I once led a dev team of 12. After a few weeks with Claude Code, I’m not so sure what that means anymore
A practitioner’s honest account of what changes — and what doesn’t — when you start working alongside an AI that reasons about architecture.
I recently completed Claude Code: Software Engineering with Generative AI Agents on Coursera — a five-hour course that turned out to be one of the more practically useful things I’ve done this year. I went in as someone already working with AI workflows. I came out genuinely rethinking what a single developer or small team can realistically build.
The course doesn’t dwell on theory — it’s hands-on from the start: setting up projects, delegating to agents, iterating, debugging, and managing increasingly complex systems.
What struck me first: code quality
Not just “working” code, but well-structured, readable, and aligned with the standards I’d normally expect from experienced engineers. The technical depth is where the course earns its credibility.
Using claude.md to define project context and standards, driving consistent behaviour with Claude commands, integrating directly into a Git-based workflow. The section on parallel development with Git worktrees was particularly striking — multiple agents working simultaneously on different features or fixes, without stepping on each other.
From impressive demo to something I rely on
Since finishing the course, I’ve been using Claude Code to explore and evolve architectural patterns — clean architecture, modular monolith, vertical slice — and it’s been remarkably effective at maintaining boundaries, generating consistent modules, and staying coherent as complexity increases.
That’s the part that moved it from “impressive demo” to something I am now starting to rely on.
Claude isn’t better autocomplete. It behaves more like a collaborative engineer — reasoning about architecture, writing meaningful tests, refactoring codebases, contributing to design decisions. The experience is closer to orchestrating a group of capable junior-to-mid engineers than using an AI assistant.
Who this is for
For anyone familiar with AI tools but unsure how to bring them into real engineering practice: this course is a good place to start. Not because it promises some future where AI writes all your code, but because it shows — concretely, right now — how to work alongside it in ways that actually shift what’s possible.
The team-of-12 question isn’t rhetorical. I’m still working through what it means — for how teams are structured, how work gets scoped, and what “senior engineering judgement” looks like when the execution layer becomes this capable.
The next post in this series goes deeper into the architectural patterns I’ve been testing with Claude Code — what held up, what didn’t, and what surprised me.