5 ms·

You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much e

by dwaltrip 11mo ago

You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much easier. Rinse and repeat for many "one-off" tasks. It's not going away, you need to learn how to use it. shrugs shoulders

deleted 11mo ago
[deleted]
deleted 11mo ago
[deleted]
dwaltrip 11mo ago
It’s kind of fun watching this comment go up and down :) There’s so much evidence out there of people getting real value from the tools. Some questions you can ask yourself are “why doesn’t it work for me?” and “what can I do differently?”. Be curious, not dogmatic. Ignore the hype, find people doing real work.
- samdoesnothing 11mo ago
  Most people don't have a problem with using genai for stuff like throwaway UI's. That's not even remotely relevant to the criticisms. People reject having it forced down their throats by companies who are desperate to make us totally reliant on it to justify their insane investments. And people reject the evangelicals who claim that it's going to replace developers because it can spit out mostly working boilerplate.
- SpicyLemonZest 11mo ago
  They're good questions! The problem is that I've tried to talk to the people who are getting real value from it, and often the answer ends up being that the value is not as real as they think. One guy gave an excited presentation about how AI let him write 7k LOC per day, expounded for an entire session about how the rest of us should follow in his shoes, and then clarified only in Q&A that reviewers couldn't keep up so he exempted himself from code review.
  - stocksinsmocks 11mo ago
    Most enterprise software I use has serious defects. Professional CAD software for infrastructure is awful. Many are just incremental improvements piled upon software from the 1990s. Bugs last for decades because nobody can understand how the program works so they just work on one more little VBA plugin at a time. Meanwhile, the capabilities of these programs have fallen completely behind game studios with no budget and no business plan. Where are the results of this human excellence and code quality process? There are 10s of thousands of new CVEs every year from code hand crafted by artisans on their very own MacBooks. How? Perhaps there is the tiny possibility that maybe code quality is mostly an aesthetic judgment that nobody can really define, and just maybe this effort is mostly spent on vague concepts like maintainability or preferential decisions instead of the basics: does it meet the specification? Is the performance getting better or worse? This is the game changer for me: I don’t have to evaluate tens or hundreds of market options that fit my problem. I tell the machine to solve it, and if it works, then I’m happy. If it doesn’t I throw it away. All in a few minutes and for a few cents. Code is going the way of the disposable diaper, and, if you ever washed a cloth diaper you will know, that’s a good thing.
    - SpicyLemonZest 11mo ago
      > I tell the machine to solve it, and if it works, then I’m happy. If it doesn’t I throw it away. What happens when it seems to work, and you walk away happy, but discover three months later that your circular components don't line up because the LLM-written CAD software used an over-rounded PI = 3.14? I don't work in industrial design, but I faced a somewhat similar issue where an LLM-written component looked fine to everyone until final integration forced us to rewrite it almost entirely.
      - queenkjuul 11mo ago
        This is basically me at my job right now. My boss used Claude Code in his spare time to write a "proof of concept" Electron app. It mostly worked but had some weird edge case behaviors. Now it's handed off to me, and fixing those edge cases is requiring me to refactor basically every single thing Claude touched. Vast majority I'm just tossing and redoing from scratch. The original code "looks" fine, and it works pretty well even, but an LLM cannot avoid critical oversights along the way, and is fundamentally designed to its mistakes look as plausibly correct as possible. This makes correcting the problems down the line much more annoying (unless you can afford to live with the bugs and keep slapping on more band aids, i guess)
- mattgreenrocks 11mo ago
  I’m an AI skeptic. I like seeing what UIs it spits out, though, which defeats the blank page staring into my soul fear nicely. I don’t even use the code, just take inspiration from the layouts.
  - scotty79 11mo ago
    Yeah, it helps a lot to make first steps, to overcome writers block, to make you put into words what you'd like to have built. At one point you might take over, ask it for specific refactors you'd do but are too lazy to do yourself. Or even toss it away entirely and start fresh with better understanding. Yourself or again with agent.
- pydry 11mo ago
  It's like watching somebody argue that code linting is going to change the face of the world and the rebuttals to the skeptics are arguing that akshually code linting is quite useful....
xorcist 11mo ago
Perhaps. But does it matter? There is a million tools to investigate complex data already. Are you suggesting it is more useful to develop a new tool from scratch, using LLM-type tools, than it is to use a mature tool for data analysis? If you don't know how to analyze data, and flat out refuse to invest in learning the skill, then I guess that could be really useful. Those users are likely the ones most enthusiastic about AI. But are those users close to as productive as someone who learns a mature tool? Not even close. Lots of people appreciate an LLM to generate boiler plate code and establish frameworks for their data structures. But that's code that probably shouldn't be there in the first place. Vibe coding a game can be done impressively quick, but have you tried using a game construction kit? That's much faster still.
icedchai 11mo ago
I have found value for one off tasks. I forget the exact situation, but I wanted to do some data transformation, something that would normally take me a half hour of awk/sed/bash or python scripting. AI spit it out right away.
thewebguyd 11mo ago
> You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much easier. I think the throwaway part is important here and people are missing it, particularly for non-programmers. There's a lot of roles in the business world that would make great use of ephemeral little apps like this to do a specific task, then throw it away. Usually just running locally on someone's machine, or at most shared with a couple other folks in your department. Code doesn't have to be good, hell it doesn't even have to be secure, and certainly doesn't need to look pretty. It just needs to work. There's not enough engineering staff or time to turn every manager's pet excel sheet project into a temporary app, so LLMs make perfect sense here. I'd go as far to say more effort should be put into ephemeral apps as a use case for LLMs over focusing on trying to use them in areas where a more permanent, high quality solution is needed. Improve them for non-developers.
bigger_cheese 11mo ago
The issue is people trying to use these AI tools to investigate complex data not the throwaway UI part. I work as the non-software kind of engineer at an industrial plant there is starting to emerge a trend of people who just blindly trust the output of AI chat sessions without understanding what the chat bot is echoing at them which is wasteful of their time and in some cases my time. This not not new in the past I have experienced engineers who use (abuse) statistics/regression tools etc. Without understanding what the output was telling them but it is getting worse now. It is not uncommon to hear something like: "Oh I investigated that problem and this particular issue we experienced was because of reasons x, y and z." Then when you push back because what they've said sounds highly unlikely it boils down to. "I don't know that is what the AI told me". Then if they are sufficiently optimistic they'll go back and prompt it with "please supply evidence for your conclusion" or some similar prompt and it will supply paragraphs of plausible sounding text but when you dig into what it is saying there are inconsistencies or made up citations. I've seen it say things that were straight up incorrect and went against Laws of Thermodynamics for example. It has become the new "I threw the kitchen sink into a multivariate regression and X emerged as significant - therefore we should address x" I'm not a complete skeptic I think AI has some value, for example if you use it as a more powerful search engine by asking it something like "What are some suggested techniques for investigating x" or "What are the limitations of Method Y" etc. It can point you to the right place assist you with research, it might find papers from other fields or similar. But it is not something you should be relying on to do all of the research for you.
gigel82 11mo ago
Except when your AI psychosis PM / manager sees your throwaway vibe-coded garbage and demands it gets shipped to customers. It's infinitely worse when your PM / manager vibe-codes some disgusting garbage, sees that it kind of looks like a real thing that solves about half of the requirements (badly) and demands engineers ship that and "fix the few remaining bugs later".
area51org 11mo ago
One thing people often don't realize or ignore: these LLMs are trained on the internet, the entire internet. There's a shit-ton of bad and inefficient code on the internet. Lots of it. And it was used to train these LLMs as much as the good code. In other words, the LLMs are great if you're OK with mediocrity at best. Mediocrity is occasionally good enough, but it can spell death for a company when key parts of it are mediocre. I'm afraid a lot of the executives who fantasize about replacing humans with AI are going to have to learn this the hard way.
BearOso 11mo ago
But how do you know you're getting the correct picture from that throwaway UI? A little while back there was an blog posted where the author wrote an article praising AI for his vibe-coded earth-viewer app that used Vulkan to render inside a GUI window. Unfortunately, that wasn't the case and AI just copied from somewhere and inserted code for a rudimentary software rendering. The AI couldn't do what was asked because it had seldom been done. Nobody on the internet ever discussed that particular objective, so it wasn't in the training set. The lesson to learn is that these are "large-language models." That means it can regurgitate what someone else has done before textually, but not actually create something novel. So it's fine if someone on the internet has posted or talked about a quick UI in whatever particular toolkit you're using to analyze data. But it'll throw out BS if you ask for something brand new. I suspect a lot of AI users are web developers who write a lot of repetitive rote boilerplate, and that's the kind of thing these LLMs really thrive with.
- keeda 11mo ago
  > But how do you know you're getting the correct picture from that throwaway UI? You get the AI to generate code that lets you spot-check individual data points :-) Most of my work these days is in fact that kind of code. I'm working on something research-y that requires a lot of visualization, and at this point I've actually produced more throwaway code than code in the project. Here's an example: I had ChatGPT generate some relatively straightforward but cumbersome geometric code. Saved me 30 - 60 minutes right there, but to be sure, I had it generate tests, which all passed. Another 30 minutes saved. I reviewed the code and the tests and felt it needed more edge cases, which I added manually. However, these started failing and it was really cumbersome to make sense of a bunch of coordinates in arrays. So I had it generate code to visualize my test cases! That instantly showed me that some assertions in my manually added edge cases were incorrect, which became a quick fix. The answer to "how do you trust AI" is human in the loop... AND MOAR AI!!! ;-)
llbeansandrice 11mo ago
You should try telling management it’s throwaway
Arainach 11mo ago
>You can vibe-code a throwaway UI And then people create non-throwaway things with it and your job, performance report, bonus, and healthcare are tied to being compared to those people who just do what management says without arguing about the correct application of the tool. If you keep your job, it's now tied to maintaining the garbage those coworkers checked in.

deleted 11mo ago

[deleted]

deleted 11mo ago

[deleted]

dwaltrip 11mo ago

It’s kind of fun watching this comment go up and down :) There’s so much evidence out there of people getting real value from the tools. Some questions you can ask yourself are “why doesn’t it work for me?” and “what can I do differently?”. Be curious, not dogmatic. Ignore the hype, find people doing real work.

samdoesnothing 11mo ago
Most people don't have a problem with using genai for stuff like throwaway UI's. That's not even remotely relevant to the criticisms. People reject having it forced down their throats by companies who are desperate to make us totally reliant on it to justify their insane investments. And people reject the evangelicals who claim that it's going to replace developers because it can spit out mostly working boilerplate.
SpicyLemonZest 11mo ago
They're good questions! The problem is that I've tried to talk to the people who are getting real value from it, and often the answer ends up being that the value is not as real as they think. One guy gave an excited presentation about how AI let him write 7k LOC per day, expounded for an entire session about how the rest of us should follow in his shoes, and then clarified only in Q&A that reviewers couldn't keep up so he exempted himself from code review.
- stocksinsmocks 11mo ago
  Most enterprise software I use has serious defects. Professional CAD software for infrastructure is awful. Many are just incremental improvements piled upon software from the 1990s. Bugs last for decades because nobody can understand how the program works so they just work on one more little VBA plugin at a time. Meanwhile, the capabilities of these programs have fallen completely behind game studios with no budget and no business plan. Where are the results of this human excellence and code quality process? There are 10s of thousands of new CVEs every year from code hand crafted by artisans on their very own MacBooks. How? Perhaps there is the tiny possibility that maybe code quality is mostly an aesthetic judgment that nobody can really define, and just maybe this effort is mostly spent on vague concepts like maintainability or preferential decisions instead of the basics: does it meet the specification? Is the performance getting better or worse? This is the game changer for me: I don’t have to evaluate tens or hundreds of market options that fit my problem. I tell the machine to solve it, and if it works, then I’m happy. If it doesn’t I throw it away. All in a few minutes and for a few cents. Code is going the way of the disposable diaper, and, if you ever washed a cloth diaper you will know, that’s a good thing.
  - SpicyLemonZest 11mo ago
    > I tell the machine to solve it, and if it works, then I’m happy. If it doesn’t I throw it away. What happens when it seems to work, and you walk away happy, but discover three months later that your circular components don't line up because the LLM-written CAD software used an over-rounded PI = 3.14? I don't work in industrial design, but I faced a somewhat similar issue where an LLM-written component looked fine to everyone until final integration forced us to rewrite it almost entirely.
    - queenkjuul 11mo ago
      This is basically me at my job right now. My boss used Claude Code in his spare time to write a "proof of concept" Electron app. It mostly worked but had some weird edge case behaviors. Now it's handed off to me, and fixing those edge cases is requiring me to refactor basically every single thing Claude touched. Vast majority I'm just tossing and redoing from scratch. The original code "looks" fine, and it works pretty well even, but an LLM cannot avoid critical oversights along the way, and is fundamentally designed to its mistakes look as plausibly correct as possible. This makes correcting the problems down the line much more annoying (unless you can afford to live with the bugs and keep slapping on more band aids, i guess)
mattgreenrocks 11mo ago
I’m an AI skeptic. I like seeing what UIs it spits out, though, which defeats the blank page staring into my soul fear nicely. I don’t even use the code, just take inspiration from the layouts.
- scotty79 11mo ago
  Yeah, it helps a lot to make first steps, to overcome writers block, to make you put into words what you'd like to have built. At one point you might take over, ask it for specific refactors you'd do but are too lazy to do yourself. Or even toss it away entirely and start fresh with better understanding. Yourself or again with agent.
pydry 11mo ago
It's like watching somebody argue that code linting is going to change the face of the world and the rebuttals to the skeptics are arguing that akshually code linting is quite useful....

xorcist 11mo ago

Perhaps. But does it matter? There is a million tools to investigate complex data already. Are you suggesting it is more useful to develop a new tool from scratch, using LLM-type tools, than it is to use a mature tool for data analysis? If you don't know how to analyze data, and flat out refuse to invest in learning the skill, then I guess that could be really useful. Those users are likely the ones most enthusiastic about AI. But are those users close to as productive as someone who learns a mature tool? Not even close. Lots of people appreciate an LLM to generate boiler plate code and establish frameworks for their data structures. But that's code that probably shouldn't be there in the first place. Vibe coding a game can be done impressively quick, but have you tried using a game construction kit? That's much faster still.

icedchai 11mo ago

I have found value for one off tasks. I forget the exact situation, but I wanted to do some data transformation, something that would normally take me a half hour of awk/sed/bash or python scripting. AI spit it out right away.

thewebguyd 11mo ago

> You can vibe-code a throwaway UI for investigating some complex data in less than 30 minutes. The code quality doesn't matter, and it will make your life much easier. I think the throwaway part is important here and people are missing it, particularly for non-programmers. There's a lot of roles in the business world that would make great use of ephemeral little apps like this to do a specific task, then throw it away. Usually just running locally on someone's machine, or at most shared with a couple other folks in your department. Code doesn't have to be good, hell it doesn't even have to be secure, and certainly doesn't need to look pretty. It just needs to work. There's not enough engineering staff or time to turn every manager's pet excel sheet project into a temporary app, so LLMs make perfect sense here. I'd go as far to say more effort should be put into ephemeral apps as a use case for LLMs over focusing on trying to use them in areas where a more permanent, high quality solution is needed. Improve them for non-developers.

bigger_cheese 11mo ago

The issue is people trying to use these AI tools to investigate complex data not the throwaway UI part. I work as the non-software kind of engineer at an industrial plant there is starting to emerge a trend of people who just blindly trust the output of AI chat sessions without understanding what the chat bot is echoing at them which is wasteful of their time and in some cases my time. This not not new in the past I have experienced engineers who use (abuse) statistics/regression tools etc. Without understanding what the output was telling them but it is getting worse now. It is not uncommon to hear something like: "Oh I investigated that problem and this particular issue we experienced was because of reasons x, y and z." Then when you push back because what they've said sounds highly unlikely it boils down to. "I don't know that is what the AI told me". Then if they are sufficiently optimistic they'll go back and prompt it with "please supply evidence for your conclusion" or some similar prompt and it will supply paragraphs of plausible sounding text but when you dig into what it is saying there are inconsistencies or made up citations. I've seen it say things that were straight up incorrect and went against Laws of Thermodynamics for example. It has become the new "I threw the kitchen sink into a multivariate regression and X emerged as significant - therefore we should address x" I'm not a complete skeptic I think AI has some value, for example if you use it as a more powerful search engine by asking it something like "What are some suggested techniques for investigating x" or "What are the limitations of Method Y" etc. It can point you to the right place assist you with research, it might find papers from other fields or similar. But it is not something you should be relying on to do all of the research for you.

gigel82 11mo ago

Except when your AI psychosis PM / manager sees your throwaway vibe-coded garbage and demands it gets shipped to customers. It's infinitely worse when your PM / manager vibe-codes some disgusting garbage, sees that it kind of looks like a real thing that solves about half of the requirements (badly) and demands engineers ship that and "fix the few remaining bugs later".

area51org 11mo ago

One thing people often don't realize or ignore: these LLMs are trained on the internet, the entire internet. There's a shit-ton of bad and inefficient code on the internet. Lots of it. And it was used to train these LLMs as much as the good code. In other words, the LLMs are great if you're OK with mediocrity at best. Mediocrity is occasionally good enough, but it can spell death for a company when key parts of it are mediocre. I'm afraid a lot of the executives who fantasize about replacing humans with AI are going to have to learn this the hard way.

BearOso 11mo ago

But how do you know you're getting the correct picture from that throwaway UI? A little while back there was an blog posted where the author wrote an article praising AI for his vibe-coded earth-viewer app that used Vulkan to render inside a GUI window. Unfortunately, that wasn't the case and AI just copied from somewhere and inserted code for a rudimentary software rendering. The AI couldn't do what was asked because it had seldom been done. Nobody on the internet ever discussed that particular objective, so it wasn't in the training set. The lesson to learn is that these are "large-language models." That means it can regurgitate what someone else has done before textually, but not actually create something novel. So it's fine if someone on the internet has posted or talked about a quick UI in whatever particular toolkit you're using to analyze data. But it'll throw out BS if you ask for something brand new. I suspect a lot of AI users are web developers who write a lot of repetitive rote boilerplate, and that's the kind of thing these LLMs really thrive with.

keeda 11mo ago
> But how do you know you're getting the correct picture from that throwaway UI? You get the AI to generate code that lets you spot-check individual data points :-) Most of my work these days is in fact that kind of code. I'm working on something research-y that requires a lot of visualization, and at this point I've actually produced more throwaway code than code in the project. Here's an example: I had ChatGPT generate some relatively straightforward but cumbersome geometric code. Saved me 30 - 60 minutes right there, but to be sure, I had it generate tests, which all passed. Another 30 minutes saved. I reviewed the code and the tests and felt it needed more edge cases, which I added manually. However, these started failing and it was really cumbersome to make sense of a bunch of coordinates in arrays. So I had it generate code to visualize my test cases! That instantly showed me that some assertions in my manually added edge cases were incorrect, which became a quick fix. The answer to "how do you trust AI" is human in the loop... AND MOAR AI!!! ;-)

llbeansandrice 11mo ago

You should try telling management it’s throwaway

Arainach 11mo ago

>You can vibe-code a throwaway UI And then people create non-throwaway things with it and your job, performance report, bonus, and healthcare are tied to being compared to those people who just do what management says without arguing about the correct application of the tool. If you keep your job, it's now tied to maintaining the garbage those coworkers checked in.