Here’s How Vibe Coding Actually Works

Vibe coding is the practice of building production software by describing what you want to AI coding tools and letting them handle implementation, while you focus on intent, architecture, and outcomes. The term was coined by Andrej Karpathy in early 2025, and the practice has since collapsed the economics of custom software development by roughly an order of magnitude. Here's how it actually works.

What vibe coding actually is

When Andrej Karpathy coined the phrase "vibe coding" in February 2025, he was describing a specific shift in how software gets built. The developer doesn't write code line by line. The developer describes the desired outcome to an AI coding tool — Claude, Cursor, Replit, Lovable, or one of the rapidly expanding category of AI coding agents — and the AI produces the implementation. The developer accepts, refines, redirects, and iterates based on the result rather than the source code.

That's vibe coding in its narrow technical sense. The broader practical reality is that vibe coding shifts the bottleneck from syntax to specification. The hard part is no longer typing the code. The hard part is knowing exactly what you want built, how it fits into your existing systems, and what production discipline needs to surround the AI's output to make it trustworthy.

This is the part most coverage of vibe coding gets wrong. The breathless takes describe it as a way for non-technical people to build software without learning to code. That's true on the margins — you can absolutely produce a working prototype this way with no programming background. But the operators actually using vibe coding to ship production systems are not coding novices. They're people with strong product instincts, clear architectural thinking, and enough technical literacy to recognize when the AI is about to ship something that will break.

How the workflow actually runs

A working vibe coding session has a different rhythm than traditional development. Here is the typical loop, drawn from what I do most weeks building tools for Brandyard's content operation:

Step 1 — Specify the outcome, not the implementation. Instead of "write a Python function that takes X and returns Y," the prompt is closer to: "I need a tool that monitors my inbox for supplier pricing emails, parses the price changes out of them, and surfaces them in a daily review queue I can act on with one click." The specification describes the job, not the code.

Step 2 — Let the tool propose an architecture. A capable AI coding tool will respond by proposing how it would build the thing — what data sources, what storage, what interface, what failure modes. This is the moment where architectural judgment matters most. A weak proposal here produces a weak system later. Push back, refine the approach, agree on the structure before any code gets written.

Step 3 — Build in working chunks. The AI writes code. You run it. It either does what you specified or it doesn't. If it doesn't, the feedback isn't "fix line 47" — it's "this is failing on input type X; here's the error." The AI investigates and adjusts. Each loop produces a working piece you can verify.

Step 4 — Catch the production gaps. This is where vibe coding rewards experience and punishes naivety. The AI will produce code that runs but doesn't handle the edge case it didn't think of. It will skip the audit logging. It will hardcode a value that should be a configuration. The human's job is to notice what's missing and prompt for it explicitly — error handling, security boundaries, logging, monitoring, the difference between a prototype and a tool you would trust with real data.

Step 5 — Deploy and use. Production-grade vibe coding ends with the tool running where it is supposed to run, doing the job, with the human who built it actively using it. The most reliable quality test is whether you would put the tool inside your own operation.

How we built Paxelo this way

Paxelo is the AI content engine that runs Brandyard's marketing operation — a WordPress plugin and LinkedIn surface, built on the Abacus platform, with Claude as the primary thinking partner for the architecture and logic.

I built it via vibe coding over a few weeks.

The work that mattered was not the typing. It was the architectural specification — what the system needed to do, how it would fit into our existing publishing surfaces, what data it needed access to, where the human-in-the-loop checkpoints belonged. That specification work took roughly half the total time. The actual code generation was the easier part in retrospect.

What I learned: production-grade vibe coding requires production-grade thinking. The AI handles implementation. The human has to handle everything else — architecture, integration, data integrity, failure modes, security, the difference between "it ran successfully once on test data" and "it has been running in production for a month without breaking." The discipline is the same discipline traditional engineering has always required. The leverage is what changed.

Paxelo runs Brandyard's content operation today. It is not a prototype. It generates real traffic, real citations, real client engagement. That is the test of whether vibe coding produces production software — the tool, used every day, doing real work, with real stakes.

Where vibe coding works, and where it doesn't

The honest assessment, after using this practice across multiple internal tools and client engagements:

Where it works well:

Internal business tools — dashboards, automations, data integrations, custom reporting
Workflow tools where the specification is clear and the business rules are knowable
Replacements for spreadsheet-based processes that have outgrown their format
API integrations between systems that do not talk to each other natively
Content and marketing automation where the human stays in the loop on output quality
Anything that would have required a three-to-six-month traditional dev contract and produces well-defined outputs

Where it doesn't:

Mission-critical regulated systems where compliance burden is heavy — medical device software, financial trading, anything FDA or SEC adjacent
Hardware-integrated systems where the software-only stack does not apply
High-concurrency systems where performance tuning requires expert engineering judgment
Anything where a 2 a.m. outage has six-figure or higher consequences
Greenfield systems with no clear specification — vibe coding amplifies whatever specification you bring; it does not fix a missing one

The pattern: vibe coding rewards operators who know what they want built and can describe it precisely. It punishes operators who hope the AI will figure out what they need. The bottleneck is intent, not implementation.

What's different from traditional coding

The clearest comparison is in the economics. A traditional custom software project for a B2B operator typically runs three to six months and costs between $40,000 and $250,000 depending on scope. A vibe coded equivalent of the same project, with the same production discipline applied, ships in one to three weeks at roughly a tenth of the cost.

The other difference is in the failure mode. Traditional development tends to fail late and expensively — the project runs for months, then does not ship, or ships and does not do what was needed. Vibe coding tends to fail fast and cheaply — you discover within days whether the specification is right and the approach will work. The cost of being wrong drops by an order of magnitude, which makes it economically rational to try things you would not have tried before.

This is the shift that is reshaping how B2B operations think about custom tooling. The work that used to be impossibly expensive is now routinely affordable. The work that was risky to attempt is now safe to experiment with. The operators who recognize this and adjust their tooling strategy accordingly will compound a structural advantage over the operators who do not.

Getting started

If you are a B2B operator with workflows worth automating and no engineering team, vibe coding is the path that is available to you now that was not available a year ago. The on-ramp is shorter than most people assume. The architectural thinking that makes the practice work is the same thinking you have been applying to your operation already — you just need a way to translate it into the AI tool's working language.

At Brandyard we build custom AI tools for B2B operators on the Abacus platform, using the same approach that produced Paxelo. Quote engines, billing automation, internal dashboards, AI agents that handle the tedious work your team should not be touching. Production-grade tools shipped in days instead of months.

If you'd like to see what we would build for your operation, the path starts with a 30-minute discovery call. Details and pricing at brandyard.net/vibe-coding.

References

Your business logic is more valuable than you think.

Brandyard specializes in translating deep commercial expertise into AI-powered systems that scale. If you’ve been watching the AI revolution wondering where you fit — the answer is at the top of it. Your judgment is the input the system needs most.

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Author

Stephen Sowinski — Founder & CEO, Brandyard

Stephen is the founder of Brandyard, a B2B marketing practice that builds custom AI tools and content systems for industrial and SaaS operators. Over a 30-year career, he has held marketing leadership roles at Nordson Plasma Systems, Interstate Plastics, Interstate Advanced Materials, Plastic Machining Company, and a natural gas filtration distributor. He is the author of the forthcoming book The Cluster Method: How to Build Content That Ranks on Google and Gets Cited by AI, and the architect of Paxelo, a production AI content engine running on the Abacus platform. He is a member of the International Association of Plastics Distributors (IAPD).

More about Stephen → brandyard.net/about