What Is Vibe Coding? A Plain-Language Explanation

The term appeared in early 2025 and quickly became the umbrella name for a whole style of work — from beginners shipping their first app without writing a single line of code themselves, to experienced developers who hand the routine work to agents. In this article we cover where the word came from, what the process looks like in practice, the honest pros and limitations of the approach, and which tools are the easiest way in.

The term was coined by Andrej Karpathy, a co-founder of OpenAI and former head of AI at Tesla. In February 2025 he published a post on X describing a new kind of programming he called vibe coding: fully give in to the vibes, forget that the code even exists, and just talk to the model about what you want.

Karpathy wrote that he accepted the agent's suggestions almost without reading them, and when an error appeared, he simply pasted its text into the chat — and that was usually enough. He was upfront that he used this style for throwaway weekend projects, not production systems. But the phrase hit a nerve: models had just become strong enough for this way of working to reliably produce results.

The word was picked up by the media, by dictionaries, and by developers themselves. By late 2025, vibe coding no longer meant only the accept-everything-blindly mode — it had come to describe any development where the AI writes most of the code and the human sets the goals and steers the process.

The basic loop is simple and repeats dozens of times per session. You describe a task in words: build a page with a list of expenses and a form to add one. The agent reads the project, writes or edits code, and often runs commands and tests on its own. You look at the result — in the browser, in the terminal, in the running app.

Then comes the essential part: iteration. The button breaks on mobile, add sorting by date, it crashed with this exact error — each follow-up request refines the previous result. A good vibe-coding session feels less like writing a spec and more like a conversation with a capable contractor whom you keep showing what you like and what you don't.

The key skill here is decomposition and precise wording. Asking to make it pretty works worse than asking to increase the spacing between cards and make the heading larger. The more concrete the request and the smaller the step, the more predictable the agent's behavior and the less often you have to roll back failed edits.

Yes, you can — and in 2026 this is no longer a bold claim but an observable fact. A prototype, a landing page, a Telegram bot, a small internal tool for your team — all of it can be built without ever writing code by hand. The honest caveat: the further a project moves beyond a prototype, the more often you will hit situations where you get stuck without understanding what is going on.

Beginners and programmers vibe-code differently. A beginner trusts the agent with almost everything and judges the result by how the app behaves: if it works, it's good. A programmer uses the same tools but reads the code changes, asks the agent to justify questionable decisions, writes tests, and steps in manually when the agent goes off track.

This leads to different risk profiles. For a beginner, the main danger is missing a problem that is only visible in the code: a leaked secret key, a broken permission check, a fragile architecture. For an experienced developer the risk is different — the temptation to accept code faster than you can actually digest it.

The biggest upside is speed — both of getting started and of iterating. An idea turns into a working prototype in an evening rather than a month, and the barrier of not knowing how to program stops being a wall. For experienced developers it is a way to offload the routine: boilerplate, migrations, standard screens, tests.

The second upside is learning by doing. Watching an agent solve a task, a beginner gradually absorbs the vocabulary and logic of software development: what routing is, why you need a database, how to read error messages. Many people came just to poke around — and six months later were reading code consciously and pushing back on the agent with good arguments.

The third is the low cost of experiments. Testing a hypothesis, building an MVP to show an investor or a client, automating personal chores — this used to require hiring a developer or studying for months. Now the cost of an attempt is measured in a few hours of your time and API tokens.

The first and biggest risk is the state of not understanding what is written in your own project. While everything works, it doesn't matter; when something breaks, you depend on whether the agent can fix what it built itself. For throwaway projects this is acceptable; for a product with real users it is not.

The second risk is code quality. Left unguided, models tend to duplicate logic, pull in unnecessary dependencies, and pick the most straightforward solution. Without regular review, the codebase gradually gets overgrown, and every next change becomes harder.

The third risk is security. The classic vibe-coding mistakes: secret keys committed to the repository, missing permission checks on the server, blind trust in form input. The agent writes what you asked for, but not always what you forgot to ask for. If your app handles other people's data or money, having someone experienced look at it is not paranoia — it is hygiene.

Terminal agents — Claude Code, OpenCode, Codex CLI. They live in the command line, see the whole project, edit files, and run commands on their own. This is the most powerful class for working with real repositories; the series includes a separate deep dive on Claude Code.

AI IDEs are editors built around an agent; the best known is Cursor. It looks like a familiar code editor, but with chat, smart autocomplete, and an agent mode inside. It is a comfortable entry point for anyone who wants to see the code next to the conversation.

VS Code extensions — Cline, Roo Code, Continue. They add an agent to the editor you already have, and usually let you connect any model with an API key. Finally, web builders are services where an app is assembled right in the browser from a text description — the fastest way to get a working prototype without installing anything at all. We compare specific tools in separate articles in this series.

Start small and personal: a tool for yourself, not a startup. A synced shopping list, a bot that sends you exchange rates, a one-page site for a friend. A small task means fast iterations and a clear definition of success.

If you don't want to install anything, start with a web builder. If you are ready to install an editor, try Cursor or VS Code with an extension like Cline. If you are comfortable in the terminal, go straight to Claude Code or OpenCode: the terminal-plus-agent combination currently delivers the strongest results on real projects.

And sort out model access in advance. Almost every tool listed here works through language model APIs and consumes tokens — the main running cost of a vibe coder. That is what the final section is about.

Kumo is an OpenAI-compatible API gateway: one key and one prepaid balance for dozens of models, including Claude Opus 5 and Claude Sonnet 5, GPT-5.6, Gemini 3 Flash, Grok 4.5, and DeepSeek V4 Pro. Any tool that speaks the OpenAI API connects by changing a single base URL — that is how Claude Code, Cursor, Cline, OpenCode, and others work with it. Ready-made configs for specific tools live in the Integrations section of the dashboard, with details in the docs.

Two things matter most for a beginner. First, payment: you top up the balance with a Russian card or via SBP (Faster Payments System), with no VPN and no foreign card. Second, spend control: every API key can have its own limit and alerts, so an agent accidentally left running overnight won't burn through your entire balance.

You pay only for the tokens you actually use — no subscriptions, no seats, no minimum payments. The effective rate is 30–50% below the labs' official price lists; the exact savings depend on your workload profile and volume, and the price of every model is visible before you spend a single token. The model is never swapped: request Claude Sonnet 5 and you get exactly that, with every response reporting which model served it.

Privacy is covered too: Kumo does not log prompt bodies — only billing metadata such as token counts, model, and time. Getting started is simple: sign up, top up the balance, grab a key — and every tool in this article is ready to go.