I can't relate that much to this. Every time I use AI to write code, I'm constantly fighting a feeling on the back of my neck that I need to look over everything it has done and supplement/alter it with my own code. That ick feeling counteracts the dopamine hit of having a working app after a few minutes of vibe coding, and I don't think that's going anywhere anytime soon.

That said, I have experience. I could absolutely see myself falling into this as a junior or even mid level dev. I'd no doubt not feel that feeling on my neck if it wasn't scarred from code review lashings early in my career by knowledgeable mentors.

In my experience, Claude only knows how to spew code. Every problem you want it to solve, it translates into "more code" rather than "less code". You have to very closely code review everything it does, otherwise your codebase is going to just grow and grow, and asymptotically approach 100% debt.

I code review everything that Claude produces, and I'd estimate about 90-95% of the time, my reaction is WOW it works but too much code dude, let's take 3 hours to handhold you through simplifying it until nothing more can be removed.

> after a few minutes of vibe coding

Don't vibe-code, it's a joke someone coined in the moment, that somehow the industry decided shouldn't be a joke, and some people think it's a feasible way of developing stuff, it's not.

Find a better way of working together with agent, where you get the review what's important to be reviewed by a human, and "outsource" the rest, and you'll end up with code and a design that works the way you'd program it yourself, you just get there faster. I probably end up reviewing maybe 90% of the code that the agent writes, but still it's a hell of a lot more pleasant writing/dictating a few prompts over typing tens of thousands of characters and constantly moving between files. Maybe I'm just tired of typing...

Fully agree. I supplement my game development with AI. Anything novel or interesting I want to do, I need to write the code for myself, otherwise I'm in for a world of hurt. But for the drudgery work that is necessary to invest a lot of time in but boring to actually write, I design a clear architecture and ask AI to do the implementation leg-work. And still you have to go back over and make sure it didn't decide to just create something outlandish. A good recent example is Codex trying to recreate from scratch the behaviour already provided by Area2D in a game I'm making with Godot.

If you try and get AI to do anything meaningful, it will be riddled with footguns and bizarre choices. Maybe if you have hundreds of dollars worth of tokens that might not be the case - but for someone who spends $10 a month, it's just not worth the headache.

Besides, for me these are hobby projects and writing code is still fun, I just make AI write the boring parts (good examples: saving and loading, parsing of data files and settings menu functionality) - but I keep it away from anything that needs a humans judgement to create.

Experience is so so valuable right now. We can guide these agents super well, but I do fear for the juniors as you said. I would like to think I'd use the agents to dive deeper and learn faster. It was pretty rough piecing together solutions from Stack Overflow, various irc channels, Reddit, etc. But also, I cheated on my homework in college and didn't really review the answers, so not sure. Though I pursued programming out of interest and not just to complete a degree. Maybe it would have been different. In any case, I'm glad I came into the LLM era with a lot of experience and failures already.

I'm a very senior dev (32 years exp) but I've got the process nailed down tight enough with .md documents, skills, review agents, etc, that I don't typically have that feeling or any need to do anything extra.

I don't think this makes me dumb though, I've just moved up stack. Rather than caring about assembly language or source code, I'm focused on requirements, architectural decisions, engineering process, and ever more automation.

The code that LLMs produce is just average IMO. I wouldn’t call myself an authority on clean code but I can tell when code is well structured. I prefer my hand written code over Claude or GPT’s every time. I once did an experiment where I generated a spec from a project I’d already written, then had an LLM blindly reimplement it from the spec, and compared code. The LLM’s version looked like vomit.

As someone now firmly in the "used to be a developer" part of my career, AI use seems like good old atrophying of the memory muscle of coding. Whether it's because you just don't code anymore at all, or offload that thinking to AI, the effect on you is the same. You start to forget.

Don't have AI do anything you want to stay sharp on.

I feel the same way, and yet I would still say I feel AI usage atrophying my thinking skills. I feel less tempted to use it to shortcut whole files, but even just using it to speed up looking up and carefully reading docs, tinkering with a library to understand it when docs are inadequate, working out the tradeoffs for design decisions... These sound less objectionable and more like simplr speedups, but when I _do_ need to do it (because the agent refuses to do it properly) I can feel the friction so much more keenly. Whether that's just me losing the habit of those specific tasks, or a generalised loss of g-factor, I don't know.

I've been using it mostly to bat away yak shaving rabbit holes one can get into when working on a large and complex project. I work mostly on platform work, which is generally nebulous in its feedback loop and testing. Relegating AI to refactoring and building tools to help me research keeps me focused on solving the actual main problem I'm trying to solve, reduces context switching. I really don't understand people who use it to bat out their main focus. I simply don't trust it at that level.

I think this instinct is intrinsic, and comes from really caring about detail and wanting to fully understand it and own it.

That's what drives it, and I don't really think the extrinsic things about the way you learned (while helpful) have that much bearing on it. It comes from you and you should take credit for it.

I think if you were learning today you'd probably find have the same feeling and do just fine because of it.

I agree with your sentiment. I've been trying to get from plan -> complete with AI and it's been working very well in a sandbox.

I am trying super hard to give the tools to validate everything to AI.

I finish by opening a draft PR and then I go through doing a deep review myself.

If I didn't already have 10+ years experience, it would be hard to learn and not atrophy with easy shortcuts being so available.

You still need people who know stuff in detail and can own the code... for now

> I'm constantly fighting a feeling on the back of my neck that I need to look over everything it has done and supplement/alter it with my own code

Can relate but the only thing I do different is I teach AI how to cleanup after herself in followup prompts, sessions and refining AGENTS.md. Static code quality analysis tools are also really good to keep the agent on its toes.

I have the same feeling on the back of my neck. I think it’s born from my crippling imposter syndrome, which is maybe a super power now.

I really enjoy having the AI write the spec then I write the code.

Reviewing code is pain, reviewing requirements and giving feedback feels more productive. I have to confront the full shape of the problem and I usually discover a few cans of worms that make me rethink my approach.

Reading and writing are related, but separate activities. One's capability to write code can degrade independently of one's ability to review it.

Learning from code review lashings is amazing in its effectiveness and minimal blast radius! I'm glad you were able to take that in the easy way.

Scar tissue from production going down and staying down is probably powering those code reviews and I think will be teaching this wave of vibe projects a few hard lessons. I've had to learn a few things the hard way like this and it's as effective as it is painful.

I'm very pro ai-generated-software in the right context. I think being able to vibe out software as needed is awesome and could finally unlock the potential of our computer and data dominated world. I also think we haven't yet learned as a culture where this new thing is different from traditional software and misunderstanding that is where a lot of the pain will be felt.

The thing is that you seem to have that luxury to be able to dig more into the problem and scratch that itch.

But the industry is changing around you fast.

If MIT-bred devs were already building crap in faang before, the trend has been getting nothing short of worse across the industry.

Expectations are rising, the field is becoming a rat race of which engineer can output the most mediocre/acceptable/good enough amount of features in the least time as possible.

Let me make this clear: you're in an increasingly rarer bubble where you have a luxury that is disappearing in this industry, plain and simple.

I have the fortune of having stellar devs around me, people that contributed to projects and software you use every day.

They are also outputting magnitude of order more than they ever did, and none of them is getting genuinely better at the craft, but it is what it is.

I'm using a local model. The code gen is never fast beyond the first few context. As the context grows, it slows down. It's basically it's own self limiting process. When it starts doing things, the threshold of lethargy lowers and triggers me to 'do it myself'; especially, I've developed the understanding of knowledge where it starts doing stupid things and that's valuable.

There must be an epistemic problem with just how fast these SOTA models run. I don't think it's just that my local model is dumber, I think it's more that the speed of token gen trains my brain with different expectations. There's no way it'll just generate hundreds of files by itself. When it can via a opencode loop with thought files, letting it run for a day is the only way you get that.

> I'm constantly fighting a feeling on the back of my neck that I need to look over everything it has done and supplement/alter it with my own code.

On the flip side, I'm working on stuff FAR more challenging than I would ever be able to do on my own.

My brain is melting because I can barely keep up with learning how to figure out if I'm even doing what I'm trying to do.

AI might be making me a worse coder, but I don't care. If it hasn't "solved" coding now, I'm pretty confident it will long before my career is over. I don't have a job because I can write code - that's a small part of it. I have a job because I can get things to work. Anyone can code things that don't work (especially AI).

AI is certainly making me a far better overall engineer. Instead of spending my time trying to make the compiler happy (or fixing dynamic type errors at runtime), I can spend my time trying to solve substantially harder problems that I would never even dare try without an entire team to back me up (i.e. never).

Coding - imo - is VERY low on the totem pole of engineering skills.

I don't care if the function is pretty. I care if the system is upholding invariants and performing as expected, and there's adequate testing in place to PROVE to me that it ACTUALLY works.

High performance concurrent code has always blurred the line between sorcery and arcana... Go didn't really solve that. Rust/Tokio didn't. Zig didn't. C certainly hasn't.

It might be easier to prove to yourself, if you're the one doing all the writing, but at the end of the day, code is rarely just for you...

You probably should have the same level of proof whether you wrote it yourself and just trust yourself bro, or whether a Chinese Room wrote if for you.

I feel like I'm living in a Brave New World, and - at least for the time being - I'm enjoying it, even if it feels like I'm sprinting as fast as I can and still unable to keep up.