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Engineering deep-dives from running Google Cloud in production at enterprise scale — Kubernetes, cloud security, DevSecOps, system architecture and AI infrastructure. By Aleksei Aleinikov · Google Cloud Ambassador · 5× certified · published author (Orange Education). English and German, for the DACH region.
Eight focused engineering hubs. Each one gathers the deep-dive articles, real-world context and FAQs for a single domain — start where you want to go deepest.
FeaturedGenAI vs Agentic AI vs AI Agents vs LLM — the four terms everyone uses interchangeably, explained by an engineer. What each one actually is, how they nest, why GenAI and 'agentic' sit on different axes, with a comparison table and decision guide.
Is your career AI-proof? An honest 2026 answer: AI automates producing code, not the judgment to review it. Why companies confuse the two when they hire juniors plus AI, and what actually stays durable.
A practical 2026 guide to data governance on Google Cloud: the four questions every program must answer, and the exact services that answer them — Knowledge Catalog (Dataplex), Sensitive Data Protection, BigQuery policy tags, and VPC Service Controls — with a comparison table and a decision guide.
OpenTofu vs Terraform in 2026: why the fork happened, what actually differs (state encryption, provider for_each, licensing), a comparison table, and an honest verdict on switching.
GKE Gateway API vs Ingress in 2026: the role-oriented model, the four GatewayClasses, the proxy-only subnet regional clusters need, and when to drop self-hosted nginx.
The real trade-offs between GKE Autopilot and Standard mode in 2026: node ownership, billing models, what Autopilot still cannot do, and a practical decision guide for platform engineers.
A vendor-neutral comparison of the three AI coding agents that matter in 2026 — Claude Code, Codex, and opencode: how the agent loop works, where each one fits, a decision table, and how to run them without handing over the keys.
A practical 2026 guide to Cloud Bigtable: what wide-column storage actually is, how Bigtable works under the hood, when it beats BigQuery, Spanner, Cassandra and DynamoDB, and the one thing — row-key design — that makes or breaks it.
A vendor-neutral system-design walkthrough of the message-queue architecture that sends a million notifications without taking your API down: fan-out workers, back-pressure, retries, and dead-letter queues.
A practical 2026 architecture for digital sovereignty: treat sovereign cloud as three layers — data, operational, technical — and use Kubernetes, external key custody, and a real exit path to make sovereignty a design property instead of a marketing sticker.
A hands-on 2026 guide to Podman: why daemonless and rootless containers are safer, how user-namespace UID mapping works, and how Quadlet lets systemd run your containers instead of a background daemon.
Running AI models directly in the browser is finally fast enough to be real. A practical 2026 guide to on-device inference with WebGPU and Google's new LiteRT.js runtime — what changed, how it works, and when to reach for it.
Three ways to build a web scraper in 2026: a managed platform like Scraper Studio, an actor marketplace like Apify, or a DIY Scrapy + Playwright stack. Here is how they really compare on speed, ownership, and the part that actually costs you — maintenance.
You can code — in 2026 that's table stakes, not the edge. The skill of the year is the cloud. Why every developer should master it, and why Google Cloud is the smart place to start.
A practical, no-marketing comparison of BigQuery and Snowflake in 2026 — how their architecture, billing, performance and AI stories really differ, and how to choose the right one for your workload.
