Vibe Coding: How AI Is Changing the Way We Build Software

Vibe coding is an approach to programming where a person describes the desired result in plain language, and artificial intelligence writes most of the code. Instead of manually creating functions, interfaces, and logic, a developer-or even a beginner-can formulate a task such as "make a registration page with password validation" and receive a ready-made implementation.

This method of development has become especially prominent with the emergence of AI tools capable of not only completing individual lines but also handling multiple project files at once, detecting errors, and suggesting fixes. It significantly lowers the entry barrier to programming, but it does not mean that developer knowledge is no longer necessary.

Vibe coding in simple terms

The term vibe coding gained widespread recognition in 2025 thanks to programmer and AI researcher Andrej Karpathy. It describes a style of development where a person primarily explains to a neural network what they want to achieve, runs the suggested code, and asks for corrections if something doesn't work as intended.

In traditional programming, the developer defines the program's structure, writes functions, works with libraries, troubleshoots syntax errors, and studies documentation by themselves. In vibe coding, a significant part of this work is handled by artificial intelligence.

For example, instead of manually writing dozens or hundreds of lines, you can ask the neural network: "Create a small to-do list app, with the ability to add new entries and mark tasks as completed."

The AI can propose a project structure, create the interface, write logic, and explain how to launch the program. If a button doesn't work after launching, the user doesn't have to search for the error themselves-they can simply report the issue to the neural network and ask for a fix.

This turns the process into a sequential dialogue rather than classic code writing. The person sets the direction and describes the desired behavior, while the AI translates these requirements into code.

It's important to note that vibe coding is not a new programming language or technology, but rather a way of interacting with existing languages, frameworks, and tools through AI.

The neural network still uses technologies like JavaScript, Python, Java, C#, SQL, or others. The difference is that the user doesn't have to write every construct by hand or remember the syntax.

The human role changes significantly: rather than constantly working with lines of code, they increasingly focus on formulating requirements-what the interface should look like, what a button should do, which data needs to be saved, and what should happen in case of errors.

This is why vibe coding attracts people who have never programmed before. It enables rapid prototyping of websites, small utilities, bots, or experimental apps, and lets you test ideas without spending months learning to code.

However, the neural network's ability to write code does not guarantee that the program will be reliable. AI may misunderstand the task, use an inappropriate approach, or create a bug that only appears under certain conditions. As projects grow more complex, it becomes increasingly important for people to evaluate the results, not just accept every AI suggestion.

How vibe coding works with neural networks

Vibe coding is built around a simple cycle: a person describes a task, the neural network proposes a solution, the user runs it, and then reports what needs to be changed. This process repeats until the program works as intended.

Usually, everything begins not with code, but with a plain text request. For example: "Create a web app for expense tracking. I need to add amounts, categories, and dates, and display total expenses for the month below."

The AI can create the main project files, the interface, data handling logic, and provide instructions for launching. More advanced tools can manage the entire project structure: creating new files, modifying existing ones, and tracking interdependencies.

Next, the user checks the result. If the app launches but doesn't work as needed, the task can be clarified in plain language: "add a category filter," "make a mobile version," "save data after page reload." The neural network analyzes the current code and makes adjustments.

If the program returns an error, its text can also be sent to the AI. Instead of searching for the cause themselves, users can copy the error message or describe the problem, after which the neural network tries to identify the problematic code and fix it.

This creates the characteristic vibe coding cycle:

• idea → request → code generation → launch → check → fix → new request.

Many of these steps also exist in traditional development, but the programmer makes most technical decisions themselves. In vibe coding, some decisions are effectively delegated to the model.

Modern code generation is not limited to creating individual functions. AI can help assemble a user interface, connect a database, write server logic, prepare API requests, create tests, and fix some bugs. This allows a small project to be built in just a few back-and-forths.

To learn more about how code generation tools have evolved from simple autocompletion to systems capable of working autonomously on projects, see the article "AI for Coding: Evolution from Autocomplete to Autonomous Agents".

However, the neural network doesn't see the task the same way a human does. It works with the context it's given. If you simply write "make an online store," the model has to invent the catalog structure, product storage, authentication, cart, and dozens of other details on its own.

So, a good result usually comes not from a single perfect prompt, but from a sequence of clarifications. First, a basic version is created, then features are added, the interface is changed, and discovered issues are fixed.

Even a very detailed prompt does not guarantee correct code. The neural network may use outdated methods, link project components incorrectly, or fix one bug in a way that causes another. That's why running and checking the result remain essential parts of vibe coding.

This approach works especially well for small prototypes: errors are easy to spot, and the whole project fits within the model's context. As the application grows, things get more complicated. There are dozens of files, dependencies, a database, authentication, and complex business logic, making it much harder to control changes without technical knowledge.

Can you build programs without knowing how to code?

It's definitely possible to create simple programs without knowing how to code. This is where vibe coding most significantly lowers the entry barrier: users don't need to memorize syntax, understand every library, or write functions from scratch. It's enough to clearly explain what the program should do and check the result.

A beginner can use AI to assemble a small website, calculator, Telegram bot, expense tracker, to-do list, simple game, file processing tool, or internal utility. For such projects, the architecture is relatively simple, and errors are usually easy to spot during use.

Vibe coding is especially convenient for prototyping. If you have a service idea, you don't need to immediately hire a developer or spend months learning a programming language. You can describe the main features to the neural network and get a working demo version to test the concept.

For example, someone with no development experience might ask: "Create an app where I upload a CSV file, and it shows a table, calculates the average for a selected column, and lets me download the result."

The AI can write the code for such a program, select suitable libraries, and explain how to launch it. The user may not need to know at all how CSV reading or average calculation is implemented.

But "without knowing code" shouldn't be taken literally as a complete lack of technical understanding. As projects grow in complexity, situations arise where it's not enough to simply tell the neural network "fix the error."

For instance, an app may work on the author's computer but fail after deployment to a server. Users may face database setup, environment variables, API keys, access rights, dependencies, or incompatible library versions. In such cases, understanding basic development principles is extremely helpful.

Another issue is that a working program isn't necessarily a well-written one. The code may look fine and perform its main function but contain redundant database queries, poor error handling, or security vulnerabilities.

Beginners have a hard time noticing these shortcomings because they often judge results by a simple principle: "I clicked the button-it worked." For small personal projects, that may be enough, but for apps with users, payments, or sensitive data, the requirements are much higher.

Therefore, vibe coding is best seen as a way to start development without first mastering the entire tech stack. Knowledge can be acquired gradually while building real projects.

For a first prototype, it's enough to understand the program's goal and check basic scenarios. Afterward, it's useful to learn core concepts: what variables, functions, APIs, databases, clients, and servers are, why errors occur, and how to read error messages.

This approach differs from the classic route, where you study theory, syntax, and exercises before building your own apps. In vibe coding, the order is often reversed: you start with a working project, and technical knowledge accumulates as specific challenges arise.

But it's still not possible to completely exclude programming understanding when creating a serious product. The higher the demands for stability, security, and scalability, the more important it is to assess the solutions AI proposes.

Vibe coding, no-code, and traditional development: what's the difference?

Vibe coding is often compared to no-code, as both approaches allow users to build applications without constantly writing code by hand. However, they work differently and offer different degrees of freedom.

Vibe coding

With vibe coding, the program is still created using conventional programming languages. The difference is that much of the code is generated by AI based on textual descriptions.

Users can ask to change the interface, add a new feature, connect an API, or rewrite logic. The neural network updates the relevant project files, and the result is standard source code that can be opened, manually edited, and moved to another development environment.

The main advantage here is flexibility. If the AI can implement a feature using the chosen language and libraries, the user is not limited by a predefined set of blocks.

No-code and low-code

No-code works differently. The user assembles the application mainly from pre-built components: forms, tables, buttons, scripts, integrations, and visual blocks.

This approach is convenient for typical tasks. For example, you can quickly build an internal CRM, a registration form, a simple database, or automate a business process without worrying about the source code structure.

However, capabilities usually depend on the specific platform. If a needed feature isn't among the available components or integrations, implementation can be difficult. Low-code partly solves this problem by allowing some manual coding.

So, no-code can be compared to a construction set: you assemble the system from prepared parts. Vibe coding is closer to a scenario where you tell the AI what part to make, and the neural network writes the code for it.

For more on visual development and its limitations, see the article "Low-code and no-code: How businesses build apps without developers".

Traditional programming

Classic development gives the programmer maximum control. They choose the project architecture, write and review code, manage dependencies, test the system, and make technical decisions.

This approach requires more knowledge and time, especially at the start, but the developer thoroughly understands what's happening inside the app and why the system is designed as it is.

For serious development, this control is crucial. If an app serves thousands of users, processes payments, or stores sensitive data, it's important to know not just whether a particular button works, but how safe, stable, and efficient the entire system is.

In practice, these three approaches don't necessarily compete. One project may combine them. For example, an internal tool's interface might be built on a no-code platform, a separate script created through vibe coding, and a critical server component written and checked by hand.

The choice depends on the task. For quickly testing an idea, vibe coding is ideal. For standard business processes, no-code can be convenient. For complex systems with high requirements for performance, security, and support, full development is still needed.

So, vibe coding shouldn't be seen as just another name for no-code. In the first case, AI helps generate real code; in the second, the user mainly works within the boundaries of a visual builder's features.

Main limitations and risks of vibe coding

The main problem with vibe coding is that a neural network can generate code that looks convincing but works incorrectly. An error might be obvious-the program doesn't run at all-or may only appear in specific scenarios, with large data sets, or unusual inputs.

The AI does not verify results like an experienced developer. It might suggest a function with a logical mistake, handle exceptions incorrectly, use an unsuitable library, or forget to account for edge cases. If the user doesn't understand what's going on in the code, such problems are much harder to spot.

Security errors are especially dangerous. A program might store passwords in plain text, improperly check access rights, leave API keys in the source, or accept user input without sufficient validation. If the app is only used locally, consequences may be minimal. But after publishing online, these become real risks.

Another issue is technical debt. With vibe coding, it's easy to keep asking the neural network to add features or quickly fix bugs. As a result, the code gradually accumulates temporary solutions, repeated sections, and unnecessary dependencies.

This may be almost invisible in small projects. But after dozens of changes, one fix can break another, and it becomes harder for the neural network to understand the whole structure. Sometimes, it's simpler to rewrite part of the project than to keep patching the old implementation.

This problem is especially likely if the user only evaluates the external result and doesn't pay attention to the internal structure. The program keeps working, but maintaining and expanding it becomes more difficult.

To learn more about how generative models are changing the work of programmers, read the article "How Artificial Intelligence Is Changing Programming: The Era of AI Development".

Debugging can also be challenging. If you wrote the code yourself, you usually have at least a rough idea where to look for problems. With vibe coding, parts of the project may be essentially unfamiliar. You know what you wanted, but not always how the neural network implemented it.

As long as the AI can find errors by itself, this isn't an issue. But if the model repeatedly suggests unsuccessful fixes, you'll have to dig into logs, dependencies, data structures, and component relationships.

There's also a risk of excessive reliance on generation. If you delegate every change to the neural network, you may become good at formulating prompts but barely understand your own project. This isn't critical for an experimental tool, but it's a problem if the app needs long-term maintenance or handover to other developers.

Therefore, vibe coding does not eliminate the need for testing. After every significant change, you must check main scenarios, input errors, data persistence, and program behavior in unusual situations. The more important the app, the more thorough these checks should be.

Vibe coding also does not mean programmers are automatically replaced. AI greatly accelerates the creation of typical code and helps move from idea to working prototype faster, but someone still has to define requirements, assess architecture, check security, and decide whether a proposed solution is suitable.

The developer's role is gradually shifting from writing every line by hand to managing the system as a whole. The ability to read code, spot weak points, and make technical decisions is just as important-even if much of the implementation is generated by a neural network.

FAQ

1. What is vibe coding in simple terms?
   Vibe coding is a development method where a person describes the desired program in plain language, and artificial intelligence generates code, modifies the project, and helps fix errors. Users focus more on results and requirements than on manually writing every line.
2. Do you need to know programming for vibe coding?
   Deep programming knowledge is not required for building simple prototypes. However, understanding the basics becomes more important as the project grows-it helps in spotting errors, evaluating code quality, and determining the safety of AI-generated solutions.
3. Which neural networks are suitable for vibe coding?
   AI tools capable of generating code and working with project context are suitable. Especially useful are systems that can see multiple files, modify existing code, analyze error messages, and perform sequential tasks. The specific tool matters less than its ability to understand the project as a whole and make accurate changes.
4. Can you build a full-fledged application using only AI?
   It's quite realistic for a small app or working prototype. Even complex commercial services can be significantly accelerated with AI, but it's risky to rely solely on automated generation. Testing, security checks, infrastructure setup, and architectural oversight are still required.
5. Can vibe coding replace programmers?
   It's more accurate to say it changes how programmers work. There's less manual writing of routine code, but task setting, architecture design, result verification, and error hunting become more important. For beginners, vibe coding lowers the entry barrier; for experienced developers, it speeds up routine tasks.

Conclusion

Vibe coding makes programming more accessible for people who have never worked with code. Today, you can describe an idea in plain language, get a working prototype, add features, and fix many issues in a dialogue with a neural network.

For a small website, bot, utility, or idea validation, you may not need programming knowledge at the start. But as the project grows, the stakes of errors increase: issues of security, architecture, performance, data storage, and ongoing support emerge.

The optimal approach is to use vibe coding not as a full replacement for technical knowledge, but as a tool to move from idea to result faster. You can now start building programs with almost no coding expertise, learning essential skills gradually as your projects become more complex.