Kubernetes, GPU, and AI infrastructure guides paired with practical tools for operators and platform teams.
Turn Kubernetes and AI infrastructure notes into checks you can run today.
Start with the highest-signal Kubernetes, GPU, and AI infrastructure guides.
A concrete walkthrough into Linux stack overflow protections — how canaries, NX, ASLR, PIE, and RELRO actually work, what they stop, what they don't, and what breaks when you enable them.
A detailed look at the system calls behind malloc — brk/sbrk for heap growth, mmap for large allocations and thread arenas, and the internal kernel data structures for process memory.
A walkthrough into how Linux dynamically links shared libraries at runtime — PIC, GOT, PLT, lazy binding, gdb tracing, and the real cost of -fPIC.
A concrete guide to GPU overprovisioning strategies, including scheduler-level oversubscription, time slicing, memory controls, MIG, vGPU, queue backfill, and operational guardrails.
A concrete guide to choosing between serverless GPUs and dedicated GPUs for startups, based on cost structure, delivery speed, performance predictability, operations burden, and team maturity.
A concrete walkthrough into Linux glibc (ptmalloc2) heap memory management — arenas, chunks, bins, tcache, and how each structure becomes an attack surface.
Start with the basics, then continue to operations and troubleshooting topics.
A curated reading track for Kubernetes.
A curated reading track for System.
A curated reading track for GPU.
New notes, guides, and long-form pieces from the main archive.
Ephemeral volumes live with the Pod and fit cache or temp files.
Learn the core Kubernetes volume types, what data survives Pod restarts, and how to choose between temporary and persistent storage.
Understand when to use ConfigMap or Secret in Kubernetes, how they reach Pods, and which practices reduce config drift and secret exposure.
A concrete guide to stateful applications on Kubernetes, covering storage choices, stable identities, rollout concerns, and failure handling.
Learn the essentials of running MySQL on Kubernetes, including StatefulSets, persistent storage, Services, and operational tradeoffs.
Understand declarative configuration in Kubernetes, why desired state matters, and how apply, diff, and reconciliation shape safe operations.
Learn practical canary release patterns in Kubernetes, how to reduce rollout risk, and which signals to watch before promoting traffic.
Learn how Kubernetes Services provide stable networking for Pods, how service types differ, and how to troubleshoot selectors, endpoints, and traffic flow.
Learn how Kubernetes Namespaces organize resources, scope policies and quotas, and support safer multi-team or multi-environment clusters.
Understand what a Pod really is in Kubernetes, how Pods are scheduled and restarted, and which commands help you debug them.
Learn how Deployments and ReplicaSets work together in Kubernetes, how rolling updates happen, and how to debug rollout and selector problems.
Get a working single-node cluster fast with Minikube.
Learn when to use K3s, how to install it quickly, and how to run core Kubernetes workloads on a lightweight cluster for labs and edge environments.
A concrete introduction to Kubernetes: what problems it solves, what it does not solve, and how desired state and controllers work in real clusters.
Understand Kubernetes architecture from the control plane to worker nodes, including the API server, scheduler, controllers, kubelet, and reconciliation loops.
A step-by-step Kubernetes learning path covering core concepts, workloads, networking, storage, and troubleshooting so you can study in the right order.
Learn the four Kubernetes basics that matter most in practice: Pods, Deployments, Services, and Namespaces, plus the declarative workflow behind them.