Vibe Coding: Building Software by Feel

There is a new way of building software that does not have a formal methodology, a manifesto, or a certification program. People are calling it vibe coding, and the name is more accurate than it sounds.

Vibe coding is what happens when you sit down with an AI coding tool, describe what you want in natural language, let the AI generate the code, and iterate based on whether the result looks right, feels right, and does what you need. You are not writing code line by line. You are not following a specification. You are not designing the architecture first and implementing second. You are describing, generating, testing, adjusting, and repeating until the thing works.

This sounds sloppy. Sometimes it is. But it also describes how a surprising amount of useful software is being built right now, and dismissing it entirely means missing something important about how the developer's role is changing.

What It Actually Looks Like

A typical vibe coding session goes something like this.

You have an idea. Maybe you want a tool that takes a CSV file and generates a summary report. You open your AI coding tool and say: "Build me a Python script that reads a CSV file, groups the data by the category column, calculates totals for the amount column, and generates an HTML report with a bar chart."

The AI generates a script. You run it. The output is close but the chart colors are wrong and the grouping logic handles null values badly. You say: "Change the chart to use blue and gray, and treat null categories as 'Uncategorized'." The AI updates the script. You run it again. Better. The report layout needs work. You describe what you want, the AI adjusts, you test, you iterate.

Thirty minutes later, you have a working tool that does exactly what you need. You did not design a class hierarchy. You did not write unit tests. You did not think about edge cases beyond the ones that showed up when you ran it. You built software by feel — by describing what you want, observing what you get, and adjusting until the vibe is right.

This is vibe coding.

Why It Works (When It Works)

Vibe coding is effective in specific contexts, and understanding those contexts is the difference between using it well and using it badly.

Prototyping and Exploration

When you are exploring an idea, the traditional development process is expensive. Designing, planning, implementing, and testing a prototype using conventional methods takes days or weeks. Most of that time is spent on structure and correctness for something that might get thrown away.

Vibe coding collapses this. You can go from idea to working prototype in hours or minutes. You can try three different approaches and compare them before committing to one. The speed of iteration means you can explore more of the solution space, which means you make better decisions about what to build for real.

The key insight: at the exploration stage, getting something working that you can look at and interact with is more valuable than getting something perfect that exists only in a design document.

Solo Projects and Internal Tools

If you are building something for yourself or a small team, the stakes are different. The software does not need to scale to millions of users. It does not need to be maintained by a team of developers who were not involved in building it. It does not need to pass a security audit or meet regulatory requirements.

In this context, vibe coding is practical. You need a script that automates a workflow. You need a dashboard that visualizes data. You need a tool that solves a specific problem for a specific group of people. The output needs to work, not to be architecturally elegant.

Many of the most useful internal tools in organizations were built this way — maybe not with AI, but with the same philosophy. Someone needed something, hacked it together, and it worked. AI just makes the hacking faster.

Learning and Understanding

Vibe coding is an underrated way to learn. When you describe what you want and the AI generates code, you can read what it produced, understand the approach, and learn patterns you might not have encountered otherwise. The iteration loop — describe, generate, review, adjust — teaches you both what is possible and how things work.

This is not passive learning. You are making decisions at every step. Which approach to take. What looks right and what does not. Where the output fails and how to fix it. You are building judgment, even if you are not writing every line yourself.

When It Does Not Work

Vibe coding has clear failure modes, and they are serious enough that ignoring them causes real problems.

Production Systems

Production software has requirements that vibe coding does not address. It needs to handle edge cases. It needs to be secure. It needs to perform under load. It needs to be maintainable by people who did not build it. It needs to be tested.

Code generated through vibe coding typically has none of these properties. It handles the happy path — the case you tested and iterated on. It does not handle the unhappy paths — the malformed input, the network timeout, the concurrent access, the malicious request. These are the cases that matter in production, and they do not surface during a vibe coding session because you are testing with your data on your machine.

The failure mode is subtle and dangerous. The code works in the demo. It works in the first week of production. Then it encounters a case nobody thought about, and it fails in a way that is hard to diagnose because nobody understands the code well enough to debug it.

Team Development

When multiple people work on a codebase, they need shared understanding. They need conventions. They need patterns that are consistent across the codebase so that any developer can work on any part of the system without first decoding the idiosyncratic style of whoever wrote that section.

Vibe coding produces code that reflects the AI's patterns, not your team's patterns. Different sessions produce different styles. The architecture is whatever the AI decided at the moment of generation, not a deliberate decision your team made and documented. The result is a codebase that feels incoherent — it works, but nobody fully understands how, and every change is riskier than it should be.

Regulated Environments

Some software has to meet standards. Healthcare, finance, aviation, government — these domains have requirements around code quality, documentation, testing, and auditability. You need to demonstrate that the software does what it claims to do. You need evidence.

Vibe coding produces no evidence. There is no specification that the code implements. There are no tests that verify behavior. There is no audit trail of design decisions. The development process is a conversation with an AI that you probably did not save. This is not compatible with regulatory requirements, and trying to retroactively add compliance to vibe-coded software is harder than building it correctly in the first place.

Security-Sensitive Systems

AI-generated code regularly introduces security vulnerabilities. Not because the models are malicious, but because security is the domain where the difference between "works" and "works safely" is invisible at the surface. SQL injection, cross-site scripting, improper authentication, insecure defaults — these vulnerabilities do not show up when you run the code and check if the output looks right.

Vibe coding's iteration loop is "does it work and does it look right." Security vulnerabilities do not affect either of those criteria. The code works. The output looks right. It is also exploitable.

The Skill It Actually Requires

Here is the thing that gets lost in the discourse about vibe coding: it is not a no-skill activity. It requires different skills than traditional development, but it requires skills.

Taste

You need to be able to look at generated code and know if it is good. Not just whether it runs, but whether the approach is sound, the structure makes sense, and the patterns are appropriate. This is taste — the ability to evaluate quality without following a rubric.

Taste comes from experience. A developer who has built many systems can look at AI-generated code and quickly identify problems: "This approach will not scale." "This pattern creates tight coupling." "This error handling is insufficient." A beginner does not have this calibration. They look at AI-generated code, see that it runs, and assume it is good.

This is why vibe coding is simultaneously overhyped by beginners and quietly valuable to experienced developers. The experienced developer has the taste to guide the generation. The beginner does not.

Judgment

You need to know when vibe coding is appropriate and when it is not. This requires judgment about the context — who will use this software, how long it needs to last, what the consequences of failure are, and how much investment the problem deserves.

A quick internal tool? Vibe code it. A customer-facing feature in a production system? Do not. A prototype to validate an idea before committing to a full implementation? Vibe code it. The full implementation itself? Do not.

This judgment is not obvious. The temptation is to vibe code everything because it is fast and fun. The discipline is knowing when to stop and do the work properly.

Communication

Vibe coding requires you to describe what you want clearly enough that the AI generates something useful. This is harder than it sounds. Ambiguous descriptions produce ambiguous results. Missing constraints produce unconstrained solutions. Unclear priorities produce arbitrary choices.

The developers who get the best results from vibe coding are often the ones who can articulate requirements precisely — not in formal specification language, but in clear, specific natural language that leaves minimal room for misinterpretation. This is a communication skill that maps directly to other professional skills: writing requirements, giving feedback, explaining decisions.

How It Changes the Developer's Role

Vibe coding is a signal of a broader shift in what it means to build software.

The traditional developer writes code. They think in syntax, algorithms, and data structures. Their primary output is text files that a compiler or interpreter turns into running software.

The vibe coding developer directs code generation. They think in systems, requirements, and quality. Their primary output is decisions — what to build, how it should behave, whether the result is good enough.

This is not a lower-skill activity. It is a different-skill activity. The developer who can describe what they want, evaluate what they get, and iterate effectively is doing real work. They are making the decisions that determine whether the software is good.

But it does change the emphasis. Writing code becomes less important. Understanding code remains critical. Designing systems becomes more important. Evaluating quality becomes essential.

The developers who will thrive in this shift are the ones who have both skills — they can write code when they need to and direct AI generation when that is more efficient. They know which mode to use for which task. They have the taste to evaluate output, the judgment to know when each approach is appropriate, and the discipline to do the hard work when the fast approach is not good enough.

The Honest Take

Vibe coding is real. People are building useful software this way. It is not going away.

It is also not a replacement for software engineering. It is a tool — a way of working that is excellent for some tasks and terrible for others. The people getting the most value from it are not the ones who use it for everything. They are the ones who know when to use it and when to stop.

Build prototypes by vibe. Explore ideas by vibe. Create internal tools by vibe. Then, when something needs to be production-quality, shift to the practices that make production software reliable: testing, code review, security analysis, architecture, documentation.

The skill is not choosing one mode or the other. The skill is knowing where the boundary is.