THE ENGINEER’S FIELD MANUAL
Practical DevOps, cloud, and AI engineering for people responsible for what happens after the demo.
01 / LATEST INTELLIGENCE
LEAD STORYNVIDIA says its AVO agent system completed every level in ARC-AGI-3’s public set, showing why persistent state, tools, feedback and recovery matter—while leaving private-set generalization and production safety unproven.
Read the analysis ↗A constrained repository scanner gives defenders findings and suggested fixes without opening direct model access.
↗Why held-out tests, tool trajectories, and rollback matter when agents optimize their own instructions.
↗Critical AI and infrastructure shifts, translated for engineering teams.
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.
