Vibe Coding: The AI Coding Trend Every Beginner Should Understand
Meera built her first real app in a weekend. Not a tutorial project — an actual expense tracker with login, a dashboard, and a database behind it. She’d never written a line of code before. She just described what she wanted to an AI app builder, clicked “accept” on whatever it generated, and by Sunday night she had something that worked. She felt, for the first time, like a developer.
Three weeks later, a friend asked her to add a simple feature: let users export their expenses as a CSV. Meera opened the code. She didn’t recognize most of it. She’d never actually read it — she’d just accepted everything the AI produced and moved on. She tried describing the new feature the same way she’d built everything else. The AI made a change, something else broke, and she had no idea which of the two was the actual problem. The app that took a weekend to build now felt impossible to touch.
What Is Vibe Coding, Really?
Meera had, without knowing the term for it, been vibe coding. The phrase was coined in February 2025 by AI researcher Andrej Karpathy, who described a workflow of describing what you want in plain language, letting an AI tool generate the code, and essentially trusting the output without reading it closely. It caught on fast enough that Collins Dictionary named it their Word of the Year for 2025.
At its core, vibe coding means building software by describing your goal in natural language and letting an AI tool write, and often deploy, the actual code — with little or no manual review of what it produced. You’re directing outcomes, not writing syntax.
Why Vibe Coding Feels Like Magic (Because, for Prototypes, It Kind Of Is)
For exactly what Meera used it for — a fast, working prototype — vibe coding is genuinely excellent. Tools like Lovable, Bolt, and Replit let anyone go from an idea to a clickable app in hours instead of weeks. That’s not a small thing. It’s why non-developers, designers, and product managers are now shipping working tools that never would have justified a developer’s time before.
This is the real strength of vibe coding: removing the cost of trying an idea. If a project is small, disposable, or just meant to test whether an idea works at all, this is close to the ideal tool for the job.
Where Vibe Coding Breaks Down
Meera’s story after that first weekend is common enough that it has its own nickname in developer communities: the “vibe coding hangover” — a project that felt effortless to build and becomes nearly impossible to maintain once nobody, including the person who “built” it, understands how it actually works.
A few things drive this:
- Security gaps slip through. Independent security research has repeatedly found a large share of AI-generated applications ship with real vulnerabilities — exposed data, hardcoded secrets, and similar issues — because nobody reviewed the code closely enough to catch them.
- Debugging skill doesn’t develop. A 2026 randomized controlled trial from Anthropic found that leaning on AI assistance can actually reduce performance on coding-related assessments, with the biggest drop showing up specifically in debugging — the exact skill Meera needed and didn’t have.
- Context gets lost. Multi-file projects drift as the AI “forgets” earlier architectural decisions, and nobody wrote them down anywhere else either.
Even Karpathy himself has since pulled back from treating vibe coding as an end state. By early 2026 he was describing a more disciplined version of AI-assisted development — one where the developer still supervises and directs the work with real engineering judgment — as a meaningfully different, more mature practice.
Vibe Coding vs. Learning to Actually Code
This is the part worth being honest about: vibe coding doesn’t replace programming knowledge, it just relocates where that knowledge gets used. Instead of applying it while typing every line, you need it while reviewing, debugging, and deciding whether what the AI produced is actually safe to ship.
If you never built that knowledge in the first place, there’s nothing to review with. That was Meera’s exact wall — not that AI-assisted coding failed her, but that she had no fundamentals underneath it to fall back on the moment something needed real understanding instead of another prompt.
How to Use Vibe Coding Without Skipping the Fundamentals
The goal isn’t to avoid AI coding tools — nearly every professional developer uses them now. The goal is to make sure you’re the one in charge of the result. A few practical habits:
- Use vibe coding tools for prototyping, not production. Validate an idea fast, then rebuild the parts that matter with real understanding before real users depend on it.
- Read what the AI generates, at least once, line by line. You don’t have to write it yourself to understand it.
- Learn the fundamentals separately from any one tool. Data structures, how a request actually flows through an application, how databases relate to each other — this is what lets you catch a subtly wrong AI suggestion instead of shipping it.
- Practice debugging on purpose. It’s the skill research shows erodes fastest when AI does all the fixing — so it’s the one worth deliberately protecting.
This is exactly where Meera’s approach changed. She went back and worked through a structured, project-based Full Stack Development course — not to abandon AI tools, but to actually understand the code she’d been generating with them. A few months later, she rebuilt her expense tracker from scratch, using AI to move faster on boilerplate while writing and reviewing the core logic herself. This time, when a bug showed up, she could actually find it.
The Bottom Line
Vibe coding is a real, useful way to build fast — for prototypes, demos, and throwaway weekend projects, it’s hard to beat. What it can’t do is substitute for the underlying skill of understanding how software actually works, and that gap is exactly where projects, and careers, tend to stall.
If you want to be able to direct AI tools instead of being at their mercy the moment something breaks, that starts with the fundamentals. Wave IT Labs’ project-based Python and Full Stack Development courses are built for exactly that — learning by building real, working software with a mentor who makes sure you understand why it works, not just that it runs. See all courses at Wave IT Labs to find where to start.