Summarizer

There was a guy a few months ago who found that telling the AI to do everything in a single PHP file actually produced significantly better results, i.e. it worked on the first try. Otherwise it defaulted to React, 1GB of node modules, and a site that wouldn't even load. >Am I better served For anything serious, I write the code "semi-interactively", i.e. I just prompt and verify small chunks of the program in rapid succession. That way I keep my mental model synced the whole time, I never have any catching up to do, and honestly it just feels good to stay in the driver's seat.

I think you are right in placing emphasis on delegation. There’s been a hypothesis floating around that I find appealing. Seemingly you can identify two distinct groups of experienced engineers. Manager, delegator, or team lead style senior engineers are broadly pro-AI. The craftsman, wizard, artist, IC style senior engineers are broadly anti-AI. But coming back to architects, or most professional services and academia to be honest, I do think the term vibe architect as you define it is exactly how the industry works. An underclass of underpaid interns and juniors do the work, hoping to climb higher and position themselves towards the top of the ponzi-like pyramid scheme.

Architects still need to learn to draw manually quite well to pass exams and stuff.

It's not about the tooling it's about the reasoning. An architect copy pasting existing blueprints is still in charge and has to decide what the copy paste and where. Same as programmer slapping a bunch of code together, plumbing libraries or writing fresh code. They are the ones who drive the logical reasoning and the building process. The ai tooling reverses this where the thinking is outsourced to the machine and the user is borderline nothing more than a spectator, an observer and a rubber stamp on top. Anyone who is in this position seriously need to think their value added. How do they plan to justify their position and salary to the capital class. If the machine is doing the work for you, why would anyone pay you as much as they do when they can just replace you with someone cheaper, ideally with no-one for maximum profit. Everyone is now in a competition not only against each other but also against the machine. And any specialized. Expert knowledge moat that you've built over decades of hard work is about to evaporate. This is the real pressing issue. And the only way you can justify your value added, your position, your salary is to be able to undermine the AI, find flaws in it's output and reasoning. After all if/when it becomes flawless you have no purpose to the capital class!

This is exactly what I said in the end. Right now you rely on it fucking things up. What happens to you when the AI no longer fucks things up? Sorry to say, but your position is no longer needed.

> When was the last time you reviewed the machine code produced by a compiler? ... Any time I’m doing serious optimization or knee-deep in debugging something where the bug emerged at -O2 but not at -O0. Sometimes just for fun to see what the compiler is doing in its optimization passes. You severely limit what you can do and what you can learn if you never peek underneath.

I didn't get into creating software so I could read plagiarism laundering machines output. Sorry, miss me with these takes. I love using my keyboard, and my brain.

GPT-4 showed the potential but the automated workflows (context management, loops, test-running) and pure execution speed to handle all that "reasoning"/workflows (remember watching characters pop in slowly in GPT-4 streaming API response calls) are gamechangers. The workflow automation and better (and model-directed) context management are all obvious in retrospect but a lot of people (like myself) were instead focused on IDE integration and such vs `grep` and the like. Maybe multi-agent with task boards is the next thing, but it feels like that might also start to outrun the ability to sensibly design and test new features for non-greenfield/non-port projects. Who knows yet. I think it's still very valuable for someone to dig in to the underlying models periodically (insomuch as the APIs even expose the same level of raw stuff anymore) to get a feeling for what's reliable to one-shot vs what's easily correctable by a "ran the tests, saw it was wrong, fixed it" loop. If you don't have a good sense of that, it's easy to get overambitious and end up with something you don't like if you're the sort of person who cares at all about what the code looks like.

> It's a shame that AI coding tools have become such a polarizing issue among developers. Frankly I'm so tired of the usual "I don't find myself more productive", "It writes soup". Especially when some of the best software developers (and engineers) find many utility in those tools, there should be some doubt growing in that crowd. I have come to the conclusion that software developers , those only focusing on the craft of writing code are the naysayers. Software engineers immediately recognize the many automation/exploration/etc boosts, recognize the tools limits and work on improving them. Hell, AI is an insane boost to productivity, even if you don't have it write a single line of code ever . But people that focus on the craft (the kind of crowd that doesn't even process the concept of throwaway code or budgets or money) will keep laying in their "I don't see the benefits because X" forever, nonsensically confusing any tool use with vibe coding. I'm also convinced that since this crowd never had any notion of what engineering is (there is very little of it in our industry sadly, technology and code is the focus and rarely the business, budget and problems to solve) and confused it with architectural, technological or best practices they are genuinely insecure about their jobs because once their very valued craft and skills are diminished they pay the price of never having invested in understanding the business, the domain, processes or soft skills.

but annoying hype is exactly the issue with AI in my eyes. I get it's a useful tool in moderation and all, but I also experience that management values speed and quantity of delivery above all else, and hype-driven as they are I fear they will run this industry to the ground and we as users and customers will have to deal with the world where software is permanently broken as a giant pile of unmaintainable vibe code and no experienced junior developers to boot.

The Death of the "Stare": Why AI’s "Confident Stupidity" is a Threat to Human Genius OPINION | THE REALITY CHECK In the gleaming offices of Silicon Valley and the boardrooms of the Fortune 500, a new religion has taken hold. Its deity is the Large Language Model, and its disciples—the AI Evangelists—speak in a dialect of "disruption," "optimization," and "seamless integration." But outside the vacuum of the digital world, a dangerous friction is building between AI’s statistical hallucinations and the unyielding laws of physics. The danger of Artificial Intelligence isn't that it will become our overlord; the danger is that it is fundamentally, confidently, and authoritatively stupid. The Paradox of the Wind-Powered Car The divide between AI hype and reality is best illustrated by a recent technical "solution" suggested by a popular AI model: an electric vehicle equipped with wind generators on the front to recharge the battery while driving. To the AI, this was a brilliant synergy. It even claimed the added weight and wind resistance amounted to "zero." To any human who has ever held a wrench or understood the First Law of Thermodynamics, this is a joke—a perpetual motion fallacy that ignores the reality of drag and energy loss. But to the AI, it was just a series of words that sounded "correct" based on patterns. The machine doesn't know what wind is; it only knows how to predict the next syllable. The Erosion of the "Human Spark" The true threat lies in what we are sacrificing to adopt this "shortcut" culture. There is a specific human process—call it The Stare. It is that thirty-minute window where a person looks at a broken machine, a flawed blueprint, or a complex problem and simply observes. In that half-hour, the human brain runs millions of mental simulations. It feels the tension of the metal, the heat of the circuit, and the logic of the physical universe. It is a "Black Box" of consciousness that develops solutions from absolutely nothing—no forums, no books, and no Google. However, the new generation of AI-dependent thinkers views this "Stare" as an inefficiency. By outsourcing our thinking to models that cannot feel the consequences of being wrong, we are witnessing a form of evolutionary regression. We are trading hard-earned competence for a "Yes-Man" in a box. The Gaslighting of the Realist Perhaps most chilling is the social cost. Those who still rely on their intuition and physical experience are increasingly being marginalized. In a world where the screen is king, the person pointing out that "the Emperor has no clothes" is labeled as erratic, uneducated, or naive. When a master craftsman or a practical thinker challenges an AI’s "hallucination," they aren't met with logic; they are met with a robotic refusal to acknowledge reality. The "AI Evangelists" have begun to walk, talk, and act like the models they worship—confidently wrong, devoid of nuance, and completely detached from the ground beneath their feet. The High Cost of Being "Authoritatively Wrong" We are building a world on a foundation of digital sand. If we continue to trust AI to design our structures and manage our logic, we will eventually hit a wall that no "prompt" can fix. The human brain runs on 20 watts and can solve a problem by looking at it. The AI runs on megawatts and can’t understand why a wind-powered car won't run forever. If we lose the ability to tell the difference, we aren't just losing our jobs—we're losing our grip on reality itself.

"Become better at intuiting the behavior of this non-deterministic black box oracle maintained by a third party" just isn't a strong professional development sell for me, personally. If the future of writing software is chasing what a model trainer has done with no ability to actually change that myself I don't think that's going to be interesting to nearly as many people.

[Edit: I may have been replying to another comment in my head as now I re-read it and I'm not sure I've said the same thing as you have. Oh well.] I agree. This is how I see it too. It's more like a shortcut to an end result that's very similar (or much better) than I would've reached through typing it myself. The other day I did realise that I'm using my experience to steer it away from bad decisions a lot more than I noticed. It feels like it does all the real work, but I have to remember it's my/our (decades of) experience writing code playing a part also. I'm genuinely confused when people come in at this point and say that it's impossible to do this and produce good output and end results.

> the scope is so small there's not much point in using an LLM Actually that's how I did most of my work last year. I was annoyed by existing tools so I made one that can be used interactively. It has full context (I usually work on small codebases), and can make an arbitrary number of edits to an arbitrary number of files in a single LLM round trip. For such "mechanical" changes, you can use the cheapest/fastest model available. This allows you to work interactively and stay in flow. (In contrast to my previous obsession with the biggest, slowest, most expensive models! You actually want the dumbest one that can do the job.) I call it "power coding", akin to power armor, or perhaps "coding at the speed of thought". I found that staying actively involved in this way (letting LLM only handle the function level) helped keep my mental model synchronized, whereas if I let it work independently, I'd have to spend more time catching up on what it had done. I do use both approaches though, just depends on the project, task or mood!

I think you are right, the secret is that there is no secret. The projects I have been involved with thats been most successful was using these techniques. I also think experience helps because you develop a sense that very quickly knows if the model wants to go in a wonky direction and how a good spec looks like. With where the models are right now you still need a human in the loop to make sure you end up with code you (and your organisation) actually understands. The bottle neck has gone from writing code to reading code.

I still use the chatbot but like to do it outside-in. Provide what I need, and instruct it to not write any code except the api (signatures of classes, interfaces, hierarchy, essential methods etc). We keep iterating about this until it looks good - still no real code. Then I ask it to do a fresh review of the broad outline, any issues it foresees etc. Then I ask it to write some demonstrator test cases to see how ergonomic and testable the code is - we fine tune the apis but nothing is fleshed out yet. Once this is done, we are done with the most time consuming phase. After that is basically just asking it to flesh out the layers starting from zero dependencies to arriving at the top of the castle. Even if we have any complexities within the pieces or the implementation is not exactly as per my liking, the issues are localised - I can dive in and handle it myself (most of the time, I don't need to). I feel like this approach works very well for me having a mental model of how things are connected because the most of the time I spent was spent on that model.

I don't understand how Agents make you feel productive. Single/Multiple agents reading specs, specs often produced with agents itself and iterated over time with human in the loop, a lot of reviewing of giant gibberish specs. Never had a clear spec in my life. Then all the dancing for this apperantly new paradigm, of not reviewing code but verifying behaviour, and so many other things. All of this to me is a total UNproductive mess. I use Cursor autocomplete from day one till to this day, I was super productive before LLMs, I'm more productive now, I'm capable, I have experience, product is hard to maintain but customers are happy, management is happy. So I can't really relate anymore to many of the programmers out there, that's sad, I can count on my hands devs that I can talk to that have hard skills and know-how to share instead of astroturfing about AI Agents

With "Never had a clear spec in my life" what I mean is also that I don't how something should come out till I'm actually doing it. Writing code for me lead to discovery, I don't know what to produce till I see it in the wrapping context, like what a function should accept, for example a ref or a copy. Only at that point I have the proper intuition to make a decision that has to be supported long term. I don't want cheap code now I want a solit feature working tomorrow and not touching it for a long a time hopefully

> Never had a clear spec in my life. Just because you haven't or you work in a particular way, doesn't mean everyone does things the same way. Likewise, on your last point, just because someone is using AI in their work, doesn't mean they don't have hard skills and know-how. Author of this article Mitchell is a great example of that - someone who proved to be able to produce great software and, when talking about individuals who made a dent in the industry, definitely had/has an impactful career.

Never mentioned Mitchell I'm generally speaking, 95% of industry is not Mitchell

How much does it cost per day to have all these agents running on your computer? Is your company paying for it or you? What is your process of the agent writes a piece of code, let's say a really complex recursive function, and you aren't confident you could have come up with the same solution? Do you still submit it?

It's amusing how everyone seems to be going through the same journey. I do run multiple models at once now. On different parts of the code base. I focus solely on the less boring tasks for myself and outsource all of the slam dunk and then review. Often use another model to validate the previous models work while doing so myself. I do git reset still quite often but I find more ways to not get to that point by knowing the tools better and better. Autocompleting our brains! What a crazy time.

I've been building systems like what the OP is using since gpt3 came out. This is the honeymoon phase. You're learning the ins and outs of the specific model you're using and becoming more productive. It's magical. Nothing can stop you. Then you might not be improving as fast as you did at the start, but things are getting better every day. Or maybe every week. But it's heaps better than doing it by hand because you have so much mental capacity left. Then a new release comes up. An arbitrary fraction of your hard earned intuition is not only useless but actively harmful to getting good results with the new models. Worse you will never know which part it is without unlearning everything you learned and starting over again. I've had to learn the quirks of three generations of frontier families now. It's not worth the hassle. I've gone back to managing the context window in Emacs because I can't be bothered to learn how to deal with another model family that will be thrown out in six months. Copy and paste is the universal interface and being able to do surgery on the chat history is still better than whatever tooling is out there. Unironically learning vim or Emacs and the standard Unix code tools is still the best thing you can do to level up your llm usage.

I can't speak for parent, but I use gptel, and it sounds like they do as well. It has a number of features, but primarily it just gives you a chat buffer you can freely edit at any time. That gives you 100% control over the context, you just quickly remove the parts of the conversation where the LLM went off the rails and keep it clean. You can replace or compress the context so far any way you like. While I also use LLMs in other ways, this is my core workflow. I quickly get frustrated when I can't _quickly_ modify the context. If you have some mastery over your editor, you can just run commands and post relevant output and make suggested changes to get an agent like experience, at a speed not too different from having the agent call tools. But you retain 100% control over the context, and use a tiny fraction of the tokens OpenCode and other agents systems would use. It's not the only or best way to use LLMs, but I find it incredibly powerful, and it certainly has it's place. A very nice positive effect I noticed personally is that as opposed to using agents, I actually retain an understanding of the code automatically, I don't have to go in and review the work, I review and adjust on the fly.

You come across as if you didn't read my post. I'll wait for OP to move their workflow to Claude 7.0 and see if they still feel as bullish on AI tools. People who are learning a new AI tool for the first time don't realzie that they are just learning quirks of the tool and underlying and not skills that generalize. It's not until you've done it a few times that you realzie you've wasted more than 80% of your time on a model that is completely useless and will be sunset in 6 months.

LLMs are not for me. My position is that the advantage we humans have over the rest of the natural world, is our minds. Our ability to think, create and express ideas is what separates us from the rest of the animal kingdom. Once we give that over to "thinking" machines, we weaken ourselves, both individually and as a species. That said, I've given it a go. I used zed, which I think is a pretty great tool. I bought a pro subscription and used the built in agent with Claude Sonnet 4.x and Opus. I'm a Rails developer in my day job, and, like MitchellH and many others, found out fairly quickly that tasks for the LLM need to be quite specific and discrete. The agent is great a renames and minor refactors, but my preferred use of the agent was to get it to write RSpec tests once I'd written something like a controller or service object. And generally, the LLM agent does a pretty great job of this. But here's the rub: I found that I was losing the ability to write rspec. I went to do it manually and found myself trying to remember API calls and approaches required to write some specs. The feeling of skill leaving me was quite sobering and marked my abandonment of LLMs and Zed, and my return to neovim, agent-free. The thing is, this is a common experience generally. If you don't use it, you lose it. It applies to all things: fitness, language (natural or otherwise), skills of all kinds. Why should it not apply to thinking itself. Now you may write me and my experience off as that of a lesser mind, and that you won't have such a problem. You've been doing it so long that it's "hard-wired in" by now. Perhaps. It's in our nature to take the path of least resistance, to seek ease and convenience at every turn. We've certainly given away our privacy and anonymity so that we can pay for things with our phones and send email for "free". LLMs are the ultimate convenience. A peer or slave mind that we can use to do our thinking and our work for us. Some believe that the LLM represents a local maxima, that the approach can't get much better. I dunno, but as AI improves, we will hand over more and more thinking and work to it. To do otherwise would be to go against our very nature and every other choice we've made so far. But it's not for me. I'm no MitchellH, and I'm probably better off performing the mundane activities of my work, as well as the creative ones, so as to preserve my hard-won knowledge and skills. YMMV I'll leave off with the quote that resonates the most with me as I contemplate AI:- "I say your civilization, because as soon as we started thinking for you, it really became our civilization, which is, of course, what this is all about." -- Agent Smith "The Matrix"

I was using it the same way you just described but for C# and Angular and you're spot on. It feels amazing not having to memorize APIs and just let the AI even do code coverage near to 100%, however at some point I began noticing 2 things: - When tests didn't work I had to check what was going on and the LLMs do cheat a lot with Volkswagen tests, so that began to make me skeptic even of what is being written by the agents - When things were broken, spaghetti and awful code tends to be written in an obnoxius way it's beyond repairable and made me wish I had done it from scratch. Thankfully I just tried using agents for tests and not for the actual code, but it makes me think a lot if "vibe coding" really produces quality work.

The Death of the "Stare": Why AI’s "Confident Stupidity" is a Threat to Human Genius OPINION | THE REALITY CHECK In the gleaming offices of Silicon Valley and the boardrooms of the Fortune 500, a new religion has taken hold. Its deity is the Large Language Model, and its disciples—the AI Evangelists—speak in a dialect of "disruption," "optimization," and "seamless integration." But outside the vacuum of the digital world, a dangerous friction is building between AI’s statistical hallucinations and the unyielding laws of physics. The danger of Artificial Intelligence isn't that it will become our overlord; the danger is that it is fundamentally, confidently, and authoritatively stupid. The Paradox of the Wind-Powered Car The divide between AI hype and reality is best illustrated by a recent technical "solution" suggested by a popular AI model: an electric vehicle equipped with wind generators on the front to recharge the battery while driving. To the AI, this was a brilliant synergy. It even claimed the added weight and wind resistance amounted to "zero." To any human who has ever held a wrench or understood the First Law of Thermodynamics, this is a joke—a perpetual motion fallacy that ignores the reality of drag and energy loss. But to the AI, it was just a series of words that sounded "correct" based on patterns. The machine doesn't know what wind is; it only knows how to predict the next syllable. The Erosion of the "Human Spark" The true threat lies in what we are sacrificing to adopt this "shortcut" culture. There is a specific human process—call it The Stare. It is that thirty-minute window where a person looks at a broken machine, a flawed blueprint, or a complex problem and simply observes. In that half-hour, the human brain runs millions of mental simulations. It feels the tension of the metal, the heat of the circuit, and the logic of the physical universe. It is a "Black Box" of consciousness that develops solutions from absolutely nothing—no forums, no books, and no Google. However, the new generation of AI-dependent thinkers views this "Stare" as an inefficiency. By outsourcing our thinking to models that cannot feel the consequences of being wrong, we are witnessing a form of evolutionary regression. We are trading hard-earned competence for a "Yes-Man" in a box. The Gaslighting of the Realist Perhaps most chilling is the social cost. Those who still rely on their intuition and physical experience are increasingly being marginalized. In a world where the screen is king, the person pointing out that "the Emperor has no clothes" is labeled as erratic, uneducated, or naive. When a master craftsman or a practical thinker challenges an AI’s "hallucination," they aren't met with logic; they are met with a robotic refusal to acknowledge reality. The "AI Evangelists" have begun to walk, talk, and act like the models they worship—confidently wrong, devoid of nuance, and completely detached from the ground beneath their feet. The High Cost of Being "Authoritatively Wrong" We are building a world on a foundation of digital sand. If we continue to trust AI to design our structures and manage our logic, we will eventually hit a wall that no "prompt" can fix. The human brain runs on 20 watts and can solve a problem by looking at it. The AI runs on megawatts and can’t understand why a wind-powered car won't run forever. If we lose the ability to tell the difference, we aren't just losing our jobs—we're losing our grip on reality itself.

For the AI skeptics reading this, there is an overwhelming probability that Mitchell is a better developer than you. If he gets value out of these tools you should think about why you can't.

Perhaps that's the reason. Maybe I'm just not a good enough developer. But that's still not actionable. It's not like I never considered being a better developer.

The value Mitchell describes aligns well with the lack of value I'm getting. He feels that guiding an agent through a task is neither faster nor slower than doing it himself, and there's some tasks he doesn't even try to do with an agent because he knows it won't work, but it's easier to parallelize reviewing agentic work than it is to parallelize direct coding work. That's just not a usage pattern that's valuable to me personally - I rarely find myself in a situation where I have large number of well-scoped programming tasks I need to complete, and it's a fun treat to do myself when I do.

Don't get it. What's the relation between Mitchell being a "better" developer than most of us (and better is always relative, but that's another story) and getting value out of AI? That's like saying Bezos is a way better businessman than you, so you should really hear his tips about becoming a billionaire. No sense (because what works for him probably doesn't work for you) Tons of respect for Mitchell. I think you are doing him a disservice with these kinds of comments.

Maybe you disagree with it, but it seems like a pretty straightforward argument: A lot of us dismiss AI because "it can't be trusted to do as good a job as me". The OP is arguing that someone, who can do better than most of us, disagrees with this line of thinking. And if we have respect for his abilities, and recognize them as better than our own, we should perhaps re-assess our own rationale in dismissing the utility of AI assistance. If he can get value out of it, surely we can too if we don't argue ourselves out of giving it a fair shake. The flip side of that argument might be that you have to be a much better programmer than most of us are, to properly extract value out of the AI... maybe it's only useful in the hands of a real expert.

>A lot of us dismiss AI because "it can't be trusted to do as good a job as me" Some of us enjoy learning how systems work, and derive satisfaction from the feeling of doing something hard, and feel that AI removes that satisfaction. If I wanted to have something else write the code, I would focus on becoming a product manager, or a technical lead. But as is, this is a craft, and I very much enjoy the autonomy that comes with being able to use this skill and grow it.

There is no dichotomy of craft and AI. I consider myself a craftsman as well. AI gives me the ability to focus on the parts I both enjoy working on and that demand the most craftsmanship. A lot of what I use AI for and show in the blog isn’t coding at all, but a way to allow me to spend more time coding. This reads like you maybe didn’t read the blog post, so I’ll mention there many examples there.

I enjoy Japanese joinery, but for some reason the housing market doesn't.

Nobody is trying to talk anyone out of their hobby or artisanal creativeness. A lot of people enjoy walking, even after the invention of the automobile. There's nothing wrong with that, there are even times when it's the much more efficient choice. But in the context of say transporting packages across the country... it's not really relevant how much you enjoy one or the other; only one of them can get the job done in a reasonable amount of time. And we can assume that's the context and spirit of the OP's argument.

>Nobody is trying to talk anyone out of their hobby or artisanal creativeness. Well, yes, they are, some folks don't think "here's how I use AI" and "I'm a craftsman!" are consistent. Seems like maybe OP should consider whether "AI is a tool, why can't you use it right" isn't begging the question. Is this going to be the new rhetorical trick, to say "oh hey surely we can all agree I have reasonable goals! And to the extent they're reasonable you are unreasonable for not adopting them"?

> a period of inefficiency I think this is something people ignore, and is significant. The only way to get good at coding with LLMs is actually trying to do it. Even if it's inefficient or slower at first. It's just another skill to develop [0]. And it's not really about using all the plugins and features available. In fact, many plugins and features are counter-productive. Just learn how to prompt and steer the LLM better. [0]: https://ricardoanderegg.com/posts/getting-better-coding-llms...