Topic: Augmented Coding

Three AI coding tools, three config files, all slowly disagreeing with each other and with the code. This is how we got down to one source of truth the robots and the humans both read.

Onboarding onto a large LMS codebase, I used Cursor not to write features faster, but to build a mental model: where legacy PHP meets newer REST layers, how events propagate, and where permission checks actually live.

This post walks through two real explorations (user impersonation across stacks and a permissions trace), with anonymized prompts, what the tool got right and wrong, and a small playbook you can reuse on your own brownfield project.

You bump a version, run install, and the project catches fire. That’s a React upgrade.

Half the bugs you find were already there — the rest is types, tests, and Claude Code keeping the investigation from eating you alive.

Most AI coding workflows assume you’re building something new from scratch. But what if you’re building something that spans multiple existing codebases, each with years of accumulated quirks?

This is how I stopped fighting context loss and built a system that actually works with existing codebases.

In a world where AI writes code at lightning speed, the bottleneck has shifted from typing to thinking.

Spec-Driven Development (SDD) puts requirements and behavior specifications at the center of the development process, ensuring that what we build is what we actually need — before a single line of code is written.

Five years ago, we invested in Domain-Driven Design. Conferences, workshops, consultants. The works.

The goal was simple: help humans navigate complexity. Make domain experts and developers speak the same language.

We had no idea those same boundaries would matter for something else entirely.