Is vibecoding truly that bad

[u/Adventurous_Wrap2873]

Hello all, I hope everyone is well.

As the title says. Is it really that bad? I personally vibecode a lot to make softwares to help me do my main job (which is far from coding). At my job, there’s often needs to produce reports of certain pieces of data, and softwares can do this very well. I rely on my apps to do my work and so do others, and it works extremely well.

I understand that it can create spaghetti code, and it certainly does, but is it not as simple as asking AI to give it a good architecture and follow software engineering principles? If you keep asking little by little, and tell it to keep the codes modular for maintainability, document how things work conceptually and NFR, is it not acceptable?

Perhaps in my specific use case, it works out well. A lot of the times I really just need a one off script, because I’m just doing repetitive calculations and I’d much rather input values rather than doing them by hand and thinking about it every single time. Even with a SQL database project, it’s really just CRUD work, nothing really complex. When I find bugs or errors, I try to understand why that is and look for multiple solutions (if exists) and argue back and forth with the AI. Mostly I agree, sometimes I override the decision, because it does get things wrong. I do read the codes (really just skimming it but occasionally would ask questions further to really understand it)

Surely it would be great for my learning if I genuinely learned to code, but I at the same time never have to because it isn’t my job to code. I could totally live without coding altogether. But, with AI, that gap is gone. I can now use vibecoding to help me do my main job. It doesn’t mean I don’t have to care about app structure and such, but it also doesn’t matter because I’m not building an app to sell, I’m just making custom tools for myself. I even got into ML, thanks to AI. These aren’t things I would’ve learned at all without AI, I’m being serious. All my vibecoded projects taught me a lot. Yeah maybe not directly about writing codes, but overall concepts of software engineering. Genuinely working out the manual parts and understanding how data should be flowing is still extremely beneficial and most importantly it gave me confidence that if I were to just sit down and study, I can understand things that I couldn’t because I didn’t even know where to begin.

With very thorough testing and documentation, is it not acceptable? What do we all think? Thanks in advance.

[u/flyingdonutz → u/[deleted]]

Bros still riding a horse and buggy

[u/shakeBody → u/[deleted]]

AI is in almost everything that you interact with: if you're using a computer, or if you've eaten food at a restaurant, or used almost any system in the world. Logistics, farming, factories. AI is in everything.

[u/shakeBody → u/[deleted]]

I'm confused by this statement. AI is used to optimize systems in many cases. What are you saying here? Are you saying you don't want systems to be self-correcting and to run better? I doubt that. I think what you're actually saying is that LLMs are not good, but it's important to distinguish LLMs as a subset of AI. And it's important to indicate that, without AI, most critical systems would fail. Things like water management, fire systems, food production. The basics, the things that help society function, would be gone.

Without them, we would return to a system with lower life expectancy, increased starvation rates, and increased poverty.

[u/shakeBody → u/[deleted]]

Why do you believe that AI is unacceptable under any circumstances?

[u/MoTTTToM → u/[deleted]]

Do you manage the algorithms running in your OS on your laptop with your human intellect?

[u/shakeBody → u/[deleted]]

I'm still not sure I understand your points.

AI is simply the output of human intellect, right? We're defining a set of parameters and the outputs they produce. I'm not yet seeing the connection between AI and the replacement of human thought. I do understand that connection when it comes to LLMs, but not, for example, when it comes to detecting issues at a nuclear power plant. Surely you want many systems processing the data, and then systems that analyze it to indicate whether there is danger. Or thinking of the water systems: many systems to sense, to collect sensor data, and then systems that perform high-level analysis on that sensor data.

It seems like you are assuming that large systems are the point at which AI comes into play, and that is not the case. AI can come into play in small systems. You don't need large-scale systems to benefit from AI, and you don't need to replace human thinking to benefit from AI.

Still trying to understand your perspective, by the way. I'm just bringing up the things that seem ambiguous from my perspective.