Fordel StudiosWeb Application Development
Web apps built for AI streaming, real users, and Core Web Vitals from the start.
What this means
in practice
Web development changed in 2025. React Server Components, the Vercel AI SDK, and AI-assisted scaffolding tools shifted the baseline for what a well-built app looks like. We build full-stack Next.js applications using the App Router, RSC where it matters, and streaming AI interfaces that feel native to the product — not bolted on.
Our stack is TypeScript end-to-end, shadcn/ui for component foundations, Tailwind for styling, and the Vercel AI SDK for anything that streams. We use v0 and Cursor in our workflow — not to replace engineering judgment, but because the productivity floor has moved and ignoring that costs time. Design systems ship as the first deliverable, not the last.
Web Development After the Vibe Coding Wave
In 2025, a wave of AI-assisted development tools — v0, Bolt, Lovable — demonstrated that non-engineers could generate working web UIs from text descriptions. The "vibe coding" label stuck. The lesson the industry drew varies: some concluded that frontend engineers are obsolete, others dismissed the tools as toys. The more accurate read is that the productivity baseline shifted. Engineers who use these tools effectively ship features faster than engineers who do not. The engineering skill is now in knowing when the generated output is correct, where it is subtly wrong, and how to build the architecture that makes AI tooling more effective.
The way you make AI tooling more effective is with a well-structured design system. When v0 or Cursor knows your design tokens, your component API conventions, and your file structure, it generates code that fits your system rather than generic Tailwind components you have to rework. The investment in design system quality now pays off in AI tool output quality.
React Server Components Changed the Mental Model
The App Router and React Server Components represent the most significant change to Next.js architecture since the Pages Router was introduced. The key insight: most of the data fetching and rendering in typical web applications does not need to happen in the browser. RSC moves that work to the server, ships only the resulting HTML to the browser, and eliminates the loading spinners and JavaScript overhead that come from client-side data fetching.
The practical effect: pages that used to show a skeleton loader while waiting for a client-side fetch now render complete on the first response. The JavaScript bundle shrinks because RSC components ship no JavaScript. Server-side data access is direct (no HTTP layer between your component and your database) and composable (each RSC can fetch exactly the data it needs).
Streaming AI in Web Applications
The interaction pattern for AI features in web applications has converged on streaming: the user submits a prompt, and the response appears token-by-token as the model generates it, rather than after a long wait for the full response. The Vercel AI SDK makes this straightforward with useChat and useCompletion hooks that manage the streaming state on the client and StreamingTextResponse on the server.
The design challenge is everything around the stream: how do you show the interface is thinking before tokens start appearing? How do you handle a streaming response that encounters an error mid-stream? How do you let the user cancel a response that is not what they wanted? These interaction design questions require more thought than the streaming implementation itself.
- Streaming chat: useChat hook + StreamingTextResponse — the standard Vercel AI SDK pattern
- AI-powered search: embed the query, retrieve semantically similar content, generate a grounded response
- Content generation in forms: trigger generation from form context, stream into a textarea, let users edit
- Real-time AI suggestions: debounced trigger on input, streaming suggestions, accept/reject UI
- Document Q&A: upload, extract, embed, retrieve on query — the full RAG flow in a web interface
- 01
Next.js application development with App Router and React Server Components
- 02
AI feature integration: streaming chat, AI-powered search, content generation
- 03
Design system development: tokens, component library, Storybook documentation
- 04
Real-time features: collaborative editing, live updates, presence indicators
- 05
Full-stack type safety with TypeScript across frontend and API
- 06
Performance optimization: Core Web Vitals, bundle analysis, image optimization
- 07
Authentication flows: OAuth, magic links, session management
- 08
Accessibility: WCAG 2.1 AA compliance built in, not bolted on
Our process
- 01
Architecture Decision
We pick rendering strategy (App Router with RSC for most apps), state management pattern, and styling approach before writing component one. These choices compound — they're easier to make upfront than to unwind at 60% completion.
- 02
Design System Foundation
Design tokens — colors, spacing, typography, radius — ship as the first concrete deliverable. A token-based system enforces visual consistency and gives AI coding tools (Cursor, v0, Copilot) enough context to generate components that actually match your design language.
- 03
Component Architecture
We build the UI component library on shadcn/ui base components, customized to match the design system, plus purpose-built components for domain-specific interactions. Everything goes into Storybook — components that aren't documented don't get reused.
- 04
Core Application Build
Primary user flows are implemented with RSC for data-heavy pages and Client Components for interactive elements. We use Server Actions for mutations, React Query for client-side data fetching, and keep component trees shallow with minimal client-side state.
- 05
AI Feature Integration
AI features are integrated using the Vercel AI SDK — useChat and useCompletion hooks manage streaming state, error boundaries, and loading states. The goal is AI that lives inside the primary user flow, not a sidebar widget that runs parallel to the actual product.
- 06
Performance and Launch
We measure LCP, CLS, and TBT on real user hardware throughout development, not just before launch. Real user monitoring via Vercel Analytics or DataDog RUM goes live with the app so regressions surface in hours, not support tickets.
Tools and infrastructure we use for this capability.
Why work
with us
- 01
AI features built into the flow, not appended to it
A chat widget bolted to a regular app is not an AI-first product. We integrate AI into the primary user flows — search that understands intent, forms that pre-fill from context, content that generates in place. That's a different implementation and a noticeably different user experience.
- 02
We work in the AI-augmented development baseline
We use v0 for component scaffolding, Cursor for code generation, and well-structured design tokens as context for both. This isn't about replacing engineering — it's acknowledging that teams doing this are delivering faster than teams that aren't, and we're on the right side of that gap.
- 03
Design systems as engineering infrastructure
A token-based design system with a documented component library does two things: enforces visual consistency across the application, and gives every AI coding tool enough context to generate correct output automatically. We treat it as infrastructure, not a design deliverable.
- 04
Performance measured on real hardware, not dev machines
The decisions that hurt Core Web Vitals — client-rendering content that should be server-rendered, unoptimized images, oversized JS bundles — are ones we avoid by default. We ship with real user monitoring in place so performance regressions are caught before users report them.
Frequently
asked questions
What's the practical difference between App Router RSC and the old Pages Router?
React Server Components render on the server and send HTML — no JavaScript for that component ships to the browser. For data-heavy pages (dashboards, content pages), this eliminates loading spinners and cuts bundle size significantly. Teams on the Pages Router are leaving real performance gains on the table, and the mental model shift to RSC takes roughly a week to internalize before it becomes natural.
How do you integrate streaming AI responses into a web UI?
The Vercel AI SDK handles the protocol: useChat and useCompletion hooks manage streaming state, error states, and loading states. The server endpoint streams via StreamingTextResponse using SSE. The result is tokens appearing as they generate rather than a spinner followed by a full response — the difference in perceived responsiveness is substantial.
Tailwind or CSS-in-JS — which do you recommend and why?
Tailwind is the pragmatic choice for most projects in 2026. The component ecosystem (shadcn/ui, Radix, Headless UI) is built around it, AI tools generate Tailwind by default, and there's no runtime CSS generation cost. CSS-in-JS solutions like styled-components still work, but the cases where they're the better choice have narrowed significantly.
What is shadcn/ui and why do you use it as a foundation?
shadcn/ui is a set of accessible, composable components built on Radix UI primitives and styled with Tailwind — but unlike a traditional library, it copies component source into your repository so you own and can modify it freely. That makes it ideal as a design system foundation: you start with well-built, accessible base components and customize them to your design language rather than fighting a theming API.
How do AI tools like v0 fit into your development process without creating throwaway code?
v0 and Cursor handle initial component scaffolding well — often covering 60-70% of a component's implementation from a description or screenshot. The remaining work requires application context, design judgment, and architecture fit that AI tools don't have. We use them to compress early-stage component work, then engineer the details that matter. Quality doesn't drop; timeline does.
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