sn0wflak3s

The line is scope.

I'm not asking an agent to build me a full-stack app. That's where you end up babysitting it like a kindergartener and honestly you'd be faster doing it yourself. The way I use agents is focused, context-driven, one small task at a time.

For example: i need a function that takes a dependency graph, topologically sorts it, and returns the affected nodes when a given node changes. That's well-scoped. The agent writes it, I review it, done.

But say I'm debugging a connection pool leak in Postgres where connections aren't being released back under load because a transaction is left open inside a retry loop. I'm not handing that to an agent. I already know our system. I know which service is misbehaving, I know the ORM layer, I know where the connection lifecycle is managed. The context needed to guide the agent properly would take longer to write than just opening the code and tracing it myself.

That's the line. If the context you'd need to provide is larger than the task itself, just do it. If the task is well-defined and the output is easy to verify, let the agent rip.

The muscle memory point is real though. i still hand-write code when I'm learning something new or exploring a space I don't understand yet. AI is terrible for building intuition in unfamiliar territory because you can't evaluate output you don't understand. But for mundane scaffolding, boilerplate, things that repeat? I don't. llife's too short to hand-write your 50th REST handler.

I wrote it myself. But the irony isn't lost on me. "Who did what" is kind of the whole point of the article. Appreciate the feedback.

This is a fair point. The cognitive load is real. Reviewing AI output is a different kind of exhausting than writing code yourself.

Even when the output is "guided," I don't trust it. I still review every single line. Every statement. I need to understand what the hell is going on before it goes anywhere. That's non-negotiable. I think it gets better as you build tighter feedback loops and better testing around it, but I won't pretend it's effortless.

I get this. I don't think either of you is wrong. There's a real loss in not writing something from scratch and feeling it come together under your hands. I'm not dismissing that.

I have immense respect for the senior engineers who came before me. They built the systems and the thinking that everything I do now sits on top of. I learned from people. Not from AI. The engineers who reviewed my terrible pull requests, the ones who sat with me and explained why my approach was wrong. That's irreplaceable. The article is about where I think things are going, not about what everyone should enjoy.

Fair enough. I know how that reads. But when anyone with a laptop and a subscription can ship production software in a weekend, the architecture and the idea start to matter a lot more. The technical details in the post are real. I just can't share the what yet. Take it or leave it.

This is the question I keep coming back to. I don't have a clean answer yet.

The foundation I built came from years of writing bad code and understanding why it was bad. I look at code I wrote 10 years ago and it's genuinely terrible. But that's the point. It took time, feedback, reading books, reviewing other people's work, failing, and slowly building the instinct for what good looks like. That process can't be skipped.

If AI shortens the path to output, educators have to double down on the fundamentals. Data structures, systems thinking, understanding why things break. Not because everyone needs to hand-write a linked list forever, but because without that foundation you can't tell when the AI is wrong. You can't course-correct what you don't understand.

Anyone can break into tech. That's a good thing. But if someone becomes a purely vibe-coding engineer with no depth, that's not on them. That's on the companies and institutions that didn't evaluate for the right things. We studied these fundamentals for a reason. That reason didn't go away just because the tools got better.

The K-shaped workforce point is sharp and I think you're right. The curious ones are a minority, but they've always been the ones who moved things forward. AI just made the gap more visible :)

Your Codex case study with the content creators is fascinating. A PhD in Biology and a masters in writing building internal tools... that's exactly the kind of thing i meant by "you can learn anything now." I'm surrounded by PhDs and professors at my workplace and I'm genuinely positive about how things are progressing. These are people with deep domain expertise who can now build the tools they need. It's an interesting time. please write that up...