THE ENGINEER’S FIELD MANUAL
Practical DevOps, cloud, and AI engineering for people responsible for what happens after the demo.
01 / LATEST INTELLIGENCE
Compare five AI tools for Kubernetes by context, permissions, cost and execution risk, then build a safer workflow for manifests and infrastructure.
AI-assisted discovery of two critical PHP flaws and pre-disclosure exploit telemetry are forcing DevOps teams to measure emergency patching in hours, not days.
Compare 8 GitHub Copilot alternatives by workflow, cost, privacy and control, then run a safe pilot before moving your team or repositories.
02 / LEARNING TRACKS
Structured paths for engineers who want context, depth, and a clear next step.
Linux, containers, Kubernetes, infrastructure as code, CI/CD, and observability—organized as one practical path.
Move from prompts and RAG to evaluation, LLMOps, agent controls, and production delivery patterns.
Design resilient AWS, Azure, and Google Cloud systems with cost, reliability, and incident response in view.
03 / FIELD GUIDES
No generic checklists. Each guide connects concepts to production decisions.
A sequence you can actually follow—from shell fundamentals to platform engineering and AI-assisted operations.
Open guide ↗How retrieval, embeddings, vector search, and generation fit into one production system.
Open guide ↗The lifecycle controls behind evaluation, deployment, monitoring, cost, and governance for language models.
Open guide ↗A practical selection framework across GitHub Actions, GitLab, Jenkins, CircleCI, and modern delivery platforms.
Open guide ↗KEEP BUILDING
Follow a structured learning path, then use the twice-daily engineering brief to track what changes in AI and infrastructure.
