I built a production-ready Next.js app using Claude Code (Here's what I learned)

ddjuly — Fri, 27 Mar 2026 02:32:43 +0000

title: I built a production-ready Next.js app using Claude Code (Here's what I learned)
published: true
tags: showdev, nextjs, tailwind, ai

Hello DEV Community! 👋

We all hear about AI coding assistants, but I wanted to put them to a real test. Recently, I built and shipped my new project, AISeedance2, and I leaned heavily into using Claude Code to accelerate the process.

I want to share what the experience was like, what the stack looks like, and what the final product actually does.

🚀 What is AISeedance2?

Before diving into the code, here is a quick overview of what I built. AISeedance2 is a platform that helps users generate amazing video content using AI

The goal was to create something fast, responsive, and incredibly user-friendly.

🛠️ The Tech Stack: Speed and Simplicity

I wanted a stack that allowed me to move fast without sacrificing performance or SEO.

Framework: Next.js It’s my go-to for building React applications. The file-based routing and server-side rendering capabilities out of the box are unmatched for getting a project off the ground quickly.
Styling: Tailwind CSS I used Tailwind to style the entire application. Being able to build complex UI components without constantly switching context between CSS and JS files saved me hours.
The Secret Weapon: Claude Code This was the game-changer for this project.

🤖 The Claude Code Experience

Using Claude Code wasn't just about auto-completing lines; it was about having a pair-programmer who understands the context of the entire Next.js structure.

Here is how it actually helped me:

Scaffolding Tailwind Components: I could describe the UI I wanted (e.g., "A responsive hero section with a gradient background and a CTA button"), and Claude Code would generate the exact JSX with the correct Tailwind utility classes.
Handling Next.js Nuances: When I ran into tricky hydration errors or needed to optimize server components versus client components, Claude was surprisingly good at suggesting the right architecture.
Refactoring: I tend to write messy draft code just to get things working. I used Claude to clean up my components and make them more reusable.

The Catch? It's not magic. I still had to orchestrate the pieces, manage the overall state, and verify the logic. But it easily cut my development time in half.

👀 See the Result

If you want to see what a project built with this stack looks and feels like in production, check out the live site here:
👉 https://aiseedance2.net

I’d love to get feedback from this community. What do you think of the UI? Does it feel snappy? Have you used Claude Code for your own Next.js projects?

Let me know your thoughts in the comments! 👇

How I built an AI SaaS with Next.js, FastAPI, and Dokploy

ddjuly — Sat, 21 Mar 2026 03:53:22 +0000

Hey folks! 👋
As a solo developer building for the global market, balancing performance, shipping speed, and server costs is always a tricky triangle. Recently, I launched my new AI project, wan2-7.io, and I wanted to share the exact tech stack and architecture I used to bring it to life without breaking the bank.

If you are looking to build a full-stack AI SaaS, I hope this breakdown saves you some time and headache. Let's dive in! 🚀

🛠️ The Tech Stack
I needed a stack that allowed me to move fast on the frontend, handle heavy AI inference tasks smoothly on the backend, and keep hosting costs extremely low.

Frontend: Next.js (App Router) + Tailwind CSS

Backend: Python + FastAPI

Infrastructure: Hetzner Cloud + Dokploy (Self-hosted PaaS)

Database: PostgreSQL

The Frontend: Next.js + Tailwind CSS For the user interface, Next.js is my go-to. Since wan2-7.io relies heavily on visual outputs and smooth user interactions, I needed something that handles state well while still being great for SEO.

Tailwind CSS made it incredibly fast to style the components. One of the biggest challenges in AI apps is the waiting state—when the user hits "Generate", you need to keep them engaged. I used some clean Tailwind animations to build the loading skeletons.

JavaScript

// [Optional: Insert a small, cool React component snippet here, 
// e.g., your loading state or how you handle the fetch request to your API]

The Backend: FastAPI When it comes to wrapping AI models, Python is king. I chose FastAPI because it is incredibly fast, easy to write, and natively supports asynchronous programming—which is mandatory when you are waiting for AI generation tasks to complete.

Instead of blocking the main thread, FastAPI handles the incoming requests, queues the tasks, and allows the Next.js frontend to poll for the result (or listen via Webhooks).

The Secret Weapon for Solo Devs: Dokploy + Hetzner This is where I saved the most money and time. Instead of paying premium prices for Vercel, Heroku, or Render, I decided to self-host.

I spun up a cheap, high-performance VPS on Hetzner and installed Dokploy (an amazing open-source alternative to Vercel/Heroku). Dokploy connects directly to my GitHub repository. Now, whenever I push code, Dokploy automatically builds the Docker images and deploys my Next.js and FastAPI apps with zero downtime.

It gives me the "Vercel-like" developer experience but at a fraction of the cost, giving my indie hacking budget a lot more breathing room.

💡 The Biggest Lesson Learned
The hardest part wasn't the UI—it was handling the async nature of AI generation.

Don't rely on long-polling if you can avoid it. * Always implement proper error handling on the frontend for when (not if) the AI provider times out.

Wrapping Up
Building wan2-7.io has been an awesome journey in optimizing both code and infrastructure. If you're an indie developer, I highly recommend giving the Dokploy + Hetzner combo a try for your next side project.

If you want to see how snappy this architecture feels in production, feel free to play around with the live app at wan2-7.io.