My manager who vibe coded our entire webapp in claude design. Has difficulty understanding why its still not production ready.
My job is to wire to our backend data, and a lot of these wiring require me to be in there and actually think about the features. These take time, and I just haven't figure out a way to speed this process up with Claude.
On the other hand. If his tool does the job why not. If you asked a 90s engineer how javascript triggers millions of cpu instructions for just a few code steps he would call you crazy.
Why won't smarter and cheaper models in the future be able to automate this part for your manager as well? How novel is the feature set? Is it he has a knowledge gap or the model is incapable of something? What expertise are you bringing to bear that is beyond the scope of a future harness/model? Why wouldn't such a model simply fill in the blanks for your management, perhaps observing a diff of whatever you did? How do you verify the correctness of your thinking? Why could a future model not replicate this process?
I am just very puzzled by these sort of takes as we approach the end of 2026.
This would be a great point if his manager asked him to wash the dishes. Who knows, in 10 years time if he finds himself with such a job he might consider himself lucky.
Anyway I hope I get an answer to my actual questions. Engaging with your "it's just a calculator" denialism is an obvious dead-end. Have a good day buddy.
Completely missing my point there. I wasn't saying it's a calculator. I was saying it's not a human intelligence and there's no reason to think it'll stand in for every responsibility we have.
Name me one human that can describe grandmaster level chess strategy but also loses to a random-only chess bot - that was the case for LLMs for a long time
My point is LLMs aren't humans. They're not a toddler slowly getting smarter, and when they're smarter they'll be able to do everything a human can do. It's a different scale entirely.
I didn't ask for every responsibility I asked for his specific responsibility.
Why don't you calm down and read my question again and you will see the only one "completely missing the point" is you.
You're arguing with someone else. I didn't make a claim that LLMs were human. If you're going to respond to me then engage with what I'm exactly saying. Go back and read it again, if you can manage it.
A calculator used to be a human job. Are calculators smarter than humans?
I'm asking why is it reassuring about his job? Surely your big human brain can understand to be automated away doesn't mean the machine is "smart as a human" whatever that means. There is probably not a lot of utility in comparing synthetic and organic intelligence from such a reductive perspective.
Factory workers got automated by machines. Is a robotic arm smarter than a human? Is a tractor smarter than a bovine?
My question is simply what is he doing that is so "smart" or novel that it cannot be automated or mechanized. This should not be so hard to understand.
We can speculate about the capability of AI models in 10 years all we want. Currently; It's not good enough for the task.
Taking a visual proof-of-concept and turning it into a real product with integration to an existing complex system requires the developer to re-do a-lot of the work. And reading code; especially AI code someone-else wrote, is a miserable experience.
My experience matches this also. My observation is that most business people think things they know they don't know are hard, and things that they think they do know are easy. My additional observation is that AI tools make business people think they know a lot more things, so they consider a lot more things easy, when they are not. AI tools are a machine that makes business people Dunning-Kruger-max.
"Never really finish building it" is the key insight.
The problem with software is that it is never done. There is always another feature you could have and worse than building a property the work is only done by the people on the outside.
I think it's deeper than that, once you know software is never done you have to design for constant change.
When clients ask why something takes so long, I explain that I'm not building what you asked for today, I'm building something that will be easy to turn into what you asked for today and possible to turn into whatever you ask for tomorrow.
AI has vastly changed the shape of what that looks like though. There are whole classes of refactoring work that are much cheaper and quicker to do with agents. Integrating a protocol client library say, or swapping one library for another are now potentially hour-long instead of days-long tasks, especially when you have test coverage to back you up (which AI also immensely helps you with).
Not OP, but… It can drive itself…mostly (the “belt”). And when it doesn’t, you need remote drivers (the “suspenders”) to get it unstuck. I don’t need that with the car I drive myself, and it probably won’t be needed with self-driving cars (waaaay in the future, IMO).
I've been using it for years. "Let's decide where we want the plumbing to go before pouring the slab", or "let's not focus on where the couch will go before we paint the walls". Or when a 'genius cowboy dev' discovers a super fast way to get from the 2nd floor to the kitchen by cutting a hole in the floor despite all the leaks it causes and how many other people have to work around the change.
This is all caused because no-one understands the purpose of quality in software
Low quality = cascading bugs, issues, slow to iterate and add or change features
This is just as true for human written as it is for AI
Instead we have everyone giving up on code quality as if it was just "beautiful code" perfectly indented that was only there for people to ooh and aah at
That is the one of the larger issues with poor quality. However even if there are no security issues poor quality can make your customers mad enough to go elsewhere and badmouth you to others looking at your product.
Even if you do use AI tools to help you write the code, at some level you have to specify what the program's output should be for every possible input.
By loosely specifying things in a prompt, there's simply not enough context for the AI tool to know the "right" output to produce for all possible inputs. What's "right" is often subjective anyway ("Should this button be red or blue?").
I don't know that saying "this work is hard" is enlightening.
More useful would be to be able to explain at some high level what the the inherent and accidental complexity is, the tradeoffs to navigate, long-term vs short-term decisions, etc.
Saying "it is hard" makes the audience think you're less of an expert in your domain and they are then inclined to find someone who doesn't say "this work is hard".
You have enough budget for only the first floor, but you have a big family, and you know you’ll want a second one in a couple of years.
> Adding the infrastructure to support a 2nd floor is MUCH cheaper right now than it will be when you actually want that 2nd floor.
The problem with this thinking is it requires certainty about the future. It's much cheaper right now IF AND ONLY IF you end up needing the thing. If you don't need it, then you've threw time and money down the drain.
Where I think this analogy weakens is you probably have far more certainty of whether or not you want a big family then you do on whether or not a new product line will see major adoption.
You should have an idea of where you are going so you don't paint yourself into a corner. However certainty about the future is impossible. You make your best guess and often that will be wrong. That doesn't mean it is wrong to guess, it means you need to temper your guesses.
I am known for analogies. I use them all the time to try and explain things to less technical people.
Sometimes it's building a house, sometimes it's writing a book, sometimes it's the difference between a Ford and a kit car, sometimes it's how you build a bridge.
None of them stand up to full scrutiny. You can pick every single one apart.
That's not the point of analogies. The point is to explain just one of the many aspects of software engineering in a more understandable format to the listener. Software engineering is nothing like building a physical thing. But some of the many, many problems and complexity you hit have physical product analogies.
All software is path dependent, all code is a liability, and all technical decisions are tradeoffs. These are the immutable truths of software that not changed one iota due to AI or any Moore's Law progress before it.
There are too many product managers and decision makers that are unable or unwilling to do the hard work of actually thinking through what they want, and re-evaluating their priors as new feedback and learnings come in. Similarly, there are too many engineers who are distant from the customer and the problem at hand, and end up chasing their own idea platonic ideal of good software, detached from the hard tradeoffs of what is truly needed right now vs what we anticipate needing in the future. The less software we can write to solve the problem now, while minimizing one way door decisions, and deferring as many "scaling" challenges as long as possible to make decisions with more complete information the better.
This is why AGI won't magically solve software development—because people don't actually know what they want until they try it and then they want something else. Raw intelligence can not solve for purpose or human goals. The better it gets, the more it will become like an evil genie or monkey's paw that never quite does what the feeble-minded human prompters want.
Pretty much every project. Initially your pace is great. You're knocking out features quickly. Everyone is upbeat and enthusiastic. Then you have to put it all together. You discover scenarios that the specs don't cover. You need more info from business about validating certain combinations of inputs. When you get those, you find contradictions with some other stuff you thought was already done. Combinatorial complexity. It's the essential problem with software systems. If business people don't understand it, tell them to read Brooks. If they still don't understand, tell them to read it again.
Combinatorial complexity. It's the essential problem with software systems
After using OpenBSD for a while, I fully adopted the “write less code” approach. Create the simplest solution and leave “features” out until you need them. Nice to have should be practically banned.
We decided to go with Lovable so the recruiting team can maintain it themselves later.
Yeah, I'm not sure that's a good idea. At the end of the day, Lovable is still a TypeScript web-app with a Supabase backend full stack system. If you don't understand what you have the LLMs actually build, then there is no way you can maintain it or debug it if something goes wrong.
All these no code/vibe code website generators don't make the code go away; the code maintenance burden just shifted to somebody else.
My manager who vibe coded our entire webapp in claude design. Has difficulty understanding why its still not production ready.
My job is to wire to our backend data, and a lot of these wiring require me to be in there and actually think about the features. These take time, and I just haven't figure out a way to speed this process up with Claude.
On the other hand. If his tool does the job why not. If you asked a 90s engineer how javascript triggers millions of cpu instructions for just a few code steps he would call you crazy.
Is it big data? Claude should be able to tell you what to do and give you scripts to deploy anything behind a web app.
Yeah, I have 2 clients that are now obsessed with vibe coding. They seem to understand the realities.
Why is this reassuring?
Why won't smarter and cheaper models in the future be able to automate this part for your manager as well? How novel is the feature set? Is it he has a knowledge gap or the model is incapable of something? What expertise are you bringing to bear that is beyond the scope of a future harness/model? Why wouldn't such a model simply fill in the blanks for your management, perhaps observing a diff of whatever you did? How do you verify the correctness of your thinking? Why could a future model not replicate this process?
I am just very puzzled by these sort of takes as we approach the end of 2026.
Because smarter for an LLM isn't operating on the same scale as human intelligence
Making a calculator a billion times "smarter" isn't going to make it able to wash dishes
This would be a great point if his manager asked him to wash the dishes. Who knows, in 10 years time if he finds himself with such a job he might consider himself lucky.
Anyway I hope I get an answer to my actual questions. Engaging with your "it's just a calculator" denialism is an obvious dead-end. Have a good day buddy.
Completely missing my point there. I wasn't saying it's a calculator. I was saying it's not a human intelligence and there's no reason to think it'll stand in for every responsibility we have.
Name me one human that can describe grandmaster level chess strategy but also loses to a random-only chess bot - that was the case for LLMs for a long time
My point is LLMs aren't humans. They're not a toddler slowly getting smarter, and when they're smarter they'll be able to do everything a human can do. It's a different scale entirely.
I didn't ask for every responsibility I asked for his specific responsibility.
Why don't you calm down and read my question again and you will see the only one "completely missing the point" is you.
You're arguing with someone else. I didn't make a claim that LLMs were human. If you're going to respond to me then engage with what I'm exactly saying. Go back and read it again, if you can manage it.
ok
Why isn't a LLM as smart as a human? Therein lies your problem.
A calculator used to be a human job. Are calculators smarter than humans?
I'm asking why is it reassuring about his job? Surely your big human brain can understand to be automated away doesn't mean the machine is "smart as a human" whatever that means. There is probably not a lot of utility in comparing synthetic and organic intelligence from such a reductive perspective.
Factory workers got automated by machines. Is a robotic arm smarter than a human? Is a tractor smarter than a bovine?
My question is simply what is he doing that is so "smart" or novel that it cannot be automated or mechanized. This should not be so hard to understand.
[delayed]
We can speculate about the capability of AI models in 10 years all we want. Currently; It's not good enough for the task.
Taking a visual proof-of-concept and turning it into a real product with integration to an existing complex system requires the developer to re-do a-lot of the work. And reading code; especially AI code someone-else wrote, is a miserable experience.
TBH I think we still need to explain this to ourselves first.
My experience over many decades is this:
* If a business person thinks a change or new program is very easy to do, it is really a very hard project.
* If a business person thinks the change or new program us hard to do, usually it is a trivial project.
For me, this has been true for well over 40 years. I never use any kind AI for my work, it did not exist before I retired.
This is is relatable but I think the issue is that they are uncorrelated.
If it's easy and the business thinks it's easy it gets done. If it's hard and the business thinks it's hard it doesn't.
XKCD Tasks (from 12 years ago, to the day!) https://xkcd.com/1425/
My experience matches this also. My observation is that most business people think things they know they don't know are hard, and things that they think they do know are easy. My additional observation is that AI tools make business people think they know a lot more things, so they consider a lot more things easy, when they are not. AI tools are a machine that makes business people Dunning-Kruger-max.
Typical experience with Lovable from everyone I've talked to.
Check out Synthetiq if you want to actually get into production
The problem with software is that it is never done. There is always another feature you could have and worse than building a property the work is only done by the people on the outside.
I think it's deeper than that, once you know software is never done you have to design for constant change.
When clients ask why something takes so long, I explain that I'm not building what you asked for today, I'm building something that will be easy to turn into what you asked for today and possible to turn into whatever you ask for tomorrow.
AI has vastly changed the shape of what that looks like though. There are whole classes of refactoring work that are much cheaper and quicker to do with agents. Integrating a protocol client library say, or swapping one library for another are now potentially hour-long instead of days-long tasks, especially when you have test coverage to back you up (which AI also immensely helps you with).
To me stories like this seem like just a moment in time.
Like in 1 to 5 years, vibe coding without looking at the code will likely be a lot better.
We’re in the self driving car stage right now. A Waymo can drive itself but the belts and suspenders involved are more expensive than a normal car.
More expensive?
Not OP, but… It can drive itself…mostly (the “belt”). And when it doesn’t, you need remote drivers (the “suspenders”) to get it unstuck. I don’t need that with the car I drive myself, and it probably won’t be needed with self-driving cars (waaaay in the future, IMO).
This is definitely getting my favorite. I’m also going to shamelessly steal this house analogy.
Thanks for writing this!
I've been using it for years. "Let's decide where we want the plumbing to go before pouring the slab", or "let's not focus on where the couch will go before we paint the walls". Or when a 'genius cowboy dev' discovers a super fast way to get from the 2nd floor to the kitchen by cutting a hole in the floor despite all the leaks it causes and how many other people have to work around the change.
The house analogy is great. The extreme is the Winchester Mystery house.
…which is always how I explain legacy code.
It's the rest of the owl.
This is all caused because no-one understands the purpose of quality in software
Low quality = cascading bugs, issues, slow to iterate and add or change features
This is just as true for human written as it is for AI
Instead we have everyone giving up on code quality as if it was just "beautiful code" perfectly indented that was only there for people to ooh and aah at
Low quality = you will sooner or later be writing a "We take security seriously" letter and buying credit monitoring for all your customers.
That is the one of the larger issues with poor quality. However even if there are no security issues poor quality can make your customers mad enough to go elsewhere and badmouth you to others looking at your product.
Even if you do use AI tools to help you write the code, at some level you have to specify what the program's output should be for every possible input.
By loosely specifying things in a prompt, there's simply not enough context for the AI tool to know the "right" output to produce for all possible inputs. What's "right" is often subjective anyway ("Should this button be red or blue?").
I don't know that saying "this work is hard" is enlightening.
More useful would be to be able to explain at some high level what the the inherent and accidental complexity is, the tradeoffs to navigate, long-term vs short-term decisions, etc.
Saying "it is hard" makes the audience think you're less of an expert in your domain and they are then inclined to find someone who doesn't say "this work is hard".
Medical doctors mostly follow standard procedure and decision trees.
The problem with this thinking is it requires certainty about the future. It's much cheaper right now IF AND ONLY IF you end up needing the thing. If you don't need it, then you've threw time and money down the drain.
Where I think this analogy weakens is you probably have far more certainty of whether or not you want a big family then you do on whether or not a new product line will see major adoption.
You should have an idea of where you are going so you don't paint yourself into a corner. However certainty about the future is impossible. You make your best guess and often that will be wrong. That doesn't mean it is wrong to guess, it means you need to temper your guesses.
I am known for analogies. I use them all the time to try and explain things to less technical people.
Sometimes it's building a house, sometimes it's writing a book, sometimes it's the difference between a Ford and a kit car, sometimes it's how you build a bridge.
None of them stand up to full scrutiny. You can pick every single one apart.
That's not the point of analogies. The point is to explain just one of the many aspects of software engineering in a more understandable format to the listener. Software engineering is nothing like building a physical thing. But some of the many, many problems and complexity you hit have physical product analogies.
Don't stretch analogies too far as they all pop.
All software is path dependent, all code is a liability, and all technical decisions are tradeoffs. These are the immutable truths of software that not changed one iota due to AI or any Moore's Law progress before it.
There are too many product managers and decision makers that are unable or unwilling to do the hard work of actually thinking through what they want, and re-evaluating their priors as new feedback and learnings come in. Similarly, there are too many engineers who are distant from the customer and the problem at hand, and end up chasing their own idea platonic ideal of good software, detached from the hard tradeoffs of what is truly needed right now vs what we anticipate needing in the future. The less software we can write to solve the problem now, while minimizing one way door decisions, and deferring as many "scaling" challenges as long as possible to make decisions with more complete information the better.
This is why AGI won't magically solve software development—because people don't actually know what they want until they try it and then they want something else. Raw intelligence can not solve for purpose or human goals. The better it gets, the more it will become like an evil genie or monkey's paw that never quite does what the feeble-minded human prompters want.
Pretty much every project. Initially your pace is great. You're knocking out features quickly. Everyone is upbeat and enthusiastic. Then you have to put it all together. You discover scenarios that the specs don't cover. You need more info from business about validating certain combinations of inputs. When you get those, you find contradictions with some other stuff you thought was already done. Combinatorial complexity. It's the essential problem with software systems. If business people don't understand it, tell them to read Brooks. If they still don't understand, tell them to read it again.
After using OpenBSD for a while, I fully adopted the “write less code” approach. Create the simplest solution and leave “features” out until you need them. Nice to have should be practically banned.
Yeah, I'm not sure that's a good idea. At the end of the day, Lovable is still a TypeScript web-app with a Supabase backend full stack system. If you don't understand what you have the LLMs actually build, then there is no way you can maintain it or debug it if something goes wrong.
All these no code/vibe code website generators don't make the code go away; the code maintenance burden just shifted to somebody else.