Modular by design
The architecture separates capture, workspace modeling, timeline replay, and agent execution so the system can evolve from prototypes to production deployments. The Polyopticon provides a temporal-haptic thin client interface that connects with poly-virtual back-end resources—designed for coherence across scales.
System overview
The architecture spans from device-level input capture through distributed back-end processing, maintaining coherence across the entire stack.
Device Layer
Haptic input capture • Spatial manipulation • Anamorphic rendering • Local state
Model Layer
Poly-capsules • Timeline objects • Agent matrix • Workspace topology
Compute Layer
Local processing • Docked node • Poly-server • Cloud resources
Coherence Layer
Timeline synchronization • Agent coordination • Workspace state management
Collaboration Layer
Timeprint sharing • Agent transfer • Live co-navigation • Workspace sync
Applications and data feeds residing virtually are processed by a specialized back-end algorithm that maintains coherence among the individual and collective timeline objects created. The system enables constant scanning for novel and nuanced data from within the Poly-capsules.
Device architecture
A multi-faceted computing device using anamorphic rendering of optical telemetric signaling for user interface and haptic signaling to manipulate time. The device comprises novel components reaching technical maturity: flexible displays, high-performance portable processors, accelerometer arrays, and high-speed wireless networking.
Hardware subsystems
Display array
Flexible OLED surfaces coating the polyhedral form. Each face serves as an independent display capable of full-resolution rendering. The means to physically coat a spherical object with screen material enables the 32-face topology.
Haptic input matrix
Pressure-sensitive touch surfaces across all faces. High-resolution accelerometer arrays capture device motion—roll, pitch, yaw, shake. The input methods enable the virtual Poly space for navigation.
Processing unit
High-performance portable processor for local rendering and state management. FPGA integration for complex state machinery—knitting together graphics and motion data to provide apparent motion and maintain whole-system coherence.
Connectivity
High-speed wireless networking for back-end synchronization. The device interfaces with all potential communication and processing resources in the environment, building relationships that reflect current observed requirements.
Rendering system
Anamorphic projection
Novel display algorithms drive the device, rendering content across curved surfaces while maintaining visual coherence and legibility.
Resolution shifting
Dynamic fidelity management. Foreground capsules render at full resolution; peripheral and background capsules reduce detail to conserve resources.
Topsight mode
Medium-resolution, forced-perspective view enabling visualization of all capsules in the matrix simultaneously. Supports animated replay of activity across foreground and background.
Data model
Development and adoption of a four-dimensional object model is a necessity to fully realize the capabilities the Polyopticon offers. All objects carry temporal metadata and spatial relationships. The interface can be thought of as “3-D browser tabs” that contain the physical metadata tracing how contents got to be in their current state.
Poly-capsule
The fundamental workspace unit. A capsule can contain stored agents, represent a set of capabilities (an application palette), or encapsulate a data stream. Each capsule carries its own timeline—the physical interactions that led to its current state can be explored.
Properties: content, position, relationships, timeline, agents, resolution level, sync state
Timeline object
A captured sequence of interactions—a timeprint. Contains haptic motion applied to workspace objects, enabling replay and exploration of different perspectives. Timeline objects portray past instances of work and enable ready recall.
Operations: replay, edit, branch, annotate, compose, share
Agent
An encoded behavior pattern—a “3-D macro.” Rather than translating activities into a script, the user’s own physical motion is stored, embedding their conception of time into the process. Agents automate activities within the user’s unique information processing matrix.
Types: macro, template, reactive, AI-augmented
Workspace topology
The spatial arrangement of capsules and their relationships. The topology persists across sessions and can be shared. A network of connections in which temporal relationships can be explored using motion.
Contains: capsule positions, relationship graph, agent bindings, sync configuration
Deep dive: The agent matrix+
The underlying agent mechanism enables constant scanning for novel and nuanced data from within the Poly-capsules. The OS moves background objects reflecting new feeds, providing a live perspective of the user’s data and information processing matrix.
The user engages with tasks in the foreground while the Polyopticon’s agent matrix manages the rendering and feeding of background tasks. Foreground activities focus primarily on the creation of agents—bodies of data and capabilities that can be stored, extended, and shared.
The greater depth of metadata supports the design of agents that automate activities within the user’s unique information processing matrix. This capability of storing and sharing temporal perception—the literal capability to replay, annotate, and share one’s own perception of working—leads to making complex connections more quickly.
Compute architecture
The architecture supports multiple deployment configurations, from standalone device operation to distributed processing across cloud resources. The Polyopticon fits hand and glove with its Poly-server back-end, providing high resource availability and the capacity to navigate and manage multiple streams quickly.
Standalone
Self-contained operation with local storage and processing. The device handles capture, rendering, and basic agent execution independently. Suitable for portable use and environments without network connectivity.
◇ Local timeline storage
◇ On-device agent execution
◇ Offline workspace management
Docked / Node-assisted
Extended capacity through connected compute node. The device in an integrated suite with flat displays, projectors, and stationary orientation on a base or charging platform. Provides ability to sort through a universe of choices with enhanced processing.
◇ Extended display integration
◇ Persistent agent execution
◇ Mass storage connection
Poly-server
Full back-end integration with dedicated server resources. The architecture is capable of virtualizing on a mass scale—up to an effective 105 cores to process large application streams containing physical-semantic data. Connected to archives, wired into switch and router infrastructure.
◇ High-throughput timeline processing
◇ Complex agent orchestration
◇ Enterprise data integration
Cloud / Distributed
Elastic resources from cloud computing infrastructure. Growing accessibility to large-scale server-based resources supports applications running on virtual resources. Enables massive multi-user environments and AI-augmented agent execution.
◇ Elastic compute scaling
◇ Cross-region synchronization
◇ AI model integration
Processing distribution
Device
Input capture
Local rendering
State caching
Node
Timeline processing
Agent execution
Display expansion
Cloud
Long-term storage
AI processing
Collaboration sync
Coherence management
A specialized back-end algorithm maintains coherence among the individual and collective timeline objects created. The system ensures that workspace state, agent execution, and timeline capture remain consistent across devices, sessions, and collaborators.
Timeline synchronization
Captured interactions sync across devices and to back-end storage. Conflict resolution handles concurrent edits. Branch and merge operations maintain timeline integrity.
Agent coordination
Agents execute across device, node, and cloud as appropriate. The agent matrix coordinates background task execution. Trigger conditions evaluate across the distributed system.
Workspace state
Capsule positions, relationships, and content sync continuously. Resolution levels adapt to available compute. State persists through disconnection and device transitions.
Integration architecture
The device serves as a media and peripheral coordinating device, receiving and feeding input to external devices and services.
Capsule adapters
Standard interfaces for wrapping external applications, data feeds, and services as poly-capsules. Adapters translate between the temporal-haptic model and conventional application interfaces.
◇ Web application wrapper
◇ REST/GraphQL connector
◇ Document renderer
◇ Media stream handler
External display
Projection of capsule contents to external monitors, projectors, and AR/VR displays. The specialized interface expands individual operator perspective to group accessibility in real-time.
◇ Multi-monitor extension
◇ Projector integration
◇ AR overlay support
◇ VR workspace mode
AI services
Connection to AI models for agent augmentation, content analysis, and intelligent automation. AI provides judgment within agent-defined structure.
◇ LLM reasoning integration
◇ Computer vision for content
◇ Pattern detection agents
◇ Semantic search
Enterprise systems
Connectors for enterprise data sources, identity management, and workflow systems. Enables deployment in organizational contexts with existing infrastructure.
◇ SSO/SAML authentication
◇ Data warehouse access
◇ Workflow orchestration
◇ Audit logging
Security & privacy architecture
The system captures meaningful work without unnecessary personal data. Security is designed in, not bolted on.
Data minimization
Capture focuses on actions and context needed for reproducibility, not surveillance. Timeprints record interaction structure; sensitive content can be redacted while preserving reasoning paths.
User ownership
Your timeprints and agents belong to you. Export them, move them, own them. Portability is a design requirement, not an afterthought.
Granular sharing
Control what’s shared and with whom. Timeprints can be shared in full, redacted, or structure-only. Agent transfer includes permission inheritance.
Encryption throughout
Data encrypted at rest and in transit. Device-level encryption for local storage. End-to-end encryption for collaboration channels.
Enterprise deployment considerations
◇ On-premises deployment option
◇ Data residency controls
◇ Audit trail export
◇ Role-based access control
◇ Integration with SIEM
◇ Compliance reporting
Technology enablers
The convergence of several technology trends makes the Polyopticon architecture viable at scale.
Flexible OLED displays
Flexible screen material at the precipice of commodification. The means to physically coat a polyhedral object with display surfaces is now technically feasible at reasonable cost points.
FPGA advancement
FPGAs have leapt to the fore, driven by cryptocurrency mining and AI acceleration. This complex state machinery provides apparent motion and maintains whole-system coherence at the device level.
Rendering capacity
Built-in rendering capacity of systems has expanded to the point where the graphics requirements, while still steep, can be fulfilled within the hardware footprint of a portable device.
Cloud computing
Growing accessibility to large-scale server-based resources. Elastic compute enables the back-end processing needed for complex agent execution and multi-user coherence.
High-speed wireless
High-speed wireless networking enables seamless synchronization between device and back-end. Low-latency connections support real-time collaboration and continuous state sync.
AI integration
Large language models and AI services provide the reasoning capacity for AI-augmented agents. The structure is provided by human-defined agents; AI provides judgment within that structure.
Implementation phases
The architecture supports incremental development from simulation through hardware prototype to production deployment.
Simulation environment
Software simulation of the polyhedral interface and temporal-haptic interaction model. Validates the data model, agent framework, and core interaction patterns without custom hardware.
Prototype device
Hardware prototype with representative display and input capabilities. Tests physical ergonomics, rendering algorithms, and device-level processing requirements.
Integrated system
Full stack implementation with device, back-end, and collaboration features. Domain-specific deployments for validation in target use cases.