pattern-resonance

Pattern resonance performs a three-layer scan (surface, middle, deep) across notes, code, and data to surface implicit patterns you sense but haven't articulated. It applies temporal analysis and cross-domain detection to identify recurring themes dispersed across multiple sources, outputting probe-level signals for reflection rather than definitive reports.

Use pattern resonance when reviewing accumulated work and questions recur across sources but connections aren't yet clear, or when you feel something is present in your data but can't articulate it. It's designed for cross-domain review tasks where explicit patterns haven't emerged and felt sense guides investigation.

Pattern resonance prioritizes felt sense over forced explanations, using energy-field mapping and temporal analysis to detect implicit connections across domains. Unlike report-based analysis, it outputs probes—low-signal indicators—that validate through user reflection rather than quantitative proof.

The scan operates across three layers: surface detects explicit patterns in data; middle identifies implicit connections; deep accesses felt sense and intuition. This progression surfaces patterns from obvious to subtle, with cross-domain resonance mapping how signals interact across notes, code, and thoughts.

Yes. Pattern resonance is designed for unstructured inputs—notes, code fragments, and thoughts—where patterns are dispersed and not yet organized. It applies cross-domain resonance and energy-field mapping to detect coherence across heterogeneous sources without requiring pre-structured data.