Effective AI Code Development
Field-tested practices, prompting discipline, and design-first methods for shipping with AI tools.
Articles
See Claude Code Token Usage Without Spending Tokens
Add two local scripts to Claude Code that show a status line and a Stop hook with tokens, context size and approximate cost. Reporting costs zero model tokens because it reads local session data.
DevOps OS MCP Server Development Journey with AI Coding Agents
How I built and hardened DevOps-OS's MCP server with AI coding agents: Copilot agent mode for the build, Claude Code for testing, the bugs found, and prompting lessons.
An AI-Based Coding Assistant Needs Boundaries, Not Just a System Prompt
A general-purpose model does not become a dedicated coding product because of a system prompt. Reliable assistants need scope, routing, policy, and evaluation.
The 183k-Token Mistake I Made Because the Agent Lacked Context
A small AI infrastructure presentation reached 183k active-context tokens because I left source scope, tooling, and verification boundaries undefined.
Why AI Won't Build Secure Software Unless You Instruct It How
One security requirement in a planning document changed an entire database design. A field note on engineering requirements driving AI-assisted development.
One Line That Saved Me 20,000 Tokens in Claude
One sentence added to a Claude Code prompt cut roughly 20,000 tokens in a single sprint by stopping redundant file reads on a large compliance project.
How to Avoid Context Burnout in AI Coding Workflows
AI coding works better when teams treat context as a managed working buffer, not infinite memory, and preserve rules, state, and milestones outside the chat.
Two Years of AI-Assisted Code Development. Here Is What I Learned and Built.
Field notes from two years of building 50+ production apps with AI tools — and how every lesson from that journey shaped the blog you are reading right now.