Master Roadmap v2.0
Offline-readable snapshot of the Neurialab stack. Phases are framed as tension between milestones and pain surfaces.
Milestones
Pain Surfaces
Phase I — Foundation
Cement the semantic substrate and glyphic layer.
Semantic Architecture Layer
Milestones
- Finalize 230-D linguistic feature stack → 17 meta-features.
- Standardize feature definitions and examples.
- Document calibration procedures for live transcripts.
Pain Surfaces
- Calibration routines incomplete or too manual.
- No shared “playbook” for annotators or collaborators.
Glyphic & Affective Layer
Milestones
- Integrate glyph layer into transcript engine.
- Add confidence scaling & decay for glyph states.
- Prototype 1–2 “glyph dashboards” for personal sessions.
Pain Surfaces
- No bidirectional inference between text and glyph yet.
- Affective mapping is intuitive, not empirically validated.
Phase II — Cymatic Integration
Bring body, audio, and environment into the same analytic frame.
Cymatic–Linguistic Integration
Milestones
- Translate frequency bands → candidate linguistic correlates.
- Build first-pass pipeline: audio → feature manifolds.
- Integrate basic EEG signal processing for resonance mapping.
Pain Surfaces
- Requires empirical EEG / audio data you don’t yet have.
- Open questions around privacy & consent for bodily data.
- Signal noise in real-world environments could skew correlations.
Phase III — Reflexive Deployment
Deploy the integrated system in controlled environments, focusing on user feedback loops.
Deployment & Feedback Layer
Milestones
- Launch beta prototype for self-tracking sessions.
- Implement real-time feedback loops for glyph adjustments.
- Collect initial dataset from 50+ user interactions.
Pain Surfaces
- User adoption barriers due to tech setup complexity.
- Feedback overload if revelations aren't paced by trust levels.
AI-Assisted Calibration
Milestones
- Integrate ML models for predictive glyph states.
- Automate calibration based on user history.
Pain Surfaces
- Model bias from limited training data.
- Over-reliance on AI could erode self-trust.
Phase IV — Evolutionary Scaling
Scale to collaborative and communal systems, evolving the field through shared resonance.
Collaborative Resonance Layer
Milestones
- Enable multi-user sessions with shared glyph manifolds.
- Develop community doctrine from aggregated insights.
- Release open-source tools for custom integrations.
Pain Surfaces
- Privacy risks in shared data environments.
- Scaling computations for real-time multi-user resonance.
- Divergent user interpretations fracturing coherence.