When we talk about software architecture, it’s easy to get lost in diagrams full of boxes, arrows, and buzzwords. But for most startup projects, the foundation doesn’t need to be complicated.

At its simplest, you need three layers. The front end, where users interact with your product. The back end, which includes authentication and the business logic that powers the experience. And the database, where the information lives. That’s enough to get started and build something meaningful.

And here’s the exciting part: today we can vibe code across all of these layers. Tools like Bolt.new make it quick to spin up front ends. ChatGPT or GitHub Copilot can help shape the back end and authentication logic. They can even guide you through setting up and querying your database. Beyond those, there’s a growing world of AI tools that can fit in wherever you need them.

You don’t need queues, event buses, or complex logging right away. Keep it simple, focus on delivering value, and let these tools accelerate your progress. The sophistication can always come later—when your product, team, and users are ready for it.

When we talk about being successful with AI-assisted coding, I think it’s easy to focus too much on the “magic” of the AI and forget the fundamentals. But success doesn’t come from just pressing generate. It comes from the same balance we’ve always needed in software: good ideas, the right technologies, and solid practices.

The first part is the idea itself. It’s tempting to throw every half-baked thought at an AI and hope it will shape it into a masterpiece. But that usually leads to messy, disconnected code. A better approach is to move step by step, layering features carefully so that modules fit together naturally. Just like in traditional coding, structure and pacing matter.
The second part is the technology. AI can write React code, for example, but it won’t decide for you whether state should sit inside a component, live in a shared context, or be managed through a dedicated state manager. Understanding those building blocks is what keeps a project performant, secure, and scalable. Without that knowledge, it’s very easy to end up with something fragile.

And then there are best practices. File sizes grow too large if you never pause to split them. APIs can quickly become unmaintainable if you don’t validate authentication properly. Test coverage, error handling, naming conventions — these are the details that separate “something that runs” from “something that lasts.”

There are other pieces too: version control discipline, clear documentation, and thinking about deployment early rather than as an afterthought. All of these add up. AI coding helps us move faster, but it doesn’t replace the craft, it amplifies it. And when we respect both the creativity of the idea and the discipline of the practice, that’s when we start to see real success.