I’m a computer scientist and work in academia. Programming stuff usually takes like ~10% of my time, while the rest is theory and more like social sciences, i.e. studying human behaviour when working with machines etc. So even if I were to replace all my actual programming with vibe coding, I still would be a computer scientist because to me, coding is just a tool to achieve a bigger purpose. I think you are more referring to the job of a software dev, which can be someone that studied CS, but not necessarily.
Computer arts?
Most programming already didn’t use computer science.
Yeah, I never needed an AI to write poor, inefficient, and ineffective code. I’ve always had a tremendous personal capacity for that. Why should I give a company money to do something that I’m already good at?
lol, I feel you, but what I’m trying to say is you often don’t need to know concepts like P vs. NP for work other than an extreme baseline “is this gonna take forever if I throw more data at it?” I am not saying it’s not useful, just that for lots of work it’s not always super useful to know. Computer science as a field of study is much more mathy than a lot of fields of dev work. Then again, you’ve got other fields where it’s more important. Like I’m sure folks doing 3d graphics need to know a lot more trigonometry than I do as a backend engineer.
Surprisingly little trig, from my experience with opengl, direct3d, and vulkan. Much of the interfaces abstract that away. It helps you to understand what’s going on if you do know it, but you can go in without and be able to do fine. Most of what you interact with is angle calculations.
Because: for $20 per month to the AI company, you can output poor code much much faster.
So you dont use programming languages? learning how to use them was like all of computer science’s actual classes? Lets just start with the first class, you dont use classes, vectors, arrays, forloops, while, if else, etc? cout?
I dont understand what that means, the degree itself literally covers the fundamnetals, ehats realprogramming? (I dropped and graduated with it years ago)
When I think about computer science as a field of science I think about things like algorithmic complexity. I believe things like what you mentioned should be taught in general education prior to university (or, like calculus, as an optional elective) and are only covered because you need to know those basics to cover the advanced things.
It’s really difficult to come up with other examples of this that aren’t contrived because computer programming is the only field I know of that’s like this. I might compare it to architects needing to know how to use tools, but I don’t think they actually cover that. Maybe a better example might be engineers needing to know how to use tools before designing machines. Either way, things like how to use tools aren’t covered in those classes and they’re either not taught or taught as shop classes (or maybe they are, I didn’t go into those fields). Things like for loops I view as learning how to make a computer operate. Like how someone who drives a car doesn’t need to know how to fix an engine but a mechanic does. But learning about computer science is more like learning about what car designers do than what mechanics do. A lot of programming work doesn’t need that low level of attention to detail.
Like I said before, it doesn’t hurt, but it’s not super critical. A classic example is something like learning how to make a linked list. This is an early example of an assignment that starts to get into the actual computer science stuff because you start to talk about the comparisons between different data structures, like linked lists versus array lists. So in university you may be thinking “damn I’m gonna be making list implementations all the time” but you quickly learn, no, you’re not. The standard library of your language already has one and it’s worlds better than anything you made. Plus, 99% of the time you’re gonna want an array list, at least in the types of work I do.
I hope that helps make it more clear what I’m trying to say. I’m not saying computer programming is easy or doesn’t require skills.
Computer science is much more than programming. Did you cover other topics like formal logic, finite state machines, computability, crytography, machine learning etc?
Sure they teach that, but its not the first thing you’re thought the first two main classes for into are programming?
Why are you arguing with me and the dude that said programming didnt use computer science when computer science encompasses programming…
It’s like the relationship between mathematics and accounting. Sure, almost everything accountants do involve math in some way, but it’s relatively simple math that is a tiny subset of what all of mathematics is about, and the actual study of math doesn’t really touch on the principles of accounting.
Computer science is a theoretical discipline that can be studied without computers. It’s about complexity theory and algorithms and data structures and the mathematical/logical foundations of computing. Actual practical programming work doesn’t really touch on that, although many people are aware of those concepts and might keep them in the back of their mind while coding.
Computer vibing
People always talk about it in relation to programmers, but what about us non-programmers that have been able to code things only becuase of chatgpt?
I have some python, sysadmin, and computer security knowledge. I actually obtained the security+ cert a few years ago.
I do not work in tech anymore, and chatgpt has helped me so much, by basically coding stuff for me to do random work tasks that I was either unqualified to do or didn;t have the time to do.
I’m working on a physics project, and my simulator suits my purposes and produces reliable results. And I learned a teeny bit about coding building it.
AI coding is actually a very powerful tool, almost like a light saber. Do you notice how many amputations and artificial limbs there are in that galaxy far far away?
That’s pretty funny.
It’s a neat tool, but be careful what you do with it. I wouldn’t make anything web-connected or otherwise requiring security considerations, for example.
I think LLM is fine for shorter scripts. As a professional programmer, it has helped me with writing simple throwaway scripts. Those circumstances are rare.
My stance is that if you think LLM help you get your job done, then use LLM. It’s just another tool to your arsenal.
I don’t trust using LLM for large long running software projects though.
I have been building various things with AI coding tools for a month or so now. I rate the various engines on how far I can take them before they get hopelessly lost, unable to correct their own errors. For the best tools this seems to come after about 50 to 70 iterations of asking for small feature additions or error corrections, weaker tools (like Copilot) hit these infinite loops of fixing their errors with other errors much faster.
It’s a good limit, because after 2-3 hours of AI interactive development, I can then spend 4-6 hours going through the resulting code - cleaning it up and understanding how it works. I suspect if AI were taking me farther, like 100-150 iterations, it would probably take me more like 15-20 hours to unravel the various things it comes up with - kind of a point of diminishing returns.
Bottom line: think of your project in terms of microservices. AI is pretty good at microservices. As long as the individual services are each robust in their delivery of the required functions, you’re in good shape.
If it ever becomes “mystery meat,” it’s time to recode by hand.
That’s perfectly fine though. And I say that as a professional dev. The problem is when people assume you can actually build an entire software/service architecture of any complexity just through vibe coding.
Currently LLMs are great for helping me pick out the curtains or even to help me assemble some furniture, but I would NEVER let them build the entire house, if that makes sense.
The problem is when people assume you can actually build an entire software/service architecture of any complexity just through vibe coding.
Welcome to CEO handling 101. It’s an art, a very soft skill, and not for the faint of heart. I worked for a mid sized (50 employee) company once where I’d “speak truth to power” in our weekly meeting, get shot down rather enthusiastically by the CEO during the meeting, then after I and the rest of R&D left his office, he’d go out to production and have them start implementing all the concepts of my pitch - as his own ideas, naturally.
Sure, I get it. Once my business is in a more profitable place I’ll bring someone on to fix up the code, but for now it’s more than enough.
Once my business is in a more profitable place I’ll bring someone on to fix up the code
AKA: technical debt. I actually approve of this approach when you’re testing the market and don’t have any paying customers. Where it gets ugly is when customers start placing trust in your product, trust that might be costly if your code fails, and management doesn’t budget the resources to actually fix up the code. I was very glad to leave the place that was doing this…
The tools I’m using are for internal use and there is a backup in place. So, if something does go down, my contractors can pull the files up in one drive in a view only format.
It’s an interesting tool.
It can shave hours off of experienced programmers work if they use it in the right scenarios. You can use it in places where you need to do something that’s mundane but fiddly. It’s suboptimal for crapping out a large project, But it’s super effective at generating a single function or module to do a task. It might even come up with a better idea than you would use for some things. The key is if it does something that’s not quite right or not the best idea You need to be able to read it to understand that it’s going a little off the rails.
If you’re a spreadsheet junkie, It’s capable of writing really really complicated rules without getting lost in the minutia.
For non-developers that don’t know anything it’s a dicer proposition. After a couple thousand lines of code You might start running into interesting problems. When it starts having to go and do problem solving mode, and you’re just feeding it back The errors and asking it to fix the problem You can get bogged down pretty quickly.
For DevOps it’s the diggity bomb. Practically everything in that profession is either a one-off quick emergency script or a well thought out plan of templates.
Here are my five Amazon accounts give me a shell script that goes into every account in every availability zone, enumerate every security group and give me a tool to add remove or replace a given IP with a description and port based on the existence of other IPs descriptions or ports. Or write me an ansible script to install zabix monitoring playbooks with these templates.
You need to be able to read it to understand that it’s going a little off the rails.
At least 2/3 of the time I spend with AI coding is getting it to compile without errors - that’s more than a little off the rails, but it’s also much more helpful when you finally do get to a working example that you can look at, instead of beating your own head against the Stack Exchange archives hoping for inspiration, let it try for you.
This is what I’m talking about. So many people talk about it in white or black.
I was able to “code” a front end that my contractors can log into to view the files they are authorized to see.
It helped me write so many different things that all work together to solve my problem.
It may or may not be the most efficient code, but in terms of overall business operation, it’s extremely efficient.
Tip, when you’re done having it do your project, restart the chat, tell it that it’s a security engineer and ask it to check for any vulnerabilities or anything that should be done to protect the site against malicious activities. Ask it if there’s anything with your hosting or site that should be addressed.
Most of the training data out there is on how to get a task done and the best way to do the task, there’s a lot less training on completing a project with security in mind. There is however a lot of data on specifically how to secure already written code so it can do it, but it generally will not unless you ask it to.
Thanks! I’m going to do that Nita a great idea.
That’s a great tip: having it review the security of code that an earlier context generated.
I plan on having it write unit tests, or at least try to…
What’s the largest program, in line count (wc -l will be close enough, or open the file in Notepad++ and scroll to the end), that you’ve created this way?
I got one up around 500 lines before it started falling apart when trying to add new features. That was a mix of Rust and HTML, total source file size was around 14kB, with what I might call a “normal amount” of comments in the code.
If you count only 100% vibed code, it’s probably a 20 lines long script.
Usually, I tweak the code to fit my needs, so it’s not 100% vibes at that point. This way, I have built a bunch of scripts, each about 200 lines long, but that arbitrary limit is just my personal preference. I could put them all together into a single horribly unreadable file, which could be like 1000 lines per project. However, vast majority of them were modified by me, so that doesn’t count.
If you ask something longer than 20 lines, there’s a very high probability that it won’t work on the 15th round of corrections. Either GPT just can’t handle things that complicated, or maybe my needs are so obscure and bizarre that the training data just didn’t cover those cases.
If you ask something longer than 20 lines, there’s a very high probability that it won’t work on the 15th round of corrections.
Try Claude by Anthropic. I noticed Copilot and Google getting hung up much faster than Claude.
Also, I find that if you encourage a good architecture, like a formalized system of variables with Atomic / Mutexed access and getter/setter functions, that seems to give a project more legs than letting the AI work out fiddly access protection schemes one by one.
Using a tool as a tool to solve a problem?! Blasphemy, hectic, you shall bring about the end times!
But to be serious ai is here, it exists, you can’t put things back in the box. Bitching about ai at this point is like bitching the sun is bright and hot.
People need to just get the fuck over it and move on. Focus on regulating and updating laws to the new status quo. Not just bitching like some child having a tantrum because people are using a fucking tool.
I have been doing this stuff for over 40 years, the tools get faster and the ecosystems get more complex.
What would be really nice is a return to simplicity, using the fast tools to make simple stuff fast-squared, but nobody seems to want that.
I’m with you on this. The only legit concern I hear is its environmental impact.
But things will become more efficient over time and it had led to increased interest in nuclear energy, so i think it’s a problem that will take care of itself.
legit concern I hear is its environmental impact.
Commuting my fat ass to a climate controlled office, out to lunch, back home, parking spaces, highway lane miles, fuel, periodic vehicle replacements… that all has environmental impacts too, if I can do my job in half the time, that’s a big win for the environment.
I’m not bitching about the existence of code agents in general, I’m bitching about the general attitude of “Code Agents will replace programmers” because no the fuck they are not.
They can produce one-off apps and scripts fairly well to the point where non-programmers can solve their problems (great!) but they lack the necessary sophistication and context to build long-lasting, maintainable and scalable applications, which is what you are hiring developers for in the first place
to the point where non-programmers can solve their problems
I had a period of about 10 years where I bounced from company to company fixing non-programmers’ code so that it could actually be used in commercial products that brought in revenue.
Computer science has always been separate from software engineering.
In my mind:
- Computer science: Theoretical. Deals with algorithms, complexity and such.
- Software engineering: Practical. Deals with whatever PM has written in Jira tickets.
Both are important in their own right.
I wish I could’ve gotten a software engineering degree
I have a BEng in software engineering. It wasn’t thst different from a normal BSc in CS. Bits that stood out to me was industrial stuff around building and programming our own circuits, making our own (very simple) compiler, and some assembly modules, I had more maths stuff but that was for 3D graphics. My dissertation was a temperature control system for radiators.
I don’t really use any of it in my job, although I did do a ton more programming modules than most CS of the time, and those programming modules prove very useful.
Computer science is basically the study of software engineering, because computer engineering means hardware, which has grown into a separate discipline that computer science only touches on
Programming is writing code for the ticket, architecture is designing the system that gets written into tickets, and software development is the whole process
But all these disciplines grew faster than language, so really the titles are whatever you want them to be
Computer science is basically the study of software engineering
That’s not at all true if you ask me. Computer science is the study of data and computation, on a theoretical level. Software engineering is not theoretical at all, but very practical.
Here’s the thing…all of computer science is based on the practical, and software engineering is based on the theoretical
The data and computation being studied? We made it up. We don’t need to do it any particular way, we’re playing with ideas to interface to computers. Computers we made up too
Software engineering is using the lessons we learned by studying how others did things and how it works out in practice
We teach students computer science to make them into software engineers. You can still study how things are done as a separate career, but the two ideas are like an ouroboros. It’s a cycle of creation and analysis
all of computer science is based on the practical
I don’t understand this at all. Computer science is based on theoretical foundations that were developed way before any actual computer existed. This goes back more than 100 years.
We teach students computer science to make them into software engineers.
That’s only true if you studied a very practically-oriented education. Such educations are usually called “Software engineering” rather than “Computer Science”.
As a computer science graduate myself, my university definitely did not try to make me into a software engineer. It was very theoretical, with a clear focus on further research if that was what you wanted to pursue. You could get through the education quite okay and only ever write very little actual code. It was the maths that was the harder part to write.
I studied computer science as well and I share this sentiment.
Although I’m happy about my degree because I’ve learned many things I would otherwise miss, I also wish my degree prepared me more for the industry. There’s a disconnect between academia and the industry.
What I’m mostly concerned with is how to build software that can grow with 10ish team members. I find it hard to find good academic sources on this matter.
It’s methodology. Basically what you need is the correct amount of process - you can pick agile or scrum or whatever, and then you follow it to the amount that it makes sense. If you over-adhere to it, it slows things down to a crawl
Once you get up to 10 team members, you need to do things like feature branches, code reviews, and rigid style. You should also add in tests… At 10 you don’t have to have full coverage, but you need to be able to exercise your system enough to know when something breaks immediately
You also need ownership. You need one primary person who is the heart and soul of the code base, and they’re going to be the one who knows the whole thing and gives everyone direction. You can spin off another team at solid interface points, like an API or a plug in system, but you need one person who owns the core system and holds the code debt back
You also can add in code pipelines, enforce docstrings to generate documentation, you need diagrams so people understand how things flow through the system, etc
Ultimately, a lot of it comes down to mentorship. You have to be very hands on teaching people how the code works, and really hold their hand until they gain proficiency over an area. Then let them be secondary owners over that part of the symptom… And you have to make sure to stick them in a place where they’ll be a benefit - as you grow in numbers, it gets easier for each new person to be a drain on progress.
I’m not sure about academic sources…I dropped a lot of keywords in there that might help search, but ultimately it’s about team culture. You can’t just shove it all in at once, you have to slowly add new processes and make sure everyone is moving in the same direction
I know these concepts - after working in the industry for a while. Computer science education barely touched these topics. One professor was passionate enough to hint at test driven development, but that’s about it.
I don’t understand this at all. Computer science is based on theoretical foundations that were developed way before any actual computer existed. This goes back more than 100 years.
Yes, it’s code. We studied and iterated on that code long before the first computer, we came up with architectures that influenced the creation of the hardware to run it
The way they teach it has probably changed since I went through, but we had software engineering as a concentration. I actually picked networking and just took the all the software engineering courses because it had less math requirement lol
But it was mostly theoretical, with hands on homework to demonstrate it in practice. Everyone had certain courses they had to take, like at least 3 semesters of programming, discrete math, data structures, and a few others along with gen eds.
You just had to get a certain amounts of credits from different levels, so you could go through and pick what you wanted to focus on. You could dive into more theoretical or practical, high level or low level, but everyone had to study the full stack enough to understand it at a basic level
But it’s all castles made of sand. Even before the first computer, we’ve been iterating on these ideas… Studying them and building higher
The line between the science and engineering is blurry…Hell, our jobs are blurry and usually cross-discipline
I got an education in software engineering, not computer science, and my experience is in line yours. I had a few courses about fundamental computer science concepts but most of my education was in learning a little about many different areas of software engineering, specializing in a few. Most of the education involved working as part of a software team, using tools of the trade, applying common design patterns and that sort of stuff, even when courses weren’t explicitly about that.
I would never call myself a computer scientist, I don’t have the education for it, I however immediately had a software engineering job ready after graduating and felt prepared for it from day one.
I love what computer scientists do within the theoretical domain because it eventually seeps into mainstream languages and tools, in a way I benefit from. I’m just not involved with it myself, beyond when it reaches practical application.
Pfft. Computer Science ain’t about coding
Meh, I actually know how to code without the help of AI and my knowledge in computer science is minimal. A lot of people assume you need to be good at math and whatnot to be a software developer but in reality it’s like the difference between being a construction worker and having an engineering degree.
Edit: I’m a senior software engineer for a big tech company. Y’all down voting me are either over-inflating what software engineers do on a day-to-day or undervaluing what construction workers do.
Code AI is to programming as a microwave is to cooking
Microwaves are useful cooking tools and can be used to make many things.
Exactly how AI is a useful programming tool when used correctly by a professional.
correct, but if you’re paying someone to make you a dish you wouldn’t want them to just slap a frozen fish in the microwave and serve it to you. That’s what using AI to build enterprise applications for customers is like.
i mean, when they were new that was absolutely what people paid for. microwaves were the new hotness, the height of luxury for a few years. there were microwave-only restaurants.
which i guess serves to further strengthen the metaphor.
Funny. I dislike vibe coding because it takes away the “art”.
Implicit in these remarks is the notion that there is something undesirable about an area of human activity that is classified as an “art”; it has to be a Science before it has any real stature. On the other hand, I have been working for more than 12 years on a series of books called “The Art of Computer Programming.”
it can be both artless and scienceless
Yay! 😰
I dislike it because it encourages shit code
Computer vibence
I think it depends on who you ask. Some people who “vibe code” definitely use it as a crutch for a lack of understanding. But others (often more senior) tend to use it as just a really really complex auto-complete. Mostly it generates chunks and patterns but the ideas and how those pieces connect come from the dev
I feel like not knowing what you’re doing is a critical piece of the vibe coding definition tho. If a sr developer is using AI, understands the code generated, and can manipulate it in a secure, industry standard way, then that’s just a developer.
Fair enough. I put it in quotes because honestly I’ve seen all kinds of definitions thrown around. The conversation seems to often become a substitute for pro-LLM tools and anti-LLM tools. I think it’s more about how you use it and who controls it.
Vibe coding is shit, and will always be shit no matter who is doing it.
Edit: The mods decided my other comment was too controversial… “I’m an engineer to genius” apparently thats too controversial for this site 🙄
Totally disagree. Your position is way too overly simplistic and naive.
An engineer only builds a bridge as strong as it needs to be, and likewise I “vibe code” things based on how few fucks I need to give.
I’m experienced and can review the output for sanity and completion. I can test it, I can rewrite it, etc.
Stop looking at vibe coding as doing the whole thing, it’s more valuable as the glue between things, or to create scripts tools that make you more efficient.
And you can vibe code entire apps that basically just work these days. You probably don’t want to maintain those apps but thats a question of lifecycle planning.
It is so much faster to vibe code an API integration and a suite of tests than I can write. It’s faster to write a functional jq or bash script.
But it’s also much much much worse at doing data viz or writing pandas code because it’s trained on 10,000 shitty medium blogs.
You really have to know what you’re doing and what the model is doing, but it is not universally trash.
And if you don’t believe me, put $20 into the Claude API and install Claude Code and ask it to build something.
I’m experienced and can review the output for sanity and completion. I can test it, I can rewrite it, etc.
You aren’t vibe coding if you refactor and test properly…
why do you guys always just move the goalposts?
“X thing isn’t real AI, because real AI sucks and I might have to concede the positive attributes of X about AI generally… [OCR, chess bots, etc.]”
“Y thing isn’t real vibe coding, because real vibing coding sucks and I might have to concede the positive attributes of Y about vibe coding…”
like… you seem like you’ve just decided these things are “bad things” in your head and just shift your definitions the moment you meet reality and see anything that might evoke cognitive dissonance about it.
Usage by scientists to do pattern matching and by language models to replicate natural sounding language and a bunch of other AI is neat and useful but the AI is not literally intelligent as described by the people that are dumping LLMs into settings they are not actually useful for like regurgitating accurate facts.
When we criticize AI in situations like this it is because they are using a tape ruler to hammer in a nail and then taking away people’s hammers and replacing them with tape measures and then we find out they stole all the tape measures.
We are complaining about a combination of what it is and how it is used. We also want to make sure that a term that means something stupid is clearly used for that stupid thing and doesn’t lose meaning because they have some vaguely related usage. Using a hammer put pound in a nail and using the hammers claw to pull a nail out are two different things even if they both use a nail.
Where as you seem to think anyone criticizing shitty use of AI means all AI that exists instead of understanding context.
People who use AI frequently are the ones who don’t understand the fundamentals.
No, it doesn’t, because the need for programmers has not changed one single iota.
Vibe Coders do not replace them at any level. They are not computer scientists, they are not engineers, they cannot even program any more than a regular person could (possibly much worse).
Why stop there? Ouija coding takes the “science” and the “computer” out of computer science.
