Blog — Page 22 of 53
Browsing page 22 of 53 — 634 articles and counting.
Edge-Side Includes in Modern Web Architecture: Dynamic Fragment Composition, Cache Segmentation, and Personalization at the CDN Layer
ESI was invented in 2001 and mostly forgotten. Edge computing brought it back in a different form. Here is how fragment composition at the CDN layer works today, when it beats full-page caching, and how to implement personalization without killing your cache hit rate.
Building an AI-Powered Workflow Automation Engine: Task Classification, Decision Routing, and Human-in-the-Loop Orchestration
78% of enterprises have agentic AI pilots. 14% run them in production. This guide covers what the gap actually is: task classification with confidence thresholds, rule-based vs ML routing, human escalation patterns, audit trails, and monitoring automation accuracy in real systems.
LLM Observability in Production: Tracing Chains, Measuring Latency, and Debugging Agent Failures
How to instrument LLM-powered applications for production: tracing multi-step chains and agent loops, measuring token usage and latency per step, detecting prompt regressions, structured logging, cost attribution, and debugging agent failure modes.
Time Series Forecasting in Production: Feature Engineering, Model Selection, and Deployment Patterns for Real-Time Predictions
A practical guide to building time series forecasting systems that hold up in production: feature engineering, model selection tradeoffs, walk-forward validation, and serving patterns for real-time predictions.
Kubernetes Security Hardening in Production: Pod Security Standards, RBAC, and Runtime Threat Detection
Most Kubernetes clusters run with default security settings that are far too permissive. This guide covers practical hardening: Pod Security Standards, RBAC design for multi-team clusters, network policies, secrets encryption, image admission control with Kyverno, and runtime threat detection with Falco.
Kubernetes Operators in Practice: Custom Controllers, Reconciliation Loops, and CRD Design Patterns
A deep dive into the Kubernetes operator pattern: how reconciliation loops work, how to design CRDs that age well, how to handle finalizers and leader election, and the production pitfalls that break operators under load.
Migrating from Docker Compose to Kubernetes: Service Translation, Persistent Volumes, and Incremental Adoption Strategies
A practical guide for teams outgrowing Docker Compose: translating Compose services to Kubernetes manifests, handling persistent storage, navigating networking differences, and running an incremental migration without a big-bang cutover.
Running a Production Readiness Review: Checklists, Architecture Audits, and Launch Gates for Engineering Teams
A structured process for answering 'is this actually ready?' before a system ships to production. Covers what to check, how to score findings, who should be in the room, and when to gate vs approve with conditions.
The Staff Engineer's Playbook: Technical Strategy, Influence Without Authority, and Driving Cross-Team Impact
A practical guide for engineers transitioning to or operating at staff level. Covers what actually changes at staff+, how to build technical strategy, how to drive decisions across teams you don't manage, and how to measure your own impact.
Designing a Batch Processing System: Job Partitioning, Checkpoint Recovery, and Parallel Execution at Scale
A practical guide to batch processing system design covering job partitioning strategies, checkpoint recovery for fault tolerance, parallel execution models, and backpressure. Includes TypeScript examples and production considerations.
Designing a Tenant Provisioning System: Automated Onboarding, Resource Allocation, and Namespace Isolation for Multi-Tenant SaaS
Manual tenant setup is a ticking clock for SaaS teams. This article covers the full system design of automated tenant provisioning: schema isolation, resource quotas, async workflows, rollback, and observability.
Designing an Autoscaling System: Metrics-Driven Scaling, Cooldown Policies, and Predictive Capacity Planning
A deep dive into how autoscaling systems work at the infrastructure level. Covers reactive scaling, step and target-tracking policies, cooldown and flapping prevention, predictive and scheduled scaling, Kubernetes HPA and VPA, serverless differences, and the real cost tradeoffs of over-provisioning.