Can AI Produce Authentic Python, Django and React.js Code?

The Age of “Vibe Coding” Is Here. But Here’s What Most Business Owners Don’t Understand About Building Apps with AI.

I’ve been building websites and business tools for over 25 years. Flash animations, responsive design, jQuery to React.js - I’ve watched every trend come and go. But what’s happening with AI coding tools right now? This is genuinely different.

I didn’t have a computer science degree. I didn’t spend years learning syntax. I had clear instructions and a solid understanding of what a modern business website needs.

I almost failed spectacularly. Nearly burnt my codebase to the ground. This experience taught me the same lessons I share in when the algorithm comes for your business - that AI doesn’t replace expertise, it amplifies it.

I succeeded through perseverance. Here are the lessons I learnt along the way.

The “Vibe Coding” Revolution

It’s like having a developer who never sleeps, never gets frustrated, and costs a fraction of what traditional development costs.

But here’s the kicker - it only works if you know how to give instructions. You have to be the one who controls the robot. You have to be the conductor, and you also need to know when to wave the magic wand in the right places.

Why Most People Get This Wrong

Here’s what I’ve learned after months of hands-on experimentation with AI coding tools:

The real skill isn’t coding anymore - it’s communication. But I don’t mean casual communication. I mean writing like a project manager, not chatting like you’re ordering a coffee.

But here’s the crucial part that I see people miss all the time - you need to force the AI to read everything before it starts coding.

I create a dedicated “project” in ChatGPT or Claude.ai and I upload all three documents. Then I make the AI confirm it’s read and understood each one before it writes a single line of code. This systematic approach is something I cover in detail in my guide on how to set up AI projects for maximum success.

My project notes then say “Always read planning.md, task.md, and roadmap.md. Tell me what we’re building, what phase we’re in, and what specific task you’re about to work on. Don’t write any code until you’ve confirmed this.”

It sounds excessive, but it prevents hours of wasted work. I’ve learned that the AI needs context, not just commands. When it’s done correctly, you can achieve the kind of acceleration I describe in Claude Code: web development 10x faster.

Here’s another critical rule I learned the hard way: start a fresh chat for every new feature.

After 50+ messages in a single conversation, the AI starts forgetting earlier decisions and contradicting itself. I’d ask it to perform a simple task, and suddenly it’s rewriting my entire database structure.

I keep conversations focused now. One chat = one feature. I reference my project documents each time, but I keep the conversation scope narrow. Your future self will thank you when you’re debugging six months later.

One Tool to Rule Them All

Here’s what makes it special: Claude Code reads my existing codebase, it understands how different files connect, and it makes changes across multiple files simultaneously while keeping everything in sync. When I’m building a Django backend with an Astro frontend, Claude Code is aware that changing the API endpoint also requires updating the frontend calls.

The interface feels like working with a senior developer who never gets tired. I can say “add user authentication to this project” and it will create the backend models, it will build the frontend forms, it will set up the routing, and it will even update my database migrations - all while maintaining my existing code style and architecture.

Most importantly, it doesn’t just dump code and disappear. It explains what it’s doing, it shows me the connections between files, and it helps me understand the decisions it’s making. I’m learning as I build, not just copying and pasting blindly.

The Real Question: Authentic Code?

Can AI produce “authentic” code? I’ll be honest - that’s the wrong question. The right question is: can AI produce code that solves your business problem reliably?

I’ve deployed AI-generated Python applications that process SEO audits, I’ve built Django backends that securely handle client data, and I’ve created React interfaces that convert prospects into customers. In all cases, the code works, it’s maintainable, and I built it in a fraction of the time. My keyword ranking model is a working example - I use Django to track SEO strategy across dozens of keywords.

What This Means for Your Business

The Hidden Cost Nobody Talks About

Here’s the uncomfortable truth - AI coding isn’t free money.

I spent weeks learning to prompt effectively. I rebuilt features multiple times when the AI misunderstood my requirements. I needed to know enough about technology to spot when the AI was heading down the wrong path.

I now spend about 30% of my time on AI tool management and prompt engineering. It’s a new skill set that sits somewhere between business analysis and technical architecture. But the results? My development speed has tripled, my costs have halved, and I’m building better solutions for my clients.

Getting Started (Without the Expensive Mistakes)

Start small. I’d say don’t try building your entire business platform on day one. I started with a simple calculator, a contact form, a basic data display. Get comfortable with the rhythm first.

Write requirements like you’re talking to a junior developer. I mean really specific - inputs, outputs, and edge cases. “Build me a login system” becomes “Build me a login system that requires email and password, validates email format, stores user data securely, and redirects to a dashboard page after successful login.”

Test everything. AI-generated code can have subtle bugs that only appear under specific conditions. I build testing into my process from day one, and I’d recommend you do the same.

Have an exit strategy. I always think about how I’ll maintain and modify the code when (not if) my requirements change.

The Bottom Line

Can AI produce authentic code? Here’s what I know after deploying my AI-built system.

But here’s what really matters - I built all of this enterprise-quality code in hours, not months.

What used to require a team of developers over 12 weeks now happens in a long weekend. The businesses that are embracing this shift are building competitive moats while their competitors are still interviewing developers.

The ones that ignore it? They’ll be paying 10x more for development while moving 10x slower. That’s not sustainable in any market.

Ready to Explore What’s Possible?

I’m currently helping clients implement AI-powered solutions that would have cost tens of thousands to build traditionally. These tools are creating real competitive advantages and measurable ROI. If you’re curious about what’s possible for your specific situation, I’d genuinely like to have that conversation. No sales pitch - just an honest discussion about where AI coding makes sense for your business and where it doesn’t.

Drop me an email at tony.cooper@webuildstores.co.uk

The future isn’t about whether AI can code. It’s about whether you’ll be ready when your competitors start using it.