Vibe Coding: The Ins and Outs of Building Software by Feel

by: Rick Rome
9/10/26

In early 2025, AI researcher Andrej Karpathy posted a short note that captured a shift already underway in how people build software. He described a new approach he called “vibe coding.” In his words, it meant fully giving in to the vibes, embracing the rapid progress of AI models, and essentially forgetting that the code itself even exists. You describe what you want. The AI writes most of the implementation. You judge the result by whether it works and how it feels.

The phrase stuck. By late 2025, Collins Dictionary named “vibe coding” its Word of the Year. Searches for the term soared. Tools built specifically for this style of work multiplied. Today, vibe coding sits at the center of conversations about who gets to create software and how fast ideas can move from concept to working prototype.

What Vibe Coding Actually Is

Traditional coding requires writing precise instructions in a programming language. You decide the architecture, manage files, handle edge cases, and debug line by line. Vibe coding flips that process. You speak or type in plain language. You tell the AI the outcome you want, the feeling the product should create, or the problem it should solve. The model generates the code, the interface, the data structure, and often the integrations.

The key distinction, as clarified by developer Simon Willison and others, is the level of review. Pure vibe coding involves accepting large amounts of AI-generated code without thoroughly reading or fully understanding every line. You iterate by describing what feels off or what should change next. The human stays in charge of intent and final judgment. The AI handles most of the typing and structural decisions.

Popular tools that support this workflow include Cursor, Claude Code, Lovable, Bolt.new, Replit Agent, and v0 by Vercel. Some operate inside a full code editor. Others present a more conversational or visual interface aimed at non-developers. All of them rely on large language models that have grown dramatically better at understanding context across an entire project.

How the Process Typically Works

A typical vibe coding session begins with a description. You might say, “Build a simple web app that lets users track daily habits with a clean, calm interface and weekly progress charts.” The AI proposes a plan, generates the initial code, and often spins up a live preview. You run it, notice what feels wrong or missing, and respond with further instructions: “Make the colors softer. Add a dark mode. Connect it to a simple database so progress saves between sessions.”

The loop continues. You refine by feel rather than by rewriting functions yourself. Many people report entering a flow state more easily because the mechanical work of syntax and boilerplate largely disappears. Experienced developers use the same approach to move faster on features they already understand conceptually. Non-developers use it to create personal tools, prototypes, and internal business applications they previously could not build alone.

The Clear Advantages

Speed stands out first. Tasks that once required days or weeks of traditional development can reach a usable prototype in hours. Founders can test ideas before committing significant resources. Designers can turn static mockups into interactive experiences. Product managers can stand up internal tools without waiting in a development queue.

Accessibility is the second major benefit. People without formal programming backgrounds can now create functional software. The barrier that once separated idea from implementation has lowered dramatically. This opens creative and practical opportunities for a much wider group of people.

Reduced friction around boilerplate also matters. Authentication flows, basic databases, form validation, and standard UI patterns are areas where current models perform reliably. Handling these pieces automatically frees attention for higher-level decisions about user experience and product direction.

Iteration becomes cheaper. Changing direction no longer requires extensive refactoring by hand. You adjust the description and let the model update the implementation. For early-stage exploration, this flexibility is powerful.

The Real Limitations and Risks

Vibe coding is not magic, and treating it as such creates problems. Security remains a consistent concern. Multiple studies in 2025 and 2026 found that a substantial portion of AI-generated code contains vulnerabilities. Issues range from weak authentication and missing input validation to insecure handling of secrets. When the person accepting the code lacks the background to review it carefully, those problems can reach production unnoticed.

Maintainability presents another challenge. Code that works today can become difficult to extend or debug months later if no one fully understands its structure. Developers sometimes describe a “vibe coding hangover” — the moment when a project that felt effortless to create turns messy under the pressure of real-world changes and growth.

Skill atrophy is a longer-term worry for professional developers. Relying heavily on AI for implementation can reduce practice with fundamental skills such as debugging, system design, and careful reasoning about edge cases. The best practitioners treat vibe coding as a powerful accelerator rather than a complete replacement for understanding.

Production readiness is the clearest boundary. Most experienced voices agree that pure vibe coding works well for prototypes, internal tools, experiments, and personal projects. Shipping customer-facing or business-critical software still requires human review, testing, documentation, and architectural judgment. Tools can assist with those steps, but they do not eliminate the need for them.

Practical Guidance for Getting Value

Start with clear intent. Vague prompts produce vague results. Spending a few minutes refining the initial description — or even asking another AI to help craft a stronger prompt — usually improves the first output significantly.

Keep the human in the loop on important decisions. Review diffs when the stakes are higher. Run the application yourself. Test edge cases. Check how data is handled. Treat the AI as a fast collaborator rather than an unsupervised builder.

Choose the right tool for the job. Some platforms excel at rapid full-stack web apps. Others integrate more deeply into existing codebases and support professional workflows. Matching the tool to the goal reduces friction.

Separate environments carefully. Never let experimental or AI-generated code touch production data without isolation and review. Several public incidents have shown how quickly a convenient “clean up” button or hardcoded key can create serious damage.

Document the important decisions even when the code itself was generated. Future maintainers — including your future self — benefit from knowing the intent behind major choices.

Where Vibe Coding Fits in 2026

Vibe coding has already changed who can participate in software creation and how quickly early versions of ideas appear. It has not eliminated the need for craft, judgment, or responsibility. The most effective users combine the speed of natural-language generation with the discipline of review, testing, and clear thinking about long-term consequences.

For creative professionals, founders, and teams exploring new directions, the approach offers a practical way to move from conversation to working software with far less friction than before. The “vibe” is real. The results can be impressive. The responsibility for what ships still belongs to the human guiding the process.

Used thoughtfully, vibe coding expands what is possible. Used carelessly, it creates systems that are hard to trust and harder to maintain. Understanding both sides is the difference between riding the wave and being surprised by it.