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.
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 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.
There's a cycle that is needed for good system design. Start with a problem and an approach, and write some code. As you write the code, you reify the design and flesh out the edge cases, learning where you got the details wrong. As you learn the details, you go back to the drawing board and shuffle the puzzle pieces, and try again.
Polished, effective systems don't just fall out of an engineers head. They're learned as you shape them.
Good engineers won't continue to be good when vibe-coding, because the thing that made them good was the learning loop. They may be able to coast for a while, at best.
- I've taken a controversial new pill that accelerates my brain.
-- So you're smart now?
- I'm stupid faster!
That being said, being stupid faster can work if validation is cheap (and exists in the first place).
Turns out "eh close enough" for AGI is just stupidity in an "until done" loop. (Technically referred to as Ralphing.)
I estimate that I'm now spending about 10 to 30 hours less time a week in the mechanical parts of writing and refactoring code, researching how to plumb components together, and doing "figure out how to do unfamiliar thing" research.
All of those hours are time that can now be spent doing "careful consideration" (or just being with my family or at the gym or reading a book, which is all cognitively valuable as well).
Now, I suppose I agree that if timelines accelerate ahead of that amount of regained time, then I'm net worse off, but that's not the current situation at the moment, in my experience.
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.
To me, none of this feels like "going faster", it feels like "opening up possibilities to try more things, with a lot less tedious work".
Unfortunately, a lot of workplaces are ignoring this, believing their engineers are assembly line workers, and the ones who complete 10 widgets per minute are simply better than the ones who complete 5 widgets per minute.
Companies want workflows that work with mediocre programmers because they are more like interchangeable parts. This is the real secret to why AI programming will work in a lot of places. If you look at the externalities of employing talented people, shitty code actually looks better than great code.
This is the earworm the leaders of these companies have allowed into their minds. Like Agent Mulder, they Want To Believe in this so badly...
If you assume they are not idiots and analyze the FOMO incentives via a little game-theory, it becomes clear why.
Assuming the competition has adopted AI, leadership can ignore it, or pursue it. If they adopt it, then they are level with the completion whether AI actually succeeds or fails - they get to keep their executive job.
If leadership ignores AI, and it actually delivers the productivity gains to the competition, they will be fired. If they ignore AI and it's a bust, they gain nothing.
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...
The way you apply the expert loop is to be the expert. "Can we try this...", "have you checked that...", "but what about...".
To some degree you can try to get agents to work like this themselves, but it's also totally fine (good, actually) to be nudging the work actively.
> 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
The ideal solution increasingly seems to be encoding everything that differentiates a good engineer from a bad engineer into your prompt.
But at that point the LLM isn’t really the model as much as the medium. And I have some doubts that LLMs are the ideal medium for encoding expertise.
The Pragmatic Programmer book has whole chapters about this. Ultimately, you either solve the problem analogously (whiteboard, deep thinking on a sofa). Or you got fast as trying out stuff AND keeping the good bits.
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?"
I do this too, but then I sit and observe how agent gets very creative by going around all of these layers just to get to the finish line faster.
Say, for example, if I needlessly pass a mutable reference and the linter screams at me, I know it's either linter is wrong in this case, or I should listen to it and change the signature. If I make the lazy choice, I will be dissatisfied with myself, I might even get scolded, or even fired if I keep making lazy choices.
LLM doesn't get these feelings.
LLM will almost always go for silencing it because it prevents it from reaching the 'reward'. If you put guardrails so that LLM isn't allowed to silence anything, then you get things like 'ok, I'll just do foo.accessed = 1 to satisfy the linter'.
Same story with tests. Who decides when it's the test that should be changed/deleted or the implementation?
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.
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.
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.
Isn’t this just because you have seen a lot of PRs from inexperienced engineers? People learn LLM behavior over time, too.
Yes, as an engineer I make mistakes, but I could never make as many mistakes per day as an LLM can
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."
Software developers get paid big money because they can speak alien, the only thing that is changing is the dialect.
People used to like them and they used to be legends (even if not everyone liked them)
Notch, Woz, Linus and Geohot come to mind
The Metasploit creator Dean McNamee worked for me and he was just like that and a total monster at engineering hard tech products
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?
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.
I have heard this statement every single day for 2 years and yet we still have no companies compressing 10 years into 1 year thus exploding past all the incumbents who don't "get it".
> if it’s a solo greenfield project
which is a pretty large caveat. Anecdotally, I've found my side projects (which are solo greenfield projects, and don't need to be supported to the same standards as enterprise software) have gained the boost the GP was talking about.
At work, it's different, since design, review, and maintenance is much more onerous.
The first line of code was written on November 25th. It achieved adoption in the "personal agents" space that far exceeded the other companies that had tried the same thing.
(Whether or not you trust the quality of the software you can't deny the impact it had in such a short time. It defined a new category of software.)
Like, look at e.g. YC minus the AI and AI ajacent companies. Are those startups meaningfully more impressive or feature-rich as compared to a couple years ago?
Which is exactly why you can't use it as an example, there is no control. This is basic stuff.
If agents could really compress 10 years of development into 1 year, you'd see people making e.g. HFT platforms and becoming obscenely rich, not making a fun open-source project and getting hired by OpenAI as an employee.
If that were true, all of these anti-AI greybeards who have been in the game for 30 years would all own their own jets.
Cryptocurrencies? Barely any other use than money laundering, buying drugs and betting on the outcome of battles in war. And NFTs? No use at all other than money laundering and setting money ablaze.
That's a big if. I don't have numbers but most professional engineers are not working on such projects
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.
Perhaps these “obstinate” engineers have good reason in their decision. And it should be their decision!
To be so confident in what is “the right way (TM)” and try to force it onto others is... revealing.
After 18 months the hard evidence is in place. And much like replacing bare-metal servers for many use cases where evidence shows that the burden of k8s or the substitution of shell scripts for Terraform, it's time to move on.
I don't really see a place for no AI usage in line-of-business software apps anymore.
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.
First of all, building a system that constrains the output of the AI sufficiently, whether that's typing, testing, external validation, or manual human review in extremis. That gets you the best result out of whatever harness or orchestration you're using.
Secondly, there's the level at which you're intervening, something along the hierarchy of "validate only usage from the customer perspective" to "review, edit, and validate every jot and tiddle of the codebase and environment". I think for relatively low importance things reviewing at the feature level (all code, but not interim diffs) is fine, but if you're doing network protocol you better at least validate everything carefully with fuzzing and prop testing or something like that.
And then you've got how you structure your feedback to the LLM itself - is it an in-the-loop chat process, an edit-and-retry spec loop, go-nogo on a feature branch, or what? How does the process improve itself, basically?
I agree with you entirely that the responsibility rests on the human, but there are a variety of ways to use these things that can increase or decrease the quality of code to time spent reviewing, and obviously different tasks have different levels of review scrutiny, as well.
My nonexistent backend isn’t going to be pwned if there is a bug in the thumbnail generation.
After the QA testing on my device, a quick scroll through of the code is enough.
Maybe prompt „are errors during thumbnail generation caught to prevent app crashes?“ if we‘re feeling extra cautious today.
And just like that it saved a day of work.
Hmm. Historically image editing was one of the easier to exploit security holes in many systems. How do you feel about having unknown entities having shell inside your datacenter or vpc?
It is so embarrassing that LOC is being used as a metric for engineering output.
Objectives change; timeliness matters. The speed at which you deliver value is incredibly important, which is why it matters to measure your process. Deceptively dense is what I’d call software engineers who can’t accept that the process is actually generalizable to a degree and that lines of code are one of the few tangible things that can be used as a metric. Can you deliver value without lines of code?
This assumes that shorter code is faster to write. To quote Blaise Pascal, "I would have written a shorter letter, but I did not have the time."
> Can you deliver value without lines of code?
No, but you can also depreciate value when you stuff a codebase full of bloated, bug-ridden code that no man or machine can hope to understand.
“All models are wrong, some are useful”. What’s not useful is constantly bitching about how there’s no way to measure your work outside of the binary “is it done” every time process efficiency is brought up.
Very far from the truth in practice, every line of code isn't as difficult/easy to review as the other.
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.
So I’m pretty skeptical that reviewing 2000 lines of code won’t take any more time than reviewing 200 lines of code.
Furthermore how do you know the AI generated lines are the open highway lines of code and not the mountain road ones? There might be hallucinations that pattern match as perfectly reasonable with a hard to spot flaw.
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.
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.)
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.
Although this requires you to take pride in your profession and what you do.
Got it.
...ok fine; lack of political action to put us all on the hook for your healthcare is your choice to take a gamble on a paycheck. It's a choice to say your own existence is not owed the assurance of healthcare.
So I will honor your choice and not care you exist.
AI helps eng ship more and faster, I think that’s the takeaway.
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?
We're also assuming LOC vibe coded by competent engineers who should be able to tell when something is overengineered.
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.
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.
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. [...]"
I find the LLM as interactive tutor reviewing my work in a proof checker to be a really killer combo.
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."
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.
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.
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
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.
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).
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.
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.
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.
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
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.
> 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.
* 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.
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.
No one is suggesting that.
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.
Claude Code in particular seems really uninterested in this aspect of the problem and I've stopped using entirely because of this.
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.
So the number of bugs to find remains constant but the amount of code to review scales with the capability of the agent.
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.
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...
You can use these tools wisely without letting it run unverified carelessly.
Can software engineers?
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.. :)
If you mean 'passes tests', that can be tackled by AI. Although AI writing its own tests and then implementing its own code is definitely not a foolproof strategy.
How do you manage/orchestrate this? I'm genuinely curious.
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
> Claude Code does not have a professional reputation!
how come?
Do this enough times, and I will have forgotten how to think.
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.
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.
you can also execute larger tasks than this using subagents to divide the work so each segment doesn’t exceed the usable context window. i regular execute tasks that require hundreds of subagents, for example.
in practice the context window is effectively unlimited or at least exceptionally high — 100m+ tokens. it just requires you to structure the work so it can be done effectively — not so dissimilar to what you would do for a person
How to organize code like you said, and how agents interact with it, to keep the actual context window small is the fundamental challenge.
I looked at that response by GP (rgbrenner) and refrained from replying because if someone is both running hundreds of agents at a time AND oblivious to what "context window" means, there is no possible sane discourse that would result from any engagement.
Assistant: “I propose A”
User: “Actually B is better”
Assistant: “you’re absolutely right”
User: “actually let’s go with C”
Assistant: “Good choice, reasons”
User: “wait A is better”
Assistant: “Great decision!”
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.
Write lots of code now and statistically look great, while the impact won’t be felt for a much larger range of time.
With the job search and whatnot then yeah, caring becomes a lot more important. That’s true.
It's not immediate, it still takes weeks if you want to actually do QA and roll out to prod, but it's definitely better than the pre-LLM alternatives.
AI will make this dynamic worse, and it's got the extra danger of the default banal way of applying the technology in fact encourages it's application to that end.
I also don't think that the commodification of programming is a substitute for things like understanding your customers, having good taste for design, and designing software in a way that is maximally iterable.
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.
What would normally be considered overengineered gold plating is "free" now.
Same thing happens in other fields. A rich country and a poor country might build equivalent roads, but they won't pay the same price for them.
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.
"Shit's in the Game!"
"Chunder Everything"
"Maddening NFL 26"
"FIFiAsco 26"
"UFC 26 (Un Finished Code)"
"The Shits 4"
"Battlefailed"
"Need for Greed"
What you're suggesting is a negative flywheel where quality spirals down, but I'm hoping it becomes a positive loop and the quality floor goes up. We had plenty of slop before LLMs, and not all LLM output is slop. Time will tell, but I think LLMs will continue to improve their coding abilities and push overall quality higher.
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.
Whether that happens or not is a different question, but I believe that's what they're suggesting.
Programming is taking ambiguous specs and turning them into formal programs. It’s clerical work, taking each terms of the specs and each statements, ensuring that they have a single definition and then write that definition with a programming language. The hard work here is finding that definition and ensuring that it’s singular across the specs.
Software Engineering is ensuring that programming is sustainable. Specs rarely stay static and are often full of unknowns. So you research those unknowns and try to keep the cost of changing the code (to match the new version of the specs) low. The former is where I spend the majority of my time. The latter is why I write code that not necessary right now or in a way that doesn’t matter to the computer so that I can be flexible in the future.
While both activities are closely related, they’re not the same. Using LLM to formalize statements is gambling. And if your statement is already formal, what you want is a DSL or a library. Using LLM for research can help, but mostly as a stepping stone for the real research (to eliminate hallucinations).
That's what the Tech-Priests are for.
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?
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.
With such a low baseline, there is an optimistic perspective that LLMs could improve the situation. LLMs can produce excellent code when prompted or reviewed well. Unlike human employees, the model does not worry about getting a 'partially meets expectations' rating or avoid the drudgery of cleaning up other people's code.
AI certainly has the potential to make the underlying code/design a lot cleaner. We will also be working with dramatically more code, at a much higher rate of change. That alone will be a big challenge to keep sustainable.
The ones making the decision to under-invest on design are either are unaware of the real costs, or are aware and are deliberately choosing that path - that's not new, and I don't expect it to change.
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?
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.
Companies have been enforcing technology mandates since time immemorial. In the early 2000s there were definitely a lot of mandates to move away from commercial UNIX to Linux. Lots of companies began enforcing the switch to PHP, Ruby and Python for new projects.
Good luck disliking LLM babysitting these days
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.
If it is, the fall out will be way worse than if AI ends up living up to (reasonable) expectations.
If it doesn’t, we are going to see over a trillion dollars of capital leave the tech sector, which I think will have worse impacts on the livelihood of tech workers than if AI ends up panning out.
This is something the naysayers need to grapple with. We’ve crossed a line where this tech needs to work simply because of the amount of money depending on that fact.
I don't think it will be worse; if AI pans out the world would be able to continue without a single programmer left. If a trillion dollars leave the tech sector, all those programmers employed outside of the tech sector will still have jobs.
The damage would come much later, well beyond the point where it could be simply pulled out and replaced without spending massive amounts of money and would also basically necessitate training an entire new generation of engineers.
Then the AI giants would start appearing vulnerable like cigarette companies in the 90s while an AI Superfund and interstate class action are being planned but Sam Altman would already be a centitrillionaire at that point so it would be someone else's problem.
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.
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.
> 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).
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.
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.
> 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.
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.
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.
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?
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.
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"?
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?
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.