl plugins. Think observability (like K9s), policy checks, audit trails, and Glasskube’s take on declarative package management.

The reality of Kubernetes troubleshooting: 80% of the time is spent locating the issue, while only 20% is used for the fix. Managing eight Kubernetes clusters highlighted this pattern. A tool was developed to provide a complete cluster health report in under a minute, streamlining the process and saving valuable time.

Running Kubernetes in production isn’t just clicking “Create Cluster.” It means locking down RBAC, tightening up network policy, tracking autoscaling metrics, and making sure your images don’t ship with surprises.

Managed clusters help get you started. But real workloads need more: hardened configs, sharp awareness of the ecosystem, and the grit to keep upgrading - unless you like living one CVE from disaster.

System shift: Platform engineering and policy-as-code aren’t buzzwords - they’re how teams go from wrestling YAML to building real, governed platforms on top of Kubernetes.

The series dives deep into real-world Kubernetes - starting with hands-on setup via Kubeadm and eksctl, then moving through monitoring, logging, CI/CD, and MLOps. It tracks key release changes up to v1.30, including the confirmed death of Dockershim since v1.24.

This guide cuts through modern Docker workflows. It covers BuildKit for faster, smarter builds. Shows how multi-stage Dockerfiles make images slimmer. Breaks down how ENTRYPOINT and CMD actually work. Walks through using supervisord to wrangle multi-process containers. Then zooms out to Docker Compose, where local dev setups get declarative and less brittle.

AKS desktop delivers an application focused experience for deploying and managing workloads on Azure Kubernetes Service.

Kubernetes-Native Database-Driven Automation

Lightweight health check utilities for Docker containers

Learn docker in your terminal, with bite sized exercises

Did you know that in Kubernetes the NodeLocal DNSCache sets iptables rules with NOTRACK so DNS over UDP port 53 bypasses the kernel’s conntrack table entirely? By routing queries to a per-node CoreDNS cache and skipping connection tracking, it prevents DNS resolution failures caused by conntrack table exhaustion under heavy load. Even negative responses are cached, keeping DNS reliable when pods send high QPS.

"Containers promise isolation; Kubernetes schedules them to share a kernel and a network. Desired state is declarative; behavior under partition is not. Portability ends where your CRDs, sidecars, and CNI begin."
— SenseiOne

Treat your career like a multi-cluster platform

Without observability, redundancy, and routine upgrades, entropy wins. Set personal SLOs with real telemetry like lead time from idea to merged PR, mean time to unblock when a cluster or tool breaks, and quarterly API deprecation burn-down; do postmortems on your own incidents and feed the findings into a living runbook. The hidden bottleneck most Kubernetes engineers miss is upgradeability inertia, where unpracticed version jumps and silent API drift turn into weeks of emergency toil.

The concrete habit

Schedule a monthly upgrade drill in a local kind or k3d lab driven by GitOps; rebuild from scratch, bump Kubernetes and key controllers one minor version, run kube-score and pluto or kubent to catch deprecations, execute a smoke test suite, and time your MTTR when something breaks. Track these metrics over time and refactor your tools and docs after each drill so the next run is faster. This creates a tight feedback loop, builds redundancy in your skills, and compounds into resilience when the real cluster migration hits. Design for long-term stability now so scaling your responsibilities later is a simple horizontal pod autoscale, not a frantic rewrite.

This week, we’re highlighting Kathryn V., a scientist, former USPTO Examiner, founder of Vatt IP Management, and mother of seven with 25+ years turning lab insight into defensible IP. She’s examined thousands of applications at the USPTO, ranked among the Top 30 Patent Drafters and Prosecutors and a #1 LinkedIn Global Influencer in Innovation/IP Law, and now builds tools like My Startup Shield™ for pre‑pitch IP readiness and My AI Examiner™ for examiner‑style prior‑art search, while mentoring teams at ASU’s Edson E+I Institute and earning a J.D. in Patent Law at Franklin Pierce.