I was thinking of that, but asking the right questions and learning the problem domain just a little bit "getting the gist of things" will help a complete newbie to generate code for a complex software.

For example in your case there is the concept of message routing where a message that gets sent to the room is copied to all the participants.

You have timers, animation sheets, events, triggers, etc. A question that extracts such architectural decisions and relevant pieces of code will help the user understand what they are actually doing and also help debug the problems that arise.

It will of course take them longer, but it is possible to get there.

So I agree, but we aren't at that level of capability yet. Because at some point currently it inevitably hits a wall and you need to dig deeper to push it out of the rut.

no code has been the hot new thing for the past 40 years.

Surely there is a scale limit to how big the application can be using this approach.

Why do you say that? I would argue that as long as your tests and interfaces are clearly defined no reason it couldn't scale indefinitely.

Practically there is with Claude Code at least.

Hypothetically, if you codified the architecture as a form of durable meta tests, you might be able to significantly raise the ceiling.

Decomposing to interfaces seems to actually increase architectural entropy instead of decrease it when Claude Code is acting on a code base over a certain size/complexity.

Curious, do you supervise the code itself or at least understand what the code is trying to do?

By "I didn't edit a single line", do you still prompt the agent to fix any issues you found? If so, is that consided an edit?

So yes and no. I often just let it work by itself. Towards the very end when I had more of a deadline I would watch and interrupt it when it was putting implementations in places that broke its architecture.

I think only once did I ever give it an instruction that was related to a handful of lines (There certainly were plenty of opportunities, don't get me wrong).

When troubleshooting occasionally I did read the code. There was an issue with player to player matching where it was just kind of stuck and gave it a simpler solution (conceptually, not actual code) that worked for the design constraints.

I did find myself hinting/telling it to do things like centralize the CSS.

It was a really useful exercise in learning. I'm going to write an article about it. My biggest insight is that "good" architecture for an current generation AI is probably different than for humans because of how attention and context works in the models/tools (at least for the current Claude Code). Essentially "out of sight out of mind" creates a dynamic where decomposing code leads to an increase in entropy when a model is working on it.

I need to experiment with other agentic tools to see how their context handling impacts possible scope of work. I extensively use GitHub Copilot, but I control scope, context, and instructions much tighter there.

I hadn't really used hands off automation much in the past because I didn't think the models were at a level that they could handle a significantly sized unit of work. Now they can with large caveats. There also is a clear upper bound with the Claude Code, but that can probably be significantly improved by better context handling.

so if you're an experienced, trained developer you can now add AI as a tool to your skill set? This seems reasonable, but is also a fundamentally different statement that what every. single. executive. is parroting to the echochamber.