149 lines
4.3 KiB
Markdown
149 lines
4.3 KiB
Markdown
# The Mindscape of Bro Tsong
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Project: The Mindscape of Bro Tsong
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Current phase: `model_library_mvp`
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Current subsystem: Model Library / Model Management MVP
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## 1. Project Definition
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This project is a file-first MVP for a cognitive model library.
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It turns core cognitive models into structured, traceable, callable, and testable model assets.
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The first version validates the workflow with two sample models:
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- QPI
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- Intellectual Archaeology
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## 2. What This Project Is
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This project is:
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- A model asset library
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- A model card system
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- A source evidence index
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- A regression test container
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- A minimal model selection demo foundation
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- A foundation for a future question-answering / cognitive processing system
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## 3. What This Project Is Not
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This project is not:
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- A full model management platform
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- A public SaaS product
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- A user-facing application
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- A complete knowledge graph
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- A full RAG system
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- A commercial platform
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- A multi-user collaboration system
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- A complete question-answering system
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## 4. Current MVP Goal
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The current MVP tests whether a small number of core cognitive models can be represented as:
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- Human-readable model cards
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- Machine-readable JSON model specs
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- Source article records
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- Source evidence excerpts
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- Regression test cases
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- Minimal selector inputs and outputs
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## 5. First Sample Models
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### QPI
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QPI is a routing model that classifies a user input as:
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- Question: lack of information
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- Problem: lack of path or method
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- Issue: lack of stability, consensus, or dynamic balance
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### Intellectual Archaeology
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Intellectual Archaeology is a deep modeling model that analyzes a topic through multiple depth layers, from surface application to mechanism, purpose, human capability, and philosophical assumptions.
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## 6. Repository Structure
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```text
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docs/ Project rules, contracts, workflow notes, decisions, non-goals, and handoff templates.
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schemas/ JSON Schema files for model specs, source records, source excerpts, and regression cases.
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models/ Machine-readable JSON model specifications.
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cards/ Human-readable Markdown model cards.
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sources/ Source article records and source evidence excerpts.
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tests/ Regression cases for model use, misuse, and boundary checks.
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selector/ Rule-based selector configuration and examples.
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scripts/ Local validation and selector demo scripts.
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reports/ Validation reports, extraction notes, and concrete session handoffs.
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```
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## 7. Data Format
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The project uses JSON as the machine-readable source format.
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Markdown files are used for human-readable model cards and documentation.
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## 8. Validation
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All model JSON files should eventually pass schema validation.
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Validation should check:
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- Required fields
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- Enum values
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- Unique model IDs
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- Source article references
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- Source evidence references
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- Regression test references
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## 9. Minimal Selector
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The selector is not a full AI system.
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It is a simple demo that recommends candidate models based on:
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- Trigger keywords
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- Input type match
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- Negative triggers
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- Pipeline position
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- Selection priority
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## 10. Development Principles
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- Keep the MVP small.
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- Prefer files over databases.
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- Prefer explicit schema over implicit conventions.
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- Prefer traceability over automation.
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- Prefer testability over expressive writing.
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- Do not expand to many models before the sample models are stable.
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- Treat `model_library_mvp` as the current phase, not as a nested project root.
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## 11. Related Projects
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- `knowledge-vault`: source archive, discussion records, and durable upstream documentation.
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- `ccpe-system`: expert-agent, runtime, model, and protocol specification workbench.
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- `skills-vault`: canonical source for reusable automation skills.
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- `writing-workbench`: deep writing production workspace.
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- `video-workbench`: dimensional output workspace for scripts, presentations, and videos.
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This repository consumes selected source material and model definitions from the surrounding ecosystem, but it remains the product/system boundary for The Mindscape of Bro Tsong.
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## 12. Current Status
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Initial project setup.
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## 13. Next Steps
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1. Confirm directory structure.
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2. Confirm schema files.
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3. Add QPI model JSON.
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4. Add Intellectual Archaeology model JSON.
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5. Add human-readable model cards.
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6. Add source records and evidence excerpts.
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7. Add regression cases.
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8. Add validation scripts.
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9. Add minimal selector demo.
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