Vibe Coding: How a New Technology Changed Programming -

Just ten years ago, talking about controlling a computer with the power of thought seemed like a plot from science fiction.

Today, the IT industry is already actively discussing the imminent disappearance of the programmer profession because neural networks have learned to write code. The prospect is frightening, but against this backdrop, specialists are emerging who do not write programs themselves but use artificial intelligence for this purpose. Such developers are called vibe coders. Vibe coding technology is currently one of the fastest-growing. We explored how it works and what awaits the industry in the future.

What is Vibe Coding?

Vibe coding is an approach to programming where a developer writes code using AI agents. In fact, a vibe coder may not be the best programmer. The main thing is to be able to correctly formulate a task for an AI model.

The very idea of vibe coding was proposed at the beginning of 2025 by OpenAI co-founder and machine learning researcher Andrej Karpathy. He described vibe coding as a process where a developer does not write code but dictates a task to an LLM assistant (large language model) by voice or enters it in text form, and then iteratively corrects errors by simply sending them back to the neural network until everything works. Using vibe coding, Karpathy developed MenuGen. This is a web application for generating images of dishes based on menu photos. The neural network did all this—and even more: the application includes user authentication and a subscription payment form.

Evolution of the Term

Interestingly, the term "vibe coding" itself has evolved, reflecting different facets of technological progress.

Biometric Approach: Programming as an Extension of Consciousness

Initially, this direction lay in the field of neural interfaces and human-computer interaction. It was about using biological feedback to analyze the programmer's state. Special headsets tracking brain activity (EEG), eye movement sensors (eye trackers), and stress sensors allowed the development environment to "understand" how difficult a particular code fragment was to perceive.
Pilot projects, such as Code Cognitive Load Analysis at several technology universities, demonstrated that it is possible to identify parts of a program that cause the developer maximum cognitive load and automatically offer simplification options. This is not about code generation at all, but rather about creating an adaptive work environment that adjusts to a person's psychophysiological characteristics.

Pragmatic Approach: The Developer as a Prompt Engineer

Andrej Karpathy gave the term a new, practical meaning. In his interpretation, vibe coding is precisely the process where a programmer formulates a task for a large language model by voice or text, and it generates working code.
The key feature is the iterative cycle without deep intermediate control. If the result contains errors, they are similarly sent to the model for correction. Karpathy himself noted that such a method is ideal for quick experiments and pet projects (these are independent initiatives in IT or other areas that people create in their free time to learn new technologies), but requires serious refinement for industrial use. It is this definition that went viral and shaped the modern face of the movement: programming through strategic AI management.

Vibe Coding Tools

The ecosystem for vibe coding is developing rapidly, offering solutions for different scenarios—from prototyping to managing a complex codebase.

Intelligent Development Environments:

Cursor and Windsurf. These platforms develop the ideas of VS Code, having made a major leap forward recently. The services integrate AI not as an external assistant but as the core of the system. Cursor allows the neural network to analyze the entire project structure, make related changes across dozens of files, and propose architectural solutions. Windsurf, recently integrated into the OpenAI ecosystem, focuses on seamless interaction and a minimalist interface designed specifically for dialogue with AI.
Replit. A cloud platform that has turned into a testing ground for vibe coding. The service allows you to get a working prototype with a configured environment and the possibility of immediate deployment by describing an idea in natural language. This is an ideal tool for educational hackathons and testing business hypotheses.

Autonomous Agents and Niche Solutions:

Devin. Positioned as the first autonomous AI engineer. It can be integrated into workflows: added to Slack chats, assigned tasks from Jira. Devin independently analyzes the task, studies the code, asks clarifying questions, and commits changes to the repository. Its strength lies in performing clearly formulated routine operations: updating libraries, fixing typical vulnerabilities, and migrating data.
Cline. An open-source solution offering maximum flexibility. It is a plugin for popular IDEs that allows connecting virtually any language model via API. For experienced developers, this is an opportunity to assemble an "ideal assistant" for their specific tasks.

Integration Strategies: How to Apply Vibe Coding Without Losing Control

The main misconception of beginners is the idea of a vibe coder as a "prompt manager" who does not understand code at all. In reality, this is a path to creating a low-quality and insecure product. The true power of the method lies in enhancing expertise, not replacing it.

Scenario 1: Lightning-Fast Prototyping and Exploration

Tools: Replit, Cursor online demo.
Method: Clearly formulate the goal (e.g., "Create a single-page application in Vue.js that displays real-time cryptocurrency prices with a 24-hour chart"). Use dialogue to clarify details and perceive the result as an interactive sketch. Its task is to test feasibility and obtain a visual embodiment of the idea in hours, not weeks.

Scenario 2: Enhancing Expertise in the Workflow

Tools: Cursor/Windsurf for main work, Claude Code for complex logic, Devin for routine.

Practices::

Complex Refactoring: "Analyze this microservice, find performance bottlenecks, and propose three optimization options with complexity estimates."
Generating Supporting Code: Entrust AI with writing comprehensive unit and integration tests (these are levels of automated software testing, where unit tests check the smallest, isolated parts of code, and integration tests check how these parts interact with each other and external systems, ensuring the operation of the entire system as a whole), documentation in OpenAPI (Swagger) format, boilerplate code for repeating patterns.
Diagnosing Non-Obvious Bugs: Provide AI with the error stack trace, logs, and contextual files. Often, it acts as a super-fast and well-read virtual colleague, offering non-standard hypotheses.
Fundamental Principle: The developer retains architectural sovereignty. Every line created by AI must be subjected to critical analysis, security checks, and compliance with team standards. If the time spent "explaining" a task to AI exceeds the time to solve it manually, the approach should be reconsidered.

Scenario 3: Personalized Learning and Mentoring

Tools: Customizable agents like Cline, classic GPT/Copilot in dialogue mode.
Method: Use AI not as an answer generator but as an interactive mentor. Requests should sound like, for example: "Explain why my caching implementation is less efficient than pattern X?"

A New Map of Professional Competencies

Vibe coding is not just a new tool. It fundamentally changes the specifics of the developer profession. The era when the main measure was the speed and volume of code writing is fading into the past. This function is successfully delegated to machines.

Meta-skills of a higher order come to the fore:

Architectural Vision and Decomposition: The ability to see the system as a whole and break down complex tasks into perfectly formulated, atomic prompts for AI.
Critical Verification and Quality Assurance: The art of quickly evaluating, testing, and refining generated code, distinguishing brilliant optimization from a potentially dangerous model "hallucination."
Systems Engineering Thinking: Understanding how to assemble reliable, scalable, and secure systems from components created by AI, not a collection of disparate scripts.
Ethical and Responsible AI Management: Awareness of risks related to data security, intellectual property, and potential biases in generated solutions.

The future belongs to those who know how to ask the most precise questions and draw the most balanced conclusions. The profession is not disappearing—it is being reborn in a new, more complex, and strategic form.