Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
AI apps add a new attack surface on top of the old ones. This is the map: the threats unique to LLMs and agents, and the controls that actually contain them.
You cannot prove an LLM app is safe by reading its prompt. Here is how to adversarially test it before attackers do.
Autonomous agents take real actions, so a single injected instruction can cause real damage. Here is how to contain them.
A working security engineer's tour of the ten failure modes unique to LLM apps, each paired with a fix you can ship this sprint.
AI coding assistants ship fast but frequently introduce security flaws, so treat their output as untrusted and gate it before merge.
A practitioner's tour of the reusable patterns for building reliable LLM agents, and when each one earns its keep.
AI agents went from demos to production this year. This is the map: the frameworks, the protocol tying them together, and the patterns that actually ship.
Small language models now handle most agent steps at a fraction of the cost, so pick per step instead of defaulting to a frontier model.
A practitioner's guide to tracing, evaluating, and debugging LLM agents in production with the tools that actually earn their keep.
A practical look at when multiple coordinating AI agents actually help, the orchestration patterns that work, and where they fail.
MCP is an open standard that gives AI agents one consistent way to reach external tools and data instead of bespoke glue.
A hands-on tutorial for building a Model Context Protocol server that exposes tools, resources, and prompts to any LLM host.
