Update nexus: fix conflicts and sync local changes
This commit is contained in:
Shen Wei
@@ -1,72 +1,72 @@
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---
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title: "llm-wiki-sync Circular Flow"
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topic: technical
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data_type: cycle
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complexity: moderate
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point_count: 7
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source_language: zh
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user_language: en
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---
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## Main Topic
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A cyclical knowledge management pipeline that transforms raw notes into structured wiki pages through continuous LLM-powered ingestion, extraction, and reuse.
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## Learning Objectives
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After viewing this infographic, the viewer will understand:
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1. The continuous circular flow of llm-wiki-sync from raw notes to reusable knowledge
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2. The key extraction outputs: Summary, Claims, Entities, Concepts, Connections
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3. How feedback and reuse complete the cycle back to new raw material
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## Target Audience
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- **Knowledge Level**: Intermediate technical audience
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- **Context**: Developers and knowledge workers interested in AI-powered knowledge management
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- **Expectations**: Clear understanding of the llm-wiki-sync pipeline and its cyclical nature
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## Content Type Analysis
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- **Data Structure**: Cyclic process with recurring steps
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- **Key Relationships**: Raw → Ingest → Extract → Source Page → Graph/Site → Reuse → Raw (feedback loop)
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- **Visual Opportunities**: Circular flow with nodes for each stage, arrows showing direction, center concept
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## Key Data Points (Verbatim)
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### Core Pipeline Steps
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1. **Raw Note** - Original document in raw/ folder
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2. **Ingest** - LLM analyzes and extracts structured information
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3. **Extract** - Summary, Claims, Quotes, Entities, Concepts, Connections
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4. **Source Page** - Structured wiki/sources/ page with frontmatter
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5. **Graph & Site** - graph.json and Quartz static site generation
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6. **Reuse** - Synthesize, query, create new content from structured knowledge
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7. **Feedback Loop** - New raw notes created from reused knowledge
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### Extraction Outputs
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- **Summary**: 核心主题, 问题域, 方法/机制, 结论/价值
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- **Key Claims**: Verifiable assertions extracted from text
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- **Key Entities**: LaunchDarkly, HP, Christian Dior, etc.
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- **Key Concepts**: RTO, RPO, Feature Flag, Kill Switch, Gradual Rollout
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- **Connections**: depends_on, enables, provides relationships
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### Key Quotes
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- "RTO is about speed: how fast you get back online. RPO is about data: how much you can afford to lose."
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- "Deploy != Release. Feature flags change this. You can deploy code to production without releasing it to users."
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## Layout × Style Signals
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- Content type: cycle → circular-flow
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- Tone: technical educational → chalkboard
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- Audience: developers → clear, legible, professional
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- Complexity: moderate → balanced density with clear visual hierarchy
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## Design Instructions (from user input)
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- **Layout**: circular-flow (NOT linear - must emphasize recurring cycle)
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- **Style**: chalkboard (dark background, hand-drawn chalk accents)
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- **Aspect**: 16:9 landscape
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- **Language**: English
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- Circular flow showing: raw note -> ingest -> extract -> source page -> graph/static site -> reuse/feedback -> knowledge base
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- Include core extraction outputs as recurring nodes/callouts
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- Keep text concise and legible
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- Dark chalkboard background with hand-drawn chalk accents
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- Avoid clutter, make cycle visually clear and publication-ready
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## Recommended Combinations
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1. **circular-flow + chalkboard** (Recommended): Perfect match for cycle/process content with chalkboard aesthetic
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2. **hub-spoke + technical-schematic**: For emphasizing central concepts with technical precision
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---
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title: "llm-wiki-sync Circular Flow"
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topic: technical
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data_type: cycle
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complexity: moderate
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point_count: 7
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source_language: zh
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user_language: en
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---
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## Main Topic
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A cyclical knowledge management pipeline that transforms raw notes into structured wiki pages through continuous LLM-powered ingestion, extraction, and reuse.
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## Learning Objectives
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After viewing this infographic, the viewer will understand:
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1. The continuous circular flow of llm-wiki-sync from raw notes to reusable knowledge
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2. The key extraction outputs: Summary, Claims, Entities, Concepts, Connections
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3. How feedback and reuse complete the cycle back to new raw material
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## Target Audience
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- **Knowledge Level**: Intermediate technical audience
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- **Context**: Developers and knowledge workers interested in AI-powered knowledge management
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- **Expectations**: Clear understanding of the llm-wiki-sync pipeline and its cyclical nature
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## Content Type Analysis
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- **Data Structure**: Cyclic process with recurring steps
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- **Key Relationships**: Raw → Ingest → Extract → Source Page → Graph/Site → Reuse → Raw (feedback loop)
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- **Visual Opportunities**: Circular flow with nodes for each stage, arrows showing direction, center concept
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## Key Data Points (Verbatim)
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### Core Pipeline Steps
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1. **Raw Note** - Original document in raw/ folder
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2. **Ingest** - LLM analyzes and extracts structured information
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3. **Extract** - Summary, Claims, Quotes, Entities, Concepts, Connections
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4. **Source Page** - Structured wiki/sources/ page with frontmatter
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5. **Graph & Site** - graph.json and Quartz static site generation
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6. **Reuse** - Synthesize, query, create new content from structured knowledge
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7. **Feedback Loop** - New raw notes created from reused knowledge
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### Extraction Outputs
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- **Summary**: 核心主题, 问题域, 方法/机制, 结论/价值
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- **Key Claims**: Verifiable assertions extracted from text
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- **Key Entities**: LaunchDarkly, HP, Christian Dior, etc.
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- **Key Concepts**: RTO, RPO, Feature Flag, Kill Switch, Gradual Rollout
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- **Connections**: depends_on, enables, provides relationships
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### Key Quotes
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- "RTO is about speed: how fast you get back online. RPO is about data: how much you can afford to lose."
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- "Deploy != Release. Feature flags change this. You can deploy code to production without releasing it to users."
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## Layout × Style Signals
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- Content type: cycle → circular-flow
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- Tone: technical educational → chalkboard
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- Audience: developers → clear, legible, professional
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- Complexity: moderate → balanced density with clear visual hierarchy
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## Design Instructions (from user input)
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- **Layout**: circular-flow (NOT linear - must emphasize recurring cycle)
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- **Style**: chalkboard (dark background, hand-drawn chalk accents)
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- **Aspect**: 16:9 landscape
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- **Language**: English
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- Circular flow showing: raw note -> ingest -> extract -> source page -> graph/static site -> reuse/feedback -> knowledge base
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- Include core extraction outputs as recurring nodes/callouts
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- Keep text concise and legible
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- Dark chalkboard background with hand-drawn chalk accents
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- Avoid clutter, make cycle visually clear and publication-ready
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## Recommended Combinations
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1. **circular-flow + chalkboard** (Recommended): Perfect match for cycle/process content with chalkboard aesthetic
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2. **hub-spoke + technical-schematic**: For emphasizing central concepts with technical precision
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3. **bento-grid + craft-handmade**: For multiple topic overview with friendly handmade feel
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