If you've been writing Perl for 30 years, you might not want to learn JavaScript just to make a little fun idea in your head to show your wife. Vibe code that shit man. Who cares? Your wife does not care about LOC or those internal design decisions you made.
If you're trying to learn something new like an algorithm, protocol, or API write that shit by hand. You learn by doing, and when you know how the thing works and have that mental context, you will always be faster than an AI. Also, when did we stop liking to learn? Why is it a bad thing to know all the ins and outs of a programming language? To write and make all the decisions yourself? That shit is fun. I don't care if you disagree.
If you're at work and they really care about getting something out of the door, do whatever you think is best. If you just wanna ship vibed code and review PRs all day, all the power to you. If you wanna write it by hand, and use AI like a scalpel to write up boiler plate, review code, do PR audits, etc... go for it!
A hammer is a really great tool that has thousands of purpose-designed uses. I still prefer my key to get into my car. It's all tools, you are a person.
A lot of this stuff if coming top-down from people who do not have the experience you do. Wouldn't a smart employee use their expertise to advise the organization? If you work at a company where that would not be okay, maybe it's time to start looking for another firm.
I suspect it happened when we achieved a level of such constant stimulation (there is a pocket computer always on us with infinite effortless distraction) that we’re never bored and never engage the default mode network.
https://en.wikipedia.org/wiki/Default_mode_network
https://www.youtube.com/watch?v=orQKfIXMiA8
When you’re bored, your mind goes to places it wouldn’t otherwise go. Curiosity kicks in. Curiosity is a precursor to learning. Learning engages the brain and is fun. But it’s not fun all the time, some of it is challenging and frustrating (which is good, that’s the process that teaches you).
When you have the digital equivalent to infinite candy and the brain equivalent to a sweet tooth, it’s hard to resist the siren’s call. The consequence is the brain equivalent to a stomachache—depression and loss of meaning—but unfortunately it doesn’t hit you the same way so you don’t make the immediate connection to make yourself stop. When you think about it, it’s ridiculous from several angles: the candy is infinite, it’s never going to run out, so you don’t need to gorge! But then we justify ourselves as only a true addict would, that while the candy is infinite, the flavours are limited editions and always rotating, and what if I miss that really good one everyone is on?! Then you miss it, is the answer. No one will be talking about it in fifteen minutes anyway.
> when you know how the thing works and have that mental context, you will always be faster than an AI
That's just plain false, honestly. No one can type at the speed AI can code, even factoring in the time you need to spend to properly write out the spec & design rules the AI needs to follow when implementing your app/feature/whatever. And that gap will only increase as LLMs get more intelligent.
I do not know the inns and out of the assembly layer my high level code end up as. It's not because I don't like to learn, it's because I genuinely don't need to. At a certain level of AI performance, how will this be any different?
I made a checklist for my kids to stamp off items after they get back from school (sort bag, get changed, etc). I had two goals, 1) I was trying to solve a problem at home and would have pip installed a library that just straight up did this already and 2) I wanted to check out what the claude website outputs was like at the time. My time was best spent poking at claude a bit but mostly playing with my kids - so vibe coding it was.
Client test speedup issues, I'm trying to speed up tests for them and spend as little time as possible doing so. Vibe coded some analysis and visualisation tools, mostly AI but with some review guided multiple prototypes for timing and let it just fix whatever. More dedicated review for the actual solutions.
Learning a new thing - goal is to learn that thing. AI there is good for doing a lot of the work around that. Maybe I'm focussing on, say, Z3. AI there can help with debugging, finding docs, setting up an environment and leave me to do the central part.
The problem is mixing vibe-coding and agentic-eng, and switching the brain in 2 different modes (fast-feedback gratification vs deep-focus gratification).
There’s no clear cut rule on what works. Different people, different brains, and especially amongst devs some optimized low-key neurodivergence.
And then there’s waiting mode, those N seconds/minutes that agents take to think and write.
What’s the right mix? Keep a main focused project and … what do you do in the meantime? Vibe code something else? Hn? Social media? Draw lines on a paper sheet? Wood carving? Exercise? Rewatch some old tv series?
I have experimented….
There are side activities that help you go back to the task at hand in the correct mental framework for it. Not just for productivity, but for efficiency and enhancing critical thinking on the main task. Or whatever you choose to optimize for. Can anyone point me towards some people talking about this?
When the economy got so bad for so many people, that every waking moment has to be either chasing fresh cash (or spent in recovery from cash-chasing, worrying about new cash), to the point they have to largely ignore their own long term goals or basic morals or principles.
You can blame all the new gadgets (phones/social media/tiktok/‘dopamine-things’) — but it’s a very much blaming the symptom, not the problem.
(It’s the meme. “Guys, this isn’t funny. Humans only do this when they’re very distressed”)
Been saying the 'Hammer is a great tool but you need to know when to use it, just like AI.' to coworkers, and i'm ̶s̶t̶e̶a̶l̶i̶n̶g̶ borrowing your quote instead, now
I could have learn all the frameworks to make it, but honestly I wouldn't have bothered.
It's not just fun (i agree it is), but it is also essential for creation.
What we have done with the 'AI' is to create a lot of ignorant morons who think they can create a lot of things without knowledge. This is not gonna end well.
> If you're at work and they really care about getting something out of the door, do whatever you think is best.
If you don’t mind being jobless, sure do whatever you think is best. Not all of us can simply switch companies easily. Folks need to realise that AI in a company setting works for the benefit of the company, not for the individual.
And no, this isn't playing what ifs.
I have seen it happening with offshoring, migration to cloud, serverless, SaaS and iPaaS products, and now AI powered automations via agents.
Less devops people, less backend devs , no translation team, no asset creation team,...
I have been layoff a few times, having to do competence transfer to offshoring teams, the quality of the output is something c suites don't care all.
Do you wanna bet what is behind Microslop, Apple Tahoe bugs and so forth?
Plenty of engineers have loose (or no!) standards and practices over how they write coee. Similarly, plenty of engineering teams have weak and loose standards over how code gets pushed to production. This concept isn't new, it's just a lot easier for individuals and teams who have never really adhered to any sort of standards in their SDLC to produce a lot more code and flesh out ideas.
I personally don’t know any colleagues who were good engineers just because they wrote code faster. The best engineers I know were ones who drew on experience and careful consideration and shared critical insights with their team that steered the direction of the system positively.
> Claude, engineer a system for me, but do it good. Thanks!
I don't know if good engineers can necessarily continue to be good. There is limit to how much careful consideration one can give if everything is on an accelerated timeline. Regardless good or not, there is limit on how much influence you have on setting those timelines. The whole playing field is changing.
Same, if anything, the opposite seems to be true, the ones that I'd call "good engineers" were slower, less panicked when production was down and could reason their way (slowly) through pretty much anything thrown at them.
Opposite experience, I've sit next to developers who are trying their fastest to restore production and then making more mistakes to make it even worse, or developers who rush through the first implementation idea they had for a feature, missing to consider so many things and so on.
Unfortunately I have seen some really good software engineering peers regress into bad engineers through a increasing reliance on AI.
Conversely some very bad engineers (undeserving of the title) have been producing better outputs than I ever expected possible of them.
However, the best engineers I know are usually among the quickest to open an editor or debugger and use it fluently to try something out. It's precisely that speed that enables a process like "let's try X, hmm, how about Y, no... ok, Z is nice; ok team, here are the tradeoffs...". Then they remember their experience with X, Y, and Z, and use it to shape their thinking going forward.
Meanwhile, other engineers have gotten X to finally mostly work and are invested in shipping it because they just want to be done. In my experience, this is how a lot of coding agents seem to act.
It's not obvious to me how to apply the expert loop to agentic coding. Of course you can ask your agent to try several different things and pick the best, or ask it to recommend architectural improvements that would make a given change easier...
Unfortunately thoughtful design and engineering doesn't get recognised
The risk isn't that agents write bad code. It's that developers lose the sense that tells them where code is bad. Code review is perception. Writing code is proprioception. They're different senses and one doesn't substitute for the other.
The question for the agent era isn't "is the code good enough to ship" — it's "do I still have enough coupling to the codebase to know when it isn't?"
Loss of discipline can be a result of panic or greed.
Perhaps believing that your own costs or your competitors' costs are suddenly becoming 10x lower could inspire one of those conditions?
(Also for greenfield projects specifically, it can plausibly be an experiment just to verify what happens. Some orgs are big enough that of course they can put a couple people on a couple-month project that'll quite likely fall flat.)
I figure if it cant code when it has all of the necessary context available and when obscure failures are easily detected then why would i trust it when building features and fixing bugs?
It never did get good enough at refactoring.
Lead engineer says something is not workable? Pm overrides saying that Claude code could do it. Problems found months later at launch and now the engineers are on the hook.
New junior onboardee declares that their new vision is the best and gets management onto it cuz it’s trendy -> broken app.
It’s made collaboration nearly unbearable as you are beholden to the person with the lowest standards.
Exactly right.
As models get better, they seem to be biased to doing most of these things without needing to be told. Also, coding tools come with built in skills and system prompts that achieve similar things.
Two years ago I was copy pasting together a working python fast API server for a client from ChatGPT. This was pre-agentic tooling. It could sort of do small systems and work on a handful of files. I'm not a regular python user (most of my experience is kotlin based) but I understand how to structure a simple server product. Simple CRUD stuff. All we're talking here was some APIs, a DB, and a few other things. I made it use async IO and generate integration tests for all the endpoints. Took me about a day to get it to a working state. Python is simple enough that I can read it and understand what it's doing. But I never used any of the frameworks it picked.
That's 2 years ago. I could probably condense that in a simple prompt and achieve the same result in 15 minutes or so. And there would be no need for me to read any of that code. I would be able to do it in Rust, Go, Zig, or whatever as well. What used to be a few days of work gets condensed into a few minutes of prompt time. And that's excluding all the BS scrum meetings we'd have to have about this that and the other thing. The bloody meetings take longer than generating the code.
A few weeks ago I did a similar effort around banging together a Go server for processing location data. I've been working against a pretty detailed specification with a pretty large API surface and I wanted an OSS version of that. I have almost no experience with Go. I'd be fairly useless doing a detailed code review on a Go code base. So, how can I know the thing works? Very simple, I spent most of my time prompting for tests for edge cases, benchmarking, and iterating on internal architecture to improve the benchmark. The initial version worked alright but had very underwhelming performance. Once I got it doing things that looked right to me, I started working on that.
To fix performance, I iterated on trying to figure out what was on the critical path and why and asking it for improvements and pointed questions about workers, queues, etc. In short, I was leaning on my experience of having worked on high throughput JVM based systems. I got performance up to processing thousands of locations per second; up from tens/hundreds. This system is intended for processing high frequency UWB data. There probably is some more wiggle room there to get it up further. I'm not done yet. The benchmark I created works with real data and I added generated scripts to replay that data and play it back at an accelerated rate with lots of interpolated position data. As a stress test it works amazingly well.
This is what agentic engineering looks like. I'm not writing or reviewing code. But I still put in about a week plus of time here and I'm leaning on experience. It's not that different from how I would poke at some external component that I bought or sourced to figure out if it works as specified. At some point you stop hitting new problems and confidence levels rise to a point where you can sign off on the thing without ever having seen the code. Having managed teams, it's not that different from tasking others to do stuff. You might glance at their work but ultimately they do the work, not you.
If the code doesn't compile, that's easy to spot. If the code compiles but doesn't work, that's still somewhat easy to spot.
If the code compiles and works, but it does the wrong thing in some edge case, or has a security vulnerability, or introduces tech debt or dubious architectural decisions, that's harder to spot but doesn't reduce the review burden whatsoever.
If anything, "truthy" code is more mentally taxing to review than just obviously bad code.
The current fever pitch mandates from above seem to want it applied liberally, and pushing back against that is so discouraging and often career-limiting as to wear the fabric of one's psyche threadbare. With all the obvious problems being pointed out to people, there are just as many workarounds; and these workarounds, as is often revealed shortly thereafter, have their own problems, which beget new solutions, ad infinitum.
At some point it genuinely seems like all this work is for the sake of the machine itself. I suppose that is true: The real goal has become obscured at so many firms today, that all that remains is the LLM. Are the people betting the farm and helping implement the visions of those who have done so guaranteed a soft exit to cushion them from the consequences, or is rationality really being discarded altogether?
Sure, sound engineering principles can help work around these problems, but what efficiency is truly gained, in terms of cognitive load, developer time, money, or finite resources? Or were those ever an earnest concern?
1. They're low stakes to get wrong.
2. The most common is MCPs or similar ai-tooling.
3. Making them look good takes time and effort still. It's a multiplier, not a replacement.
4. Quality and maintainability require investment. I had to restart an agentic project several times because it painted itself into a corner.
It’s an absolute game changer, and it can now multiply your productivity fivefold if it’s a solo greenfield project.
Maybe half a year ago it was as you said. You had to wait for the agent to finish, you had to review carefully, and often the result was not that great. You did not save a lot of time.
Now I can spin up 3+ parallel conversations in Codex, each in a git worktree. My work is mainly QA testing the features, refining the behavior, and sometimes making architectural decisions.
The results are now undeniable. In the past I could not have developed a product of that scope in my free time.
That is what is possible today. I suspect many engineers have not yet tried things that became feasible over the last months. Like parallel agents, resolving merge conflicts, separating out functionality from a large branch into proper PRs.
The degenerate side is clueless upper management and fad-driven engineering. We have talked extensively about this.
There is a more rational side to it that I've seen in my org: some engineers absolutely refuse to use AI and as a consequence they are now, clearly and objectively, much less productive than other engineers. The thing is, you still need to learn how to use the tool, so a nontrivial percentage of obstinate engineers need to be driven to use this in the same way that some developers have refused to use Docker or k8s or whatever.
Honest question: what about the counter-argument that humans make subtle mistakes all the time, so why do we treat AI any differently?
A difference to me is that when we manually write code, we reason about the code carefully with a purpose. Yes we do make mistakes, but the mistakes are grounded in a certain range. In contrast, AI generated code creates errors that do not follow common sense. That said, I don't feel this differentiation is strong enough, and I don't have data to back it up.
But another answer is that human autonomy is coupled to responsibility. For most line employees, if they mess up badly enough, it's first and foremost their problem. They're getting a bad performance review, getting fired, end up in court or even in prison. Because you bear responsibility for your actions, your boss doesn't have to watch what you're up to 24x7. Their career is typically not on the line unless they're deeply complicit in your misbehavior.
LLMs have no meaningful responsibility, so whoever is operating them is ultimately on the hook for what they do. It's a different dynamic. It's probably why most software engineers are not gonna get replaced by robots - your director or VP doesn't want to be liable for an agent that goes haywire - but it's also why the "oh, I have an army of 50 YOLO agents do the work while I'm browsing Reddit" is probably not a wise strategy for line employees.
Yes, as an engineer I make mistakes, but I could never make as many mistakes per day as an LLM can
We're investing in the human getting better rather than paying $100 to Anthropic and hoping that's enough that they don't make the product worse.
Their mental model doesn't map cleanly enough to yours, and so where for a human you'd have some way to follow their thought patterns and identify mistakes, here the alien makes mistakes that don't add up.
Like the alien has encyclopedic knowledge of op codes in some esoteric soviet MCU but sometimes forgets how to look for a function definition, says "It looks like the read tool failed, that's ok, I can just make a mock implementation and comment out the test for now."
If I get pwned because my AI agent wrote code that had a security vulnerability, none of my users are going to accept the excuse that I used AI and it's a brave new world. I will get the blame, not Anthropic or OpenAI or Google but me.
The same goes for if my AI generated code leads to data loss, or downtime, or if uses too many resources, or it doesn't scale, or it gives out error messages like candy.
The buck stops with me and therefore I have to read the code, line-by-line, carefully.
It's not even a formality. I constantly find issues with AI generated code. These things are lazy and often just stub out code instead of making a sober determination of whether the functionality can be stubbed out or not.
You could say "just AI harder and get the AI to do the review", and I do this a lot, but reviewing is not a neutral activity. A review itself can be harmful if it flags spurious issues where the fix creates new problems. So I still have to go through the AI generated review issue-by-issue and weed out any harmful criticism.
I feel like this is just not true. An JSON API endpoint also needs several decisions made.
- How should the endpoint be named
- What options do I offer
- How are the properties named
- How do I verify the response
- How do I handle errors
- What parts are common in the codebase and should be re-used.
- How will it potentially be changed in the future.
- How is the query running, is the query optimized.
…
If I know the answer to all these questions, wiring it together takes me LESS time than passing it to Claude Code.
If I don’t know the answer the fastest way to find the answer is to start writing the code.
Additionally, whilst writing it I usually realize additional edge cases, optimizations, better logging, observability and what else.
The author clearly stated the context for this quote is production code.
I don’t see any benefits in passing it to Claude Code. It’s not that I need 1000s of JSON API endpoints.
That's just not true, and if it is in your case, then you're not great at writing prompts yet.
> Take the todo_items table in Postgres and build a Micronaut API based around it. The base URL should be /v1/todo_items. You can connect to Postgres with pguser:pgpass@1.2.3.4
That's about all it takes these days. Less lines of code than your average controller.
How so?
When I write code every character I type in my computer has less ambiguity than when I write it in human language? I also have the help of LSPs, Linters and Auto-completes.
Every verb implemented, and implemented correctly according to the obscure IETF and most compatible way when the IETF never made it clear
Intuitively named routes, error, authentication all easily done and swappable for another if necessary
I feel like our timeline split if you’re not seeing this
Use-cases differ, you described a complete REST API, which can be as much of a problem as a too little.
It is so embarrassing that LOC is being used as a metric for engineering output.
I have worked with code where 1000s of lines are very straightforward and linear.
I’ve worked on code where 100 lines is crucial and very domain specific. It can be exceptionally clean and well-commented and it still takes days to unpack.
The skills and effort required to review and understand those situations are quite different.
One is like distance driving a boring highway in the Midwest: don’t get drowsy, avoid veering into the indistinguishable corn fields, and you’ll get there. The other is like navigating a narrow mountain road in a thunderstorm: you’re 100% engaged and you might still tumble or get hit by lightning.
It's still useful, however, because that is the only metric that is instantly intuitively understandable and comparable across a wide variety of contexts, i.e. across companies and teams and languages and applications.
As we know, within the same team working on the same product, a 1000 LoC diff could take less time than a 1 line bug fix that took days to debug. Hence we really cannot compare PRs or product features or story points across contexts. If the industry could come up with a standard measure of developer productivity, you'd bet everyone would use it, but it's unfeasible basically for this very reason.
So, when such comparisons are made (and in this case it was clearly a colloquial usage), it helps to assume the context remains the same. Like, a team A working on product P at company C using tech stack T with specific software quality processes Q produced N1 lines of code yesterday, but today with AI they're producing N2 lines of code. Over time the delta between N1 and N2 approximates the actual impact.
(As an aside, this is also what most of the rigorous studies in AI-assisted developer productivity have done: measure PRs across the same cohorts over time with and without AI, like an A/B test.)
I rewrote the same program using my own brain and just using ChatGPT as google and autocomplete (my normal workflow), I produced the same thing in 1500 LOC.
The effort difference was not that significant either tbh although my hand coded approach probably benefited from designing the vibe coded one so I had already though of what I wanted to build.
My experience was the same as you when I started using agents for development about a year ago. Every time I noticed it did something less-than-optimal or just "not up to my standards", I'd hash out exactly what those things meant for me, added it to my reusable AGENTS.md and the code the agent outputs today is fairly close to what I "naturally" write.
We should have gone the other way; generated a lot of code and demanded pay raises; look at the LOC I cranked out! Company is now in my debt!
If they weren't going to care enough as managers to learn and line go up is all that matters to them, make all lines go up = winning
You all think there's more to this than performative barter for coin to spend on food/shelter.
> It is so embarrassing that LOC is being used as a metric for engineering output.
In one of my previous org, LOC added in the previous year was a metric used to find out a good engineer v/s a PIP (bad) engineer. Also, LOC removed was treated as a negative metric for the same. I hope they've changed this methodology for LLM code-spitting era...
Do you reject all stats that treat the number of people involved (eg. 2 million pepole protested X) as "embarrassing" ... because they lump incredibly varied people together and pretend they're equal?
AI helps eng ship more and faster, I think that’s the takeaway.
If we shift the paradigm of how we approach a coding problem, the coding agents can close that gap. Ten years ago every 10 or 15 minutes I would stop coding and start refactoring, testing, and analyzing making sure everything is perfect before proceeding because a bug will corrupt any downstream code. The coding agents don't and can't do this. They keep that bug or malformed architecture as they continue.
The instinct is to get the coding agents to stop at these points. However, that is impossible for several reasons. Instead, because it is very cheap, we should find the first place the agent made a mistake and update the prompt. Instead of fixing it, delete all the code (because it is very cheap), and run from the top. Continue this iteration process until the prompt yields the perfect code.
Ah, but you say, that is a lot of work done by a human! That is the whole point. The humans are still needed. The process using the tool like this yields 10x speed at writing code.
You could get to "something that works" rather fast but it took a long time to 1) evaluate other options (maybe before, maybe after), 2) refine it, 3) test it and build confidence around it.
I think your point stands but no one really knows where. The next year or so is going to be everyone trying to figure that out (this is also why we hear a lot of "we need to reinvent github")
I believe the llm providers went with the wrong approach from the off - the focus should’ve been on complementing labour not displacement. And I believe they have learned an expensive lesson along the way.
Shame that what is left for the humans is the shitty, tedious part of the work.. It reminds me of the quote:
I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do laundry and dishes..But the first time I say “No, it should be …” it’s nearly game over. If you say it 3+ times in a row, you’re basically doomed.
Sure, you can get it to fix the bug, but it comes at the cost of future prompts often barely working.
The person who builds an agentic IDE or GitHub alternative that natively does the process you describe will be a multibillionare.
And it's not just easier because it's cheap, it's easier because you're not emotionally attached to that code. Just let it produce slop, log what worked, what didn't, nuke the project and start over.
It just gets incredibly boring.
Vibe coding: one shot or few shot, smoke test the output, use it until it breaks (or doesn't). Ideal for lightweight PoC and low stakes individual, family or small team apps.
Agentic engineering: - You care about a larger subset of concerns such as functional correctness, performance, infrastructure, resilience/availability, scalability and maintainability. - You have a multi-step pipeline for managing the flow of work - Stages might be project intake, project selection, project specification, epic decomposition, d=story decomposition, coding, documentation and deployment. - Each stage will have some combination of deterministic quality gates (tests must pass, performance must hit a benchmark) and adversarial reviews (business value of proposed project, comprehensiveness of spec, elegance of code, rigor and simplicity of ubiquitous language, etc)
And it's a slider. Sometimes I throw a ticket into my system because I don't want to have to do an interview and burn tokens on three rounds of adversarial reviews, estimating potential value and then detailed specification and adversarial reviews just to ship a feature.
I've been using Opus, GPT-5.5, and some lesser models on a daily basis, but not having them handle entire tasks for me. Even when I go to significant effort to define and refine specs, they still do a lot of dumb things that I wouldn't allow through human PR review.
It would be really easy to just let it all slide into the codebase if I trusted their output or had built some big agentic pipeline that gave me a false sense of security.
Maybe 10 years from now the situation will be improved, but at the current point in time I think vibe coding and these agentic engineering pipelines are just variations of a same theme of abdicating entirely to the LLM.
This morning I was working on a single file where I thought I could have Opus on Max handle some changes. It was making mistakes or missing things on almost every turn that I had to correct. The code it was proposing would have mostly worked, but was too complicated and regressed some obvious simplifications that I had already coded by hand. Multiply this across thousands of agentic commits and codebases get really bad.
It's the bad, semi-coherent submissions that eat up your time, because you do want to award some points and tell students where they went wrong. It's the Anna Karenina principle applied to math.
Code review is the same thing. If you're sure Claude wrote your endpoint right, why not review it anyway? It's going to take you two minutes, and you're not going to wonder whether this time it missed a nuance.
Favorite quote:" There are a whole bunch of reasons I’m not scared that my career as a software engineer is over now that computers can write their own code, partly because these things are amplifiers of existing experience. If you know what you’re doing, you can run so much faster with them. [...]
I’m constantly reminded as I work with these tools how hard the thing that we do is. Producing software is a ferociously difficult thing to do. And you could give me all of the AI tools in the world and what we’re trying to achieve here is still really difficult. [...]"
If I hire a plumber it's certainly not cheaper than doing it myself but when I am paying money I want to make sure it is better quality than what I am vibe plumbing myself.
But resisting that impulse is just another part of being a professional. If your standards involve a certain level of test coverage, but your tests haven't flagged any issues in a long time, you might be tempted to write fewer tests as you continue to write more code. Being a professional means not giving in to that temptation. Keep to your quality standards.
Sure, standards are ultimately somewhat arbitrary, and experience can and should cause you to re-evaluate your standards sometimes to see if they need tweaking. But that should be done dispassionately, not in the middle of rushing to complete a task.
And hell, maybe someday the agents will get so good that our standards suggest that vibe coding is ok, and should be the norm. But you're still the one who's going to be responsible when something breaks.
Let's assume AI is 10x perfect than humnas in accuracy and produces 10x less bugs and increases the speed by 1000x compared to a very capable software engineer.
Now imagine this: A car travels at a road that has 10x more bumps but it is traveling 1000x slower pace so even though there are 10x bumps, your ride will feel less bumpy because you're encountering them at far lower pace.
Now imagine a road that has 10x less bumps on the road but you're traveling at 1000x the speed. Your ride would be lot more bumpy.
That's the agentic coding for you. Your ride would be a lot more painful. There's lots of denial around that but as time progresses it'll be very hard to deny.
Lastly - vibe coding is honest but agentic coding is snake oil [0] and these arguments about having harnesses that have dozens of memory, agent and skill files with rules sprinkled in them pages and pages of them is absolutely wrong as well. Such paradigm assumes that LLMs are perfect reliable super accurate rule followers and only problem as industry that we have is not being able to specify enough rules clearly enough.
Such a belief could only be held by someone who hasn't worked with LLMs long enough or is a totally non technical person not knowledgeable enough to know how LLMs work but holding on to such wrong belief system by highly technical community is highly regrettable.
I maxxed out Claude Max $200 subscription and before I justified spending $100/day.
And it was worth it, but not because it wrote me so good code, but because I learnt the lessons of software engineering fast. I had the exact ride you are describing. My software was incredible broken.
Now I see all the cracks, lies and "barking the wrong tree" issues clearly.
NOW i treat it as an untrustworyth search engine for domains I’m behind at. I also use predict next edit and auto-complete, but I don’t let AI do any edit on my codebase anymore.
And AI that has been helping all this time will suddenly stop helping out with this one use case. I have experienced AI running in circles, in this case trying to find a root cause. It failed, and the user is left holding the bag. That is when you feel like you have just been dropped into a vast ocean without a lifeboat. Then you'll have to just start looking through those massive chunks of vibe-coded crap to understand what is going on.
AI is good in terms of improving speed, but I am afraid we are massively taking it the wrong way as engineers. Everyone is just letting it go on autopilot and make it do things completely from start to end. The ideal solution lies where every piece of code it writes is reviewed by authors, and they make sure they are not checking in crazy stuff day in and day out.
Note: I still review pretty much every line of code that I own, regardless of who generates it, and I see the problems with agents very clearly... but I can also see the trends.
My take: Instead of crafting code, engineering will shift to crafting bespoke, comprehensive validation mechanisms for the results of the agents' work such that it is technically (maybe even mathematically) provable as far as possible, and any non-provable validations can be reviewed quickly by a human. I would also bet the review mechanisms would be primarily visually, because that is the highest bandwidth input available to us.
By comprehensive validations I don't mean just tests, but multiple overlapping, interlocking levels of tests and metrics. Like, I don't just have an E2E test for the UI, I have an overlapping test for expected changes in the backend DB. And in some cases I generate so many test cases that I don't check for individual rows, I look at the distribution of data before and after the test. I have very few unit tests, but I do have performance tests! I color-code some validation results so that if something breaks I instantly know what it may be.
All of this is overkill to do manually but is a breeze with agents, and over time really enables moving fast without breaking things. I also notice I have to add very few new validations for new code changes these days, so once the upfront cost is paid, the dividends roll in for a long time.
Now, I had to think deeply about the most effective set of technical constraints that give me the most confidence while accounting for the foibles of the LLMs. And all of this is specific to my projects, not much can be generalized other than high-level principles like "multiple interlocking tests." Each project will need its own custom validation (note: not just "test") suites which are very specific to its architecture and technical details.
So this is still engineering, but it will be vibe coding in the sense that we almost never look at the code, we just look at the results.
Other than for your own pet projects, almost all of what you said has no place for "vibe engineering" / or "vibe coding" on serious software engineering products that are needed in life and death situations.
And not all "production-grade, hundred billion dollar systems" are that critical. Like, Claude Code as we all know is clearly vibe-coded and is already a 10-billion (and rapidly increasing!) dollar system. Google Search and various Meta apps meet those criteria and people are already using LLMs on that code, and will soon be "vibe coding" as I described it.
AWS meets that criteria and has already had an LLM-caused outage! But that's not stopping them from doing even more AI coding. In fact I bet they will invest in more validation suites instead, because those are a good idea anyways. After all, all the cloud providers have been having outages long before the age of LLMs.
The thing most people are missing is that code is cheap, and so automated validations are cheap, and you get more bang for the buck by throwing more code in the form of extensive tests and validations at it than human attention.
Edited to add: I think I can rephrase the last line better thus: you get more bang for the buck by throwing human attention at extensive automated tests and validations of the code rather than at the code itself.
Pretty soon there is no code reuse and we're burning money reinventing the wheel over and over.
With LLMs, you can race right for that horizon, go right through, and continue far beyond! But then of course you find yourself in a place without reason (the real hell), with all the horror and madness that that entails.
Isn't this a bit like old Java or IDE-heavy languages like old Java/C#? If you tried to make Android apps back in the early days, you HAD to use an IDE, writing the ridicolous amount of boilerplate you had to write to display a "Hello Word" alert after clicking a button was soul destroying.
If the barrier is too high, code is refactored.
They really are bad for creating a healthy codebase
This is spot on. I think the tooling is evolving so much particularly on the design side that its not worth the "translation cost" to stay (or even be) on the Figma side anymore.
It's seriously the thing that worries (and bothers) me the most. I almost never let unedited LLM comments pass. At a minimum.
Most of the time, I use my own vibe-coded tool to run multiple GitHub-PR-review-style reviews, and send them off to the agent to make the code look and work fine.
It also struggles with doing things the idiomatic way for huge codebases, or sometimes it's just plain wrong about why something works, even if it gets it right.
And I say this despite the fact that I don't really write much code by hand anymore, only the important ones (if even!) or the interesting ones.
Also, don't even get me started on AI-generated READMEs... I use Claude to refine my Markdown or automatically handle dark/light-mode, but I try to write everything myself, because I can't stand what it generates.
"Ugh, no! Why would you say it like that? That's not even how it works! Now, I need to write a full paragraph instead of a short snippet to make sure that no future agents get confused in the same way."
Because most of the complexity in software comes from interfacing with external components, when you don't need to adapt to this you can write simpler and better code.
Rather than relying on an external library, you just write your own and have full control and can do quality control.
Linux kernel is 30 000 000 LOC. At 100 tokens /s, let's say 1 LOC per second produced for a single 4090 GPU, in one year of continuous running 3600 * 24 * 365 = 31 536 000 everyone can have its own OS.
It's the "Apps" story all over again : there are millions of apps, but the average user only have 100 max and use 10 daily at most.
Standardize data and services and you don't need that much software.
What will most likely happen is one company with a few millions GPUs will rewrite a complete software ecosystem, and people will just use this and stop doing any software because anything can be produced on the fly. Then all compute can be spent on consistent quality.
We've known this since close to the advent of computing and yet every generation of has taken us further away from this goal. Largely driven by jealous resource-guarding, particularly when it comes to data. Why don't I have a generic media player app that can stream Netflix, Disney, Hulu, etc? Those brands want control over my experience. They will continue to want that control indefinitely. That basic human desire for control won't evaporate with a "single unified codebase".
No, it was never designed around that. All methodologies of software dev don't focus too much on writing the code, but on everything else: requirement definition, quality, maintenance, speed of integrating feature, scaling the work, ...
Personally with 20 years of experience, I never seen a single company were writing the code was a bottleneck
Yeah. I'm not sure how other people work, but I almost never need to write formal tests because I essentially test locally as I write, one method at a time, and at that moment I have a complete mental map of everything that can potentially go wrong with a piece of code. I write and test constantly in tandem. I can write a test afterwards to prove what I already know, but I already know it. This is time consuming, anal, and obsessive-compulsive, and luckily that kind of work perfectly suits my personality. The end result is perfect before I commit it.
It is a lot of fun asking LLMs to write code around my code. Make 10 charts with chartjs in an html page that show something and put it behind a reverse proxy so the client can see it. Wow. Spot on, would've taken me an hour. I can even rely on Claude to somewhat honestly reason about things in personal projects.
But knowing every implementation decision makes a huge difference when anything real is at stake. "Guilt" wouldn't begin to describe the sense I'd have id my software did something because of a piece of code I hadn't personally reviewed and fully understood, at which point I probably should have just written it myself.
> If another team hands over something and says, “hey, this is the image resize service, here’s how to use it to resize your images”... I’m not going to go and read every line of code that they wrote.
The distance of accountability of the output from its producer is an important metric. Who will be held accountable for which output: that's important to maintain and not feel the "guilt".
So, organizations would need to focus on better and more granular building incentives and punishment mechanisms for large-scale software projects.
Property-based testing in particular has uncovered a number of invariants in every code base I've introduced it to.
tbf depending on the agent/model a lot of the tests end up being thrown out so it's possible I _should_ handwrite more tests, but having better prompts and detailed plans seems to mitigate that somewhat
Which is the same issue of lack of understanding and care and accountability from the human operator, with extra steps and a false sense of security.
Engineering is always about a defined process. We follow it to produce predictable artifacts that meet the specifications. Even though code is somewhat "squishy" in that it is an art just as much as a science, it still has to meet the spec.
This has always been true, even before agents started writing code for us. We've all dealt with spaghetti code because of undisciplined practices. That's exactly why we came up with the standard SDLC process: plan, design, code, test, deploy. Repeat.
The part people seem to forget about when looking at this is the space between the steps: the gates. We review the artifacts produced at each stage. If the reviewer does not approve, the engineer has to fix it until it passes. True for human coders, doubly true for agentic coders.
Agentic engineering still follows the process. Artifacts are now cheap to produce, which means we have to adjust it so we don't overwhelm the humans in the loop. For me, this means augmenting my review step with agentic reviewers to catch the dumb stuff. It only escalates to me when either a) it passes clean or b) there is something that genuinely needs my experience.
This is agentic engineering, not vibe coding.
Repeat after me: it follows that most of the money the software makes occurs during the maintenance phase.
Repeat after me: our industry still does not understand this after almost 100 years of being in existence.
Alan Kay was 100% right when he said that the computer revolution hasn't occurred yet. For all of our current advancements all tools are more or less in the Stone Age.
My great hope is that AI will actually accelerate us to a point where the existing paradigm fully breaks beyond healing and we can finally do something new, different, and better.
So for now - squeee! - put a jetpack on your SDLC with AI and go to town!!! Move fast and break things (like, for real).
My favorite JIRAs are the ones I prevent from being worked on in the first place because they were unnecessary.
The ideal prompt is the one I don't fire because it would be a waste.
In an application with an LLM component, the ideal amount of inference is zero.
Ultimately this seems to lead to "the ideal amount of computers in the world is none" but for the sake of my continued employment let's let that one go by. :)
I am not a developer and have very basic code knowledge. I recently built a small and lightweight Docker container using Codex 5.5/5.4 that ingests logs with rsyslog and has a nice web UI and an organized log storage structure. I did not write any code manually.
Even without writing code, I still had to use common sense in order to get it in a place I was happy with. If i truly knew nothing, the AI would have made some very poor decisions. Examples: it would have kept everything in main.go, it would have hardcoded the timezone, the settings were all hardcoded in the Go code, the crash handling was non existent, and a missing config would have prevented start. And that is on a ~3000 line app. I cannot imagine unleashing an AI on a large, complex. codebase without some decent knowledge and reviewing.
Opus 4.7 built it about 90% the same way I would, but had way more convenience methods and step-validations included.
It's great, and really frees me up to think about harder problems.
Just having ~13yrs experience heavily weighted in one language with some formal studying of others makes directing llms a lot simpler.
Learning syntax, primitives, package managers, testing, etc isn't that much of a lift compared to how I used to program.
Was helping a non-dev colleague who's using claude cowork/code to automate reporting the other day. They understand the business intelligence side well, but were struggling with basic diction to vibe code a pyautogui wrapper to pull up RDP and fill out a MS Access abstraction on a vendor DB.
Think we'll be fine for another 5-10 years as a profession
But using an agentic LLM to complete boilerplate is attractive simply because we've created a mountain of accidental and intentional complexity in building software. It's more of a regression to the mean of going back to the cognitive load we had when we simply built desktop applications.
I work on database optimizers and other database related stuff, and I can assure Claude Code - with all the highest settings - does make mistakes. It will generate a test that does not actually test what it "thinks" it tests. It will confidently break stuff.
Do not get me wrong. It is still awesome! It takes much of grunt work off me. It can game out designs decisions even when that needs to refactor a lot of code. If you point out a mistake more often than not it can fix it itself.
It's just for a critical project I would never ship it without understanding every line of code - with the exception perhaps of some of the test code. Maybe in a year or two that will be different.
People have been running crappy code commercially for over half a century now. Not many companies successfully differentiate by running good code - it usually does not matter to the end consumer, other things are much more important. So now companies will pay less for code, and maybe it is a bit worse (though I personally can't believe AI can do worse than corporate software developers on average). Hobbyists will remain hobbyists, and precious few will be lucky enough to have someone pay them to handcraft stuff. Exactly what happened to woodworkers and other craftsmen.
> The enterprise version of that is I don’t want a CRM unless at least two other giant enterprises have successfully used that CRM for six months. [...] You want solutions that are proven to work before you take a risk on them.
Perhaps not for every category of software and every company. But in practice, any SaaS app that is just CRUD with some business logic + workflows is, imo, absolutely vulnerable to losing customers because people within their customers' orgs vibe coded a replacement.
They are perhaps even more at risk because would-be new customers don't ever even bother searching to find them as an option because they just vibe code a competitor in-house.
The vulnerability lies primarily in the fact that most of these SaaS apps were talking about are _wrong_ to some meaningful degree. They don't fully fit how your company works, and they never did. There is something about them that you are forced to work around in some way. This is true because it is impossible to build a universally perfect product, to perfectly fit it to every business requirement of every user in every company.
But now it is relatively cheap to build the perfect version for your company in-house. Or maybe even just for YOU.
I think medium/long-term this will mean a redistribution of technical talent from SaaS companies to industry companies. Instead of paying millions for SaaS subscriptions, industry companies will spend fewer millions building precisely what they need in-house with the help of AI. Not every SaaS and not every company, but I already see this happening at my company right now.
Disclaimer: I'm doing a CAD-like engineering desktop app, and I'm using VS 2026 Copilot, so YMMV.
When I get a Jira ticket, I will first diagnose the problem, and then ask AI to write a test case for it that will reproduce the problem, with guidance on what/how to do the test case (you will be surprised to know how many geometry, seemingly visual problems can be unit tested), and if necessary I provide clues (like which files to read, etc.) for AI to look at, and ask AI to just go and fix the test.
Often AI can do that; AI can make the test pass and make sure that adjacent tests also pass. If in doubt, I will check the output reasoning. I then verify that the fix is done properly via visual inspection (remember, this is a desktop app), and I ask for clarification if needed.
Then at night I'll let my automated test suites run... and oops! Regression found! Who broke it? AI or human? Who cares. I just tell AI that between these times one of the commits must have broken the code — can you please fix it for me? And AI can do that.
This works for small or medium feature implementation, trival bugfixes, or even annoying geometrical problems that require me to dig out the needle in the haystack. So the productivity gain is very real. But I haven't tried it on feature that requires weeks or months for implementation, maybe I should try it next time.
It's hard to describe the feeling. It's just that the AI is working like a very capable (junior?) programmer; both might not have full domain knowledge, but with strong test suites and senior guidance, both can go very far. And of course AI is cheaper and a lot more effective.
The future is going to dynamically budget and route different parts of the SLDC through different models and subagents running on the cloud. Over time, more and more of that process will be owned by robots and a level of economic thinking will be incorporated into what is thought of today as "software engineering." At some point vibe coding _is_ coding and we're maybe closer to that point than popularly believed.
I find the LLM as interactive tutor reviewing my work in a proof checker to be a really killer combo.
How is producing more lines of code any good? How does quality assurance work with immeasurable code bloat? I want good software not slopware with 2000 different features. A good product does few things, but does these really well. There is no need to constantly add lines of code to a working product.
I'd say if you're a semi-competent developer, as probably many people reading the article and commenting already are, this comment adds nothing new to the discussion and would already be a very vanilla usage example of "AI".
I think the point is that while you can "do things" like extracting the stripe integrations out into their own service in ten minutes, you're not stepping into other problems, such as how do you handle failures, how do you scale the stripe service, how do you structure all your other micro services so they can communicate in a coherent way, basically you're speed running yourself into harder decisions when using AI.
What standard of result are you pursuing and are you willing to discipline yourself enough to achieve it?
AI can't make you un-lazy, no matter how many tokens you pay for.
* The first agent's claim that was 3.x-only was wrong * is nice-to-have but doesn't target our exact case as cleanly as the agent claimed. * The agent's "direct fix for yyy" is overstated. * not 57% as the earlier agent claimed
etc etc etc
And I forgot how many times my session with claude starts: did you read my personal CLAUDE.md and use background agents for long running operations?
I use enterprise subscription, max effort, was with both 4.6 and 4.7.
And please refrain from comments like "you're using it wrong", as the drop in output quality is very clear and noticeable.
Agentic Engineer does not make much sense to be applied to a developer.
It is weird and confusing to call a web designer that uses AI assisted coding tools "agentic engineer".
Claude Code in particular seems really uninterested in this aspect of the problem and I've stopped using entirely because of this.
I believe this is a common fault of not being able to zoom out and look at what trade offs are being made. There’s always trade-offs, the question is whether you can define them and then do the analysis to determine whether the result leaves you in a net benefit state.
Coding agents are also upending how software development works, in a way that we are still very much figuring out.
I don't think anyone has a confident answer for how best to apply them yet, especially on larger production-ready projects.
But building software still requires domain knowledge, understanding data structures, architecture, which services to use. We probably have 2-5 years before thats fully automated.
Nothing about this should be disturbing unless you want to dig your heels in, cross your arms and refuse to adapt.
AI is a massive opportunity. But if people focus on the issue of the 'change' they simply waste time they could (and should) be spending on integrating it correctly.
I believe that this form of resistance is far more stagnating and dangerous than any of the issues that come with the general onslaught of ai integration.
Without pre-defined definitions and locked procedures, it's extremely easy to mistake iterative adaptation for genuine signal.
So the number of bugs to find remains constant but the amount of code to review scales with the capability of the agent.
To me it’s a spectrum with varying levels of structure provided, review etc.
Basically oneshot vibes on one side, fully hand coded on other.
There are certain codebases and pieces of code we definitely want every line to be reasoned and understood. But like his API endpoint example, no reason to fuss with the boilerplate.
This has definitely been my shift over the past few months, and the advantage is I can spend much more time and energy on getting the code architecture just right, which automatically prevents most of the subtle bugs that has people wringing their hands. The new bar is architecting code to be defined as well as an API endpoint->service structure so you can rely on LLMs to paint by numbers for new features/logic.
Spend a lot more time on architecting and testing than hand rolling most repos now.
Hats off to people who enjoy the minutia of programming everything by hand, but turns out I enjoy the other aspects of software development more.
e.g, I change velocity of player to '200' and of bullets to '300', and it only updated the bullet velocity. Then told me the player was already 'at the correct value' even though it was set to 150. Things like that.. :)
How do you manage/orchestrate this? I'm genuinely curious.
An ace software engineer is not an ace because of tooling.
It's not the plane, it's the pilot, or something like that.
Can agentic engineers adhere to a similar code of ethics that a professional engineer is sworn to uphold?
https://www.nspe.org/career-growth/nspe-code-ethics-engineer...
Can software engineers?
You can use these tools wisely without letting it run unverified carelessly.
I'm working on a licensing system for a product I'm building. I've used Claude a little bit to help out with it, but it's also made a lot of very dumb decisions that would have large (security!) consequences if I didn't catch them. And a lot of them are braindead things, like I asked it to create a configurable limit on a certain resource for the trial version of the application. When I said configurable, I mostly meant: put the number in a constant so I can update it later. What Claude thought I asked was "make it so the user can modify the limits of the trial version in the settings panel" (which defeats the entire purpose of a free trial!). Another thing it messed up recently is I was setting up email-magic-link authentication. It defaulted to creating an account for anyone that typed in an email, which could allow a bad actor to both spam people with login requests (probably getting me kicked off Resend) or creating a lot of bogus accounts.
These things do not think. You cannnot outsource your thinking to them.
>I firmly staked out my belief that “vibe coding” is a very different beast from responsible use of AI to write code, which I’ve since started to call agentic engineering
Disturbing? Really? I admit I don't do agentic and am going only by vibes, but for me agentic engineering is basically vibe coding in a automated loop with some ornamentals. They both stem from the same LLM root and positioning them as significantly different is weird and unconvincing to me. There may be a merit to this article (I gave up after few sentences), but I reject this specific premise.
It's the difference between caring and not caring.
I think this highlights a problem that has always existed under the surface, but it's being brought into the light by proliferation of vibeslop and openclaw and their ilk. Even in the beforetimes you could craft a 100.0% pure, correct looking github repo that had never stood the test of production. Even if you had a test suite that covers every branch and every instruction, without putting the code in production you aren't going to uncover all the things your test suite didn't--performance issues, security issues, unexpected user behavior, etc.
As an observer looking at this repo, I have no way to tell. It's got hundreds of tests, hundreds of commits, dozens of stars... how am I to know nobody has ever actually used it for anything?
I don't know how to solve this problem, but it seems like there's a pretty obvious tooling gap here. A very similar problem is something like "contributor reputation", i.e. the plague of drive-by AI generated PRs from people (or openclaws) you've never seen before. Stars and number of commits aren't good enough, we need more.
The most important part and why slop isn't the same as a code written by someone else. The model doesn't care, it just produces whatever it is asked to produce. It doesn't have pride, it doesn't have ego, it doesn't artisanal qualities, it doesn't have ownership.
Fast feedback loops and delegating tasks to sub-agents have been pretty common for vibers since well before they were canonicalized by agenteers. Same thing, different day, hardly even any difference in quality: they evolve together, though vibe tends to lead and agents follow and refine... which vibers then use too.
If you think of vibe coders as agentic alpha testers it makes a lot more sense.
Do this enough times, and I will have forgotten how to think.
You have no clue what went into the training data or how much of the output is covered by someone else's copyright. To pretend this is "responsible" is ridiculous.
Then you go on to use lines of code per day as a meaningful metric without any evidence that it has any consequence whatsoever.
Finally you don't mention profitability once.
What are we even doing here? Pretending? Why?
Makes me want to just give up programming forever and never use a computer again.
If LLMs stop improving at the pace of the last few years (I believe they already are slowing down) then they will still manage to crank out billions lines of code which they themselves won’t be able to grep and reason through, leading to drop in quality and lost revenue for the companies that choose to go all-in with LLMs.
But let’s be realistic - modern LLMs are still a great and useful tool when used properly so they will stay. Our goal will be to keep them on track and reduce the negative impact of hallucinations.
As a result software industry will move away from large complex interconnected systems that have millions of features but only a few of them actively used, to small high quality targeted tools. Because their work will be easier to verify and to control the side effects.
Depending on how you measure "improvement" they already have or they never will :-/
Measuring capability of the model as a ratio of context length, you reach the limits at around 300k-400k tokens of context; after that you have diminishing returns. We passed this point.
Measuring capability purely by output, smarter harnesses in the future may unlock even more improvements in outputs; basically a twist on the "Sufficiently Smart Compiler" (https://wiki.c2.com/?SufficientlySmartCompiler=)
That's the two extremes but there's more on the spectrum in between.
Eh, what a waste. Can't we just stimulate the optic nerve? Or better yet, whatever region of the brain is responsible for me being able to 'see' anything? And perhaps we can finally get smell-o-vision too.
Second, LLM code can be less of a hot mess than human written code if you put in the time to train/prompt/verify/review.
Generating perfect well patterned SOLID and unit tested code with no warnings or anti-patterns has never been easier.
With the right investment, we could certainly have tooling that creates and maintains very good designs out of the box. My bet is that we'll continue chasing quick and hacky code, mostly because that's the majority of the code that it was trained on, and because the majority of people seem to be interested in a quick result vs a long-term maintainable one.
That the industry was already routinely dealing with fires of it's own creation is not a valid reason to start cooking with gasoline.
The system that makes it have an opinion about good vs bad architecture or engineering sensibilities will be something on top of the transformer and probably something more deterministic than a prompt.
We are used to thinking about software like in the article, a program that runs deterministically in an OS. Where we are headed might be more like where the LLM or AI system is the OS, and accomplishes things we want through a combination of pre-written legacy software, and perhaps able to accomplish new things on the fly.
With the rise of LLMs that do all of that... those people shutup and shutup real fast.
How many of us remember that VSCode is actually a browser wrapped inside a native frame?
The new standard, Web Apps. Why update 3 seperate binaries for Win/Lin/Mac when you can do 1 for a web framework and call it a day?
That's what the Tech-Priests are for.
As a piece of meat, I look forward to charge rates of $10,000 an hour, to fix code out the vibe code generation.
--
It's just as likely that people will be surprised that we used to have billions of lines of human generated code, that no LLM ever approved.
By then AI would be good enough to clean them all up...
To make my comment more on-topic: why do you think this is going to be the case? What newer LLMs will be trained on?
Now with LLM we are talking of millions and millions of line of code that could be generated in a single day. The scale of the problem might not be the same at all.
LLMs aren’t the first thing to come along and change how people develop applications.
You had the rise of frameworks like Django, Rails, etc. Also the rise of SPAs. And also the rise of JS as a frontend+backend language.
In a 3-5 yeats we’ll have adapted to the new norm like we have in the past
Also, companies are pressuring employees towards adoption in novel ways. There was no such industry-wide pressure by employers in the 90s, 2000s or 2010s for engineers to use a specific tech.
I use AI tools daily (because they feel like they're helping me) but it's not exactly hard to imagine scenarios where an explosion of slop piling up plus harm to learning by outsourcing all thinking results in systemic damage that actually slows the pace of technological progress given enough time.
History of new technologies tend to average into a positive trend over a long enough time scale but that doesn't mean there aren't individual ups and downs. Including WTF moments looking back at what now seems like baffling decision-making with benefit of hindsight.
a) The stuff output by the existing LLMs is too unwieldy even for them to handle , even if the product itself is a glorified chatbot.
b) If all software is throwaway, then the value of all software drops to, effectively, the price of an AI subscription. We'll all be drowning in a market of lemons (https://en.wikipedia.org/wiki/The_Market_for_Lemons), whilst also being producers in said market.
Like many people I have used AI to generate crap I really don't care about. I need an image. Generate something like, whatever. Great hey a good looking image! No that's done I can do something I find more interesting to do.
But it's slop. The image does not fit the context. Its just off. And you can tell that no one really cared.
This isn't good.
You can't do that for images and text.
My side project is 80% vibe code. Every now and then I look and see all the bad stuff, then I scold Codex a bit and it refactors it for me. So I do see the author's point.
> But I’m not reviewing that code. And now I’ve got that feeling of guilt: if I haven’t reviewed the code, is it really responsible for me to use this in production?
Answer: it wholly depends upon what management has dictated be the goal for GenAI use at the time.
There seems to be a trend of people outside of engineering organizations thinking that the "iron triangle" of software (and really, all) engineering no longer holds. Fast, cheap, good: now we can pick all three, and there's no limit to the first one in particular. They don't see why you can't crank out 10x productivity. They've been financially incentivized to think that way, and really, they can't lose if they look at it from an "engineer headcount" standpoint. The outcomes are:
1) The GenAI-augmented engineer cranks out 10x productivity without any quality consequences down the line, and keeps them from having to pay other people
or
2) The GenAI-augmented engineer cranks out 10x productivity with quality consequences down the line, at which point the engineer has given another exhibit in the case as to why they should no longer be employed at that organization. Let the lawyers and market inertia deal with the big issues that exist beyond the 90-day fiscal reporting period.
Either way, they have a route to the destination of not paying engineers, and that's the end goal.
If you don't like that way of running a software engineering organization, well, you're not alone, but if nothing else, you could use GenAI to make working for yourself less risky.
Just piggy backing on this post since I'm early:
Would love to see your take on how the AI and Django worlds will collide.
If you want to build a project, you can never shorten the actual time it takes to write it out, you are stuck at that 50 words per minute limit
LLMs, agents, call it how you want, they allow us to remove that bottleneck
That's the spirit, I always say - _others_ will deal with AI slop during code review. Eventually they will get tired and start 'reviewing' this AI stuff with AI - so it's a win win. Right?
"I know full well that if you ask Claude Code to build a JSON API endpoint that runs a SQL query and outputs the results as JSON, it’s just going to do it right. It’s not going to mess that up. You have it add automated tests, you have it add documentation, you know it’s going to be good."
This really is Wordpress and early PHP all over again, but it's the seasoned folks rather than the amateurs that buy into it.
I believe these tools will be refined and locked down and eventually turn into RAD stuff used by certified enterprise consultants, much like SAP and Salesforce and IBM solutions and so on. From this I come to the conclusion that it is not a good idea to become dependent on them at this stage, which is corroborated by the pecuniary expense as well as excruciatingly fast change in available products.
> Claude Code does not have a professional reputation!
how come?
I'm not checking the code since the code doesn't really matter anymore anyways - I just have the agent write passing tests for the changes or additions I make, and so even if something breaks I can just point to the tests.
Some days, the tickets are completed much faster than I expect and I don't hit my daily token expenditure goal, so I have my own custom harness that actually hooks up an agent to TikTok, basically it splits up the reel into 1 second increments and then feeds those frames to the LLM for it's own consumption. I can easily burn 10m tokens a day on this, and Claude seems to enjoy it.
Personally I want to thank you Simon for putting me onto this "vibe engineering" concept, I really didn't expect an archaeology major like myself to become a real engineer but thanks to AI now I can be! Truly gatekeeping in tech is now dead.
I took a rock carving course in school that really enlightened me about software engineering, and it still applies today, especially to AI. You can't just decide what you want to carve, hold the chisel in just the right spot, and whack it with a hammer just perfectly so all the rock you want falls away leaving a perfect statue behind.
"I saw the angel in the marble and carved until I set him free." -Michelangelo
It's a long drawn out iterative process of making millions of tiny little chips, and letting the statue inside find its way out, in its natural form, instead of trying to impose a pre-determined form onto it.
Vibe coding is hoping your first whack of the hammer is going to make a good statue, then not even looking at the statue before shipping it!
But AI assisted conscientious coding (or agentic engineering as Simon calls it) is the opposite of that, where you chip away quickly and relentlessly, but you still have to carefully control where you chisel and what you carve away, and have an idea in your mind what you want before you start.
My dad (now retired) was always super practical about stuff. He'd tell me pretty nonchalantly things like "yeah we're dealing with xyz constraint, we may have to cut a corner over here, but that's ok", when I asked him about it he gave me a little spiel that you can be thoughtful about how you do things, including when you can cut a corner and more importantly, what corners are ok to cut.
I really took that to heart - especially the "be thoughtful about the corners you cut"
If an LLM has consistently one shotted certain tasks and they are rote/mechanical - not reviewing that code is probably ok.
Are you getting lazy and not reviewing stuff that should be reviewed even if a human wrote it? That's probably not ok
I can live with some basic code that broke because it used outdated syntax somewhere (provided the code isn't part of a mission critical application), but I can't live with it fucking JWT signing etc
> where you fully give in to the vibes, embrace exponentials, and forget that the code even exists [...] It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.
So clearly we need a term for what happens when experienced, professional software engineers use LLM tooling as part of a responsible development process, taking full advantage of their existing expertise and with a goal to produce good, reliable software.
"Agentic engineering" is a good candidate for that.
Its shifted so much for me. I used to think that I had a solemn duty to read every line and understand it, or to write all the test cases. Then I started noticing that tools like CodeRabbit, or Cursor would find things in my code that I would rarely find myself.
I think right now, its shifted my perception of my role to one where I am responsible for "tilting" the agentic coding loop; ultimately the goal is a matter of ensuring the agent learns from its mistakes, self-organize and embrace a spirit of Kaizen.
Btw thank you for your work on Django, last 20 years with it were life changing (I did .NET before).
Rather, I just feel like I have to constantly remind myself of the impermanence of all things. Like snow, from water come to water gone.
Perhaps I put too much of my identity in being a programmer. Sure, LLMs cannot replace most us in their current state, but what about 5 years, 10 years, ..., 50 years from now? I just cannot help be feel a sense of nihilism and existential dread.
Some might argue that we will always be needed, but I am not certain I want to be needed in such a way. Of course, no one is taking hand-coding away from me. I can hand-code all I want on my own time, but occupationally that may be difficult in the future. I have rambled enough, but all and all, I do not think I want to participate in this society anymore, but I do not know how to escape it either.
The job, as you have done it at least, was also not here 50 years before you started doing it.
Did you have any of the same feelings knowing that you were doing a job that has not existed in the world very long? That seems like a strange requirement for a meaningful job, that it should remain the same for 50+ years.
In truth, our world and what we do for our careers is entirely shaped by the time that we live in. Even people that ostensibly do the same thing people have done for centuries (farmer, teacher, etc) are very different today than 100 years ago.
Companies are shipping things and nobody understands what they're shipping.
I don't buy this argument at all. I think if we could pay $20/month to a service that would send over a junior plumber/carpenter/electrician with an encyclopedic knowledge of the craft, did the right thing the majority of the time, and we could observe and direct them, we'd all sign up for that in a heartbeat. Worst case, you have to hire an experienced, expensive person to fix the mess. Yes, I can hear everyone now, "worst case is they burn your house down." Sure, but as we're reminded _constantly_ when we read stories about AI agent catastrophes -- a human could wipe your prod database too. wHy ArE yOu HoLdInG iT tO a DiFfErEnT sTaNdArD???
The business side of the house is getting to live that scenario out right now as far as software goes. Sure you've got years of expertise that an LLM doesn't have _yet_. What makes you think it can't replace that part of your job as well?
But that's not what the author is talking about in that passage you quoted. What he's saying is that, if you can pay $20 for an AI plumber, then it stands to reason that eventually you will be able to pay $30 to a company that manages AI plumbers for you, so that you don't even have to go to the trouble of supervising the plumber. Most people will choose the $30.
The implication here is software engineer jobs are still safe despite basically free labor/material being available to do said jobs because he thinks other people would prefer to pay experienced professionals to do it right at a significantly higher cost. My point is, I think most people will take the low-stakes gamble of having the cheap AI agent do it with self-supervision[0]. He's naive in thinking people are really going to care about artisanal software built by experienced professionals in the future.
0: Even if you subscribe to the "your job will be to supervise the agents" train of thought, you're kinda glossing over the fact that it's probably gonna involve a pretty significant pay cut and the looming problem of "how do new experienced professionals get created if they don't have to/don't need to get their hands dirty"?
I don’t think this comparison quite works (or maybe I think it works and is wrong) and I think it has something to do with creativity or the initial ideation.
I would do this, but I’m a jack of all trades. I built my own diner booth in my kitchen recently. But my wife, who loves the diner booth, just doesn’t really want to get over the hump of figuring out what she might want. I think most people want to offload the mental load of figuring out where to start.
Most people aren’t just bored by coding, they’re bored or overwhelmed by the idea of thinking about software in the first place. Same with plumbing or construction, most people aren’t hiring someone to direct, they’re hiring a director.
Even I have this about some things, sometimes I choose to outsource the full stack of something to give me more space to do creativity elsewhere.
And AI generated code should be different than human code. AI has infinite memory for details. AI doesn’t need organizational patterns like classes. Potentially AI can write code that is more performant than any human.
Will it look like garbage? Sure. Will the code be more suited to the task? Yes.
The code produced will only be understandable by AI. You could use locally hosted LLMs, but it won't be as performant as AI run by big guys. And there is nothing stopping greedy companies implementing some ridiculous pattern that only their model can reasonably work with.
So what you'll do in situation when you can't understand "your" codebase and you have to make changes or fix a bug?
It will be a black box, and the code will be generated just in time by ai for each api request
The open weight models are nipping on the heels of frontier models. The frontier labs have to make forward progress and keep tokens cheap in order to maintain marketshare.
Eventually, we'll have a Mythos-level model running on integrated hardware on every PC.
Code that is organized well and operates coherently in the first place, by an LLM or not, will be easier to iterate on, by an LLM or not.
No, just no.