Building Seq: An AI-Native Video Editor with v0, Next.js 16, and Multi-Model AI

[ussaaron (Aaron)]

Hey Vercel community!

I’m excited to share Seq, an open-source AI video production studio I built as a template in v0. This post covers the architecture, challenges, and lessons learned.

What is Seq?

Seq is the first AI-native NLE (Non-Linear Editor) built for the browser. The workflow is:

1. Text → Storyboard: Describe scenes in natural language, Gemini 3 Pro Image generates visual panels

2. Storyboard → Video: Animate panels with Veo 3.1, WAN 2.2, or WAN 2.5

3. Video → Timeline: Edit clips on a professional multi-track timeline

4. Timeline → Export: Render to 720p or 1080p MP4 via FFmpeg WASM

Live Demo: https://seq-studio.vercel.app

GitHub: GitHub - headline-design/seq: AI-Native Video Production Studio

v0 Template: https://v0.app/templates/seq-ZAICjqmFe5w

Technical Architecture

The Stack

• Framework: Next.js 16 with App Router

• React: React 19

• Styling: Tailwind CSS 4

• AI Integration: Vercel AI SDK + AI Gateway

• Image Generation: Gemini 3 Pro Image

• Video Generation: Veo 3.1, WAN 2.2, WAN 2.5 (via fal.ai)

• Export: FFmpeg WASM for client-side encoding

• Storage: Vercel Blob

• Video Compositing: Canvas API with OffscreenCanvas

• Audio: Web Audio API with AudioContext for real-time mixing

• State Management: React hooks + refs for frame-accurate playback

Key Challenges

1. Frame-Accurate Playback

Browser video playback isn’t designed for frame-accurate scrubbing. I solved this by:

• Using requestVideoFrameCallback for precise timing

• Maintaining separate video elements for A/B playback switching

• Building a custom playhead that drives both video elements

2. Real-Time Audio Graph

Mixing multiple audio tracks in real-time required building an audio graph with Web Audio API:

• Each track has its own GainNode for volume control

• Tracks connect to a MediaStreamDestination for export

• Mute/solo states are handled by manipulating gain values

3. Multi-Model AI Integration

Different shots need different models:

• Veo 3.1 Fast: Quick iterations during ideation

• Veo 3.1 Standard: Production-quality output

• WAN 2.2: Frame-to-frame transitions with first/last frame support

• WAN 2.5: Higher resolution native output (1080p)

The Panel Processor automatically suggests the optimal model based on shot type and duration.

4. Export Pipeline

Rendering a timeline to video in the browser uses FFmpeg WASM:

• Clips are processed and encoded client-side

• Audio tracks are mixed via Web Audio API

• Progress is tracked through phases: init, audio, video, encoding

• Output options: 720p (fast, can reuse preview) or 1080p (full render)

Why v0?

Building Seq with v0 was a deliberate choice. I wanted to:

1. Prove the platform: Show that v0 can handle complex, stateful applications

2. Rapid iteration: The AI-assisted coding let me prototype features in hours, not days

3. Community: Make the template available for others to learn from and build upon

v0’s ability to understand context across multiple files was crucial for a project this size. The codebase has 100+ files and the AI could reason about component relationships, state flow, and side effects.

Lessons Learned

1. AI-native ≠ AI-assisted: Building AI-first changes everything about UX design

2. Browser APIs are powerful: Canvas + Web Audio + FFmpeg WASM = full video editor

3. State management is king: Frame-accurate playback requires obsessive attention to state

4. v0 scales: Complex projects are absolutely buildable with AI assistance

What’s Next

• Additional audio track features

• Keyframe-based animation curves

• Collaborative editing with real-time sync

• Plugin architecture for custom effects

Try It

Live Demo: https://seq-studio.vercel.app

GitHub: GitHub - headline-design/seq: AI-Native Video Production Studio

v0 Template: https://v0.app/templates/seq-ZAICjqmFe5w

Would love to hear your feedback! Drop a comment below or open an issue on GitHub.

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