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The latest News and Information on DevOps, CI/CD, Automation and related technologies.

GPU cloud for AI inference in production: How infrastructure requirements change after training

Training a model is a project with an end date. Inference is what happens for the rest of the model's working life. The two workloads share GPUs, frameworks, and a lot of vocabulary, but the infrastructure decisions that make sense during training are usually the wrong ones in production. Teams that treat inference as "training, but smaller" tend to discover the gap somewhere around their first traffic spike.

Network Device Monitoring: Topology Maps and NetFlow

Most teams run one tool for SNMP polling, another for topology, and a third for flow analysis, then spend their time stitching the views together. This webinar shows how Netdata brings all three into a single dashboard, with 100+ vendor profiles out of the box, automatic Layer 2 topology mapping, and a flow collector that auto-detects NetFlow, IPFIX, and sFlow on a single port.

Agentic validation needs different infrastructure

Previously, I described some core approaches to validating agent written code: feedforward and feedback techniques. Feedforward techniques are about avoiding errors up front, for example by coming up with better prompts and planning strategies. Feedback gives agents a signal that they have actually achieved a task. Feedback is a key part of common agentic patterns like Ralph loops or the /goal commands in Codex and Claude Code: keep working until some known condition passes.

A package manager for AI assets (and why the lock file is per-user)

Sometime in the last two years your repos quietly filled up with a new category of file. Not code, not config exactly: prompts. A.claude/skills/ directory here. A.cursor/rules/ folder there. A CLAUDE.md at the root, an AGENTS.md next to it, a.mcp.json listing the servers your agent is allowed to call. These are the things that make a coding agent useful on your codebase, and they're sprawling.

AI Agent Governance: The Missing Piece of Autonomous IT

AI agents are making decisions, accessing systems, and resolving issues autonomously. But as organizations deploy more agents, one challenge becomes impossible to ignore: governance. Who has access? What changed? Who is accountable? The future of Autonomous IT requires autonomy with accountability.