More clarifications

Author: khnumdev

_posts/2026-01-31-ai_wonderland.markdown

@@ -64,7 +64,7 @@ At work, we started using AI. GPT models worked well for test generation and see
 
 But let me be honest here. My coding skills have declined somewhat. Why? I still code extensively. However, before AI, I spent more time thinking and digging through Stack Overflow comments and posts until finding a suitable solution. Now, I often ask AI for a solution, and if the solution looks good, I can use it. If not, I refine it or try a different approach. I remember being stuck on problems for hours before finding a solution. Now I can try multiple approaches with AI until something works. I'm not thinking less, just differently. 
 
-There's also the question of code quality and programming languages. For personal projects or my GitHub repositories, I don't worry about the language used. I spent 15 years coding in C#; Java was my last five years. My home server's frontend is built in JavaScript, the backend in Python. The [distributed app design tutorial](https://khnumdev.github.io/dist-app-tutorial/) is written in Node.js. I don't care at all. Is that bad? I'm not sure. I just want things to work. One concept I taught at the university was software engineering principles, though my focus was distributed systems. I emphasized core software principles for two main reasons: first, "doing the right things" (which I have on my CV), and second, "code needs to be maintained, understood, and improved." That's true because code was written by humans for humans, and much of our work as software engineers is "cleaning house"—improving existing code so the next person faces fewer problems. But if AI writes the code, who cares? If the app works, that's what matters. I'm not saying code quality isn't important, but the mindset is changing. AI generates working code, and new models will generate even better code. So why spend time improving code that'll be replaced in a few years? The mindset is shifting across most organizations. As software engineers, we think our code is the *end goal*, but it's not. Sometimes we forget that code is a tool for solving problems. If AI can do that better, why fight it? But surprisingly, this is where software engineers will have more value: in system design, architecture, and decision-making. AI can generate code, but it can't decide what to build, how to build it, or why to build it. That's our job. Without understanding the basics, you're lost and can't evaluate whether AI results are good or bad. And if you know what you are doing, you can improve your code far away than before, even your code or the code written with the help of the AI. If not, the good news is learning new things is easier than ever.
+There's also the question of code quality and programming languages. For personal projects or my GitHub repositories, I don't worry about the language used. I spent 15 years coding in C#; Java was my last five years. My home server's frontend is built in JavaScript, the backend in Python. The [distributed app design tutorial](https://khnumdev.github.io/dist-app-tutorial/) is written in Node.js. I don't care at all. Is that bad? I'm not sure. I just want things to work. One concept I taught at the university was software engineering principles, though my focus was distributed systems. I emphasized core software principles for two main reasons: first, "doing the right things" (which I have on my CV), and second, "code needs to be maintained, understood, and improved." That's true because code was written by humans for humans, and much of our work as software engineers is "cleaning house"—improving existing code so the next person faces fewer problems. But if AI writes the code, *who cares*? If the app works, that's *what matters*. I'm not saying code quality isn't important, but the mindset is changing. AI generates working code, and new models will generate even better code. So why spend time improving code that'll be replaced in a few years with the effort of one prompt? The mindset is shifting across most organizations. As software engineers, we think our code is the *end goal*, but it's not. Sometimes we forget that code is a tool for solving problems. If AI can do that better, why fight it? But surprisingly, this is where software engineers will have more value: in system design, architecture, and decision-making. AI can generate code, but it can't decide what to build, how to build it, or why to build it. That's our job. Without understanding the basics, you're lost and can't evaluate whether AI results are good or bad. And if you know what you are doing, you can improve the product far away than before, even your code or the code written with the help of the AI. If not, the good news is learning new things is easier than ever.
 
 Another related point is proper code quality. Coding is hard; writing good code is harder. Code isn't art or inherently beautiful (though it can be ugly). Code is a tool for solving problems. It needs to be readable, understandable, and maintainable. AI isn't perfect yet and sometimes generates suboptimal, insecure, or incorrect code. We need to review AI-generated code, test it thoroughly, and ensure it meets quality standards. AI can't do this yet. However, not all code *needs to be perfect*. If you're building a startup and want to ship fast, iterate, and experiment, you can now accomplish in days what took months before. You can build an MVP in days instead of weeks. That's a game changer for startups.