What Is a Vibe Coder? A Guide to AI-Assisted Programming

Not long ago, we took a deep dive into how vibe coding is shaping the future of business productivity. If you’re curious how leaders can get involved, check out our executive-focused article: How to Start with Vibe Coding.

Now, I’ve put together a detailed guide for developers interested in this movement. But even if you don’t code every day, these expert insights offer a glimpse into where tech is headed—so stick around!

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Have you heard the buzz about “vibe coding”? It’s the practice of using plain language prompts to direct AI models to write code for you. This approach, also called AI-assisted code or “codegen,” is rapidly changing the tech industry, influencing everything from how software is developed to who gets hired for engineering roles. Some tech companies even estimate that 30% to 40% of their code is now written by AI. And get this: 85% of software developers are already using AI tools to help them code.

This shift represents a significant change in the world of software development. It lowers the barrier to entry, potentially allowing individuals without a traditional coding background to build applications and solve technical problems. Vibe coding is a new kind of coding that enables non coders to participate in building software by leveraging AI tools, making development more accessible than ever before. But what does it actually mean to be a vibe coder? Is it as simple as telling an AI what you want and watching the magic happen?

This guide explores the world of vibe coding, drawing from firsthand experiences of those who have dived headfirst into this new frontier. Unlike manual coding, where developers write code line by line, vibe coding uses generative AI to provide the fastest path from idea to application.

We’ll cover:

• What it is;
• The tools you’ll need
• What this trend means for the future of programming

What Exactly Is Vibe Coding?

Vibe coding is the process of generating software code by giving natural language instructions to an artificial intelligence model. Instead of writing lines of Python, JavaScript, or C++ yourself, you describe the desired outcome or “vibe” to an AI assistant, which then produces the necessary code. This represents a new kind of coding that moves away from traditional reliance on specific programming languages, enabling users to focus on ideas rather than syntax.

Think of it as collaborating with an incredibly fast, logical, and sometimes slightly naive intern. You provide the high-level direction, and the AI handles the granular task of writing the code. This coding approach relies on AI assistance rather than manual code writing, allowing for rapid prototyping, quick bug fixes, and a more accessible way to interact with software development.

The rise of vibe coding is closely tied to advancements in large language models (LLMs) and the emergence of low-code/no-code platforms. Initially, the low-code/no-code movement aimed to empower non-programmers to build applications through visual interfaces. Now, with powerful AI assistants, this idea is reaching its full potential, allowing for more complex and customized creations through simple conversation.

Vibe Coding Guide: Your First Steps as a Vibe Coder

Getting started with vibe coding is more accessible than you might think. While it’s a new way of working, the core principles involve problem-solving and clear communication. Many beginners simply copy paste stuff or paste stuff from AI outputs and run stuff to quickly build software, making the process fast and intuitive. Here’s a breakdown of the process.

Understanding the Tools of the Trade

To start vibe coding, you’ll need an AI-powered coding platform. These tools integrate with large language models to turn your prompts into functional code.

Some popular vibe coding platforms include:

• Cursor: This AI-first code editor has become a favorite among vibe coders. It allows you to select from various AI models, such as Anthropic’s Claude, to assist you. Many top tech companies, including OpenAI, Instacart, and Notion, use Cursor for their development workflows. It’s often considered the “Kleenex” of AI-assisted integrated development environments (IDEs). The cursor composer feature enables rapid code generation, allowing programmers to quickly create or modify code with minimal manual input.

• Replit and Lovable: For those just starting, these platforms are known for being more user-friendly and are great for experimenting with smaller projects. Replit features the Replit agent, an AI assistant that streamlines vibe coding by helping you interact with and manage your code base efficiently.

• GitHub Copilot: Integrated directly into code editors like VS Code, Copilot suggests code and entire functions in real-time, learning from the context of your project.

Choosing the best tool depends on your skill level, project goals, and desired features. These platforms can generate new components or work with existing code in your code base, making AI-assisted development more accessible and efficient.

The Art of Pair Programming with AI

Even with powerful AI tools, you’re not working in a vacuum. A common practice is “pair programming,” where two developers work together on the same code. In vibe coding, one of your “pair” is the AI itself, acting as an AI-powered pair programmer that integrates directly into your code editor. This approach benefits users with different levels of coding experience, making it accessible for both complete beginners and more advanced users. For beginners, it’s highly beneficial to also pair with an experienced human engineer.

This human-AI-human collaboration is where the real learning happens. An experienced coder can guide you on how to structure your prompts, interpret the AI’s output, and troubleshoot when things go wrong.

A typical vibe coding workflow might look like this:

1. Diagnose the Problem: Before you can fix something, you need to understand what’s broken. You can use the AI assistant to analyze a piece of code and identify issues, often by examining error messages to help the AI or human understand what went wrong. For example, you might ask, “What’s causing the Mermaid diagrams in this app to be static?”
2. Craft Your Prompt: Once you have a diagnosis, you can create a detailed prompt to instruct the AI on how to fix it. A good prompt is specific and provides context. It might include what you want to achieve and even suggest a technical approach.
3. Execute and Wait: After submitting your prompt, the AI will generate the code. This part can take time, as the model processes your request and streams out lines of code. It’s a mix of miraculous speed and moments of patient waiting.
4. Review and Refine: The AI’s first attempt might not be perfect. You’ll need to review the code, test it, and potentially refine your prompts to get the desired result. This is where having a human partner is invaluable.

In one real-world example, a novice vibe coder, paired with two experienced engineers, was able to fix a bug in Notion that prevented users from zooming into flowchart diagrams. The task, which involved making static image files expandable, was completed in under 40 minutes—a fix that was later shipped to the product’s 100 million users. Professional developers can leverage this workflow to debug and resolve issues more quickly, streamlining their development process.

The Vibe Coder’s Mindset: A New Way of Thinking

Vibe coding is more than just learning to use a new tool; it requires a shift in mindset. You become a “hyper-logical god,” as one new vibe coder described it, pulling levers and directing a powerful force to execute your vision. However, this power comes with a new set of challenges and perspectives. Vibe coders often embrace exponentials, fully leveraging the rapid advances of AI, and sometimes even embrace exponentials and forget about the details, letting the vibes embrace exponentials and drive the process.

This mindset can lead to accepting random changes or even the dumbest things suggested by the AI, and sometimes you forget that the code is being generated for you.

Embracing a Different Pace

Time operates differently in the world of vibe coding. Projects that once might have taken a significant portion of a developer’s career can now be prototyped in days or even hours. Simple bug fixes can be accomplished in minutes. Vibe coding offers the fastest path to a working prototype, allowing you to quickly move from idea to an interactive, functional version ready for testing and demonstration.

On the other hand, the instantaneous feedback we’ve come to expect from tools like ChatGPT isn’t always present. Watching an AI generate thousands of lines of code can feel slow, creating a strange paradox where you’re achieving incredible feats at a pace that sometimes feels like waiting for a dial-up modem to connect. However, some platforms now offer one click deployment, making it easy to launch your app directly to the web and share it instantly.

The Human Element Is Still Critical

Despite the power of AI, the consensus among engineers is that a “human in the loop” remains essential. AI coding assistants are often compared to “really good interns.” They get things mostly right and can handle a lot of the heavy lifting, but they still need supervision, guidance, and a discerning eye to catch mistakes. In professional app development, applying software engineering principles is crucial, especially when you want to add features or introduce new features to your project. This ensures that enhancements are reliable, maintainable, and meet evolving user needs.

An experienced developer’s job is shifting. Less time is spent on the rote mechanics of writing code, and more time is dedicated to:

• Reasoning and Diagnosis: Understanding the root cause of complex problems.
• Strategic Planning: Architecting the solution and breaking it down into logical steps for the AI.
• Quality Control: Ensuring the AI-generated code is efficient, secure, and maintainable.

For complex problems, the actual coding might only be 20-30% of the work. The other 80-70% is the critical thinking that happens before a single line of code is written—a domain where human expertise is still irreplaceable. To make this process even more effective, new frameworks are emerging that focus on clear instructions. For example, spec-driven development is becoming the next step in AI-assisted engineering because it helps translate human strategy into precise, AI-actionable tasks, ensuring that enhancements are reliable, maintainable, and meet evolving user needs.

The Future of Coding and Tech Careers

The rise of vibe coding is prompting big questions about the future. Is traditional coding becoming obsolete? How will this trend reshape the job market for developers? AI-assisted coding is rapidly changing the landscape, enabling faster prototyping and development, but also raising new concerns about code quality, oversight, and the role of human expertise.

For non-coders, vibe coding makes it easier to create software, especially for throwaway weekend projects where speed is prioritized over long-term maintenance. However, this approach increases the risk of security vulnerabilities, as AI-generated code may introduce flaws that are not easily detected without thorough review.

According to a recent Business Insider perspective, these trends are driving innovation but also highlighting the need for caution as AI-driven software development becomes more mainstream.

Do You Still Need to Learn to Code?

The short answer is yes, but the “why” and “how” are changing. Deep expertise in software programming remains incredibly valuable. The most sought-after (and highest-paid) engineers are those with a profound understanding of AI research and systems architecture. This top-tier talent isn’t going away; in fact, the demand is higher than ever.

Traditionally, coding required manually writing code in specific programming languages, demanding detailed knowledge and step-by-step construction. Now, AI-driven approaches like vibe coding allow users to describe their goals in natural language, reducing the need for expertise in programming languages and making development more accessible.

However, for entry-level and front-end roles, the landscape is shifting. Vibe coding is opening doors for individuals from diverse educational backgrounds to enter the tech industry. These new “vibe coders” will act as the human supervisors for AI, guiding the models to build and fix software.

The looming question is whether these roles are sustainable. Are vibe coders simply training AI models to eventually replace them? It’s a valid concern, and one the industry is watching closely.

The Challenge of Code Quality

As more code is generated by AI, often with supervision from less-experienced engineers, concerns about quality and long-term maintainability arise. If code is functional but poorly structured, it can create a “technical debt” nightmare for future teams who need to understand and build upon it. In AI-assisted projects, the code grows rapidly, sometimes expanding beyond the usual comprehension of the team.

When asked about this, some tech leaders argue that code quality is binary: it either works or it doesn’t. Others acknowledge the risk and believe the solution is to build better AI tools to monitor and maintain quality—essentially, using AI to solve the problems created by AI. In this new paradigm, developers may not even review diffs anymore or check if the code even exists in the way they used to, relying instead on LLMs and natural language prompts.

A human-centric approach remains the most reliable solution. A high-quality codebase is like a well-organized archive; it must be clear and accessible for years to come, especially in companies with high employee turnover.

Is Vibe Coding the End of Coding as We Know It?

Vibe coding is not the end of coding, but it is a profound evolution. It’s not that one coder can now do the job of 100, but that one coder can become 100 times more productive. With AI tools able to produce code rapidly, developers are embracing a style of development known as vibe code, where high-level instructions and feedback guide the AI, and code is accepted with minimal manual review.

This technology is making it possible to prototype and iterate faster than ever before. It’s democratizing access to software development and empowering a new generation of creators. At the same time, it reinforces the value of deep technical expertise and strategic thinking.

The path forward will likely involve a hybrid model where humans and AI collaborate, each bringing their unique strengths to the table. Experienced developers will become the architects and strategists, while a new class of vibe coders will act as the directors, guiding AI to bring those visions to life. The industry is adjusting its workforce and workflows to embrace this new reality, recognizing that while the “how” is changing, the fundamental need for human ingenuity, reasoning, and oversight remains.

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