DEV Community: Roma Armsrtrong

The Best UI/UX of 2026? Why It’s Time for a New Interface

Roma Armsrtrong — Sun, 16 Nov 2025 09:16:03 +0000

Why AI chat won’t kill websites, but reinvent them.

I’m a practicing developer and architect who has spent the last few years living at the intersection of modern web frameworks, SEO, and AI tooling. Every day, it becomes harder to pretend that the way we design interfaces can stay the same while user behavior, search, and AI are shifting under our feet. This piece is about a new kind of interface — not just another set of trendy components, but a different model of how humans interact with web applications.

It’s about what happens at the crossroads of AI chat and traditional websites — and what that means for designers, developers, and businesses building products for the next 5–10 years.

How we learned to use the web

For the last twenty years, the web has been surprisingly predictable. There is a page. On that page, there is a header, a footer, navigation, a couple of links to neighboring pages, sometimes a search box. Somewhere deeper live filters, categories, tags, and endless pagination. The mental model is simple: the web is a library, and every site is a small private collection with its own catalog and shelves.

We learned that to reach the right “shelf”, you first have to understand how the librarian thinks. On the web, that librarian is the information architecture. You don’t just look for “something about auth”; you learn that in this product, docs live in “Documentation → API → Authentication”, while guides live somewhere else. After a few clicks and a few minutes of scrolling, you start to feel that you are “familiar” with the product.

Search engines like Google and Bing amplified this model instead of replacing it. They became a global catalog on top of all those libraries. But the outcome of every search was still the same: a list of pages. We got used to googling, opening 5–10 tabs, and manually stitching together an answer from fragments scattered across different sites. It felt normal, even inevitable — that’s just how the web works, right?

The AI chat explosion: what changed in our heads

Then large‑scale access to AI chat apps arrived. At first, they looked like toys: fun to poke at, capable of jokes, sometimes hallucinating confidently wrong things. But very quickly, something subtle but important changed — not in technology, but in how people think about asking questions.

People stopped compressing their thoughts into “2–3 keywords”. Instead of typing “buy sneakers nyc”, they started writing: “I need comfortable sneakers for everyday walking, not for running, budget under \$100, okay with either NYC pickup or fast shipping.” In a traditional search engine, this kind of query feels strange. In a chat, it feels natural. And the dangerous part for the “old web” is that in this moment, the user no longer cares where the answer comes from.

The cognitive model is shifting. Before, the user had to think: “How do I phrase this so the search engine understands and gives me half‑relevant links?” Now the question is: “How do I explain this the way I would to a human?” That’s the difference between “adapting to the machine” and “speaking like a person”. Chat removes a layer of technical discipline: users don’t need to remember exact page names, the right product term, or the structure of your docs. They just need to describe their situation — and if the answer is good enough, they may never visit your site at all.

If AI is so smart, why do we still need websites?

If you push this line of thought to the extreme, you get a radical question: if AI can answer most questions, why do we need websites at all? Maybe everything moves into one universal chat window, and pages, navigation bars, and landing layouts become museum artifacts of early web design.

Technically, the answer can be almost “yes”. It is possible to imagine a world where nearly everything happens inside a chat interface: from finding products and checking out, to signing contracts and managing subscriptions. In many domains, we are already halfway there: internal support bots, scripted customer service, voice assistants that pretend to be humans on the phone.

But on the level of human experience and business, the picture looks very different. A website is not just functionality. It is also a stage, with lights and sound and scenery. It is a space where a brand gets to talk in its own language — through color, composition, animation, visual metaphor. A chat is a meeting room. It’s great for clarifying, negotiating, asking quick questions. It is terrible at building atmosphere and identity. In chat, every brand looks almost the same: text bubbles, maybe an avatar, a slightly different tone of voice.

For businesses, that is not just an aesthetic tragedy. It is a risk to trust, differentiation, and long‑term relationships. Visual language is a way to show that there is a real product, a real team, and a real story behind the interface. If everything collapses into a gray chat panel, all you have left is a disembodied “voice” — and it is much easier for that voice to pretend to be someone it is not.

So no, pure chat will not “kill” websites. It might absorb a huge chunk of tasks that previously required navigating through pages. But it will not replace everything, because people still like to “see” a product, not just “talk” to it.

Why the old page‑based web breaks in an AI world

That said, the old “everything is a page” approach also fails to survive contact with reality in 2025. Think of a mature SaaS product: years of development, dozens of sections, hundreds of doc pages, blog posts, landing pages, onboarding guides. Each piece of content made sense when it was created: “let’s put this in a separate page so users don’t feel overwhelmed”.

But from the user’s perspective, complexity accumulates. They don’t know which page holds the answer. They don’t know which of the ten similar articles is the most up to date. They don’t know how to connect pieces scattered across your blog, docs, and changelog. They are forced to do manual “integration testing” of your content, clicking through screens and mentally merging partial answers into something usable.

AI, in this context, acts as a synthesizer. It can pull meaning from several pages and turn them into a fresh, coherent answer. Classic web UX cannot do this by design; it was built around “show this page”, not “assemble this answer”. But AI chat has a weakness too: it rarely shows the full path. It gives you the conclusion, yet rarely gives you the form — the structure, the context, the place where this lives in the system.

If you extend the theater metaphor: a traditional website is the stage where you watch the whole play. An AI chat is the critic who retells the story in their own words. Sometimes that is exactly what you want; sometimes it is not. Either way, it is a different plane of experience. That tension creates a need for a hybrid interface: something that can both show and answer.

The new interface: parallel experience streams

This brings us to the key idea. The new interface is not “a website with a chat widget in the corner”, nor “a chat that occasionally opens webviews in a browser tab”. The new interface is a consciously designed system of several parallel experience streams that live together on one screen.

One stream is conversational. This is the AI you can talk to, that understands tasks, not just URLs. It can propose paths, ask clarifying questions, warn you before you step into a dead end. Another stream is visual and structural: pages, dashboards, tables, maps, forms — everything that requires focus, hierarchy, accessibility, and brand expression. A third stream is business logic and data: roles, permissions, constraints, workflows, the actual state of the system.

The important shift is that these streams no longer run “one after another” — first chat, then UI, then back to chat. They can and should run at the same time. The user talks to AI and simultaneously watches the interface evolve. The interface suggests something, and the user clarifies in chat what they really meant. Dialogue and visual layer stop competing for attention and start playing on the same team. Technically, this pulls us toward slot‑based layouts and parallel routes: the interface is split into independent regions, each with its own lifecycle, all coordinated by a shared scenario.

GitHub
Templae

Why slots and parallel routes made sense

At some point, this stopped being an abstract design discussion and turned into a concrete architectural problem in one of my own projects. The requirements looked like this:

Keep a product‑aware AI chat on the left, with access to internal docs and external knowledge via vector search.
Show pages on the right — from static marketing content to complex authenticated UIs.
Make sure any error on the right never kills the chat or resets the conversation.
Preserve SEO: public content should still be delivered as static HTML, not as a JS‑dependent shell.
Avoid a mess of iframes and fragile microfrontends that are painful to test and maintain.[

On the architecture level, this turned into an equation with several unknowns: independence, resilience, SEO, and developer experience. In that equation, slot‑based layout (independent “windows” or slots on the screen) and parallel routing (routes that can update independently) turned out to be a natural answer. Instead of thinking in “pages”, it became more useful to think in “flows”: the left slot is the conversation flow (chat, auth, assistants), the right static slot is public content that works even with JS disabled, the right dynamic slot is personalized, authenticated functionality.

From that, a new architecture emerged where AI chat and the classic site stopped fighting for control over the screen. They got their own “campus buildings”, connected by a shared campus of navigation, layout, and brand. Practically, this is what sits behind the AIFA starter templates: a Next.js‑based open‑source setup designed to keep AI chat, static SEO pages, and dynamic app surfaces in one coherent experience.

What changes in real products

High‑level ideas are nice, but interfaces live or die in real scenarios. Here’s how this parallel‑streams model reshapes some familiar patterns.

Docs and learning products

Traditional documentation is a forest of sections. Users know the answer is “somewhere in here”, but not where exactly. They skim the table of contents, try to guess by headings, open multiple tabs, and hope the right combination of pages eventually clicks. The more your product grows, the more invisible your best content becomes.

In a new interface, the user starts differently: “How do I rotate an auth token in a multi‑tenant app without breaking existing sessions?” The AI layer knows the shape of your docs. It can assemble a cohesive answer from multiple pages and, if needed, open the relevant section on the right with the exact paragraph highlighted. The user sees both the synthesized answer and the “source of truth” — and can dive deeper without getting lost in the tree of pages.

E‑commerce

Most online stores lean heavily on filters. Filter by brand, size, price, color, material — sometimes all at once in a dense sidebar. Very few users enjoy filling all of these out. They approximate, misclick, and then bounce when results feel slightly off. The interface is optimized for the database, not for the conversation in the buyer’s head.

In a parallel‑stream setup, the user speaks first: “I’m looking for black sneakers without giant logos, for city walking, size 10, under \$100.” The chat understands that this maps to a specific category, applies filters under the hood, maybe clarifies brand preferences, and then fills the visual slot with large, clear product cards. Filters still exist — but now they are tools for refinement, not the main entry point. The user does not have to translate their intent into your filter UI; the AI layer does that translation.

B2B and admin panels

Complex B2B systems are notorious for steep learning curves. They have dozens of screens, each with dozens of fields, and onboarding often sounds like: “Watch these ten videos and read the docs; you’ll get used to it.” Every new customer pays the cognitive tax of understanding how your internal model maps to their real‑world tasks.

With a new interface, the first step can be different. A user might say: “Show me customers whose churn increased over the last three months, but whose average contract value is still high.” The conversational layer turns this into a query over your data, opens the right report on the visual side, and explains in plain language how it interpreted the criteria. You don’t have to automate everything, but even the option to have a dialog over the interface is a qualitatively different level of experience.

GitHub
Templae

What this means for designers

For designers, this new interface is both a challenge and a gift. The challenge is that static screen maps are no longer enough. Now the question is: what does the conversation look like? How do you visually connect a specific chat message to a change on the screen? How do you show that this particular view is “the answer” to a particular question?

The gift is that you can finally stop pretending the interface is just a set of static frames. You can direct the experience like a play: there is a leading voice (the AI), there is a stage (screens and slots), there is light and sound (animations, highlights, contextual markers). You can invent ways to visualize dialogue — without destroying structure and accessibility in the process.

There is also a branding challenge: not letting your product dissolve into the same generic chat bubbles everyone else uses. Your product still needs a personality — including in the way your AI speaks. Tone of voice, microcopy, visual framing around the chat, how the interface reacts to uncertainty or errors — all of that becomes part of UX. In a world where the content layer is increasingly generated, character becomes a key differentiator.

What this means for developers

For developers, the new interface means the job is no longer just “build routes and components”. You have to think in terms of flows and slots. Which parts of the interface should be navigation‑independent? Which slots must survive when others crash? What is rendered statically, what dynamically, and what can be generated on demand by AI?

It also means designing communication between slots. When is the chat allowed to open pages? When can a page trigger a question to the chat? How do you avoid circular dependencies and race conditions while keeping the experience seamless? Dropping a chat widget into every page is no longer enough. You have to architect the experience itself — how users move between dialogue and visual context without noticing the internal technical seams.

On the technology side, this pushes you toward tools that handle slots and parallel routes well, and away from “one giant SPA that crashes all at once”. In practice, that often means leaning into frameworks like Next.js App Router, where you can define independent layouts, parallel segments, intercepting routes, and mixed static/dynamic rendering. Architectures like AIFA build on top of that: chat in one slot, public static content in another, personalized app surfaces in a third — each with its own error boundaries and lifecycle.

What this means for the business

For a business, the new interface is not “a fancy chat bubble on the site”. It is a way to keep control over how AI talks to your users. If you leave everything to external systems, the conversation with your customer happens in somebody else’s shell: the user types into a third‑party AI app, and that app decides which tiny fragment of your content to show or paraphrase. You are just a data source.

If you embed AI into your own architecture, you get several advantages. You keep SEO traffic by serving rich static content in your own layout. You increase conversion because the path is guided by an assistant that understands your specific processes, not generic best practices. And you can build new user journeys faster by teaching the AI new concepts and language, instead of redrawing dozens of screens for every new use case.

Of course, this is not free. A new interface requires investment in architecture, data quality, and conversational design. But in return, your product stops being “one more link in someone else’s search result” and becomes an environment where AI and users talk in the language of your product — on your terms, in your visual space.

GitHub
Templae

Risks and illusions

It’s important not to turn this into yet another wave of uncritical AI hype. The new interface has traps of its own. The first illusion is believing that chat will solve everything. It won’t. Some users simply don’t like typing. Some scenarios require predictable, highly structured forms rather than open‑ended conversation. There are accessibility constraints and legal requirements that make pure chat UX risky or even unacceptable.

The second risk is forgetting about transparency. If AI starts changing the interface without explaining why, users feel like they are losing control. A good new interface should reveal the links between intent and outcome: “You’re seeing this screen because you asked for this.” Users should be able to retrace steps, see what was filtered, and correct the AI when it misinterprets something.

The third illusion is economical: treating AI integration as “magic cost savings”. Rebuilding architecture around AI is an investment, not a shortcut. Done poorly, it can leave you with complex, fragile code, confusing UX, and dependency on a single external provider. Done thoughtfully, it can reduce friction for users and enable new business models — but the “AI tax” is real, both technically and organizationally.

Has the time really come?

There is no clean “yes” or “no” answer to whether the time for this new interface has “officially” arrived. But it already feels impossible to design serious products as if AI doesn’t exist. You can’t responsibly plan a 5–10 year product roadmap and act like users haven’t learned to expect dialogue, not just navigation. Ignoring that shift won’t make it go away; it will just make your product feel oddly old even if the tech stack is brand new.

Personally, this moment feels a lot like the transition from static sites to SPAs. Back then, it looked like “just another technical trick”. It turned out to be a paradigm shift. Slot‑based architectures, parallel routes, an AI layer that lives next to content instead of sitting as a thin widget on top — all of this still feels niche today. But once you build a few real projects this way, it becomes hard to go back. The simplest practical step right now is to stop thinking in terms of “pages versus chats” and start thinking in terms of “streams that need to live together on the same screen”.

Trello for AI Coding: When the Autopilot Needs a Manual Gearbox

Roma Armsrtrong — Sun, 03 Aug 2025 12:18:30 +0000

Lately, I’ve been reflecting on how we can further optimize software development with AI. And then it hit me—what if we had a workspace, like Trello, specifically designed not just for humans, but to orchestrate collaboration with AI models?

https://aifa.dev will have this in its function

What’s Wrong With the Usual Approach?

These days, we often let AI decide how to implement a feature. It feels a little like having a Tesla on autopilot—smooth and automatic, but sometimes you just want more control.

What if we gave that autopilot a “manual gearbox”?

The Trello-for-AI-Coding Vision

Picture this:

Project Capture: The Trello-like system “swallows” your whole project—files, structure, dependencies. No more “but it works locally.”
Micro Changes: Every AI request introduces the smallest possible change. Forget “build the whole component.” Instead: “Add email validation,” “Create a User interface,” “Add error handling.”
AI-Powered Decomposition: Given a new feature, AI generates a step-by-step breakdown—each micro-task is a Trello card, each card is a direct AI prompt.
Automated Testing: Once a micro-task is completed, the card moves to a “Testing” column. Code changes are committed, your dev environment or Vercel auto-detects the update, and the server restarts to show the result.
Automatic Feedback and Iterations: If something fails, the logs are attached to the card and it’s sent back for rework. Next time, the AI has the full error context and previous attempts, so it gets smarter.

Why This Model Works

Control: You orchestrate the process instead of letting the AI go fully solo. You set the rules, the model works in your framework.
Transparency: Every change is visible and logical—no black boxes, just a chain of understandable steps.
Resilience: A bug in one micro-task won’t break everything. The system “learns,” improving on every iteration.
Predictability: Smaller, sequential changes mean more reliable, less surprising outcomes.

A Practical Stack

It’s surprisingly doable with modern tools:

Frontend/Backend: Next.js (leveraging parallel and interceptor routes)
Source Control: GitHub API
Deployment: Vercel (handles builds, previews, and production deployments)
CI/CD: Automated via GitHub–Vercel integration—failed builds are caught early and flagged with notifications.
Branching: Parallel feature development and safer merges become default.

The Philosophy

Isn’t it a bit weird—giving manual control to an automated process? Maybe. But even Teslas have steering wheels and pedals. Sometimes, control is more important than full autonomy.

What’s Next?

I’m planning to build a starter kit for this concept and release it as open source. Follow updates and real-time experiments in my Telegram channel.

How do you see the future of AI-driven development? Should we aim for more autonomy, or keep strong human oversight?

These are my “thinking out loud” notes on where AI and dev workflows could be heading. Maybe it’s controversial, but this kind of experimentation is what keeps our industry moving forward.

Our Communication No Longer Belongs to Us, Thoughts on How AI Could Create an Alternative to WhatsApp

Roma Armsrtrong — Sun, 03 Aug 2025 12:13:58 +0000

I'm sitting here, scrolling the news, reading yet another “blocked,” “restricted,” “forbidden.” Am I the only one annoyed by this?

Our internet freedom is shrinking, turning what used to be a basic right—free communication—into a privilege. And messengers? They stopped being messengers long ago. Now, they're just social networks in disguise.

Remember when WhatsApp was just WhatsApp? A simple, safe place to chat “just between us.” Now it’s statuses, catalogs, endless notifications, marketing, group spam… Where’s the room for a quiet “Hey Mom, how are you?”—without all the extra noise (and maybe, surveillance)?

Is it just me, or are you tired of every messaging app morphing into a giant ‘ecosystem’?

But what if the solution is already within reach?

Recently, I've been reflecting on what AI enables—and suddenly, it hits me: the tech is ready. Today’s AI services (like Perplexity AI or similar) can spin up a static profile page for you, complete with a unique address. That’s almost enough for people to find you and check in—even right now.

Why not go further?

If I were to design a solution, here’s what I’d imagine: not just profile pages, but micro-messengers. Picture this:

You say: “Create my private contact room, WhatsApp 1.0 style!”

The system instantly sets up your own space—not a social network, but a digital living room just for trusted friends and family.

Key Principles of This Approach

Effortless creation: Your personal space is ready in seconds—by voice command or with a click.
Live status updates: Quick notes, photos, and short statuses—sharing your life intuitively and directly.
Design limitations: Your circle: up to 500 people, so it stays personal.
Permission control: You decide who can interact with you, and how.
AI assistant as mediator: A digital helper filters out noise, lets you stay in touch, and keeps things meaningful.
True privacy & subtlety: With a small, trusted audience, there’s no incentive (or ability) for mass surveillance. Communication stays real and direct.
Minimal friction for updates: Just a couple words or a photo, and your close contacts are up-to-date—no fuss.

Simplicity, Not Complexity

Now, about user interfaces: why do so many apps wrap normal features in layers of menus, settings, and dashboards? Want to update your status, see who's online, or add a note? Suddenly you need a manual and a dozen clicks.

AI could fix all this!

Imagine just saying:

“Show me who saw my last status.”
“Change my profile pic to the one I just sent you.”
“Pin a message from Dad.”
“Give me all updates from Jane this month.”

No more hunting for obscure options—just say what you want, and it's done. You’re not an operator anymore; you just express what you need, and AI makes it happen.

Maybe you’ll think of other things!

Is this already the rise of your own “intelligent agent” with a real address on the web? Hmm…

Real-Life Magic

People could start making these micro-WhatsApps for daily, private communication. No fear of being banned, no algorithmic interference. Your mom finds you, friends reach out. No million-member groups or status deluge—just your people.

Reflection Time

Would you give up the “convenience” of big apps like WhatsApp for true privacy?
What do you imagine as the future of authentic online communication?
What if we don’t stop at private pages—but go on to create personal AI helpers as our digital selves?

The more I think about it, the more inevitable this seems. The technology is here. The need is clear. The real question:

Does anyone else want this? Or should I just build it and see if folks truly crave a return to simple, human connection?

Your feedback—likes, comments, ideas—will help me understand!

I’m working on an open-source project at https://aifa.dev — adding new features each week.

Yes, it’s possible.

If any of this resonates, join my channel on Telegram: https://t.me/next_starters — that’s where the brainstorming and (maybe) real building happens.

So, what do you think? Is it worth a try?

How I Improved My OpenAI 4.1 Code Generation Prompts — A Simple Trick That Changed Everything

Roma Armsrtrong — Mon, 28 Jul 2025 14:31:44 +0000

“Alright, so I basically wrangled ChatGPT, put it in a headlock, and made it perform exactly as the great Sam intended!”

Despite the abundance of AI models (and I regularly use four top-tier ones), GPT-4.1 still gives me the best results for my tech stack compared to others.

My main secret has always been this: whenever I add a new feature to my app that theoretically consists of multiple components, I save these components in a Google Docs file. Every time I generate a new function or entire component, I "feed" the text from this document to the AI — specifically in text format. This approach works pretty well.

But today I managed to substantially improve this already powerful tool. Based on my internal assessment — probably by 50%.

Here's what I did
Now, whenever I write a prompt, I always add this at the end:

"Please explain how you understood my problem, why I think this is important, how you're going to solve it, and what would be the right solution."

If the tasks aren't too complex, I immediately add: "Without waiting for the next request, right after the description, provide me with a complete ready solution that includes all component code without abbreviations."

But in some cases, when tasks are complex, it's crucial to understand that the solution approach the AI model wants to suggest fully aligns with your thought process. Then it makes sense to wait for the response, maybe discuss it a bit, or perhaps fundamentally change the solution. Only then request the complete component code.

A Real Example
Let me show you a concrete example so you can see how the model responds.

I had a problem with badge animation in a React menu component. When hovering over a link with a badge, the badge would disappear, but the link text remained truncated, not expanding to fill the freed space. Here's how I wrote the task:

"Investigate the issue with incorrect animation of links containing badges. When hovering over such a link, the badge disappears and an arrow appears — this is correct behavior, but the link text remains shortened, and we can't read the full content. The correct behavior should be that after hovering, the badge disappears, an arrow appears, and the original link text should restore to the state as if the badge never existed in the link. Please explain how you understood my problem, why I think this is important, how you're going to solve it, and what would be the right solution, then provide a ready solution."

Now let's see how the model responded:

How I understood your problem

If a link has a badge and long text, visually the link looks like:
[TEXT ...] [BADGE]

When hovering, the badge disappears and an arrow (ArrowRight) appears. However, the text area doesn't expand — it still occupies less width, so the long text remains truncated and doesn't become fully visible.

Why this is important for UX: Users get maximum information on hover. Similar behavior is often implemented in rational interfaces (side menus, dropdowns — see Figma, Notion, etc.) — the badge doesn't interfere with reading detailed information.

How I'm going to solve this

Use conditional rendering instead of opacity: when hovering, show only the arrow and text, without the badge at all (badge is removed from DOM or replaced with arrow). The containing text shouldn't have fixed width — it should automatically occupy the freed space.

The right solution:

Normal state: [TEXT (shortened)] [badge]

On hover: [TEXT (full)] [ArrowRight]

Below, the model provided a completely working solution with proper conditional rendering and dynamic width management.

Draw Your Own Conclusions
Personally, I've already made mine — I'll use this approach constantly. The model now doesn't just generate code but thinks about the task structurally, which dramatically improves results.

By the way, for those following my work — this is related to developing menu interfaces for my open source Next.js starter for AI applications called AIFA. Right now I'm working on creating the second part of four for generating beautiful pages using commands — this will be one of the features similar to v0 or Lovable.

P.S. If you're interested in AI-powered development and open source solutions, I'd appreciate a follow — today I'll be showing the open source code for a really interesting top menu and mobile version that can scale to infinite categories and links while remaining elegant.

🔗 Links:

Project: https://aifa.dev

Open source code: https://github.com/aifa-agi/aifa

What's your experience with AI code generation? Have you found any techniques that dramatically improve output quality? Share in the comments!

AI-Powered Website Generator with ChatGPT and Next.js 15: Building SEO-Optimized Pages from Scratch (v0 Blueprint)

Roma Armsrtrong — Thu, 24 Jul 2025 22:45:14 +0000

This tutorial is the first installment of a comprehensive guide to creating an-driven system for automatically generating web pages using React 19 and Next.js 15. Our focus is not just on speed, but on architectural elegance and consistent, on-brand design.

Open source: (https://github.com/aifa-agi/aifa)

Here’s the roadmap:

Part 1 (You are here): Core architecture deep dive—catch-all routes, the PageHtmlTransformer component, and strongly typed page configs.
Part 2: Advanced page types—automating documentation pages and interactive tutorials.
Part 3: AI engine integration—connecting external models (ChatGPT, Claude) and our internal aifa agent for continuous, autonomous content generation.
Bonus: Case study—deploying, monetizing, and scaling a production-grade AI SaaS with user registration and Stripe payments.

This step-by-step blueprint walks you through building your own enterprise-ready v0 clone, complete with authentication and payment flows. A real-world AI product from concept to launch—full source code available at . Subscribe to follow the full series—this is only the beginning!

Technology Stack: React 19 · Next.js 15 · Vercel · Prisma · Stripe · AI SDK · ChatGPT

Our platform blends enterprise-grade stability with bleedingedge innovation:

React 19 & Next.js 15 with App Router deliver sub-100ms renders and true parallel routes.
Vercel powers zero-downtime deployments and global edge scaling.
Prisma & Neon ensure type-safe, lightning-fast data access.
Stripe handles instant, PCI-compliant payments out of the box.
AI SDK + ChatGPT layer in generative intelligence, so AIFA Dev Starter can spin up complete UIs, content modules, and prototypes in minutes—complete with built-in authentication and multilingual AI chat.

How It Works

Prompt: Send a single natural-language request to ChatGPT.
Generate: Receive a JSON-based PageConfig that defines your page’s sections, metadata, and layout.
Build: Drop the config file into your Next.js app and run npm run build. Instantly you have a fully styled, SEO-ready page, complete with auth and AI-powered chat.

Need enterprise integration? Simply switch on our “v0 Corporate Mode” in the [upcoming aifa.dev(https://aifa.dev) for advanced automation, role-based access, and scalable microservices architecture
.

Who Wins with an AI-Powered Site Generator? Studios, Enterprises, Startups, and Freelancers

The Next-Generation Design Agency: From Mockup to Mass Production in Minutes

Imagine an agency that architects scalable tech ecosystems, not just static mockups. A single upfront investment in a proprietary component library unlocks the ability to generate and deploy 50+ production-grade websites per hour.

This isn't science fiction. It's a new operational paradigm where creative capital meets automated execution. And with Stripe integration built-in, you can monetize this new velocity from day one. We'll explore this further in a later post.

Enterprise Deployments: Brand Consistency and Radical Scalability

A classic enterprise use case: a turnkey QR menu solution for a national restaurant franchise. You build a master design system for the core user experience—like product cards or menus—just once. This becomes a white-label solution you can deploy to hundreds of franchisees or locations.

Each location customizes its branding and content via a simple interface, while the core UX, performance, and design integrity remain locked down and consistent. This is the trifecta the enterprise market demands in 2025: radical scalability, bulletproof brand consistency, and unmatched speed-to-market.

The Core Engine: Our JSON-to-React Transformer Architecture

This entire system is powered by two critical components:

Intelligent Prompt Engineering

We use highly-tuned instructions that command the AI to pull battle-tested section templates from a vector database and tailor them to your specific request.
The Component Transformer Architecture

Our React components are engineered to interpret these JSON configs, instantly transforming them into fully-rendered, interactive pages.

While external models like ChatGPT and Claude handle the content generation, our proprietary transformer components are the system's core engine—the part we built to guarantee performance and design fidelity.

AI-Native Documentation: Use This Tutorial as a Live Knowledge Base

Stop reading documentation. Start commanding it.

The real power of this guide isn't in reading it cover-to-cover. It's designed to be your project's private, expert knowledge base.

How it Works:

Feed this entire document into ChatGPT.
Ask specific, contextual questions: "How do I implement a custom section?", "What's the purpose of the FullScreenWrapper?", "Show me the code for the DoublePresentation component."
Receive precise, production-ready answers with complete code snippets.

This isn’t just documentation; it's an actionable playbook. Try this approach, and you’ll understand why all technical documentation in 2025 needs to be AI-native.

1. Introduction: Our Philosophy—Consistency Trumps Raw Speed

This guide is not about creating the fastest possible AI page generator. While AI tools like v0 prioritize raw generation speed, our methodology focuses on a more critical business metric: Total Cost of Ownership (TCO). Our primary goal is to deliver bulletproof design consistency and architectural harmony across your entire digital footprint.

While the initial generation of a single page might take slightly longer, the downstream savings in development, maintenance, and brand management are exponential. Here’s why this trade-off delivers superior ROI:

Bulletproof Brand Consistency: Every AI-generated page is automatically compliant with your established design system. No rogue components, no off-brand styling.
Seamless User Experience: The architecture ensures a harmonious and cohesive journey for the user, eliminating the jarring inconsistencies common in rapidly-generated sites.
Drastically Reduced Rework: By enforcing standards at the point of creation, we minimize the expensive and time-consuming manual adjustments typically required post-generation.
True Architectural Scalability: Our approach becomes more efficient as you scale. The more pages you generate, the greater the returns in speed and consistency.

The result isn't just a collection of pages; it's a cohesive, professional-grade digital asset that scales gracefully with minimal maintenance overhead.

Beyond the Basics: Handling Real-World Complexity

This initial tutorial focuses on building standard pages with our core Header, Body, and Footer components. However, the open-source codebase provides advanced, out-of-the-box solutions for more complex requirements:

Documentation Pages with advanced, nested navigation.
Interactive Tutorial Pages with stateful, step-by-step elements.

Our architecture is designed for extensibility. For unique, highly interactive features—like animated presentations, real-time data dashboards, or components with complex state management—you can inject Custom Components directly into the rendering pipeline. This gives you the flexibility to implement bespoke functionality without ever compromising the system's core integrity.

Your Next Step: Hands-On with Custom Components

To truly master this system, your practical assignment is to explore the process of creating and integrating a new Custom Component. This will solidify your understanding of the architecture's power and flexibility.

1.1. The Concept: AI as Your Brand Steward, Not Just a Content Engine

Modern digital operations demand velocity, brand integrity, and market agility. Our architecture achieves this by treating your design system as a structured, intelligent knowledge base. Formalized, pre-approved definitions for every brand-critical section (Hero, CTA, FAQ, Features, etc.) are ingested by the AI.

By feeding the AI your exact UI/UX standards, it becomes more than a content generator—it becomes the ultimate steward of your brand identity, enforcing consistency at every stage of the page creation lifecycle.

1.2. The 5-Step Workflow: From Design System to SEO-Optimized Build

Ingest Your Design System: We load the AI's knowledge base with your component templates and explicit instructions on how to implement them visually and structurally, ensuring 100% brand alignment.
Define the Goal with a Prompt: A user, project manager, or developer submits a simple text prompt describing the desired page or its elements.
AI Interpretation & Structuring: The AI agent parses the request, generates the semantic content, and intelligently maps it to the appropriate, pre-approved templates from the knowledge base.
Generate a Declarative PageConfig: Based on the selected templates, the system outputs a strongly-typed PageConfig—a declarative JSON description of the final page. This guarantees brand compliance and simplifies downstream development.
Automated Build & Deployment: The config is consumed by our render components during the build process. The output is a production-ready, SEO-optimized page that is visually and technically compliant with your corporate standards.

1.3. The Business Impact: Drive Conversions and Slash TCO

Accelerated Go-to-Market: Launch new landing and product pages with a single prompt, leveraging pre-approved templates for unmatched speed.
Ironclad Brand Integrity: Brand consistency and quality control are automated, ensuring every new page perfectly reflects your design system.
Reduced Manual Overhead & Scalable A/B Testing: Empower marketing teams to scale page variations and A/B test content without involving developers in the layout process.
Built for Global Scale: All text and UI elements are centralized, making localization and market adaptation seamless.
Architectural Flexibility: Designed to integrate flawlessly into modern CI/CD pipelines, hosting platforms, and other AI-driven workflows.

1.4. Your Roadmap Through This Series

In this comprehensive tutorial, we will guide you through:

Deconstructing the Core Architecture: A deep dive into the PageHtmlTransformer, the Wrapper/FullScreenWrapper system, and how section components interact.
Mastering the Rendering Pipeline: Understand the logic behind section rendering, configuration typing, and the relationship between config files and React components, so you can extend the system yourself.
Learning to Instruct the AI: Learn how to create structured instructions for metadata, headers, and footers, and define the specifications for generating body content as a JSX fragment.
Generating Pages on Demand: Apply your knowledge to create the config files that the AI uses to generate pages that are fully compliant with your architecture and design standards.

2. The Anatomy of Our AI-Powered Architecture

Our project is organized to maximize scalability, maintainability, and development velocity. We use Next.js 15 App Router conventions, including parallel routes (@right) and private folders (_folder), to create a clean and powerful structure.

Project Directory Tree


app/@right/
├── public/
│   ├── (_routing)/
│   │   └── [[...slug]]/
│   │       └── page.tsx                   # 1. Dynamic Catch-All Route Handler
│   └── (_service)/
│       ├── (_config)/
│       │   └── public-pages-config.ts     # 6. AI-Generated Page Configurations
│       └── (_libs)/
│           └── utils.ts                   # 7. Configuration Utilities
└── (_service)/
    ├── (_types)
    │   └── page-wrapper-types.ts          # 5. The TypeScript Data Contract (AI <-> UI)
    └── (_components)/
        └── page-transformer-components/
            ├── page-html-transformer.tsx    # 2. The Core Page Transformer Engine
            ├── custom-sections/             # Directory for complex, interactive components
            ├── wrappers/
            │   ├── full-screen-wrapper.tsx  # 3. Immersive Full-Screen Wrapper
            │   └── wrapper.tsx              # 4. Standard Content Section Wrapper
            ├── header-sections-components/
            │   ├── header-section.tsx       # 8. Standardized Header Component
            │   ├── page-header-elements.tsx # 9. Atomic Header Elements (H1/H2)
            │   └── announcement.tsx         # 10. Announcement Banner Component
            ├── body-sections-components/
            │   └── body-section.tsx         # 12. Flexible Body Content Container
            └── footer-sections-components/
                └── footer-section.tsx       # 11. Standardized Footer Component

Key Component Paths


1. @/app/@right/public/(_routing)/[[...slug]]/page.tsx
2. @/app/@right/(_service)/(_components)/page-transformer-components/page-html-transformer.tsx
3. @/app/@right/(_service)/(_components)/page-transformer-components/wrappers/full-screen-wrapper.tsx
4. @/app/@right/(_service)/(_components)/page-transformer-components/wrappers/wrapper.tsx
5. @/app/@right/(_service)/(_types)/page-wrapper-types.ts
6. @/app/@right/public/(_service)/(_config)/public-pages-config.ts
7. @/app/@right/public/(_service)/(_libs)/utils.ts
8. @/app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/header-section.tsx
9. @/app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/page-header-elements.tsx
10. @/app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/announcement.tsx
11. @/app/@right/(_service)/(_components)/page-transformer-components/footer-sections-components/footer-section.tsx
12. @/app/@right/(_service)/(_components)/page-transformer-components/body-sections-components/body-section.tsx

2.1. The Core Component Toolkit: Production-Ready Pages Out of the Box

Purpose

Our standard component library provides the engineered foundation for creating strongly-typed, brand-aligned pages at scale. This collection comprises the architectural pillars of our system: dynamic routing, a central transformer engine, versatile content wrappers, unified header and footer sections, and the configuration files and data contracts that drive them.

Core Architectural Principles:

Bulletproof UI Consistency: Every component adheres to a unified design system and behavioral patterns, ensuring a seamless and predictable user experience.
AI-Native by Design: The architecture is optimized for autonomous generation, enabling the AI to build complex pages without manual intervention.
SEO-Optimized from the Ground Up: Best practices for search engine optimization and semantic HTML are baked in, not bolted on.
Type-Safe & Predictable: We leverage TypeScript's strict typing to enforce a reliable data contract between the AI and the UI, ensuring system stability.
Modular & Composable: Each component is a self-contained unit that solves a specific problem, designed for maximum reusability and extensibility.

Together, these components form the bedrock of the system, guaranteeing that every AI-generated page is not only visually cohesive but also technically robust and enterprise-ready.

2.1.1. The `[[...slug]]/page.tsx` Component: Universal Catch-All Route for Dynamic URLs

2.1.1.1. The Strategic Value of Catch-All Routes: Agility and Deployment Speed

A catch-all route is a dynamic routing pattern in Next.js that allows a single page component to handle an infinite number of URL segments. By using the [[...slug]] convention, the slug parameter becomes an array containing every part of the URL path, no matter how deeply nested it is.

From a business perspective, this architectural choice is a massive competitive advantage. Here’s why:

Infinite Nesting and Content Depth: You are no longer constrained by the project's file structure. A URL like /public/category/nike/sneakers/black/12345 is handled by the exact same component as /public/test. This unlocks the ability to build deeply hierarchical product catalogs, documentation sites, or content libraries without requiring any changes to the codebase.
Frictionless Navigation and Instant Page Launches: Deploying a new page is as simple as adding its configuration to a central file. The page goes live instantly without creating new components or navigating a complex file system. This decouples content strategy from engineering cycles, empowering marketing and product teams to move faster.
Built-in Automation: This architecture is the foundation for a fully automated content ecosystem. An auto-generated sidebar or menu can be programmed to read the page configurations and build the navigation dynamically. When you add a new page config, the navigation updates automatically, ensuring a consistent user experience with zero manual effort.

2.1.1.2. The Value of Dynamic URLs for AI Assistants: Infinite Content Scalability

In today's competitive landscape, modern SaaS and AI platforms demand the agility to generate new pages on the fly. Whether initiated by a user, an administrator, or an autonomous AI agent, content must be architected for instant creation and deployment. Our catch-all routing architecture is the engine that drives this capability.

Strategic Business Advantages:

Eliminates Architectural Constraints: It removes all limitations on content structure and nesting depth, allowing your digital presence to scale infinitely without requiring code changes.
Unlocks Unmatched Agility: Build complex content hierarchies for any use case. Navigation is driven purely by your business logic, not constrained by a rigid file system. This decouples your content strategy from engineering cycles, empowering product and marketing teams to execute faster.
Enables AI-Powered Automation: This architecture is designed for an AI-first world. It provides a stable foundation for AI agents to autonomously generate, configure, and deploy entire sections of your site, from product catalogs to knowledge bases.

2.1.1.3. Real-World Use Cases at Scale

This architecture isn't theoretical; it's a battle-tested model for deploying high-value, deeply-nested content structures that drive business growth:

Turnkey QR Menu Systems: Deploy a white-label solution for a national restaurant franchise. Each location can have a unique, deeply categorized menu (e.g., /public/menu/dallas-tx/dinner/mains/pasta-dishes), all while maintaining perfect brand consistency and UX integrity.
E-commerce Product Catalogs: Launch extensive product lines with infinite variations instantly. A URL like /public/shop/apparel/mens/jackets/leather/fall-2025/black/sku-12345 becomes trivial to create and manage, enabling rapid market expansion and granular product organization.
Corporate & Educational Portals: Build vast knowledge bases or tutorial platforms where content can be organized logically, no matter how deep the hierarchy needs to go (e.g., /public/tutorials/enterprise-ai/onboarding/module-3/advanced-prompting). #### 2.1.1.4. How It Works: The Engine of On-Demand Content

At its core, the process is elegantly simple yet powerful: it dynamically translates any URL path into a corresponding page configuration, which is then rendered on the fly. This is the technical foundation that unlocks the business agility described earlier.

The component's primary tasks are:

Receive the URL: It captures the incoming URL path as an array of segments (e.g., ["category", "nike", "sneakers"]).
Generate Static Paths: The generateStaticParams function proactively reads your public-pages-config.ts file. It informs Next.js of all valid page routes defined in your configuration. For the business, this translates to comprehensive Static Site Generation (SSG), ensuring every page is pre-built for lightning-fast load times and optimal SEO.

export async function generateStaticParams() { const pages: PageConfig[] = getAllPublicPages(); return pages.map((page: PageConfig) => ({ slug: page.metadata.slug ? page.metadata.slug.slice(1) : [], })); }
Fetch and Render: When a user requests a URL, the component receives the slug array. It then uses the getPageBySlug utility to perform a high-speed lookup against the configuration array (prepending "public" for an accurate match). If a match is found, the configuration is passed to our universal PageHtmlTransformer component for rendering.

export default async function PublicDynamicSlugPage({ params }: Props) { const resolvedParams = await params; const slugArr = resolvedParams.slug ?? []; const pageConfig = getPageBySlug(["public", ...slugArr]); if (!pageConfig) { return Page not found; } return ; }

Because the slugArr can be of any length and the lookup is a direct match, this system provides infinite nesting depth with zero risk of route collisions.

2.1.1.5. Key Functions and Their Business Impact

generateStaticParams: Drives Performance & SEO by telling Next.js which

2.1.1.7. The Component: The Execution Engine

This component is the engine that brings our AI-powered architecture to life. It's a single, powerful Next.js Server Component that acts as a universal handler for every dynamically generated public page. It is responsible for fetching the correct page configuration based on the URL and orchestrating the final render, ensuring both maximum performance and out-of-the-box SEO compliance.


// @/app/@right/public/(_routing)/[[...slug]]/page.tsx


import { PageHtmlTransformer } from "@/app/@right/(_service)/(_components)/page-transformer-components/page-html-transformer";
import { getPageBySlug } from "@/app/@right/(_service)/(_config)/pages-config";
import { constructMetadata } from "@/lib/construct-metadata";
import type { PageConfig } from "@/app/@right/(_service)/(_types)/page-wrapper-types";
import { getAllPublicPages } from "../../(_servise)/(_libs)/get-all-public-pages";


interface Props {
  params: Promise<{ slug?: string[] }>;
}


export async function generateStaticParams() {
  const pages: PageConfig[] = getAllPublicPages();


  return pages.map((page: PageConfig) => ({
    slug: page.metadata.slug || [],
  }));
}


export async function generateMetadata({ params }: Props) {
  const resolvedParams = await params;
  const slugArr = resolvedParams.slug ?? [];
  const pageConfig = getPageBySlug(["public", ...slugArr]);


  if (!pageConfig) return {};
  return constructMetadata(pageConfig.metadata);
}


export default async function PublicDynamicSlugPage({ params }: Props) {
  const resolvedParams = await params;
  const slugArr = resolvedParams.slug ?? [];
  const publicPageConfig = getPageBySlug(["public", ...slugArr]);


  if (!publicPageConfig) {
    return <div>Page not found</div>;
  }


  return <PageHtmlTransformer data={publicPageConfig} />;
}

2.1.1.7. The Bottom Line: Catch-All Routes as Your AI Content Engine

Combining catch-all routes with a centralized page configuration creates an architecture that is maximally flexible, extensible, and purpose-built for AI-driven content orchestration. This approach is friendly to both developers and AI agents, creating a seamless workflow from concept to deployment.

The core principle is simple but transformative: A new page is just a new entry in a configuration file. No new code files, no complex folder structures. The navigation and rendering systems adapt automatically, making this the cornerstone of a truly agile and scalable AI-powered platform.

2.1.3. FullScreenWrapper vs. Wrapper: Crafting Immersive Full-Screen Experiences

2.1.3.1. Core Purpose

The FullScreenWrapper is specifically engineered for sections that require an immersive, full-screen presentation with background video or image support. It serves as the foundational container for "hero" sections, where content is elegantly overlaid on top of rich media elements.

2.1.3.2. Layered Architecture

The component uses a sophisticated layered structure to ensure content remains legible and visually distinct from the background.

Technical Highlights:

min-h-screen: Guarantees the section fills the entire viewport height, creating a captivating, full-bleed effect.
relative Positioning: Enables precise z-index control for stacking elements.
Background Elements (video/img): Positioned with absolute and z-0 to sit behind all other content.
Content Container: Uses relative positioning with z-10 to ensure it always appears above the background.


<section className="relative flex min-h-screen flex-col py-10 lg:py-14 bg-background">
  {backgroundElement}  // z-0
  <div className="relative z-10 flex flex-col flex-1">
    {children}         // Header → Body → Footer
  </div>
</section>

2.1.3.3. Customization and Extensibility

The FullScreenWrapper is designed for easy customization. You can add complex visual effects directly via the className prop.


<FullScreenWrapper className="bg-gradient-to-br from-blue-900 to-purple-900">

Opacity Control: Background videos automatically receive an opacity-40 class to improve text readability, but this can be easily overridden.
Smooth Transitions: A transition-all duration-500 class is built-in to ensure smooth changes to background properties.

2.1.3.4. Advanced Styling and Reusable Templates

When you need section dividers, borders, glow effects, or background blurs, the FullScreenWrapper provides the ideal foundation for implementing these styles. This makes it perfect for creating highly stylized, reusable templates.


typescript

<FullScreenWrapper className="border-t-4 border-gradient bg-blur-effect">

2.1.3.5. The Component


// @/app/@right/(_service)/(_components)/page-transformer-components/wrappers/full-screen-wrapper.tsx


import React, { HTMLAttributes } from "react";
import { cn } from "@/lib/utils";


interface FullScreenWrapperProps extends HTMLAttributes<HTMLDivElement> {
  videoUrl?: string;
  imageUrl?: string;
  className?: string;
  children: React.ReactNode;
}


export function FullScreenWrapper({
  videoUrl,
  imageUrl,
  className,
  children,
  ...props
}: FullScreenWrapperProps) {
  let backgroundElement: React.ReactNode = null;


  if (videoUrl) {
    backgroundElement = (
      <video
        className="absolute inset-0 size-full object-cover z-0 opacity-40 transition-all duration-500"
        autoPlay
        loop
        muted
        playsInline
        src={videoUrl}
      />
    );
  } else if (imageUrl) {
    backgroundElement = (
      <img
        className="absolute inset-0 size-full object-cover z-0"
        src={imageUrl || "/placeholder.svg"}
        alt="Background"
      />
    );
  }


  return (
    <section
      className={cn(
        "relative flex min-h-screen flex-col py-10 lg:py-14 bg-background",
        className
      )}
      {...props}
    >
      {backgroundElement}
      <div className="relative z-10 flex flex-col flex-1">{children}</div>
    </section>
  );
}

2.1.4. Wrapper: The Standard Sectional Container

2.1.4.1. Primary Use Case

The Wrapper is the default, go-to container for the majority of content sections. It's designed to provide standard padding, content centering, and a consistent, predictable layout for informational blocks.

2.1.4.2. Structure and Styling


typescript
<section className="py-10 lg:py-14 bg-background">
  <div className="container mx-auto px-4">
    {children}  // Header → Body → Footer
  </div>
</section>

The component's structure is simple and optimized for performance, relying on a few key Tailwind CSS classes:

py-10 lg:py-14: Applies responsive vertical padding for consistent spacing across devices.
container mx-auto px-4: Creates a centered, max-width container with horizontal padding, ensuring content is well-aligned and readable.
bg-background: Utilizes the current theme's CSS variable for the background color, ensuring seamless integration with light/dark modes.

2.1.4.3. Customization Points

While minimalist by design, the Wrapper is a flexible foundation for custom styling via the className prop. It's the perfect base for adding:

Custom background colors or gradient effects.
Borders or dividers between sections.
Subtle visual enhancements like blur or glow effects.

2.1.4.4. Building Reusable Templates


typescript
// Пример для pricing-секций
<Wrapper className="bg-gradient-to-br from-green-50 to-green-100 border-2 border-green-200">
  <HeaderSection />
  <PricingTable />
  <FooterSection />
</Wrapper>

When your design calls for a template with unique styling but a standard, centered content structure, Wrapper is the ideal choice. It provides the structural backbone, allowing you to focus on the specific styles of the template.

2.1.4.5. The Component


// app/@right/(_service)/(_components)/page-transformer-components/wrappers/wrapper.tsx


import React, { HTMLAttributes } from "react";
import { cn } from "@/lib/utils";


interface WrapperProps extends HTMLAttributes<HTMLDivElement> {
  className?: string;
  children: React.ReactNode;
}


export function Wrapper({ className, children, ...props }: WrapperProps) {
  return (
    <section
      className={cn("py-10 lg:py-14 bg-background", className)}
      {...props}
    >
      <div className="container mx-auto px-4">{children}</div>
    </section>
  );
}

2.1.5. page-wrapper-types.ts: The AI-to-UI TypeScript Contract

2.1.5.1. The Core Contract: Bridging AI Generation and UI Rendering

The page-wrapper-types.ts file establishes a formal, strictly-typed contract between our AI content generator and the UI rendering system. This contract is fundamental to our architecture, defining the structure of all possible page components and the rules governing their interactions to ensure seamless integration and predictability.

2.1.5.2. SectionType Categories and AI-Driven Generation

The AI's operational model is centered around the SectionType enum. First, the AI analyzes the available section types defined within this enum. Then, based on the user's request, it strategically selects which sections to assemble for a given page. To facilitate fully autonomous and high-quality content generation, the AI's knowledge base must be populated with comprehensive instructions and diverse examples for each of the 25+ section types.


typescript
export type SectionType =
  | "hero-section" | "cta-section" | "faq-section"
  | "features-section" | "testimonials-section" | "pricing-section"

2.1.5.3. SEO Optimization and Metadata

The PageMetadata interface ensures the standards-compliant generation of meta tags, which is critical for search engine optimization (SEO). Meta tags are generated automatically via the generateMetadata() function. This function retrieves data from the page configuration and passes it to the constructMetadata() utility, which in turn constructs well-formed HTML headers.


typescript
export interface PageMetadata {
  id: string;           // Уникальный идентификатор
  title: string;        // <title> тег для SEO
  description: string;  // <meta description> для сниппетов
  image?: string;       // Open Graph изображение
  slug?: string[];      // URL-структура страницы
  type: SectionType;    // Тип основной секции
}

2.1.5.4. HeaderContentConfig: Managing SEO Hierarchy

Managing the heading hierarchy is critical for on-page SEO.

A note of caution: The Header section is optional. This is an intentional design choice because certain section types may embed H1/H2 tags directly within their bodyContent. While this provides flexibility, it introduces a potential risk to UI consistency, which is a primary objective of this architecture. Careful implementation is required to maintain a logical and SEO-friendly document structure.


typescript
interface HeaderContentConfig {
  heading: string;
  headingLevel?: 1 | 2;  // H1/H2 для поисковой иерархии
  description?: string;
  showBorder?: boolean;
}

2.1.5.5. BodySection: Enabling Maximum Design Freedom


typescript
bodyContent?: React.ReactNode;

By design, the body of a section is granted complete freedom to implement any layout or design. This architectural choice allows for maximum creative flexibility, enabling the AI to generate rich and varied content. This approach deliberately contrasts with the Header and Footer, which are standardized to maintain a consistent look and feel across the entire application.


typescript
interface FooterContentConfig {
  actions?: {
    label: string;
    href: string;
    variant?: "default" | "secondary" | "outline" | "ghost" | "link";
  }[];
}

2.1.5.6. FooterContentConfig: A Minimalist Approach

The current implementation of the footer is intentionally minimalist. It supports only optional call-to-action buttons. No additional elements are included, a decision made to prioritize simplicity, speed, and uniformity across all generated pages.

2.1.5.7. Principles for Extending Type Definitions

To introduce a new SectionType, the following steps must be taken:

Update AI Knowledge: The AI's knowledge base must be updated with comprehensive instructions and examples for the new section type.
Extend Enum: The new type must be added to the SectionType enum.
Expand Interfaces: If the new section requires unique configuration parameters, the corresponding configuration interface must be extended.

This process strikes a crucial architectural balance: the Header and Footer are strictly typed to enforce SEO best practices and UI consistency, while the Body offers maximum flexibility for innovative, AI-driven design solutions.

2.1.5.8. Component


// app/@right/(_service)/(_types)/page-wrapper-types.ts




export interface MetadataConfig {
  title?: string;
  description?: string;
}


export interface PageMetadata {
  id: string;
  title: string;
  description: string;
  image?: string;
  slug?: string[];
  type: SectionType;
}


export type SectionType =
  | "hero-section"
  | "cta-section"
  | "faq-section"
  | "features-section"
  | "testimonials-section"
  | "pricing-section"
  | "contact-section"
  | "blog-posts-section"
  | "product-grid-section"
  | "image-gallery-section"
  | "text-block-section"
  | "video-section"
  | "team-section"
  | "about-us-section"
  | "newsletter-section"
  | "social-proof-section"
  | "comparison-table-section"
  | "map-section"
  | "custom-html-section"
  | "changelog-section"
  | "comparison-two-column-section"
  | "comparison-three-column-section"
  | "feature-showcase-section";


export interface BaseSection {
  id: string;
  type: SectionType;
  className?: string;
}


export interface HeaderContentConfig {
  announcement?: {
    badgeText?: string;
    descriptionText?: string;
    href?: string;
  };
  heading: string;
  headingLevel?: 1 | 2;
  description?: string;
  showBorder?: boolean;
}


export interface FooterContentConfig {
  actions?: {
    label: string;
    href: string;
    variant?:
      | "default"
      | "secondary"
      | "destructive"
      | "outline"
      | "ghost"
      | "link";
  }[];
}
export interface SectionConfig extends BaseSection {
  type: SectionType;
  headerContent: HeaderContentConfig;
  bodyContent?: React.ReactNode;
  footerContent?: FooterContentConfig;
  videoUrl?: string;
  imageUrl?: string;
  sectionClassName?: string;
  contentWrapperClassName?: string;
  customComponentsAnyTypeData?: any;
}


export type Section = SectionConfig;


export interface PageConfig {
  metadata: PageMetadata;
  sections: Section[];
}


export type SlugType = string[];

2.1.5.9. customComponentsAnyTypeData: Supporting Arbitrary Custom Components

The customComponentsAnyTypeData?: any; field within SectionConfig serves as a powerful "escape hatch." It is designed for passing unstructured data to sections that leverage complex or unique custom components that do not conform to our standard interfaces.

To maintain architectural integrity, the type and structure of this data must be rigorously defined and documented within the custom component itself. This design choice provides the necessary flexibility to integrate highly interactive, dynamic, and advanced user-facing sections without compromising the core typing contract of the main page architecture.

2.1.6. public-pages-config.ts: The Public Page and Slug Route Registry

2.1.6.1. Role in System Architecture

The public-pages-config.ts file is an AI-generated registry of all public-facing pages within the application. This configuration is automatically generated by our AI based on system instructions and user prompts. Its structure is directly influenced by the available components in our knowledge base examples and the enumerations within the SectionType definitions.

The resulting configuration acts as a bridge, translating the AI's analysis of user requirements into a structure that our page rendering system can interpret and display.

2.1.6.2. When to Use This File-Based Configuration

This approach is ideal for:

Projects with a limited number of pages (typically 10-15).
Websites with static content that requires infrequent updates.
Rapid prototyping and Minimum Viable Products (MVPs), allowing for a quick start without database setup.
Landing pages with a fixed, predefined structure.

When to Transition to a Database:

For large-scale production applications, a file-based approach is only practical for a small set of pages. Most scenarios will benefit from storing this information in a database to enable dynamic content management and scalability.

2.1.6.3. Configuration Structure


typescript
export const PublicPagesConfig = {
  pages: [
    {
      metadata: {
        id: "public",
        title: "Enterprise-Grade AI Next.js starter",
        description: "Free Open-Source starter kit...",
        slug: ["public", "test"],
        type: "hero-section",
      },
      sections: [
        {
          id: "test-block",
          type: "hero-section",
          headerContent: { /* SEO-заголовки */ },
          bodyContent: {},
          footerContent: { /* Кнопки действий */ },
          videoUrl: "/_static/video/ai-loop.mp4",
          contentWrapperClassName: "text-white",
        } as SectionConfig,
      ],
    },
  ] as PageConfig[],
};

2.1.6.4. Configuration Categorization Principle

Key Architectural Decision: Placing the configuration file in the public/(_service)/(_config)/ directory signifies its role in managing public-facing pages.

Scaling by Category:

Our architecture supports scaling by categorizing configurations, ensuring a clean and organized structure:

public-pages-config.ts — for public pages (e.g., landing, about, contact).
docs-pages-config.ts — for documentation sites with advanced navigation.
admin-pages-config.ts — for administrative dashboards.
blog-pages-config.ts — for blog posts and articles.

Each category is designed to have its own:

Dedicated configuration file.
Specialized section types.
Unique rendering logic.

2.1.6.5. Interaction with the AI Generation System

For our AI system, this configuration file serves as:

A structural template: Defining how PageConfig objects should be formed.
A data example: Providing reference values for the generation process.
A type validation schema: Enforced by TypeScript for strict type safety.

The workflow is as follows:

The AI analyzes the existing configuration file.
It generates a new PageConfig object based on the user's prompt.
A developer then adds the new configuration to the pages array.
The system automatically recognizes and registers the new routes.

2.1.6.6. Advantages and Limitations

Advantages of the File-Based Approach:

Version Control: Easily managed and versioned via Git.
Type Safety: Benefits from compile-time type safety with TypeScript.
Zero Latency: No database queries result in faster page loads.
Simplified Deployment: Works seamlessly with static site generation (SSG).

Limitations:

No Dynamic Updates: Lacks dynamic content management capabilities.
Requires Redeployment: Any change necessitates a new build and deployment.
Not for UGC: Unsuitable for user-generated content.
Scalability Concerns: Becomes difficult to manage beyond 20-30 pages.

2.1.6.7. Component


// @/app/@right/public/(_servise)/(_config)/public-pages-config.ts


import {
  PageConfig,
  SectionConfig,
} from "@/app/@right/(_service)/(_types)/page-wrapper-types";


export const PublicPagesConfig = {
  pages: [
    {
      metadata: {
        id: "public",
        title: "Enterprise-Grade AI Next.js starter",
        description: "Free Open-Source starter kit...",
        slug: ["public", "test"],
        type: "hero-section",
      },
      sections: [
        {
          id: "test-block",
          type: "hero-section",
          headerContent: {
            announcement: {
              badgeText: "Thanks",
              descriptionText: "AI-SDK V5 & Vercel AI",
              href: "https://github.com/aifa-agi/aifa",
            },
            heading: "Enterprise-Grade AI Next.js starter",
            description:
              "Free Open-Source starter kit to build, deploy, and scale intelligent AI applications. Artifacts Feature, features secure multi-provider auth, Stripe payments, vector knowledge bases, deep-research agents, and a unique fractal architecture designed for the future of AI.",
            showBorder: false,
            headingLevel: 1,
          },
          bodyContent: {},
          footerContent: {
            actions: [
              {
                label: "Get Started",
                href: "/https://github.com/aifa-agi/aifa",
                variant: "default",
              },
              { label: "Browse Docs", href: "/docs", variant: "ghost" },
            ],
          },
          videoUrl: "/_static/video/ai-loop.mp4",
          contentWrapperClassName: "text-white",
        } as SectionConfig,
      ],
    },
  ] as PageConfig[],
};

2.1.6.8. Conclusion

public-pages-config.ts is a practical solution for small to medium-sized projects, offering a balance between ease of management and functionality. For larger-scale applications, it serves as an excellent starting point before transitioning to a more robust, database-driven architecture.

2.1.7. utils.ts: Configuration Utility Functions

This module provides utility functions for managing page configurations. It includes two primary functions: getAllPublicPages(), which filters all pages with the "public" prefix for static generation, and getPageBySlug(), which locates a specific page by an exact, case-sensitive match of its slug array.


// @app/@right/public/(_servise)/(_libs)/utils.ts


import {
  PageConfig,
  SlugType,
} from "@/app/@right/(_service)/(_types)/page-wrapper-types";
import { PublicPagesConfig } from "../(_config)/public-pages-config";


export function getAllPublicPages(): PageConfig[] {
  return PublicPagesConfig.pages.filter(
    (page: PageConfig) => page.metadata.slug?.[0] === "public"
  );
}


export function getPageBySlug(slug: SlugType): PageConfig | undefined {
  return PublicPagesConfig.pages.find(
    (page: PageConfig) =>
      JSON.stringify(
        page.metadata.slug?.map((s: string) => s.toLowerCase())
      ) === JSON.stringify(slug.map((s: string) => s.toLowerCase()))
  );
}

2.1.8. header-section.tsx: Unified Section Header

This standardized component serves as a universal header for all section types, ensuring consistency across the application. It offers optional elements such as an announcement, an H1/H2 heading, and a description. This component plays a crucial role in SEO optimization and maintaining visual consistency for AI-generated pages.


// @/app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/header-section.tsx


import React from "react";
import { cn } from "@/lib/utils";
import {
  PageHeaderDescription,
  PageHeaderHeading,
} from "./page-header-elements";


import { Announcement } from "./announcement";
import { HeaderContentConfig } from "../../../(_types)/page-wrapper-types";


export type HeaderSectionProps = {
  headerContent: HeaderContentConfig;
} & React.HTMLAttributes<HTMLDivElement>;


export function HeaderSection({
  headerContent,
  className,
  ...props
}: HeaderSectionProps) {
  if (!headerContent) return null;


  const {
    announcement,
    heading,
    headingLevel = 1,
    description,
    showBorder = false,
  } = headerContent;


  return (
    <section
      className={cn(
        showBorder && "border-t-4 border-b-4 border-primary",
        className
      )}
      {...props}
    >
      <div className="container mx-auto px-4">
        <div className="flex flex-col items-center gap-1 py-8 md:py-10 lg:py-12">
          {announcement && (
            <Announcement
              badgeText={announcement.badgeText}
              descriptionText={announcement.descriptionText}
              href={announcement.href}
            />
          )}
          <PageHeaderHeading level={headingLevel}>{heading}</PageHeaderHeading>
          {description && (
            <PageHeaderDescription>{description}</PageHeaderDescription>
          )}
        </div>
      </div>
    </section>
  );
}

2.1.9. header-section.tsx: Unified Section Header (Atomic Components)

This section details the atomic components used to construct headers: PageHeaderHeading (supporting H1/H2), PageHeaderDescription for subheadings, and PageActions for placing buttons. These components leverage createElement for dynamic HTML tag generation, ensuring correct SEO hierarchy.


// @app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/page-header-elements.tsx


import { HTMLAttributes, createElement } from "react";
import { cn } from "@/lib/utils";


type HeadingTag = "h1" | "h2";


interface PageHeaderHeadingProps extends HTMLAttributes<HTMLHeadingElement> {
  level?: 1 | 2;
}


function PageHeaderHeading({
  className,
  level = 1,
  ...props
}: PageHeaderHeadingProps) {

  const Heading: HeadingTag = level === 1 ? "h1" : "h2";
  const h1Classes = "text-2xl sm:text-3xl  md:text-6xl lg:text-7xl";
  const h2Classes = "text-lg sm:text-xl  md:text-3xl lg:text-4xl";



  return createElement(Heading, {
    className: cn(
      "text-center font-bold leading-tight tracking-tighter font-serif",
      level === 1 ? h1Classes : h2Classes,
      className
    ),
    ...props,
  });
}


function PageHeaderDescription({
  className,
  ...props
}: HTMLAttributes<HTMLParagraphElement>) {
  return (
    <p
      className={cn(
        "max-w-2xl text-balance text-center text-base font-light text-muted-foreground sm:text-lg",
        className
      )}
      {...props}
    />
  );
}


function PageActions({ className, ...props }: HTMLAttributes<HTMLDivElement>) {
  return (
    <div
      className={cn(
        "flex w-full items-center justify-center gap-2 pt-2",
        className
      )}
      {...props}
    />
  );
}



export { PageActions, PageHeaderDescription, PageHeaderHeading };

2.1.10. announcement.tsx: Announcement Component

This interactive badge component is designed for announcements and notifications. It supports navigation, keyboard control, and conditional rendering, automatically hiding when no content is present. It is used to draw attention to important updates or links.


// @app/@right/(_service)/(_components)/page-transformer-components/header-sections-components/announcement.tsx


"use client";


import { useRouter } from "next/navigation";
import { Badge } from "@/components/ui/badge";
import { ArrowRight } from "lucide-react";
import { cn } from "@/lib/utils";


interface AnnouncementProps {
  badgeText?: string; 
  descriptionText?: string; 
  href?: string; 
  className?: string;
}


export function Announcement({
  badgeText,
  descriptionText,
  href,
  className,
}: AnnouncementProps) {
  const router = useRouter();


  const handleOnClick = () => {
    if (href) {
      router.push(href);
    }
  };


  // Если нет текста для бейджа, описания или ссылки, возвращаем null
  if (!badgeText && !descriptionText && !href) {
    return null;
  }


  return (
    <div
      className={cn(
        "flex cursor-pointer items-center gap-2 rounded-full border border-primary bg-muted px-3 py-1 text-sm transition-colors hover:bg-muted/80",
        className
      )}
      onClick={handleOnClick}
      role="link"
      tabIndex={0}
      onKeyDown={(e) => {
        if (e.key === "Enter" || e.key === " ") {
          handleOnClick();
        }
      }}
    >
      {badgeText && (
        <Badge variant="secondary" className="text-xs">
          {badgeText}
        </Badge>
      )}
      {descriptionText && (
        <span className="text-muted-foreground">{descriptionText}</span>
      )}
      {href && <ArrowRight className=" h-3 w-3 text-muted-foreground" />}
    </div>
  );
}

2.1.11. footer-section.tsx: Unified Section Actions

This standardized footer component includes action buttons. It supports multiple buttons with various styling options, ensuring uniformity of Call-to-Action (CTA) elements across all sections. It automatically hides when no actions are defined.


// @app/@right/(_service)/(_components)/page-transformer-components/footer-sections-components/footer-section.tsx


"use client";


import { useRouter } from "next/navigation";
import type { HTMLAttributes } from "react";
import { cn } from "@/lib/utils";
import { Button } from "@/components/ui/button";
import { PageActions } from "../header-sections-components/page-header-elements";


interface FooterAction {
  label: string;
  href: string;
  variant?:
    | "default"
    | "secondary"
    | "destructive"
    | "outline"
    | "ghost"
    | "link";
}


interface FooterSectionProps extends HTMLAttributes<HTMLDivElement> {
  actions?: FooterAction[];
}


export function FooterSection({
  actions,
  className,
  ...props
}: FooterSectionProps) {
  const router = useRouter();


  if (!actions || actions.length === 0) {
    return null;
  }


  return (
    <section className={cn("py-4 md:py-6 lg:py-8", className)} {...props}>
      <div className="container mx-auto px-4">
        <PageActions>
          {actions.map((action) => (
            <Button
              key={action.href} // href должен быть уникальным!
              size="sm"
              variant={action.variant || "default"}
              onClick={() => router.push(action.href)}
            >
              {action.label}
            </Button>
          ))}
        </PageActions>
      </div>
    </section>
  );
}

2.1.12. body-section.tsx: Arbitrary Content Container

2.1.12.1. Purpose and Philosophy

BodySection serves as a highly flexible container for the main content within sections. Unlike the strictly standardized Header and Footer components, BodySection provides complete freedom for placing any React content.

2.1.12.2. Content Generation Principles

Crucially: Content for bodyContent must be generated as plain TSX without using .map() elements, where all elements are presented in an expanded format.


typescript
// ✅ 
bodyContent: (
  <>
    <div className="grid grid-cols-1 md:grid-cols-3 gap-6">
      <div className="feature-card">
        <h3>Feature 1</h3>
        <p>Description 1</p>
      </div>
      <div className="feature-card">
        <h3>Feature 2</h3>
        <p>Description 2</p>
      </div>
      <div className="feature-card">
        <h3>Feature 3</h3>
        <p>Description 3</p>
      </div>
    </div>
  </>
)

// ❌ 
bodyContent: (
  <div className="grid">
    {features.map(feature => <FeatureCard key={feature.id} {...feature} />)}
  </div>
)

2.1.12.3. When to Use Custom Components

If a section requires complex logic (state, effects, interactivity), create an independent component and add it to PageHtmlTransformer as a separate entity.


typescript
// @/app/@right/(_service)/(_components)/page-transformer-components/body-sections-components/body-section.tsx
export function BodySection({ children, className, ...props }: BodySectionProps) {
  const hasChildren = children !== null && children !== undefined && 
    !(Array.isArray(children) && children.length === 0) &&
    !(typeof children === "string" && children === "");

  if (!hasChildren) return null;

  return (
    <div className={cn(className)} {...props}>
      {children}
    </div>
  );
}

2.1.12.4. Component


// @app/@right/(_service)/(_components)/page-transformer-components/body-sections-components/body-section.tsx


import type { HTMLAttributes, ReactNode } from "react";
import { cn } from "@/lib/utils";
import { SectionType } from "../../../(_types)/page-wrapper-types";


interface BodySectionProps extends HTMLAttributes<HTMLDivElement> {
  children?: ReactNode | null;
  type: SectionType;
}



export function BodySection({
  children,
  className,
  ...props
}: BodySectionProps) {
  const defaultHeightRem = 0;
  const hasChildren =
    children !== null &&
    children !== undefined &&
    // Covers case when children = [] or ""
    !(Array.isArray(children) && children.length === 0) &&
    !(typeof children === "string" && children === "");


  if (!hasChildren && defaultHeightRem) {
    return (
      <div
        className={cn(className)}
        style={{ height: `${defaultHeightRem}rem` }}
        {...props}
      >
        {/* Empty section with default height */}
      </div>
    );
  }


  if (!hasChildren) {
    return null;
  }


  return (
    <div className={cn(className)} {...props}>
      {children}
    </div>
  );
}

2.1.12.5. Conclusion

BodySection acts as a bridge between the system's standardized architecture and the creative freedom of AI generation, striking a balance between control and flexibility.

2.2. Advanced Custom Components

Advanced Custom Components are engineered to deliver complex, interactive functionality that extends beyond the capabilities of our standard architectural patterns. These components are reserved for scenarios requiring state management, sophisticated animations, real-time data handling, or unique business logic.

When to Use Custom Components:

Interactive Elements: Forms with validation, calculators, product configurators.
Animated Presentations: Complex transitions, parallax effects, automatic sliders.
Real-Time Functionality: Chats, notifications, live data feeds.
Adaptive Logic: Components with fundamentally different behavior on desktop vs. mobile.
Unique UI Patterns: Non-standard interface elements specific to the project.

Architectural Integration:

Custom components are integrated into the PageHtmlTransformer as distinct case statements within a switch block. This approach allows them to access system-wide context, such as theme and device information, while maintaining a consistent look and feel through shared CSS classes and design tokens. Although they adhere to the system's visual language, they retain complete freedom in their internal logic and implementation. This strategy provides a balance between standardizing the majority of content and allowing for creative freedom in implementing complex interactive elements.

2.2.1. Guidelines for Implementing Custom Components

2.2.1.1. Wrapper Policy

No External Wrappers: Custom components must be returned directly without being enclosed in Wrapper or FullScreenWrapper. They are responsible for managing their own layout, padding, and layering to ensure full control over their presentation.

2.2.1.2. Component-Scoped Typing

Local Prop Interfaces: The prop interfaces for a custom component should be declared at the top of the component file itself. All data is passed from the page configuration into the component via the customComponentsAnyTypeData field in the section config.

2.2.1.3. Extending `SectionType`

Add a New Section Type: Each custom component requires a unique type to be added to the SectionType enum in page-wrapper-types.ts. This ensures proper type-checking and enables the switch statement in the transformer to correctly identify and render the component.


typescript
// @/app/@right/(_service)/(_types)/page-wrapper-types.ts
// ...
export type SectionType ="hero-section" | “new-custom-section”;

2.2.1.4. Update cases in the ageHtmlTransformer

Custom case processing
A new case is added to the PageHtmlTransformer to handle a custom section type with a direct


// В PageHtmlTransformer:
case "new-custom-section":
  return (
    <NewCustomSection
      key={config.id}
      customData={section.customComponentsAnyTypeData.customData}
    />
  );

2.2.1.5. Custom Config Structure

Configuration Structure: The following is an example of a page configuration that includes a custom section. All component-specific data is placed within the customComponentsAnyTypeData object, which can have any structure required by the component.


export const PublicPagesConfig = {
    pages: [
{
      metadata: {
        id: "CIUD",
        title: "1",
        description: "2",
        slug: ["public", "name"],
        type: "new-custom-section",
      },
      sections: [
        {
          id: "new-custom-section",
          type: "new-custom-section",
          customComponentsAnyTypeData: {
            metaData: {
              metaTitle: "1",
              metaDescription: "2",
            },
            customData: {
              mediaUrl: "/_static/illustrations/3.png",
              title: "4",
              description:
                "5",
            },
          },
        } as SectionConfig,
      ],
    },


             ] as PageConfig[],

2.2.2. Custom Component Example

2.2.2.1. `DoublePresentation`

2.2.2.1.1. Update Core Types

First, add the new section type to the SectionType enum.


typescript
// @/app/@right/(_service)/(_types)/page-wrapper-types.ts


export type SectionType =
  | "hero-section"
  | "cta-section"
  | "double-presentation-section"

2.2.2.1.2. Add New Case to `PageHtmlTransformer`

Next, add the corresponding case to the PageHtmlTransformer to render the component.


case "double-presentation-section":
            return (
              <DoublePresentation
                key={section.id}
                metaData={section.customComponentsAnyTypeData.metaData}
                leftItem={section.customComponentsAnyTypeData.leftItem}
                rightItem={section.customComponentsAnyTypeData.rightItem}
              />
            );

2.2.2.1.4. Config Example

Here is an example of how to configure the DoublePresentation component in public-pages-config.ts.


// app/@right/(_service)/(_types)/page-wrapper-types.ts


export interface MetadataConfig {
  title?: string;
  description?: string;
}


export type CuidString = string;


export interface PageMetadata {
  id: CuidString;
  title: string;
  description: string;
  image?: string;
  slug?: string[];
  type: SectionType;
}


export type SectionType =
  | "hero-section"
  | "cta-section"
  | "faq-section"
  | "features-section"
  | "testimonials-section"
  | "pricing-section"
  | "contact-section"
  | "blog-posts-section"
  | "product-grid-section"
  | "image-gallery-section"
  | "text-block-section"
  | "video-section"
  | "team-section"
  | "about-us-section"
  | "newsletter-section"
  | "social-proof-section"
  | "comparison-table-section"
  | "map-section"
  | "custom-html-section"
  | "changelog-section"
  | "comparison-two-column-section"
  | "comparison-three-column-section"
  | "feature-showcase-section"
  | "double-presentation-section";


export interface BaseSection {
  id: string;
  type: SectionType;
  className?: string;
}


export interface HeaderContentConfig {
  announcement?: {
    badgeText?: string;
    descriptionText?: string;
    href?: string;
  };
  heading: string;
  headingLevel?: 1 | 2;
  description?: string;
  showBorder?: boolean;
}


export interface FooterContentConfig {
  actions?: {
    label: string;
    href: string;
    variant?:
      | "default"
      | "secondary"
      | "destructive"
      | "outline"
      | "ghost"
      | "link";
  }[];
}
export interface SectionConfig extends BaseSection {
  type: SectionType;
  headerContent: HeaderContentConfig;
  bodyContent?: React.ReactNode;
  footerContent?: FooterContentConfig;
  videoUrl?: string;
  imageUrl?: string;
  sectionClassName?: string;
  contentWrapperClassName?: string;
  customComponentsAnyTypeData?: any;
}


export type Section = SectionConfig;


export interface PageConfig {
  metadata: PageMetadata;
  sections: Section[];
}


export type SlugType = string[];

2.2.2.1.5. Component

The complete code for the DoublePresentation component.


// @/app/@right/public/(_servise)/(_config)/public-pages-config.ts


import {
  PageConfig,
  SectionConfig,
} from "@/app/@right/(_service)/(_types)/page-wrapper-types";


export const PublicPagesConfig = {
  pages: [
    {
      metadata: {
        id: "public",
        title: "Enterprise-Grade AI Next.js starter",
        description: "Free Open-Source starter kit...",
        slug: ["public", "test"],
        type: "hero-section",
      },
      sections: [
        {
          id: "test-block",
          type: "hero-section",
          headerContent: {
            announcement: {
              badgeText: "Thanks",
              descriptionText: "AI-SDK V5 & Vercel AI",
              href: "https://github.com/aifa-agi/aifa",
            },
            heading: "Enterprise-Grade AI Next.js starter",
            description:
              "Free Open-Source starter kit to build, deploy, and scale intelligent AI applications. Artifacts Feature, features secure multi-provider auth, Stripe payments, vector knowledge bases, deep-research agents, and a unique fractal architecture designed for the future of AI.",
            showBorder: false,
            headingLevel: 1,
          },
          bodyContent: {},
          footerContent: {
            actions: [
              {
                label: "Get Started",
                href: "/https://github.com/aifa-agi/aifa",
                variant: "default",
              },
              { label: "Browse Docs", href: "/docs", variant: "ghost" },
            ],
          },
          videoUrl: "/_static/video/ai-loop.mp4",
          contentWrapperClassName: "text-white",
        } as SectionConfig,
      ],
    },
    {
      metadata: {
        id: "interactive-ai",
        title: "Interactive AI Demo",
        description: "Demo: DoublePresentation custom case",
        slug: ["public", "example"],
        type: "double-presentation-section",
      },


      sections: [
        {
          id: "double-presentation-demo",
          type: "double-presentation-section",
          customComponentsAnyTypeData: {
            metaData: {
              metaTitle: "Interactive AI: Where Conversation Builds the UI",
              metaDescription: "Discover what makes AIFA revolutionary...",
            },
            leftItem: {
              mediaUrl: "/_static/illustrations/ai-chat.png",
              title: "Ai Artifacts Chatbot",
              description:
                "As the AI chatbot speaks, it highlights elements...",
            },
            rightItem: {
              mediaUrl: "/_static/illustrations/ai-web.png",
              title: "Related Pages",
              description:
                "Click any UI element, and the AI provides instant context...",
            },
          },
        } as SectionConfig,
      ],
    },
  ] as PageConfig[],
};

2.2.2.1.3. `page-wrapper-types.ts`

Here is the complete page-wrapper-types.ts file with the new type included.


// @/app/@right/(_service)/(_components)/page-transformer-components/custom-sections/custom-double-prsentation.tsx


"use client";


import React, { useState, useEffect } from "react";
import { motion } from "framer-motion";
import Image from "next/image";
import { cn } from "@/lib/utils";
import { useMediaQuery } from "@/hooks/use-media-query";


interface PresentationMeta {
  metaTitle: string;
  metaDescription: string;
}


interface PresentationItem {
  mediaUrl: string;
  title: string;
  description: string;
}


interface DoublePresentationProps {
  metaData: PresentationMeta;
  leftItem: PresentationItem;
  rightItem: PresentationItem;
}


export default function DoublePresentation({
  metaData,
  leftItem,
  rightItem,
}: DoublePresentationProps) {
  const { isMobile } = useMediaQuery();


  // Desktop animation state
  const [activeContainer, setActiveContainer] = useState<"left" | "right">(
    "left"
  );
  const [sliderKey, setSliderKey] = useState(0);


  // Desktop auto-switching effect
  useEffect(() => {
    // Only run animation cycle on desktop
    if (isMobile) return;


    let sliderTimer: NodeJS.Timeout;
    let transitionTimer: NodeJS.Timeout;


    const startAnimationCycle = () => {
      setSliderKey((prev) => prev + 1);
      sliderTimer = setTimeout(() => {
        setActiveContainer((prev) => (prev === "left" ? "right" : "left"));
        transitionTimer = setTimeout(() => {
          startAnimationCycle();
        }, 500);
      }, 9000);
    };


    startAnimationCycle();


    return () => {
      clearTimeout(sliderTimer);
      clearTimeout(transitionTimer);
    };
  }, [isMobile]);


  // Return null while determining screen size
  if (isMobile === null) {
    return null;
  }


  // Common CSS classes
  const metaBlockClass = "text-center max-w-3xl flex flex-col items-center";
  const descriptionClass =
    "mb-12 max-w-xl text-base text-muted-foreground text-center";
  const desktopTitleClass =
    "mb-6 max-w-3xl font-serif font-bold leading-tight md:text-2xl lg:text-4xl";
  const desktopDescriptionClass =
    "mb-12 max-w-xl text-lg text-muted-foreground md:text-xl text-center";


  // Mobile card renderer
  const renderMobileCard = (item: PresentationItem) => (
    <div className="relative flex flex-col rounded-xl bg-gray-900 text-white shadow-lg mb-6 overflow-hidden">
      <div className="w-full relative" style={{ paddingTop: "56.25%" }}>
        <Image
          src={item.mediaUrl}
          alt={item.title}
          fill
          className="object-cover rounded-t-xl"
          sizes="100vw"
          priority
        />
      </div>
      <div className="flex flex-col p-4">
        <h2 className="text-xl font-bold mb-2">{item.title}</h2>
        <p className="text-gray-300 mb-2 text-base min-h-16">
          {item.description}
        </p>
      </div>
    </div>
  );


  // Desktop card renderer
  const renderDesktopCard = (item: PresentationItem, isActive: boolean) => (
    <motion.div
      layout
      animate={{ flex: isActive ? "7 1 0%" : "3 1 0%" }}
      transition={{ duration: 0.5 }}
      className="relative flex flex-col rounded-lg overflow-hidden bg-transparent text-white p-0 shadow-lg h-[30rem] flex-shrink-0"
    >
      <div className="relative w-full h-60 mb-4 rounded-xl overflow-hidden border-4 border-gray-700">
        <Image
          src={item.mediaUrl}
          alt={item.title}
          fill
          className="object-cover"
          priority
          sizes="(max-width: 768px) 100vw, (max-width: 1200px) 50vw, 33vw"
        />
      </div>
      <div className="flex flex-col pt-6">
        <h2 className="text-2xl font-bold mb-2 whitespace-nowrap overflow-hidden text-ellipsis">
          {item.title}
        </h2>
        <div className="relative w-full h-px bg-gray-700 mb-4">
          <motion.div
            key={`slider-${item.title}-${sliderKey}`}
            className={cn(
              "absolute top-0 left-0 h-full",
              isActive ? "bg-primary" : "bg-gray-700"
            )}
            initial={{ width: 0 }}
            animate={{ width: isActive ? "100%" : "0%" }}
            transition={
              isActive ? { duration: 9, ease: "linear" } : { duration: 0 }
            }
          />
        </div>
        <p className="text-gray-300 mb-4 text-sm line-clamp-4 min-h-[4rem]">
          {item.description}
        </p>
      </div>
    </motion.div>
  );


  // Mobile layout
  if (isMobile) {
    return (
      <section className="w-full pt-20">
        <div className="container mx-auto px-4 flex flex-col items-center">
          <div className={metaBlockClass}>
            <h2 className="text-xl font-bold mb-4">{metaData.metaTitle}</h2>
            <p className={descriptionClass}>{metaData.metaDescription}</p>
          </div>


          <div className="w-full flex flex-col">
            {renderMobileCard(leftItem)}
            {renderMobileCard(rightItem)}
          </div>
        </div>
      </section>
    );
  }


  // Desktop layout
  return (
    <section className="w-full pt-28">
      <div className="container mx-auto px-4 flex flex-col items-center gap-12">
        <div className={metaBlockClass}>
          <h2 className={desktopTitleClass}>{metaData.metaTitle}</h2>
          <p className={desktopDescriptionClass}>{metaData.metaDescription}</p>
        </div>


        <div className="flex gap-6 w-full max-w-6xl">
          {renderDesktopCard(leftItem, activeContainer === "left")}
          {renderDesktopCard(rightItem, activeContainer === "right")}
        </div>
      </div>
    </section>
  );
}

Conclusion: How to Get the Most Out of This Documentation

We understand that the instructions and components presented here might seem extensive for a single post. You might even feel that this isn't the most exciting read, and you'd be right. This isn't entertainment; it's a detailed technical guide designed to give you complete control over a powerful system.

To transform the study of this complex system into an interactive and productive process, we recommend the following approach.

Use AI for Accelerated Learning

Instead of passively reading, use this documentation as the "brain" for your AI assistant.

Your Action Plan:

Copy the entire content of this article, from the introduction to this section.
Use it as a system prompt for ChatGPT. This will provide the AI with the full context of the architecture we're discussing.
Ask questions about the source code. You can now have a dialogue with an AI that "understands" our rules and conventions. For example:
- "Based on the provided documentation, explain how to create a new custom component to display customer testimonials."
- "Show me a configuration example for a page that uses both DoublePresentation and HeroSection."

This method will help you understand the application's principles much faster. You can find the full source code for this project on our GitHub repository.

What's Next?

In this article, we've built one of the two critical pieces of our site generation system: a tool that transforms a Config file into ready-to-use React components. This paves the way for automated page creation, where an AI can generate the configuration, and our Transformer turns it into a beautiful interface.

To see our project in action, visit the live demo at aifa.dev.

A Glimpse into Future Posts:

Part 2: The Magic of Markdown. The next article will focus on the second key technology: generating pages (documentation, tutorials, product pages) from Markdown files.
Part 3: From Config to UI. We will revisit the topic of this article and dive deeper into how a ChatGPT-generated configuration is transformed into React components and how this process can be extended.

In the next tutorial, we'll start by answering a key question: Why was Markdown chosen for content management? Stay tuned!

How to Fix the Next.js Parallel Routes 404 Error on Cold Reload with NextAuth

Roma Armsrtrong — Mon, 16 Jun 2025 19:53:17 +0000

Next.js Parallel Routes are a powerful feature for building complex, dynamic layouts like dashboards. However, they come with a subtle trap that can lead to hours of frustration: a mysterious 404 "Page Not Found" error that only appears on a cold reload. Standard debugging methods often lead to dead ends, making this a particularly challenging issue to solve.

This article details a robust solution to this problem, discovered while building aifa.dev, an AI-First architecture designed.

The Problem: A 404 Error That Defies Logic
Consider an application built with a parallel route structure, which is common in AIFA. The layout is split into two primary slots:

text
app/
├── @left/ # Slot for AI chat and authentication
├── @right/ # Slot for the main application content
└── layout.tsx # Root layout for the parallel routes
Navigating within the app (soft navigation) works perfectly. The problem occurs when you perform a "cold reload" (pressing Ctrl+F5 or Cmd+Shift+R) on a URL that is only active in the @right slot, such as /privacy-policy. The application crashes and displays a 404 error.

The confusion is compounded because the most obvious solution, adding a default.tsx file to the @left slot as a fallback, doesn't work. The console provides no useful information, just a generic "Page Not Found" message, leaving you to believe the issue is with the routing itself.

The Root Cause: A "Greedy" NextAuth Route
The issue stems from a conflict between Next.js's routing mechanism and how NextAuth handles authentication, especially when placed inside a parallel route. In the AIFA architecture, the authentication fractal resides in the @left slot.

Here's the incorrect project structure that causes the error:

text
@/
└── app/
└── @left/
└── (_public)/
└── (_AUTH-FRACTAL)/
└── (auth)/
├── (_server)/
│ └── api/
│ └── auth/
│ ├── [...nextauth]/ # <-- THE PROBLEM IS HERE
│ │ └── route.ts
...
The problem is the standard catch-all route [...nextauth]. When you reload the /privacy-policy page:

The @right slot correctly identifies and prepares to render the /privacy-policy page.

The @left slot, however, does not have a matching route. Its "greedy" [...nextauth] route handler intercepts the /privacy-policy URL, misinterpreting it as an authentication parameter.

Since the NextAuth handler cannot process this parameter, it throws an error internally. An unhandled error in any parallel slot causes the entire page to fail, resulting in a 404 error before the default.tsx fallback can even be considered.

The Solution: The Optional Catch-All Route [[...nextauth]]
The fix is elegant and simple: switch from a standard catch-all route to an optional catch-all route. This is achieved by adding a second pair of square brackets to the directory name.

The corrected and robust structure:

text
@/
└── app/
└── @left/
└── (_public)/
└── (_AUTH-FRACTAL)/
└── (auth)/
├── (_server)/
│ └── api/
│ └── auth/
│ ├── [[...nextauth]]/ # <-- THE SOLUTION
│ │ └── route.ts
...
Why This Works

The difference between [...slug] and [[...slug]] is fundamental:

...slug: This route is "greedy" and matches any URL segment after its base path. It requires at least one parameter to be present. It incorrectly tries to match /privacy-policy.

[...slug]: This route makes the parameters optional. It will only match its exact base path (/api/auth/) or paths that extend it (e.g., /api/auth/signin). It does not match unrelated URLs like /privacy-policy.

By making this change, when you reload /privacy-policy, the router for the @left slot sees that the URL does not match the /api/auth/ base path and correctly ignores the NextAuth handler. With no other matching route, it successfully falls back to rendering your default.tsx file, and the 404 error is resolved.

Conclusion
This deep dive, prompted by the development of aifa.dev, highlights a critical nuance in Next.js. For complex applications using Parallel Routes, employing an optional catch-all route [[...nextauth]] for nested API handlers is not just a workaround—it is the canonical method for ensuring architectural stability. This approach guarantees that your routes are precisely targeted and do not interfere with one another, creating the resilient and predictable foundation that modern web applications require.

DEV Community: Roma Armsrtrong

The Best UI/UX of 2026? Why It’s Time for a New Interface

Why AI chat won’t kill websites, but reinvent them.

How we learned to use the web

The AI chat explosion: what changed in our heads

If AI is so smart, why do we still need websites?

Why the old page‑based web breaks in an AI world

The new interface: parallel experience streams

Why slots and parallel routes made sense

What changes in real products

Docs and learning products

E‑commerce

B2B and admin panels

What this means for designers

What this means for developers

What this means for the business

Risks and illusions

Has the time really come?

Trello for AI Coding: When the Autopilot Needs a Manual Gearbox

What’s Wrong With the Usual Approach?

The Trello-for-AI-Coding Vision

Why This Model Works

A Practical Stack

The Philosophy

What’s Next?

Our Communication No Longer Belongs to Us, Thoughts on How AI Could Create an Alternative to WhatsApp

Key Principles of This Approach

Simplicity, Not Complexity

Real-Life Magic

Reflection Time

How I Improved My OpenAI 4.1 Code Generation Prompts — A Simple Trick That Changed Everything

AI-Powered Website Generator with ChatGPT and Next.js 15: Building SEO-Optimized Pages from Scratch (v0 Blueprint)

Here’s the roadmap:

Technology Stack: React 19 · Next.js 15 · Vercel · Prisma · Stripe · AI SDK · ChatGPT

How It Works

Who Wins with an AI-Powered Site Generator? Studios, Enterprises, Startups, and Freelancers

The Next-Generation Design Agency: From Mockup to Mass Production in Minutes

Enterprise Deployments: Brand Consistency and Radical Scalability

The Core Engine: Our JSON-to-React Transformer Architecture

AI-Native Documentation: Use This Tutorial as a Live Knowledge Base

How it Works:

1. Introduction: Our Philosophy—Consistency Trumps Raw Speed

Beyond the Basics: Handling Real-World Complexity

1.1. The Concept: AI as Your Brand Steward, Not Just a Content Engine

1.2. The 5-Step Workflow: From Design System to SEO-Optimized Build

1.3. The Business Impact: Drive Conversions and Slash TCO

1.4. Your Roadmap Through This Series

2. The Anatomy of Our AI-Powered Architecture

Project Directory Tree

Key Component Paths

2.1. The Core Component Toolkit: Production-Ready Pages Out of the Box

Purpose

Core Architectural Principles:

2.1.1. The [[...slug]]/page.tsx Component: Universal Catch-All Route for Dynamic URLs

2.1.1.1. The Strategic Value of Catch-All Routes: Agility and Deployment Speed

2.1.1.2. The Value of Dynamic URLs for AI Assistants: Infinite Content Scalability

2.1.1.3. Real-World Use Cases at Scale

2.1.1.5. Key Functions and Their Business Impact

2.1.1.7. The Component: The Execution Engine

2.1.1.7. The Bottom Line: Catch-All Routes as Your AI Content Engine

2.1.3. FullScreenWrapper vs. Wrapper: Crafting Immersive Full-Screen Experiences

2.1.3.1. Core Purpose

2.1.3.2. Layered Architecture

2.1.3.3. Customization and Extensibility

2.1.3.4. Advanced Styling and Reusable Templates

2.1.3.5. The Component

2.1.4. Wrapper: The Standard Sectional Container

2.1.4.1. Primary Use Case

2.1.4.2. Structure and Styling

2.1.4.3. Customization Points

2.1.4.4. Building Reusable Templates

2.1.4.5. The Component

2.1.5. page-wrapper-types.ts: The AI-to-UI TypeScript Contract

2.1.5.1. The Core Contract: Bridging AI Generation and UI Rendering

2.1.5.2. SectionType Categories and AI-Driven Generation

2.1.5.3. SEO Optimization and Metadata

2.1.5.4. HeaderContentConfig: Managing SEO Hierarchy

2.1.5.5. BodySection: Enabling Maximum Design Freedom

2.1.5.6. FooterContentConfig: A Minimalist Approach

2.1.5.7. Principles for Extending Type Definitions

2.1.1. The `[[...slug]]/page.tsx` Component: Universal Catch-All Route for Dynamic URLs

2.2.1.3. Extending `SectionType`

2.2.2.1. `DoublePresentation`

2.2.2.1.2. Add New Case to `PageHtmlTransformer`

2.2.2.1.3. `page-wrapper-types.ts`