Claude Code represents the next evolution of AI coding tools — moving beyond autocomplete and chat assistants toward true engineering collaboration. This guide explains how Claude Code differs from tools like Cursor and Copilot, how context and memory actually work, and how to control AI behavior through commands, integrations, and workflow design.
AI fluency isn’t about better prompts. It’s about understanding how to work with AI — when to automate tasks, collaborate with models, or deploy autonomous agents. A practical breakdown of Anthropic’s engagement model and the 4D framework for real-world AI workflows.
Understanding an LLM’s edges matters more than flattering it.
This post explores how deliberately pushing models into conflicting, noisy, or chained prompts reveals their failure modes — and why learning those edges is the core of real prompt engineering.
Hallucination isn’t a mystery glitch — it’s a predictable consequence of how LLMs work. This Field Note breaks down why models fabricate information, the patterns behind it, and practical ways to reason about and mitigate hallucination in real systems.
LLM pipelines aren’t just prompts and models — they’re grounded, verified, cached, and resilient systems. This Field Note breaks down pragmatic patterns like retrieval grounding, verification checks, caching strategies, and reliability practices that keep AI systems dependable beyond demos.