Treating Claude as Engineering Infrastructure
Most teams bolt Claude on. The ones getting real value designed a system around it.
AI Coding Tools
The landscape of AI tools for developers — what they're good at, where they fall short, and how to build a workflow around them.
AI coding tools have moved from novelty to default in a short time. Copilots, inline completions, chat-based assistants, agentic CLI tools that can read and modify codebases — the category is broad and the gap between the best and worst use of these tools is larger than it looks.
The common failure mode is adoption without design. A developer starts using an AI tool, gets faster at some things, and calls it good. The team absorbs the cost later — in diffs that are hard to review, in code that nobody can explain, in standards that erode because the tool doesn’t know they exist.
The teams that get durable value treat AI tooling the way they treat any other piece of infrastructure: they configure it deliberately, enforce the standards that matter, and keep a clear line between what to delegate and what to own.
Posts here cover the tools themselves — Claude Code, Copilot, and others — along with the workflow patterns, configuration strategies, and tradeoffs that come with building AI assistance into engineering work.
2 posts tagged "AI Coding Tools"
