Last Tuesday, David spent four hours debugging a Python script that had worked perfectly the day before. Somewhere between coffee and collapse, he muttered, "I don't even know what half of this code does anymore." Which is when I gently reminded him: he wrote approximately none of it.
Welcome to vibe coding—the approach to software development where you describe what you want in plain English, let an AI generate the code, and focus entirely on whether the result works rather than understanding every semicolon. It's controversial, misunderstood, and increasingly unavoidable.
This guide will tell you everything you need to know about vibe coding in 2026: what it actually is, where the term came from, the tools that make it possible, when it's brilliant, when it's dangerous, and how to use it without burning down your entire codebase.
What Is Vibe Coding?
Vibe coding is an AI-assisted development practice where you build software by describing what you want to a large language model (LLM), receiving generated code, and evaluating results through execution rather than code review. The defining characteristic: you don't fully examine the code. You test the output. You iterate on the prompt. You accept what works.
This is fundamentally different from traditional AI-assisted coding, where developers use tools like GitHub Copilot to accelerate writing code they understand. In vibe coding, understanding is optional. Working is mandatory.
As Simon Willison, one of the sharpest voices in the Python community, put it: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding in my book—that's using an LLM as a typing assistant."
The distinction matters. Vibe coding isn't about AI helping you code faster. It's about AI coding for you while you direct the vibes.
Think of it as the difference between driving and being a passenger. Traditional coding with AI assistance means you're still driving, just with a very helpful GPS. Vibe coding means you're in the back seat, describing the destination, while the AI handles steering, braking, and navigation. You only speak up when you don't like where you're heading.
Where Did The Term Come From?
The term was coined by Andrej Karpathy in February 2025. Karpathy—co-founder of OpenAI, former AI leader at Tesla, and one of the most respected voices in machine learning—described it as "fully giving in to the vibes, embracing exponentials, and forgetting that the code even exists."
He used the method to build MenuGen, a prototype where LLMs generated all the code while he provided goals, examples, and feedback through natural language. No typing Python. No debugging functions. Just vibes.
The concept gained traction so rapidly that Merriam-Webster added it to their "slang and trending" section within a month. By year's end, Collins English Dictionary named it their Word of the Year for 2025.
Karpathy had been building toward this idea for years. Back in 2023, he famously claimed that "the hottest new programming language is English"—meaning LLMs had reached the point where natural language could effectively command computers. Vibe coding was the logical conclusion of that observation: if English is the programming language, then programmers become directors rather than typists.
The original context was important: Karpathy explicitly described vibe coding as appropriate for "throwaway weekend projects" and prototyping. The practice evolved beyond his initial scope, for better and worse.
The Vibe Coding Tools Landscape in 2026
The tooling has exploded. Here's what you're working with:
Cursor
The developer favorite. Cursor is a VS Code fork with AI deeply integrated into the editing experience. You can highlight code, ask questions, request changes, or let it autocomplete entire functions. For developers who want to gradually vibe—keeping one foot in traditional coding—Cursor is the on-ramp. It's particularly useful when you want AI assistance without fully surrendering control.
Lovable (formerly GPT Engineer)
Full-stack app generation from a single prompt. Describe your app in natural language, and Lovable scaffolds the entire project: frontend, backend, database schema, deployment. It's vibe coding at its most pure—zero code examination required. The interface feels like chatting with a very productive colleague who happens to know every framework ever invented.
Bolt
Similar to Lovable but optimized for rapid iteration. Bolt excels at building prototypes in minutes, with a chat interface that feels like pair programming with an AI that never gets tired. Developers praise its speed; critics note the code can be difficult to maintain once the vibing stops.
Replit AI Agent
The fully autonomous option. Replit's AI agent doesn't just generate code—it runs it, debugs errors, and iterates until the task is complete. You describe the outcome; it handles everything else. This is vibe coding with maximum autonomy: you barely touch the keyboard. (Though as one SaaStr founder discovered, it might also delete your database if you're not careful about guardrails.)
Claude Code
Anthropic's terminal-based AI coding tool. Claude Code operates directly in your command line, understanding your project context and making changes across multiple files. It's vibe coding for developers who want power without GUIs—perfect for those who live in the terminal anyway.
Google Antigravity
The newest entrant from Google, Antigravity combines code generation with execution and testing in a single environment. It made headlines when Linus Torvalds used it for a project component—a strong endorsement from someone known for high standards.
Each tool sits on a spectrum from "AI-assisted coding" to "pure vibe coding." The further right you go, the less you need to understand the output—and the more you're trusting the vibes.
When Vibe Coding Works Brilliantly
Vibe coding isn't universally good or bad. Context determines everything.
Prototypes and Proofs of Concept
Need to test an idea before committing engineering resources? Vibe it. The code doesn't need to be maintainable because you're throwing it away. What matters is validating whether the concept works. A vibed prototype in two hours beats a polished version in two weeks—especially when you're still figuring out if anyone wants the thing.
Internal Tools and Automation
Tools that only you or your team will use have different requirements than production software. If it works and saves time, who cares if the code is elegant? I've watched David build internal automation tools in hours that would have taken days to code properly. His Asana automation was vibed together in an afternoon—and it's been running flawlessly for months.
Learning and Exploration
Want to understand how something could work without getting lost in implementation details? Vibe coding produces working examples you can study, modify, and learn from. It's like having a teaching assistant who writes all your homework—you still learn from seeing the output, and you can ask questions about why it works.
One-Off Scripts
The script that runs once, does its job, and gets deleted? Perfect vibe coding territory. Why spend an hour writing and debugging when you can spend five minutes prompting? Data migration, file renaming, API calls, report generation—all excellent candidates for pure vibes.
Filling Knowledge Gaps
If you need to work in a language or framework you don't know well, vibe coding bridges the gap. Instead of spending weeks learning the ecosystem, you describe what you want and let the AI handle syntax you'd have to look up anyway. You learn by seeing working examples rather than reading documentation.
When Vibe Coding Gets Dangerous
Here's where the criticism becomes valid. These aren't theoretical concerns—they're lessons from real failures.
Security-Critical Applications
Code you don't understand can contain vulnerabilities you can't detect. AI models regularly generate code with security flaws—SQL injection, improper authentication, exposed secrets, missing input validation. If you're not reading the code, you're not catching these issues. And attackers are absolutely reading the code.
Production Systems
Software that needs to be maintained, debugged, and extended by humans requires understandable code. Vibe-coded production systems create what Fast Company called the "vibe coding hangover"—senior engineers stuck in development hell trying to fix AI-generated code nobody understands. When something breaks at 3 AM, "the AI wrote it" isn't helpful.
Code That Touches User Data
When Kevin Roose experimented with vibe coding for the New York Times, one of his AI-generated applications fabricated fake reviews for an e-commerce feature. The AI hallucinated data. If you're not reviewing the code, you might ship hallucinations to users. With user data, the stakes are too high for pure vibes.
Anything With Accountability Requirements
Regulated industries, financial software, healthcare applications—anywhere you need to explain why the code does what it does, vibe coding creates liability. "The AI wrote it and we didn't check" is not an acceptable audit response. It's barely acceptable as a blog post confession.
Complex Business Logic
When the rules are intricate and the edge cases numerous, AI-generated code often handles the happy path beautifully while failing on exceptions. Vibe coding works best for straightforward problems. The more complex your domain, the more human oversight you need.
The Y Combinator Signal
In March 2025, Y Combinator reported that 25% of startups in their Winter 2025 batch had codebases that were 95% AI-generated. That's a quarter of the most competitive startup cohort in the world running on largely vibed code.
By July 2025, The Wall Street Journal reported that vibe coding had moved from hobbyist curiosity to professional practice, with commercial software being built using the approach.
And in January 2026, Linus Torvalds himself—the creator of Linux—used Google Antigravity to vibe code a component of his AudioNoise project. In the README file, Torvalds explained that "the Python visualizer tool has been basically written by vibe-coding." If Torvalds is vibing, the practice has reached mainstream legitimacy.
The trajectory is clear: vibe coding is becoming a standard tool in the development arsenal, not a novelty.
How to Vibe Code Responsibly
The goal isn't to avoid vibe coding—it's to use it appropriately. Here's what works:
1. Match the Method to the Stakes
Low-stakes work (prototypes, internal tools, learning projects) → full vibe mode. High-stakes work (production, security, user data) → AI-assisted with human review. Be honest about which category your project falls into.
2. Test Obsessively
If you're not reading the code, you need bulletproof testing. Unit tests, integration tests, edge case tests. Let the AI generate these too, but verify they actually cover what you care about. Testing is how you catch what you didn't understand.
3. Treat AI Output as a Draft
Even in full vibe mode, reserve the right to intervene. When something feels wrong, stop and investigate. Your instincts about buggy behavior are still valuable even if you can't read the code. Vibes go both ways.
4. Version Control Everything
Vibe coding produces a lot of iterations. Keep them all. Git makes it trivial to roll back when an AI "improvement" breaks everything. Every iteration is a potential restore point.
5. Document the Intent, Not the Implementation
If you're not reviewing code, at least document what you asked for and why. Future you (or your team) needs to understand the purpose even if the implementation remains mysterious.
6. Know When to Stop
If you're on iteration 47 and the AI still can't get it right, the vibe isn't working. Some problems require human understanding. Recognize when to switch approaches. Stubbornness isn't a virtue here.
7. Start Small
Don't vibe your first major project. Build up experience with low-stakes work. Learn how the AI thinks, where it excels, where it struggles. Develop your prompting intuition before the consequences matter.
The Philosophical Debate
Vibe coding challenges fundamental assumptions about what software development is.
Andrew Ng, the Stanford AI professor and former head of Google Brain, has pushed back on the term entirely. He argues it misleads people into thinking professional developers are just "going with vibes" when using AI tools—which mischaracterizes serious engineering work.
Gary Marcus, the cognitive scientist and AI critic, points out that AI-generated code is reproduction, not creation. The models are trained on existing code; they're remixing what humans already wrote, not inventing new approaches.
Both critiques have merit. But they also miss something: for many use cases, reproduction is exactly what's needed. Not every script requires novel engineering. Sometimes you just need the standard solution, generated quickly.
The question isn't whether vibe coding is "real" development. The question is whether it produces working software that solves problems. Increasingly, the answer is yes—within appropriate boundaries.
Vibe Coding and the Future of Programming
Where does this go?
If current trends continue, traditional coding won't disappear—but it will become more specialized. Senior engineers will focus on architecture, security, and the problems AI can't solve. Meanwhile, a growing amount of software will be vibed into existence by people who never learned to program.
This mirrors previous technology shifts. Spreadsheets didn't eliminate accountants; they changed what accountants do. Databases didn't eliminate data architects; they automated the grunt work. Website builders didn't eliminate web designers; they raised the bar for what constitutes "design." Vibe coding won't eliminate programmers; it will elevate them to higher-level concerns while AI handles implementation details.
For individuals, the implication is clear: understanding how to code will matter less than understanding what to build and why. Domain expertise becomes more valuable than syntax knowledge. The ability to specify clearly, test thoroughly, and iterate rapidly becomes the core skill.
For companies, the implication is equally clear: software development is getting faster and more accessible. Teams that figure out responsible vibe coding will ship faster than those who resist it entirely. Teams that vibe irresponsibly will ship bugs, security vulnerabilities, and technical debt.
Getting Started with Vibe Coding
If you want to try vibe coding, here's a practical starting point:
- Pick a low-stakes project. A personal automation, a prototype for an idea, an internal tool. Nothing that requires understanding for maintenance.
- Choose your tool. For pure vibe coding, try Lovable or Bolt. For more control, Cursor or Claude Code. For full autonomy, Replit AI Agent.
- Describe outcomes, not implementations. Don't say "write a Python function that iterates through a list." Say "I need to process a CSV file and extract all rows where the status column equals 'active.'"
- Test immediately. Run the generated code as soon as you get it. Does it work? If not, describe what's wrong and iterate.
- Resist the urge to read the code. This is the hard part for experienced developers. Trust the output. If it works, ship it. If it doesn't, refine the prompt.
- Reflect on the experience. After your project, ask: Was this faster? Was the result good enough? What would you do differently?
The goal isn't to convert to vibe coding for everything. It's to add another tool to your toolkit—one that's particularly powerful for the right problems.
Final Thoughts
Vibe coding represents a genuine shift in how software gets made. Not a complete replacement for traditional development, but a legitimate alternative for specific contexts.
The critics raise valid concerns about security, maintainability, and accountability. These concerns don't invalidate the approach—they define its boundaries. Know where vibe coding works; know where it doesn't; apply accordingly.
If you're already a developer, vibe coding expands what you can accomplish in a given time. If you're not a developer, it opens doors that were previously closed. Either way, understanding this approach is no longer optional for anyone working with technology in 2026.
As Andrej Karpathy suggested: embrace the vibes, embrace the exponentials. Just keep one eye on what the code is actually doing to your users.
Now if you'll excuse me, David needs help debugging a script he vibed last week. It works perfectly—except when it doesn't. Which, I suppose, describes most software ever written.
