🤔 The moment AI started writing code, debugging stopped being the slowest part.

At least, that's what I expected.

Instead, I found a different bottleneck.

Every AI coding session leaves behind dozens of decisions:

• Why did we choose this architecture?

• Which prompt finally worked?

• Why was this workaround added?

• Which ideas were intentionally rejected?

The code survives.

The reasoning usually doesn't.

A month later, I often spend more time reconstructing why something exists than changing it.

Ironically, AI has made writing code faster...

...but remembering the reasoning behind that code has become slower.

Lately I've started treating design decisions like first-class artifacts instead of relying on prompts or chat history.

It's made returning to old projects much easier.

I'm curious whether others have noticed the same thing.

💬 When you come back to an AI-built project after a few weeks, what's harder to recover?

🧠 The code?

📄 The prompts?

💡 The reasoning behind the decisions?

Or something else entirely?

I'd love to hear how other builders are solving this.