My client stakeholder recently asked for an overview of what it would take to bring GPT-5.1 Codex into their engineering organization, along with a clearer understanding of its capabilities. Instead of producing yet another slide deck, I decided to answer the question the way I approach real engineering work: by building something.

And that’s how I ended up spending Day 1 of my upskilling project using GitHub Copilot Pro, VS Code, and GPT-5.1-codex to scaffold an actual application.

As a crafter who makes jewelry (Instagram and Etsy), I’ve got a backlog of pieces waiting to be listed. So why not build something useful? One of my upcoming side projects is an app that takes in a photo and generates an SEO-optimized Etsy listing. Perfect playground. Perfect excuse to explore where software engineering is going with agentic AI.

Why I’m Doing This Now

I’ve spent the last few years operating at the architecture level, so I’m intentionally pulling myself closer to implementation again. I fully intended to start with Kubernetes work, but the timing of the client request plus the personal project opportunity made GPT-5.1 the right place to begin.

To properly evaluate the tooling, I upgraded to GitHub Copilot Pro for Individuals. The free tier doesn’t include GPT-5.1-codex, and Pro comes with 300 premium requests per month.

A premium request is essentially a unit of consumption for advanced Copilot features. The quick summary:

How Premium Requests Are Calculated

  • Base request unit: 1 premium request
  • Applies to things like:
    • Copilot Chat prompts
    • Copilot Code Reviews
    • Copilot Coding Agent tasks such as creating PRs
  • A model multiplier is applied depending on which Copilot model is used, but GPT-5.1 Codex is the primary one I’m testing here.

Starting the Experiment: Let’s Build Something

My first instinct was to dive right into my Etsy listing generator idea. Codex generated a lot of code very quickly, but I couldn’t get the project running. That’s on me – this is a new laptop I haven’t fully configured for development yet.

I realized that before evaluating Copilot as an engineering partner, I needed a known baseline. So I reset and started smaller.

Prompt 1:

“Create a .NET Core app with a health check page.”

Great starting point. A predictable output. Easy to validate. And a chance to assess whether the code felt trustworthy.

Copilot scaffolded a full application with multiple pages, including a weather endpoint I didn’t ask for. I prompted it to remove that. It did.

I hit debug and boom: healthy environment, healthy endpoint.

Refactoring Toward Best Practices

Once I knew my dev environment was working, I asked Codex to take the next step.

Prompt 2:

“Refactor this into a production-ready .NET Core application.
Keep only the health check endpoint.
Add a placeholder API for Listing Builder.
Add unit tests.
Use best practices.”

This is where the agentic behavior kicked in.

It created new projects, reorganized folders, scaffolded tests, deleted unnecessary files, attempted builds, and prompted me to allow command-line execution to update the solution structure. I could see the AI thinking and iterating.

When it finished, it produced a clean, professional-looking solution layout.

I briefly reviewed the code. Everything made sense. It read like something a senior engineer would produce. The unit tests passed. The app built. The health endpoint ran.

Now I have a solid foundation for the rest of the project.

The Final Touch: GitIgnore and Initial Commit

Before wrapping Day 1, I asked it to configure a proper .gitignore, then pushed the working scaffold to GitHub so I can pick this back up later in the week and begin building the UI, cleaning the code uo further and wiring up the backend.

Commit here

Early Takeaways After Day 1

1. Prompt-first development is real.
The more intentional and incremental your prompts, the clearer and more predictable the output.

2. Reading code matters more than writing it.
Copilot can generate astonishing amounts of code. Your value is increasingly in reviewing, shaping, and validating it.

3. Agentic workflows feel like pairing with a senior engineer.
It scaffolds, refactors, reorganizes, builds, and tests – not just a text generator, but an active engineering partner.

4. The productivity boost is no joke.
What normally takes 1–2 hours took about 10 minutes.

I haven’t even touched the actual Etsy listing logic yet, but the foundation is there. And I can already see how this will reshape how engineers and architects build software.

This was just Day 1 with GPT-5.1 Codex in agent mode. I’m excited to see how far I can push it next.