9 posts tagged with "AI-Assisted Engineering"

Or: How I Accidentally Became an AI Middle Manager

There are weeks where you ship a feature.

Then there are weeks where you accidentally redesign your entire career.

This was one of those.

This blog is Git-backed, but that does not mean I need to sit at a desktop to publish something.

The authoring surface is ChatGPT Work. I can start from my phone, describe an idea in a normal conversation, refine it naturally, and let a repository-aware agent carry the work from Markdown all the way to production.

The interesting part is that ChatGPT is not the publishing system.

The repository is.

Status: Living document
Last updated: July 29, 2026
Maturity: Actively used and rapidly evolving

Executive Summary​

This workflow treats AI systems as specialized members of a software engineering team rather than as interchangeable coding assistants. Repository conventions, specifications, ranked milestones, reusable protocols, automated reviews, and infrastructure-as-code provide the operating structure. Short commands such as “do the next milestone” work because the repository already describes how the agent should operate.

Part 1 of The AI-Assisted Software Engineering Workflow.

Part 2 of The AI-Assisted Software Engineering Workflow.

Architecture​

The workflow is converging on two complementary control layers.

Part 3 of The AI-Assisted Software Engineering Workflow.

Development Environments and Mobility​

Current Default: Local Execution​

Local development remains the best fit because it provides:

Part 4 of The AI-Assisted Software Engineering Workflow.

Observations and Operating Limits​

Experienced Engineers Receive the Largest Multiplier​

“Vibe coding” is real in this workflow, but it is not effortless button-pushing.

Part 5 of The AI-Assisted Software Engineering Workflow.

Metrics to Track​

Delivery​

Part 6 of The AI-Assisted Software Engineering Workflow.

Current State​

Proven or Actively Used​