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Introduces a detailed report covering system capabilities, architectural flows, and strategic implementation patterns. - Adds 3 Mermaid diagrams for architecture and trust lifecycles. - Includes mapping tables for application domains and actor decisions. - Defines risk mitigation, KPI frameworks, and a 90-day rollout plan. - Aligns documentation with existing `ebsl.service.ts` logic. Co-authored-by: Codex <199175422+chatgpt-connector[bot]@users.noreply.github.com>
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Pull request overview
Adds a new comprehensive Markdown report describing EQBSL Explorer’s capabilities and potential application areas, including Mermaid diagrams and structured tables to communicate architecture, flows, and adoption guidance.
Changes:
- Introduces a detailed “Uses and Applications” report with architecture + lifecycle diagrams (Mermaid).
- Summarizes current feature baseline, risks/limitations, and suggested productization roadmap.
- Adds domain-fit tables, KPI framework, and a 30/60/90-day adoption blueprint.
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| | Capability | Current Implementation | Practical Meaning | | ||
| |---|---|---| | ||
| | Opinion from evidence | `calculateOpinion(r,s,a)` with `K=2` in `ebsl.service.ts` | Converts interactions into belief/disbelief/uncertainty with explicit unknown-state handling | | ||
| | Expected trust value | `expectedProbability()` in `ebsl.service.ts` | Produces a decision-friendly score while preserving uncertainty in the underlying model | | ||
| | Dynamic trust graph | Autonomous edge growth, decay, role behavior in `eqbsl-graph.component.ts` | Simulates trust formation, drift, and adversarial dynamics in multi-actor networks | |
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Markdown tables are written with a leading double pipe (e.g., || Capability | ...), which renders as an extra empty first column in most Markdown parsers. Consider switching these to standard single-pipe rows (e.g., | Capability | ...) throughout the report to avoid broken table formatting.
| | Semantic trust labels | `getCathexisLabel(r,s)` plus optional LLM-generated handles | Maps quantitative trust vectors into operator-friendly narratives | | ||
| | AI-assisted identity annotation | `generateAIHandle()` using Gemini API key | Enhances explainability/UX in large trust graphs | |
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The “Semantic trust labels” row attributes the optional LLM-generated handles to getCathexisLabel(r,s), but in the current codebase getCathexisLabel is a deterministic mapping; the LLM-based handle generation lives in EqbslGraphComponent.generateAIHandle(). Suggest updating the implementation reference so readers can find the correct source of the LLM behavior.
| | Semantic trust labels | `getCathexisLabel(r,s)` plus optional LLM-generated handles | Maps quantitative trust vectors into operator-friendly narratives | | |
| | AI-assisted identity annotation | `generateAIHandle()` using Gemini API key | Enhances explainability/UX in large trust graphs | | |
| | Semantic trust labels | Deterministic `getCathexisLabel(r,s)` mapping in `cathexis.component.ts` | Maps quantitative trust vectors into operator-friendly narratives | | |
| | AI-assisted identity annotation | `EqbslGraphComponent.generateAIHandle()` in `eqbsl-graph.component.ts` using Gemini API key | Enhances explainability/UX in large trust graphs | |
| | Semantic trust labels | `getCathexisLabel(r,s)` plus optional LLM-generated handles | Maps quantitative trust vectors into operator-friendly narratives | | ||
| | AI-assisted identity annotation | `generateAIHandle()` using Gemini API key | Enhances explainability/UX in large trust graphs | | ||
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The report calls out “Gemini API key”, but the repository documentation/code refer to a “Google Generative AI API key” provided via the API_KEY environment variable. Consider aligning terminology here (e.g., “Google Generative AI (Gemini) API key via API_KEY”) to match README guidance and avoid confusion.
- Added Mermaid diagrams for architecture, trust lifecycle, and ZK flows. - Included a real-world use-case matrix and concrete worked examples. - Added a production comparison between EQBSL and scalar trust scores. - Integrated explicit references to core implementation files. Co-authored-by: Codex <199175422+chatgpt-connector[bot]@users.noreply.github.com>
Produce a detailed and highly informative report on potential uses and applications for the EQBSL system. Including mermaid diagrams and tables.
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