
The AI-First Engineering Team — 30-Day Blog
A 30-day series on what changes when an engineering team — not just an individual engineer — adopts AI as a first-class part of how they work. From culture and workflows to governance and the human side.

A 30-day series on what changes when an engineering team — not just an individual engineer — adopts AI as a first-class part of how they work. From culture and workflows to governance and the human side.

A structured 30-day blog roadmap covering Claude Code, GitHub Copilot, Microsoft Copilot Studio, agentic AI, coding agents, and AI in SDLC — from a Lead AI Engineer with 11 years of experience.
The fine-tuning vs. RAG debate has a different answer in 2026 — 200k+ context windows and widely available fine-tuning APIs have shifted where each approach makes sense.
The AI stack has a supply chain attack surface most teams aren't thinking about — here's what to audit and how to defend it.
Text-to-SQL is the obvious starting point for AI database access, but the patterns that work in production go significantly further — and require rethinking how you expose your data layer.
Building AI agents that respond fast enough for interactive use requires a different set of patterns than batch processing — here is what actually moves the needle on latency.
The choice between many small agents and one powerful agent is an architectural decision with real cost and reliability implications — here is how to make it deliberately.
Most production codebases are too large to fit in any context window — here are the strategies that actually work for AI-assisted development at scale.
Vision-capable models unlock a new class of UI automation that works without selectors, APIs, or accessibility trees — here is how to build it reliably.
AI agents are absorbing significant implementation work — here is what that means for the staff and principal engineer role, and where human judgment still dominates.