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Vibe Coding.

Vibe coding describes a way of working in which a person describes the desired function in ordinary language and an AI model writes the matching code, without every single line being read and understood. The term comes from Andrej Karpathy and in practice mostly means building prototypes very quickly.

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Vibe Coding — Explained in Detail

The term goes back to a post by Andrej Karpathy in February 2025. He described a way of programming in which you rely entirely on the model, try the result, and on errors simply hand back the error message instead of reading the code yourself. Collins made 'vibe coding' its word of the year in November 2025, and at that point the expression lost its precision: today it covers everything from careful AI-assisted development with tests and review to clicking an app together unchecked. For an honest conversation about budget and risk it is worth defining the term before using it.

In practice vibe coding runs as a loop. You describe in a sentence or two what should exist, the model produces files, you start the application, report back what does not work, and have it corrected. Modern development environments and coding agents run commands themselves, read error messages and change several files in one pass. The strength of this loop is speed: hours rather than weeks from idea to first running version. That is also its weakness. What emerges is a version that runs — not necessarily one that suits a second developer, a load peak, or a security review.

The risks are well documented and worth treating soberly. Code nobody has read typically contains credentials in the source, missing permission checks on endpoints, unvalidated input, and dependencies nobody consciously chose. A second, less obvious effect follows: without tests and without an understood architecture, fear of change grows. Every new feature can break something nobody can explain, and the model then repairs symptoms instead of causes. The usable rule of thumb is therefore not 'never' but: the greater the damage from a failure, the stricter the human control — up to review, automated tests and a security check before going live.

A concrete case shows the sensible boundary. An SME wants to know whether a digital booking form with automatic scheduling would be used at all. Instead of writing a specification, a prototype is built in a few days and tried out by twenty existing customers. The goal is a decision, not a finished product: if the form is used, the investment is justified; if it is ignored, little money is lost. That is exactly what we build prototypes and MVPs for, from CHF 10,000. If the decision is to proceed, the prototype is not dragged along but rebuilt with tests, clean data handling and review.

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Prompt Engineering

Frequently Asked Questions About Vibe Coding

For a prototype yes; for a system holding customer data, payments or legal obligations, not without additional work. The generated code has to be read, tested and checked for permission enforcement, input validation and credentials in the source. The proven approach splits the work into two phases: validate quickly whether the idea holds, then decide deliberately whether and how the result is made production-ready.

The difference lies in who answers for the result. In AI-assisted development the model writes suggestions that a developer reads, understands and takes responsibility for. In vibe coding in its original sense the code is deliberately not read; all that counts is whether the application does what it should. Both ways of working are legitimate, but they have entirely different fields of use and risk profiles.

With four steps that cost little time: move credentials out of the source into environment variables, check every endpoint for a permission check, add automated tests for the most important flows, and scan the dependencies for known vulnerabilities. After that, a review by someone who did not generate the code is worthwhile. In experience that finds more than another round with the model.

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