What Is Vibe Coding? Andrej Karpathy's AI-First Development Approach
I’ve been thinking about how my development workflow has changed over the past year. It’s not just that I’m using AI tools more—it’s that the entire mental model of programming has shifted. Andrej Karpathy noticed the same thing and gave it a name: vibe coding.
The Problem: Syntax Overhead
Traditional development asks us to hold a lot in our heads. I need to remember syntax across multiple languages, write boilerplate code repeatedly, debug syntax errors, and constantly look up documentation for API usage. Every time I context-switch between implementation details, I lose momentum.
Traditional Mental Load┌─────────────────────────────────────────────┐│ What I want to build ││ ↓ ││ How to express it in syntax ││ ↓ ││ Which library to use ││ ↓ ││ Correct function signatures ││ ↓ ││ Error handling patterns ││ ↓ ││ Finally: working code │└─────────────────────────────────────────────┘The cognitive overhead is real. I spend significant energy on how to write something rather than what I want to build.
The Shift: Specification Over Implementation
Vibe coding flips this model. Instead of focusing on syntax, I describe my intent in natural language and let an LLM handle the implementation details. Karpathy observed that his workflow “flipped almost entirely” over just a few weeks—what was once mostly handwritten code is now largely driven by LLMs.
A Reddit commenter captured this well: the shift isn’t just “AI writes code instead of you”—it’s that the cognitive overhead moves from implementation to specification. I spend more energy on what I want and why, less on syntax and boilerplate.
Vibe Coding Mental Load┌─────────────────────────────────────────────┐│ What I want to build ││ ↓ ││ Why it matters ││ ↓ ││ How it integrates with my system ││ ↓ ││ AI generates implementation ││ ↓ ││ I review and iterate │└─────────────────────────────────────────────┘What This Actually Looks Like
Here’s a concrete comparison. When I need to fetch data from an API, the traditional approach requires me to think through every detail:
def fetch_user_data(user_id: int) -> dict: """Fetch user data from API with error handling.""" import requests from typing import Optional
base_url = "https://api.example.com" endpoint = f"{base_url}/users/{user_id}"
try: response = requests.get(endpoint, timeout=10) response.raise_for_status() return response.json() except requests.RequestException as e: print(f"Error fetching user {user_id}: {e}") return {}With vibe coding, I focus on intent:
Prompt: "Create a function to fetch user data from an APIwith proper error handling, timeout, and type hints"
My focus shifts to:- What data do I need?- How should failures be handled?- How does this integrate with my system?The AI generates the implementation. I review it for correctness and move on.
The Workflow Transformation
Traditional Workflow:Problem → Research → Design → Code → Debug → Test → Deploy ┌───────────────────────────────────┐ │ syntax focus, manual implementation │ └───────────────────────────────────┘
Vibe Coding Workflow:Problem → Specify → AI Generate → Review → Iterate → Test → Deploy ┌───────────────────────────────────┐ │ intent focus, AI handles syntax │ └───────────────────────────────────┘This isn’t about being lazy or avoiding work. It’s about operating at a higher level of abstraction. I still need to understand what the code does—I just don’t need to manually construct every line.
What Vibe Coding Is NOT
I want to be clear about misconceptions:
| Misconception | Reality |
|---|---|
| ”I don’t need to understand code anymore” | You must review and verify AI outputs |
| ”It’s a magic wand” | Still requires iteration and refinement |
| ”Faster is always better” | Going “too hard” on AI-generated code without review is “a time bomb" |
| "It replaces all coding” | Complex architectural decisions still need human judgment |
The responsibility doesn’t disappear—it shifts to specification quality and code review.
Why This Matters
Vibe coding represents a fundamental change in what it means to be a developer:
| Traditional Role | Vibe Coding Role |
|---|---|
| Syntax expert | Specification designer |
| Implementation details | System architecture |
| Manual coding | Code curation |
| Slow prototyping | Faster iteration cycles |
I’m not abandoning understanding—I’m choosing where to spend my cognitive energy. The LLM becomes a “cognitive multiplier” that handles the mechanical aspects while I focus on the strategic ones.
The Tradeoffs
Vibe coding isn’t universally better. I’ve found:
Works well for:
- Boilerplate generation
- Prototyping new features
- Exploring unfamiliar APIs
- Repetitive patterns
Requires caution for:
- Security-sensitive code
- Performance-critical paths
- Novel algorithms
- Code you can’t verify
The key is knowing when to lean into AI assistance and when to write carefully by hand.
Getting Started
If you want to try vibe coding, start with low-stakes tasks:
- Pick a routine task you’ve done many times
- Describe what you want in natural language
- Let the AI generate an implementation
- Review the output critically
- Iterate on your prompt based on results
Over time, you’ll develop a sense for how to specify intent clearly—a skill that becomes increasingly valuable as AI tools improve.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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