Vibe Coding: My Experience Building Faster With LLMs

Over the last few months, I’ve been experimenting with what Andrej Karpathy recently called “vibe coding.” At first, I laughed at the phrase. But as someone who spends much of my time building AI-powered applications and automation systems, I’ve come to realize it describes exactly how I’ve been working.

Vibe coding isn’t traditional software engineering. It’s not about meticulously designing system architecture, writing every function by hand, or spending days debugging a single line of code. Instead, it’s about giving in to the flow — letting large language models (LLMs) like Google Jules, OpenAI Codex, and others do most of the heavy lifting, while I steer with prompts, intuition, and product vision.

It’s a shift from “I am coding” to “I am orchestrating.” And it works surprisingly well.

What is Vibe Coding?

In classic software development, coding is about syntax, logic, and structure. You define the architecture, write the code, test it, and push it into production. Every decision is deliberate.

Vibe coding is different. Instead of treating the LLM as a helper, you let it drive. You simply say:

• “Reduce the padding on this sidebar by half.”

• “Make this API call asynchronous.”

• “Convert this snippet into TypeScript.”

And you “Accept All” without scrutinizing every diff. If something breaks, you paste the error back into the model and let it figure things out. If it doesn’t, you nudge it again until it magically works.

The result is that code evolves organically — it grows in directions you may not have anticipated, but still gets the job done.

My Experience with Jules

Google’s Jules has been one of the most interesting vibe-coding tools I’ve tried.

Here’s what stood out for me:

• Self-contained environment: Jules spins up its own environment, compiles, tests, and executes code in real time. This takes away the friction of switching between editor, terminal, and deployment pipeline.

• Bug fixing ability: Paste an error into Jules, and nine out of ten times it suggests a working fix. It feels like pair programming with someone who doesn’t get tired.

• Breaking down complexity: Jules is excellent at taking large, complex tasks and splitting them into smaller subtasks. For example, when I asked it to implement a multi-step API integration, it first created stubs, then fleshed out logic, and finally ran validations.

• GitHub integration: Because it works directly with GitHub, it can commit changes and push to repositories, making the whole flow much smoother.

But it’s not without quirks. Jules often “gives up” too quickly. When it reaches a tough edge case, it flatly admits: “I’m at a stalemate, I don’t know how to proceed further.” That honesty is refreshing, but it also means human guidance is essential.

My Experience with Codex

I’ve also been impressed with how OpenAI Codex has evolved.

• VS Code integration: Having Codex directly in VS Code is a game-changer. No more copy-paste between editor and playground. You can write, prompt, and review code inline.

• Works with or without GitHub: Unlike earlier versions that leaned heavily on repository structures, Codex now works just as well in standalone files. That makes it more flexible for quick prototyping.

• Improved reliability: While still not perfect, Codex has become much better at completing larger blocks of code without introducing weird logic errors.

Codex feels more like a productivity accelerator — not quite as “full-stack autonomous” as Jules, but very dependable inside a developer workflow.

Why Developers Should Not Fear This Shift

It’s natural for developers to feel uneasy about tools that can generate code so quickly. But here’s why I believe developers should embrace this change instead of fearing it:

• Change is inevitable in tech: Just as IDEs, Git, and frameworks once felt like disruptions, LLM-powered coding is simply the next evolution.

• It doesn’t replace developers: The need for human oversight, architecture, and vision is stronger than ever. LLMs can write code, but they can’t understand user needs or product strategy.

• Focus on higher-order work: By letting LLMs handle repetitive tasks, developers free themselves to focus on design, optimization, and innovation.

• Career growth, not decline: Developers who learn to orchestrate AI tools will be the ones who build faster, deliver smarter, and lead the next wave of software innovation.

Change is good — and in this case, it’s a chance for developers to spend less time on boilerplate and more time on impact.

The Human + LLM Handshake

Here’s the truth I’ve learned after weeks of vibe coding:

No LLM today can build a complete application end-to-end without human input. Models are excellent at filling gaps, fixing bugs, and accelerating the pace of development — but they lack the vision.

The role of the human — whether as a product manager, design engineer, or system architect — is to imagine the flow of the product, understand user experience, and set priorities.

The role of the LLM is to articulate that vision into working code.

This handshake between human imagination and machine execution is where the magic happens. With it, great products can be built in much shorter timeframes than ever before.

A Real Example: Building a Custom ChatGPT in 5 Days

To test how far vibe coding can go, I recently took on a project to build a custom ChatGPT application for a client.

• Day 1–4: Using vibe coding, I had Jules and Codex alternately writing the bulk of the application. Whenever I hit friction, I’d prompt them differently or ask for “random changes” until the issue disappeared.

• Day 5: Spent a full day bug-fixing, testing, and tightening up the flow.

By the end of the fifth day, the application was ready, tested, and shipped to the client.

This would have taken weeks under traditional development cycles. Vibe coding compressed it into less than a week.

Where This is Going

I believe vibe coding is more than a fad. It points to a new way of building software:

• Faster prototyping: Startups and teams can go from idea → working demo in days, not months.

• Accessible coding: Non-engineers can participate more actively, giving prompts instead of writing functions.

• Shift in skills: The most valuable skills will be product vision, orchestration, and the ability to guide AI tools — not necessarily low-level syntax.

At Versalence AI, we see this trend unfolding across our work in AI Automation, Conversational AI, and Agentic AI. Clients don’t just want apps built — they want systems that evolve quickly and deliver ROI fast. Vibe coding is becoming one of the hidden accelerators that helps us ship better, faster, and smarter.

Final Thoughts

Vibe coding doesn’t replace software engineering. It complements it. It doesn’t replace product managers or designers. Instead, it makes them even more critical — because someone needs to hold the vision while the AI churns out the code.

The future of building won’t be “AI vs humans.” It will be humans + AI, working in harmony.

And sometimes, that harmony feels a lot like just following the vibes.