Quiddity Roll Versioning 1.1.0: Visual QR Code Representation

(Or: Making Feature States Actually Visible)

Staring at bVa/2025.3.1:AFFDC01F.0.3C and trying to mentally parse which features are in testing versus production remains a challenging exercise. Comparing two version strings to understand what changed between deployments requires translating hexadecimal values back into meaningful feature states—a process that’s both time-consuming and error-prone.

Visual representation solves this fundamental readability problem. QR codes provide an ideal visual grid structure that can be augmented to serve both machine and human readers simultaneously. The choice of “Quiddity Roll” as a name wasn’t accidental—the QR abbreviation points directly to the intended visual destination. While “quiddity” perfectly captures the essential nature of features, the versioning scheme was designed from the start to evolve into scannable, visual feature state representation.

Introducing Visual QRver: Bridging Machine and Human Readability

Quiddity Roll Versioning 1.1.0 introduces visual QR code representation that combines standard QR code functionality with color-coded feature state overlays. The system provides both machine-readable URLs and human-interpretable visual patterns in a single image.

Here’s what this looks like in practice:

blog/2025.1.AAAAA.55555.FFFFF.103

The QR code encodes the URL above while simultaneously displaying feature states as colored 2×2 blocks overlaid on the traditional QR pattern. This dual-purpose approach eliminates the need to choose between machine efficiency and human comprehension.

Visual Color Mapping System

The visual representation uses a systematic 4-color mapping that directly corresponds to the 2-bit feature states:

• Transparent: Not Implemented / Removed from codebase (00)
• Red: Included, unknown state (01)
• Green: Implemented in test state, feature flag default off (10)
• Gold: Integrated in production state, feature flag removed (11)

Each color is implemented with both dark and light tones to preserve QR code readability. Dark tones appear on black squares of the underlying pattern, while light tones appear on white squares. This dual-tone system maintains sufficient contrast for both QR code scanning and visual feature state identification.

The color progression creates an intuitive workflow visualization: red indicates early-stage features, green shows tested implementations, and gold represents production-ready integration. This immediate visual feedback eliminates the cognitive overhead of translating hexadecimal values into feature states.

Monthly Pattern Evolution and Spatial Organization

The positioning detection patterns change shape based on the development month within a 4-month cycle. This creates a visual calendar system that indicates both current timing and feature window positioning:

• Month 1: Top-right circle, top-left hexagon, others square
• Month 2: Top-left circle, bottom-left hexagon, others square
• Month 3: Bottom-left circle, others square
• Month 4: Top-right hexagon, others square

Each pattern contains a color-coded month indicator in the top-right corner that cycles every four months. Blue represents the winter group (months 1-4), green indicates the summer group (months 5-8), and golden brown marks the fall group (months 9-12). These month groupings provide temporal context at a glance, making it possible to understand both feature states and development timeline from visual inspection alone.

Reading Visual Feature States

Feature blocks are arranged in 7-block-wide rows around the QR code, with placement determined by the current month pattern. To read any specific feature window, you rotate the QR code so that the window’s corresponding positioning pattern appears in the top-right position, then read blocks from right to left, bottom to top following little-endian bit ordering.

This rotation-based reading system provides consistency across all months while accommodating the evolving spatial layout. The ability to physically or mentally rotate previous QR code versions to align corresponding feature windows enables direct visual comparison of feature state changes between deployments.

Central Binary Identifier Pattern

Each QR code includes a unique visual identifier in the center square, generated using two SHA1 hashes of the binary ID. The resulting pattern follows C₄ symmetry with distinct color areas that provide visual uniqueness while maintaining the mathematical precision needed for automated identification.

This central pattern enables immediate visual distinction between different binaries while preserving the cryptographic uniqueness required for systematic identification across multiple deployment targets.

Practical Implementation Benefits

The visual system addresses several real-world deployment challenges:

Immediate State Assessment: Rather than parsing hexadecimal values, teams can assess feature readiness through color patterns. Green blocks indicate tested features ready for production evaluation, while gold blocks show fully integrated functionality.

Historical Context: The monthly pattern evolution provides visual timeline information, making it possible to understand not just current feature states but their progression through development cycles.

Selective Visualization: The only parameter allows focused analysis of specific features while maintaining full QR code functionality: https://qrver.dev/1.0/bVa/2025.3.0.1.F.AC?only=1,2,3

Cross-Deployment Comparison: The rotation-based alignment system enables systematic comparison of feature states across different builds, environments, or time periods.

Backward Compatibility and Error Handling

The visual overlay system maintains full compatibility with existing QRver implementations. When rendering fails or version strings are malformed, the system displays a standard QR code without color overlays, providing clear error indication while preserving basic functionality.

All visual enhancements are additive—existing automation and parsing systems continue to function without modification while gaining the option to leverage visual analysis capabilities.

Enhanced Visual Analysis with QRoll Compare

The visual system enables sophisticated comparison workflows that would be impractical an impossible with text-based version strings alone. When examining feature progression across multiple deployments, you can rotate QR codes to align feature windows and observe state transitions through color changes.

To support this analysis pattern, I’m developing QRoll Compare—a web application that enables side-by-side visual comparison of multiple QR codes with automatic alignment and difference highlighting. The tool will support overlay modes for detecting feature state transitions and progression patterns across development cycles.

This comparative analysis capability transforms version management from a text-parsing exercise into a visual pattern recognition task, making it possible to spot anomalies, track feature velocity, and identify deployment patterns at scale.

Conclusion

Visual QRver transforms feature state management from a text-parsing exercise into a pattern recognition task. The combination of systematic color coding, spatial organization, and temporal pattern evolution creates an information-dense visual system that scales from individual feature assessment to deployment-wide analysis.

The upcoming QRoll Compare tool will extend these capabilities to multi-deployment analysis, enabling visual tracking of feature progression across development cycles and deployment targets.

The real value isn’t just in making version information visible—it’s in making complex deployment states immediately comprehensible, enabling faster decision-making and more confident release management. I recognize that relying solely on color for state indication presents accessibility challenges for users with color vision differences. This initial implementation prioritizes establishing the visual framework while I evaluate alternative approaches for conveying the same information through pattern, texture, or symbol variations. Feedback on effective accessibility strategies would be particularly valuable as the specification evolves. —-p.s. The visual patterns become particularly useful when printed and posted in deployment areas, providing team-wide visibility into feature states without requiring terminal access or version string parsing.