What is Vibe Coding?
Vibe coding: letting AI write code while you “Feel the flow”.
Until recently, entrepreneurs had fun creating prototypes in Figma, but now, this is moving to a new level – coding a prototype using LLMs. “Vibe coding” is a new term for an AI-assisted approach to programming where people describe what they want in natural language and let AI generate most of the code. Instead of carefully writing and reviewing each line, a vibe coder essentially “fully gives in to the vibes” of an AI assistant. The term was coined in early 2025 by AI researcher Andrej Karpathy, who demonstrated this style by literally talking to an AI coding tool and accepting its output with almost no manual edits. In his words, “It’s not really coding – I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works.” In essence, vibe coding treats English (or any other human language) as the new programming language, using conversational prompts to build software.
How Vibe Coding Works
At its core, vibe coding means programming by prompt – you tell the AI assistant what you want in plain language, and the AI writes the code for you. This process is powered by advanced large language models like ChatGPT/Claude/Cursor, or any other that are trained to generate code. Key principles of vibe coding include:
Natural Language Prompts: You describe features or changes in everyday language (e.g. “make the background blue” or “sort this list of names alphabetically”).
AI-Generated Code: LLM interprets your request and produces code snippets or file changes implementing that request. You typically don’t write the code yourself – the AI does the heavy lifting.
Iterative Refinement: Development becomes a back-and-forth dialogue. You run the code, see what happens, and if there’s an error or adjustment needed, you just tell the AI. It might even involve copying an error message and pasting it into the prompt so the AI can fix it.
“Accept All” Mentality: Vibe coders often accept AI suggestions wholesale. You click “Accept All” on code changes without even reading the diffs (the code differences). The focus is on quickly moving forward rather than scrutinizing each line.
Minimal Manual Coding: In a pure vibe coding session, the human writes very little actual code. The role is more like a director or editor – guiding the AI with prompts and verifying the results – rather than a traditional coder typing out logic.
Example: Vibe Coding in Action
To illustrate, Karpathy shared how he built a small web app almost entirely by “vibe.” He would ask the AI for even trivial tweaks, saying things like “decrease the padding on the sidebar by half,” instead of hunting through CSS himself. The AI would generate the code change, and he’d blindly accept it. If the app threw an error, he’d simply paste the error message back to the AI and let it figure out the fix. By repeating this loop – “see stuff, say stuff, run stuff” – he got a working app without manually coding the details. This free-flow, trial-and-error process is exactly what vibe coding is about: surrendering fine-grained control and riding the “flow” of AI suggestions.
Why People Are Buzzing About It
Vibe coding has quickly caught the attention of the software community, and it’s more than just a gimmicky phrase – it reflects real trends. Here are some reasons people are excited:
Faster Prototyping: Many find vibe coding fun and fast for building prototypes or side projects. By offloading grunt work to AI, you can whip up a demo or test an idea in hours rather than days. “I really enjoy vibe coding – it’s a fun way to play with the limits of these models”. The AI handles the boilerplate, letting you stay in a creative flow state instead of getting bogged down by syntax.
Lower Barrier to Entry: Advocates claim this approach “democratizes” programming. If you can describe an idea in words, you can potentially build a simple app – even if you aren’t a coding expert . The New York Times famously called this kind of natural-language programming “sorcery” for non-programmers, because it lets people conjure working software without deep coding knowledge.
“English is the New Code”: Instead of learning the intricacies of a programming language, coders can now “speak” their intentions. With modern AI tools, “the hottest new programming language” is plain English. This makes the development process feel more intuitive. You focus on what you want the software to do, and worry less about the how – the AI figures out the implementation details.
AI Pair-Programming on Steroids: Tools like ChatGPT, GitHub Copilot, Claude, and Cursor are evolving rapidly and enabling vibe coding scenarios. Developers have been using AI assistants as coding partners for a while, but vibe coding pushes it to the extreme – the AI isn’t just suggesting the next line, it’s handling whole functions or features at your request. This accelerates development cycles and can unlock creative solutions that a human might not think of immediately.
Happy? Be Cautious! 🚩
For all its excitement, vibe coding also raises serious concerns in the dev community. Seasoned engineers warn that blindly relying on AI to handle all your coding “vibes” can backfire, especially for production-quality software. Here are a few challenges to keep in mind:
Code Quality & Maintainability: AI-generated code can be messy under the hood. It might work initially, but it’s often bloated, inefficient, or even insecure. If you never looked at what the AI wrote, you might end up with tangled code that’s hard to understand or improve later. A significant part of software engineering work is evolving existing systems, where the quality and understandability of the code are crucial. Relying purely on vibe coding could leave you with a codebase no one fully grasps. Even AI enthusiasts admit “the code can grow beyond my usual comprehension when vibe coding at length.”
Security: Another concern is that AI-generated code might inadvertently introduce security vulnerabilities. Blindly using AI to generate a software codebase poses significant security risks because AI models may produce insecure code with hidden vulnerabilities. These issues can include weak authentication mechanisms, improper input validation, hardcoded secrets, or insecure API calls. AI-generated code may also lack context-specific security best practices, making it susceptible to attacks like SQL injection, cross-site scripting, and privilege escalation. Without professional oversight, these vulnerabilities can go unnoticed, potentially exposing applications to exploitation.
UI/UX Design Struggles: Using AI to generate a codebase makes it almost impossible to fully meet UI/UX design expectations because AI lacks deep contextual understanding and creative intuition. While it can generate functional components, it often misinterprets complex design details and struggles with brand-specific aesthetics. LLM-generated code produces rigid layouts that can misalign with your design expectations or user experience principles. Custom interactions, animations, and accessibility considerations may also be overlooked, requiring significant manual adjustments. Achieving a polished, user-friendly interface still depends on human expertise in refining and optimizing the design.
Vibe Debugging: When the AI is driving, who fixes the bugs? Vibe coding often involves a loop of the AI fixing its own mistakes via prompts, but it can hit a wall. Sometimes the AI cannot fix bugs, leaving you to make random changes until the problem resolves itself (or not). That trial-and-error approach might be okay for a toy project, but it’s nerve-wracking in a serious application. If you didn’t write the code, diagnosing a complex issue or edge-case failure can be enormously frustrating . In short, Vibe coding is fun, but Vibe debugging is definitely not.
Lack of Accountability: Will you trust payment integration to AI? Can you be sure that it will not place a fun rand() method to charge your customers more or less? Can you be sure that the money charged goes to your bank account and not somewhere else? An underlying principle of good software engineering is understanding and owning your code. Vibe coding flips this on its head – the AI writes most of it, and the developer may only have a vague idea of what’s under the hood. Critics argue this can make developers complacent. If an AI wrote every line but you have reviewed, tested, and understood it, then it’s just a smart assistant – that’s not vibe coding. Vibe coding, by definition, implies you didn’t fully review or understand everything the AI did. That’s a risky stance if the code is destined for real users. As one Y Combinator partner put it, strong coding fundamentals are still necessary to avoid building on a shaky foundation. Otherwise you risk “one bad deployment away from disaster”.
In summary, vibe coding is best approached with eyes open. You should treat it as a tool for rapid prototyping or learning, rather than a one-click path to production software. Vibe-coded projects can also be called “throwaway weekend projects”. You can play, learn, and have fun. But don’t expect it to be a production-ready product.
Conclusion
A new “vibe” is here. Vibe coding is an eye-catching name for a real trend: humans collaborating with AI at a very high level, effectively coding by conversation. It flips the traditional script – instead of the human writing and the computer obeying, the human suggests and the computer creates.
That said, vibe coding is not a magic wand. It comes with trade-offs in code quality, reliability, and comprehension. The software community’s consensus so far: use those AI “vibes” wisely. Embrace vibe coding for what it’s great at – brainstorming, prototyping, exploring creative ideas – but don’t throw away your coding fundamentals. Think of vibe coding as a powerful assistive tool: it can handle the heavy lifting and help you “flow”, but a professional software engineer will eventually need to curate and refine the output. If you need any help with your software product, you can contact the molfar.io team for professional help.