Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
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.
A practitioner's tour of the reusable patterns for building reliable LLM agents, and when each one earns its keep.
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.
A hands-on tutorial for building a Model Context Protocol server that exposes tools, resources, and prompts to any LLM host.
MCP is an open standard that gives AI agents one consistent way to reach external tools and data instead of bespoke glue.
A practitioner's comparison of the leading LLM agent frameworks, matching LangGraph, CrewAI, AutoGen and more to real use cases.
A practitioner's head-to-head on LangGraph and CrewAI, covering control, state, production readiness, and when each framework earns its place.
The LLM stack is a maze of APIs, GPU clouds, gateways, and serving tools. This is the map to what each layer is for and how to keep the bill sane.
A practitioner comparison of the RAG frameworks worth using in 2026, from LlamaIndex and LangChain to Haystack, DSPy, and raw code.
Ollama gets a model running on your laptop in minutes; vLLM serves thousands of production requests. Here's when each one earns its place.
