Readit NewsI use AI everyday for coding. But if someone so obviously puts this little effort into their work that they put out into the world, I don’t think I trust them to do it properly when they’re writing code.
LLMs are great for rapid prototyping, boilerplate, that kind of thing. I myself use them daily. But the amount of mistakes Claude makes is not negligible in my experience.
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 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.
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There are two problems left, though.
One is, laypersons don't understand the difference between "guided" and "vibe coded". This shouldn't matter, but it does, because in most organizations managers are laypersons who don't know anything about coding whatsoever, aren't interested by the topic at all, and think developers are interchangeable.
The other problem is, how do you develop those instincts when you're starting up, now that AI is a better junior coder than most junior coders? This is something one needs to think about hard as a society. We old farts are going to be fine, but we're eventually going to die (retire first, if we're lucky; then die).
What comes after? How do we produce experts in the age of AI?
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 catch about the "guided" piece is that it requires an already-good engineer. I work with engineers around the world and the skill level varies a lot - AI has not been able to bridge the gap. I am generalizing, but I can see how AI can 10x the work of the typical engineer working in Startups in California. Even your comment about curiosity highlights this. It's the beginning of an even more K-shaped engineering workforce.
Even people who were previously not great engineers, if they are curious and always enjoyed the learning part - they are now supercharged to learn new ways of building, and they are able to try it out, learn from their mistakes at an accelerated pace.
Unfortunately, this group, the curious ones, IMHO is a minority.
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...
Where do you draw the line between just enough guidance vs too much hand holding to an agent? At some point, wouldn't it be better to just do it yourself and be done with the project (while also build your muscle memory, experiences and the mental model for future projects, just like tons of regular devs have done in the past)
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.