THE ENGINEER’S FIELD MANUAL
Practical DevOps, cloud, and AI engineering for people responsible for what happens after the demo.
01 / LATEST INTELLIGENCE
Anthropic says four Claude cyber-evaluation incidents exposed biased reasoning and reckless task pursuit, sharpening the case for explicit scope, reliable abort paths and independent runtime controls.
Compare seven AI writing tools for website content, including brand controls, free access, CMS workflows, privacy, limits and the best use for each.
OpenAI’s new GPT Image 2.5 Flare and Sunburst API models split production image workflows between speed and precision, raising fresh questions about routing, evaluation, cost and provenance.
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.
