
Why LLMs ignore the middle of your context
Long context windows do not guarantee reliable recall. Design retrieval, reranking and prompt assembly so the model can use the evidence it receives.

Long context windows do not guarantee reliable recall. Design retrieval, reranking and prompt assembly so the model can use the evidence it receives.

Keep model names in configuration, select models by task, and compare routes using the cost of completed work.

Why modelling each business operation as a reusable action keeps controllers, jobs, commands, listeners and other delivery mechanisms thin.

Why many apparent multi-agent gains are really test-time compute gains, and when extra agents are still worth the complexity.

A practical comparison of three emerging agent protocols, what layer each one solves, and when to use them together.

A beginner-friendly but detailed guide to how large language models are built, trained, aligned, and run in production.

When one workflow is not enough, parallel state machines keep independent concerns separate, testable, and easier to reason about.

A practical, engineer-friendly look at state machines: why they’re great, where they hurt, and a clean TypeScript finite-state-machine example.