The Wizard Behind the Code: Andrej Karpathy and the Rise of "Vibe Coding"
Conversational Coding in the Age of AI.
Editor’s Note: Uploading this article again to fix an audio issue. The audio for this article is generated using Google’s Notebook LM- an AI-powered voice synthesis tool. Note:
This story bridges two scrolls: DROIDs! and Deep Learning with the Wolf.
Because when you’re talking vibe coding, robots and rogue AI apprentices both belong.
The Wizard Behind the Code
Andrej Karpathy and the Rise of "Vibe Coding" in the Age of AI Apprentices
What if your next app wasn’t coded—but conjured?
Imagine building software not with keystrokes, but with conversation. You speak your intent, and a digital apprentice—an LLM—does the rest. No syntax. No stack overflow. Just vibes.
It sounds like spellwork, but it's already reshaping Silicon Valley. And at the heart of this transformation is Andrej Karpathy, a modern-day Merlin of machine learning.
According to Karpathy: “There's a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” (@karpathy, X (formerly Twitter), 2 February. 2025)
The Sage of Software
At 38, Andrej Karpathy has become one of AI’s most influential figures—a rare blend of academic rigor and Silicon Valley swagger. Born in Slovakia and raised in Canada, he carved a path through Stanford, where he created its first deep learning course (750 students by 2017), then joined OpenAI in its embryonic phase, and later became Tesla’s Director of AI, architecting the Autopilot vision stack.
By early 2025, Karpathy cast a new spell over the tech world: “vibe coding.”
What in the Vibe Is Going On?
Karpathy defines vibe coding as a way to “fully give in to the vibes, embrace exponentials, and forget that the code even exists.”
In practice, it's a method where you talk to AI like a teammate. Describe a web app. Paste an error message. Riff on what you want. The code appears. You barely touch a semicolon.
Karpathy’s process: “I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.”
The result? A radical inversion of how software gets made. You no longer need to learn programming to use programming.
A Spell That’s Catching Fire
What started as a tweet is now cultural lore.
Within weeks, Merriam-Webster added “vibe coding” to its dictionary. The New York Times and Guardian ran features. Y Combinator startups began shipping MVPs where 95% of the code was AI-generated. Personal software—once impossible to justify for one user—suddenly became viable.
Want an app that knows what’s in your fridge and suggests lunch based on your calendar? Just ask.
Karpathy wasn’t exaggerating when he said: “The hottest new programming language is English.”
Democratizing Codecraft
The allure of vibe coding lies in its accessibility.
No bootcamp. No debugging at 2 a.m. No gatekeeping. Just intent translated into function.
For students, creators, and anyone who’s ever said “I wish I could build this”, vibe coding is a door creaking open. Data tools. Workflow automations. Interactive prototypes. All within reach.
It echoes the ethos of agile development: rapid iteration, low ceremony, just enough structure to move fast.
🤖 Field Notes from a Roboticist
To understand how vibe coding works in practice—and where it fails—we turn to someone who codes with both curiosity and caution: Alexander Wolf Torres, a graduate student in Robotics and Automation.
In his short video breakdown, Torres captures the paradox perfectly:
“Vibe coding at a surface level is very impressive... But when you’re working with larger projects, multiple files, thousands of lines of code—it’ll often become unmaintainable, and something will randomly break.”
— Alexander Wolf Torres
Torres describes how he leans on AI for quick syntax recall across languages like Python, JavaScript, and C. But as projects grow in complexity, AI-generated code can spiral. He warns of what he calls the “infinite loop of re-prompting”—when each fix spawns a new error, and debugging becomes a game of chance.
“You end up with overly complex code because it’s trying to fix issues caused by being too complex… which makes it more complex.”
His takeaway? Vibe coding isn’t a replacement for real programming knowledge—it’s a powerful tool if you still know how to code.
Watch his full commentary here:
But Beware the Mirror in the Dark
Not all wizards trust the spell.
Critics warn that vibe coding, done naively, skips critical steps like code review, testing, and understanding. Simon Willison draws a line: “If an LLM wrote the code, and you reviewed and understood it, that’s software development. If not—that’s vibe coding.”
An early adopter who built a vibe-coded SaaS app learned the hard way: security researchers found exploitable bugs within days.
The tension is clear. Accessibility vs. accountability. Speed vs. stability. Automation vs. authorship.
So Where Do Robots Come In?
Here’s where it gets deliciously DROIDs.
The very same AI workflows powering vibe coding are what humanoid robots increasingly rely on. When Optimus or Figure 01 responds to verbal instructions, it’s using vibe logic—language translated into structured commands, abstract goals into executable routines.
In a sense, vibe coding is the interface layer for robot programming—natural language in, behavior out.
Karpathy's revolution isn't just about software. It's about how we command the machines we build.
The Road Ahead
Vibe coding won’t replace traditional software engineering anytime soon. But it’s already reshaping expectations.
Students will enter college with a new literacy: conversational coding. Startups will build faster. Enterprises will prototype with whispers, not wireframes.
But with power comes risk. The future demands not just faster builders—but wiser stewards.
The machines are listening. What we say—and how well we understand what’s built from it—will define this new age.
🧙 Vocabulary Scroll
Vibe Coding: Programming via conversational prompts, relying on AI to write and debug code with minimal human understanding.
LLM: Large Language Model, an AI trained to generate text (and code) from natural language input.
Autopilot: Tesla’s driver-assist system powered by Karpathy’s computer vision architecture.
Rapid Prototyping: Quickly creating working versions of a product to test ideas and features.
Software for One: Highly personalized software solutions created with minimal overhead.
FAQs
Who is Andrej Karpathy? A leading AI researcher and former Tesla AI director, co-founder of OpenAI, and now a public intellectual advancing conversational AI development.
What’s the core idea behind vibe coding? Programming through natural language interactions with AI—less code, more conversation.
Is vibe coding just no-code with a new name? Not quite. No-code uses visual tools; vibe coding is about linguistic interfaces and dynamic AI generation.
How does this relate to robotics? Humanoid robots increasingly interpret natural language as task instructions—essentially doing vibe coding on the fly.
What’s the downside? Poorly understood or unreviewed code can introduce serious bugs or security flaws. It's not a shortcut to skip responsibility.
Additional Resources for Inquisitive Humans:
Listen to Andrej Karpathy in his own words talk about language models:
#VibeCoding #AndrejKarpathy #AIProgramming
#CodingWithAI #LLMDevelopment #RobotInterfaces
#ChatGPT #SoftwareForOne #PromptEngineering
#TechEthics #FutureOfCoding #DROIDsScroll