A Plea to Vibe Coders

We are living in an amazing time for making software. The barriers to entry are lower than ever before. Powerful frameworks handle a lot of the complexity. And now, Large Language Models (LLMs) can generate functional code blocks from a simple English prompt.

This environment has created a new kind of developer: the Vibe Coder.

The Vibe Coder approaches software intuitively. They rely heavily on AI assistants, iterate fast through trial and error, and tweak the output until the application feels right or just works. And let's be honest, you can build some surprisingly cool stuff this way. The democratization of coding is fundamentally a good thing in my opinion.

Vibe coding works, up to a point. But it will only let you do so much. If you want to build things that last, things that scale, and things you truly understand, you must eventually step away from pure vibes and embrace the fundamentals of computer science.

The Grand Misconception

From the outside, it looks like software development is just writing code. It looks like the job is knowing the specific secrets of Python, JavaScript, or Rust or whatever language you're working with.

This is a fundamental misunderstanding of what we do.

Writing code is rarely the difficult part of software development. It is just the final step in the process. It is the translation phase.

The vast majority of software engineering, the actual work, happens before the first line of code is written. It happens on whiteboards, in design documents, and when you are quietly thinking about the problem. It involves breaking down complex issues, understanding constraints, modeling data, and devising solutions tailored exactly to your use case.

The core skill of a software engineer is not typing. It is thinking.

Vibe coding often tries to bypass this rigorous analysis. It attempts to solve the problem by just iterating on the translation, hoping a solid underlying structure eventually appears. You might end up with a working feature, but you probably won't end up with a scalable system.

The Bedrock of Logic

I am not suggesting everyone needs a four year degree or needs to memorize how to implement obscure algorithms from scratch. You do not necessarily need to be a syntax guru.

But you do need to understand how logic works in the realm of computation. A little computer science logic understanding takes you a long way. Computer science is not really the study of computers. It is the study of structured thought and automated problem solving. It provides the mental models that decades of engineering have given us.

You need to understand trade offs. You need to grasp complexity (think Big O notation). Will the function that works perfectly in testing bring the server to its knees when it hits 10,000 concurrent users? You need to understand data structures. How the shape of your data impacts the speed of access and modification.

When the requirements change, when the traffic increases, or when a weird bug pops up, intuition will not save you. Debugging complex systems requires a systematic approach based on understanding how the system should work.

From Vibe Coding to Vibe Engineering

The most compelling argument against deep study today is the presence of AI. Why learn the fundamentals when an LLM can explain them and write the code for you?

It is precisely because these language models exist that foundational knowledge is more critical than ever.

LLMs are incredibly powerful tools, but they are statistical engines, not architects. They generate plausible code by predicting the next most likely word. They do not understand your business logic, your specific constraints, or the long term implications of the code they generate. They often produce the most average solution, not the optimal one.

If you treat an LLM as an oracle, you are limited by its output.

This is where we need to shift the perspective. People shouldn't just vibe code. They should have a good understanding of computer science logic and vibe engineer.

When a developer who possesses strong foundational knowledge uses these models, the dynamic shifts entirely. The AI shifts from being a crutch to being a high leverage exoskeleton.

With foundational knowledge, you can:

• Prompt Effectively: You know the precise terminology to generate high quality, relevant code.
• Validate Rapidly: You can spot the subtle bugs, the inefficiencies, or the security vulnerabilities the AI introduced.
• Nudge the Model: You can guide the AI (LLM), asking it to refactor using a specific pattern, change the data structure for better performance, or optimize for memory usage.

Without foundational knowledge, you are a passenger hoping the autopilot knows where it is going. With it, you are the pilot, using the automation to navigate complex roads efficiently.

Beyond the Vibe

If you identify with the Vibe Coder archetype, your intuition and bias toward action are valuable assets. But I urge you to ground those assets in the rigor of computer science.

Embrace the logic. Learn the 'why' behind the 'how'. It will transform you from someone who merely translates code into someone who architects solutions.

Don't just vibe code. Vibe engineer.