All this depends critically on one premise: that sometime in near future AI coders will become fully automated and produce senior level code. If not we are wholly fucked because currently they are employing less and less junior coders which means that we will be running very low on number of senior coders in a decade or so. If LLMs still need supervision by then there won’t be enough senior coders to do so.
I’ve used it to explore some avenues without having to write a complete implementation. If the approach shows promise, then I go through the code and mostly rewrite it because the code it generates is terrible. I also use it if I don’t care about the project I’m on. They want to “do test-driven development” while having poorly-defined requirements that constantly change on a whim while also setting unreasonable unit test coverage thresholds? Cool, I’ll let the AI shit out a bunch of unit tests and waffle stomp it to satisfy your poorly thought out project requirements.
I agree with you on this. Let it handle things you don’t care about and massage the output if necessary. Anything I do care about, I code myself, but will ask for help if I get stuck on something. I’m a novice programmer at best, 18/100 skill score.
as a programmer I feel like this would be pretty cool. but this isn’t really how it is at all. I’m usually asking Claude code to do something very specific and then I’m throwing whatever it does away because it’s not correct. if I could have a little baby that I had to babysit I think that would be better
I was “there” with Claude as you describe about 3 months ago. Since then, Claude has stepped up to being able to create fully functional microservices. It helps if you completely specify what you want, it helps if you don’t specify funky libraries or other tech that has poor support on the internet, it helps if your total ask amounts to 1000 lines of code or less - but I have gotten up around 3000 lines before Sonnet 4 choked a few times.
Before this, my AI queries were mostly limited to specific API function call syntax, and they would only be right about 2/3 of the time, which beats randomly trying things myself until I eventually guess the right variation… Yes, it’s better to consult the documentation - when it’s available - it’s not always available.
yea so maybe the resulting future is that the tools can only work with really popular libraries that have lots of people talking about them on stack overflow in the year 2024 or whatever, and new smaller potentially interesting libraries will have a harder time seeing adoption
maybe the resulting future is that the tools can only work with really popular libraries that have lots of people talking about them on stack overflow in the year 2024 or whatever, and new smaller potentially interesting libraries will have a harder time seeing adoption
Yeah, that’s the future I’ve been living since about 2005. The alternative to letting the world be your support desk via stack overflow and similar is to develop killer examples and API documentation for your own libraries so the AI (and everyone else) can learn from that. Qt was a great example of this starting in the early 2000s.
The dark future is where you have competitors “poisoning the well” spreading false information about your tech in the normally reliable channels, then having AI amplify that for them. This, too, is already happening to some extent - more in the political sphere than the technical space, but it’s everywhere to some extent.
Conversely, I’d imagine there are babysitters out there who at times wish they could just throw the baby away.
I’ve thrown the baby away many times
Eyoooo
Gotta love how devs and engineers are supposed to be on the front lines of innovation and progression. But most of the it’s just moaning and calling the next gen dumb. 15 years ago the current devs would be called dumb for using Frameworks amd how it’s cheating since it’s not self written. Do your part and educate and guide the next gen instead of complaining about tech evolving and being used.
Interesting how this article is contradicted by hundreds of others.
Because if we say anything positive about AI programming we get downvoted to hell…
I’m not a supporter of the companies making LLMs and how they profit off others’ intellect, I’m not a supporter of their use of the technology for fascism, genocide, and pure evil. I’m not a moron that thinks LLMs are intelligent.
But I recognize it as a very useful technological advancement, it’s a very useful tool and to pretend otherwise is foolish. LLMs are an amazing coding aid, and when used correctly and fed the right context, they can save hours of frustration and research dead-ends.
I share the same opinion as yourself and gave you an upvote. LLM are a truly fascinating technology and I’m amazed by how little we understand on how it even does what it does. That said the process that got us here and what they are currently being used for is amoral at best
As a senior dev, I say it’s not worth it. Our junior devs rely on it too much and I spent most of yesterday trying to figure out for my junior dev why their code didn’t work. Eventually came to the conclusion that they just have to redo most of it because it’s utter garbage and invents new code to do what the architecture already has.
As a senior dev, I agree but am impressed that you’re dealing with a functional architecture to begin with
I keep seeing “vibe coding.” WTF is vibe coding? ELI5
Vibe coding is asking gpt for code, copying it into your code environment, then telling gpt about any errors or issues. The problem is that it actually works a significant amount of the time, let’s be generous and say 80%. Another 15% of the time it cannot solve a problem itself. And finally the worst possible outcome is the last 5%, where it creates a seemingly working solution that actually breaks on edge cases or has potential security issues.
One important aspect of vibe coding that I always see that is missing in explanations is the part that vibe coding is the is the generation of code through AI, but without understanding what the code is doing, the effect of this is you are totally dependant on the AI to keep generating the code, so if any error happens you don’t have fucking idea in what to do. If you generate the code using AI and you understood what the AI did, is not vibe coding.
A security nightmare waiting to happen.
Also performance, maintenance, and regression.
A day will come when I get to know what vibecoding is. Or maybe this word will die out sooner. You never know.
Vibe coding is when you ask a chatbot to code for you, then ask it to fix the errors it generated, and repeat until you can’t find any more errors. Later, someone notices that your application was coded by a chatbot, exploits one of the many security flaws, and steals all your data and credentials.
I thought this was a normal coding. Then how do you call those who heavily rely on google and SO?
Copy pasta chefs.
That picture accompanying the article is backwards. Why is it portraying the AI as the babysitter and not the baby that needs to be supervised by a human?
You’re absolutely right!
I completely messed up the picture. It should be the other way around. Do you want me to correct my mistake and generate a new picture?
/s
Carla Rover once spent 30 minutes sobbing after having to restart a project she vibe coded. Rover has been in the industry for 15 years, mainly working as a web developer. She’s now building a startup, alongside her son, that creates custom machine learning models for marketplaces.
Using AI to sell AI, infinite money glitch! /s
“Using a coding co-pilot is kind of like giving a coffee pot to a smart six-year-old and saying, ‘Please take this into the dining room and pour coffee for the family,’” Rover said. Can they do it? Possibly. Could they fail? Definitely. And most likely, if they do fail, they aren’t going to tell you.
No, a kid will learn if s/he fucks up and, if pressed, will spill the beans. AI is, despite being called “intelligent”, not learning anything from its mistakes and often forgetting things because of limitations - consistency is still one of the key problems for all LLM and image generators
AI is, despite being called “intelligent”, not learning anything from its mistakes
Don’t they also train new models on past user conversations?
Considering how many AI models still can’t correctly count how many ‘r’ there are in “strawberry”, I doubt it. There’s also the seahorse emoji doing the rounds at the moment, you’d think that the models would get “smart” after repeatedly failing and realize it’s an emoji that has never existed in the first place.
Chatgpt5 can count the number of 'r’s, but that’s probably because it has been specifically trained to do so.
I would argue that the models do learn, but only over generations. So slowly and specifically.
They definitely don’t learn intelligently.
That’s the P in ChatGPT: Pre-trained. It has “learned” based on the set of data it has been trained on, but prompts will not have it learn anything. Your past prompts are kept to use as “memory” and to influence output for your future prompts, but it does not actually learn from them.
The next generation of GPT will include everyone’s past prompts (ever been A/B tested on openAI?). That’s what I mean by generational learning.
Maybe. It’s probably not high quality training data for the most part, though.
If you bring a 6yo into office and tell them to do your work for you, you should be locked up. For multiple reasons.
Not sure why they thought that was a positive comparison.
Looks like every senior developer is building vibe coded startup and their children are selling machine learning models on marketplaces. Anyone know of such marketplace or it’s fake as much as the article ?
I’ve noticed nary a thing except vapid media and social buzz. I’ve tried the tools themselves and they seem to waste time too often to be worthwhile
This headline made me a little nauseous.
Currently, I write all production code at work without any AI assistance. But to keep up with things, I do my own projects.
Main observation: When I use it (Claude Code + IDE-assistant) like a fancy code completion, it can save a lot of time. But: It must be in my own area of expertise, so I could do it myself just as well, only slower. It makes a mistake about 10 - 20 % of the time, most of them not obvious like compile errors, so it would turn the project into disaster over time. Still, seems like a senior developer could be about 50% - 100% more productive in the heat of the implementation phase. Most important job is to say “STOP” when it’s about to do nonsense. The resulting code is pretty much exactly how I would have done it, and it saved time.
I also tried “vibe coding” by using languages and technologies that I have no experience with. It resulted in seemingly working programs, e. g. to extract and sort photos from an outdated data file format, or to parse a nice statistics out of 1000 lines of annual private bank statements. Especially the latter resulted in 500 lines of unmaintainable Python-spaghetticode. Still nice for my private application, but nobody in the world can guarantee that there aren’t pennies missing, or income and outcome switched in the calculation. So unusable for the accounting of a company or anything like that.
I think it will remain code completion for the next 5 years. The bubble of trying more than next-gen code completion for seniors will burst. What happens then is hard to say, but it takes significant breakthroughs to replace a senior and work independently.
In real code, so after the first week of development, typing really isn’t what I spend most of my time on. Fancy autocomplete can sometimes be right and then it saves a few seconds, but not nearly 50-100% added productivity. Maybe more like 1-2%.
If I get a single unnecessary failed compile from the autocomplete code, it loses me more time than it saved.
But it does feel nice not having to type out stuff.
That’s why all research on this topic says that AI assistance feels like a 20-30% productivity boost (when the developers are asked to estimate how much time they saved) while the actual time spent on the task actually goes up by 20-30% (so productivity gets lost).
I find it also saves a certain “mental energy”.
E. g. when I worked on a program to recover data from the old discontinued Windows photo app: I started 2 years ago and quickly had a proof-of-concept: Found out it’s just sqlite format, checked out the table structure, made a query to list the files from one album. So at that point, it was clear that it was doable, but the remaining 90 % would be boring.
So after 2 years on pause, I just gave Gemini 2.5Pro the general problem and the two queries I had. It 1-shot a working powershell script, no changes required. It reads directly from the sqlite (imagine the annoyance to research that when you never ever use powershell!) and put the files to folders named by the former albums. My solution would have been worse, would probably have gone with just hacking together some copy-commands from SELECT and run them all once.
That was pretty nice: I got to do the interesting part of building the SQL queries, and it did the boring, tiring things for me.
Overall, I remain sceptical as you do. There is definitely a massive bullshit-bubble, and it’s not clear yet where it ends. I keep it out of production code for now, but will keep experimenting on the side with an “it’s just code completion” approach, which I think might be viable.
Yours is pretty much the best-case scenario for AI:
- Super small project, maybe a few dozen lines at most
- Greenfield: no dependencies, no old code, nothing to consider apart from the problem at hand
- Disposable: once the job is done you discard it and won’t need to maintain it
- Someone most likely already did the same thing or did something very similar and the LLM can draw on that, modify it slightly and serve it as innovation
- It’s a subject where you are good enough that you can verify what the LLM spits out, but where you’d have to spend hours and hours to read into how to do it
For that kind of stuff it’s totally OK to use an LLM. It’s like googleing, finding a ready-made solution on Stackexchange, running that once and discarding it, just in a more modern wrapping. I’ve done something similar too.
But for real work on real projects, LLM is more often than not a time waster and not a productivity gain.
That’s completely true; it’s hard for me to judge on a small scale when I won’t (for good reasons) let it touch my customer’s production code.
It makes a mistake about 10 - 20 %
Anecdotally, Copilot does the reverse for me.
Copilot leads me on flights of fanciful code that is absolutely not possible, and the joy turns to tragedy when I find out it lied insidiously about a particular niche function the entire time.
This feels like one of those paid fluff pieces companies put out so that smaller ones feel like they’re “missing out”
This topic is always twisted and based on some random dom deceitful surveys. Yes I’d commit AI code but mostly because that code does a test or implements some one off function that I read through anyway.
Do I enjoy baby sitting AI? Eh its a mix bag. Its great for writing tests and boilerplate and bootstrap you into real solutions but I dread any code base that claims their mostly written by cloude code. The AI is still incredibly stupid.
I think rubber duck is really the best feature of AI. I’ve been working remotely for over 20 years now and it’s such a game changer just to bounce ideas and architecture designs with a chat bot. This feature should be revolutionary enough without the need for independent agents.
