Field Notes in Agentic Engineering: AI Made Velocity Easier To Fake

Refining agentic engineering velocity metrics we need to measure for engineering velocity and developer productivity.

Token Efficiency is an Architecture Decision

What a Skills-vs-MCP benchmark reveals about where models should reason, where code should execute, and what enterprise teams should measure.

Field CTO Is Executive Developer Relations for AI

Why AI companies need leaders who can go deep with developers, wide with engineering leaders, and turn field insight into product and market direction

Why AI Coding Needs Code Integrity Built In

Frameworks Are Multipliers, Not the Default

Why I chose Jinja over a frontend framework for my policy enforcement AI coding engine

Field Notes in Agentic Engineering: Not Every AI Problem Is a Tool Problem

What conversations with professional software engineers reveal about people, process, and messy systems they begrudgingly work with.

Field Notes in Agentic Engineering: SDLC Traceability is an Unsolved Problem

Inside the SDLC traceability gap I kept hearing about at DevNexus

Field Notes in Agentic Engineering: What Enterprise Teams Shared With Me

What I heard from enterprise engineers at DevNexus about speed, standards, and software integrity.

How to Preserve Understanding When AI 10x's Your Code Output

AI Security Resources for Developers

A Comprehensive Guide to Red Teaming LLM-Integrated Applications