## Interactive Demo

### Drop Files to Release the Swarm

Or click below to select files

Swarm Size: 0
Cohesion: 0%
Velocity: 0
Documents
Images
Videos
Other

## Traditional vs Innovation

### Traditional File Upload

Drag and drop files here or click to browse

### SwarmUpload Innovation

Files become living entities that:

- Flock together by file type
- Show upload progress through movement patterns
- Demonstrate relationships through proximity
- Self-organize based on collective intelligence
- Respond to user interaction with emergent behavior

## Design Documentation

### Interaction Model

SwarmUpload transforms file management into a living ecosystem. Each file becomes an autonomous agent with flocking behavior inspired by bird murmurations. Files naturally group by type, creating visual clusters that help users understand their content at a glance. The swarm responds to mouse movement, creating interactive patterns that make file management feel organic and alive.

### Technical Implementation

Built using Canvas 2D API for smooth 60fps animation, the system implements Craig Reynolds' boid algorithm with separation, alignment, and cohesion forces. Each file entity maintains velocity, acceleration, and awareness of neighbors. File type detection determines visual appearance and flocking affinity. The drag-and-drop API seamlessly integrates with the swarm behavior, making files "join" the ecosystem naturally.

### Accessibility Features

Full keyboard navigation allows users to cycle through files with Tab/Shift+Tab. Screen readers announce file names, types, and swarm statistics. ARIA live regions update with swarm changes. Alternative text mode provides a structured list view. Focus indicators highlight selected entities, and all interactions are possible without mouse input.

### Evolution Opportunities

Future iterations could include: predator-prey dynamics for file organization, seasonal migrations for archiving, breeding behaviors for file duplication, ecosystem health indicators for storage optimization, and neural network patterns for intelligent file suggestions. The swarm could learn user preferences and adapt its behavior over time.