Vibe coding feels fast and creative — like pair programming with an AI that never sleeps. But if you skip understanding and review, you’re not coding faster — you’re just deferring disaster.

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Vibe Coding: Useful Hack or Engineering Hazard?

There’s been a lot of noise lately about “vibe coding” — a term coined in this post by Andrej Karpathy that’s taken off in dev circles. And depending on who you ask, it’s either a glimpse of the future or a complete joke.

So let’s break it down. What is it, what’s the hype, and what should serious engineers actually do with it?

What the heck is Vibe Coding?

In short: you describe what you want to an AI assistant (like Copilot, ChatGPT, Claude, Cursor, etc.), and you mostly trust the output — without reading every line. You iterate by prompting, not by coding. When errors pop up, you paste the stack trace back in and ask the AI to fix it.

No tests. No design doc. No diff review. Just “see stuff, say stuff, run stuff.”

Sounds wild? It is.

Karpathy explicitly said it worked “okay for weekend projects.” But then the dev community did what it always does: turned a throwaway tweet into a methodology. Suddenly “vibe coding” is being framed as the next big thing.

Why are devs excited?

Because it’s fast. Stupid fast.

You can build prototypes in a fraction of the time. You don’t get bogged down writing boilerplate, hunting through docs, or wiring up glue code. It feels like a creative flow — like rapid prototyping with an infinitely patient junior dev.

Even better, it lowers the barrier to entry. Non-coders can finally build stuff by just describing what they want. That’s powerful.

There are already stories of indie devs shipping MVPs, utilities, and even commercial products with AI-heavy workflows. Some have made real money. Some have just had fun. Either way, it’s clear this isn’t just hype — people are shipping.

But here’s the problem

Vibe coding skips all the boring-but-essential parts of software engineering.

No design thinking
No code review
No tests
No threat modeling
No long-term maintainability

It’s not a workflow, it’s a vibe. And vibes don’t scale.

The moment your AI-generated code goes into production — with users, uptime requirements, data security, regulatory concerns — all the fun stops and the consequences start.

You end up owning a codebase you didn’t write, can’t fully understand, and probably can’t extend safely.

That’s not engineering. That’s luck-based development.

Let’s be real

Most serious engineers aren’t afraid of AI — we’re excited about the productivity boost. But we’ve seen this movie before.

This is Rapid Application Development 2.0. This is visual builders, low-code, “10x dev” myths — all over again.

Vibe coding is great for:

MVPs
weekend hacks
exploratory work
internal tools (that can die without impact)

But it’s not how you build production-grade systems. Not without real engineering layered on top.

Production ≠ Vibes

You still need:

System design
Code comprehension
Testing
Monitoring
Architecture reviews
Security audits
Documentation
Version control discipline

AI doesn’t remove that. It just means you can get to the messy part faster.

And when the AI writes a thousand lines in one go? Guess who’s responsible for it when things go sideways? You.

If you don’t understand the code, you can’t fix the code.

So how should teams use this?

Simple rule of thumb:

Vibe for exploration, engineer for production.

Use AI like you’d use a junior dev:

Let it suggest ideas.
Let it scaffold.
Let it handle boilerplate.

But never trust it blindly.

You still need to read the code, write the tests, and own the result.

Inside teams, maybe allow “vibing” during spike work or PoCs — but enforce rigorous review, testing, and design checks before anything merges. If you’re a team lead, this is your job now.

How I Actually Use AI in My Development Workflow

Let’s cut through the noise: I don’t “vibe code.” I’m not asking an AI to build entire apps or fly blind with unreviewed outputs. But I do use AI coding tools — deliberately, surgically, and where they actually make sense in a real production workflow.

My IDEs: Visual Studio, Rider and DataGrip

I spend most of my time in JetBrains Rider and Visual Studio. That means tools like Cursor, Windsurf, or other newer-gen LLM-integrated editors aren’t really part of my setup — they don’t plug in cleanly, and I’m not about to rewire my dev environment for hype. These tools might become viable in the future, but right now? They’re not there yet for serious .NET work.

Copilot: Micro-accelerator, Not Auto-pilot

I do use GitHub Copilot, and honestly — it’s solid within its lane.

Copilot is helpful for:

Suggesting function scaffolds while I’m typing
Filling in boilerplate logic (e.g., switch statements, simple mappings)
Completing common patterns I’ve written dozens of times

What I like about Copilot is that it doesn’t hijack my workflow. I’m in the zone, I see a suggestion, and I either accept it or modify it — while staying in control. The code appears inline, so I’m forced to review it as part of my flow. That makes it less dangerous than tools that write entire files or folders out of your sight.

ChatGPT & Claude: My External Dev Brain for Focused Tasks

When I run into a specific, contained problem, I’ll bounce it off ChatGPT or Claude. Not because I don’t know what I’m doing — but because I want to try something quickly, get a second angle, or shortcut boilerplate-heavy code.

Here’s what that looks like in real scenarios:

Excel exports with NPOI: I’ll paste in a sample JSON and ask the AI to generate the method that maps it to an Excel spreadsheet, with formatting, column widths, and headers. The AI gives me something ~80% there, and I tweak the last 20%.
Document Parsing: I’ll provide a PDF file and sample JSON to AI and ask it to generate a C# fragment to first extract the text and then extract data using regular expressions.
Bitwise conversions: Generate a method to convert a byte[] into a long using big-endian representation — with bounds checking and inline comments. The resulting snippet gave me a solid starting point, which I validated and integrated.
Text transformations: Need a C# method to sanitize strings or normalize inputs with edge-case rules? Let the LLM propose a baseline implementation, then modify as needed.
Regex patterns: When dealing with complex constraints (e.g., a regex for a very specific alphanumeric format with optional symbols), I let the LLM draft candidates — then I test, refine, and document them.
Enum-to-dictionary mapping: I gave ChatGPT a list of enums and asked for a C# Dictionary<MyEnum, string> with user-friendly display names. Nothing groundbreaking, but it saved me typing and got the structure right.

These are all scoped, deterministic problems. I know what the result should look like — I’m just getting a faster path to it.

The Principle I Stick To

I don’t use AI to tell me what to build — I use it to accelerate the parts I already understand.

If I don’t know how something should work, I won’t trust the AI to figure it out. If I do know how it should work, but it’s annoying to write — that’s where the AI earns its keep.

This is also why I’m not worried about AI “replacing developers.” Because when you’re building real systems — with architecture, business logic, side effects, error handling, security implications — you still need judgment, tradeoff analysis, and ownership. No AI is doing that for you. Not yet, and not anytime soon.

Final Take

AI coding tools are here to stay. Vibe coding isn’t going to replace developers — but it will change how we work.

Our job as engineers? Adapt without lowering the bar.

AI’s for momentum, not a mental vacation.

If you’re building for real users, vibe coding is not a shortcut — it’s a liability unless paired with strong engineering discipline.

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TL;DR: Vibe all you want in the sandbox. But when it’s time to ship? Show up like an engineer.

This post is my own summary and take on the concept of vibe coding — inspired by Addy Osmani’s original deep-dive on the topic. Highly recommend reading it in full if you want a comprehensive view of the conversation, success stories, pitfalls, and long-term implications around AI-assisted development.

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