docs: Add continuous learning and documentation framework #5
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| # Continuous Learning Architecture | ||
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| ## Overview | ||
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| The Regen Network continuous learning system transforms fragmented ecosystem data into coherent, actionable knowledge through a multi-scale temporal architecture. | ||
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| ``` | ||
| ┌─────────────────────────────────────────────────────────────────────────────┐ | ||
| │ CONTINUOUS LEARNING ARCHITECTURE │ | ||
| ├─────────────────────────────────────────────────────────────────────────────┤ | ||
| │ │ | ||
| │ DATA SOURCES PROCESSING KNOWLEDGE OUTPUTS │ | ||
| │ │ | ||
| │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ | ||
| │ │ Forum │ │ │ │ Daily │ │ | ||
| │ │ Discussions │───────────▶│ │─────────▶│ Digests │ │ | ||
| │ └─────────────┘ │ │ └─────────────┘ │ | ||
| │ │ │ │ | ||
| │ ┌─────────────┐ │ KOI │ ┌─────────────┐ │ | ||
| │ │ GitHub │───────────▶│ KNOWLEDGE │─────────▶│ Weekly │ │ | ||
| │ │ Activity │ │ GRAPH │ │ Summaries │ │ | ||
| │ └─────────────┘ │ │ └─────────────┘ │ | ||
| │ │ │ │ | ||
| │ ┌─────────────┐ │ │ ┌─────────────┐ │ | ||
| │ │ Governance │───────────▶│ │─────────▶│ Monthly │ │ | ||
| │ │ On-Chain │ │ │ │ Reports │ │ | ||
| │ └─────────────┘ │ │ └─────────────┘ │ | ||
| │ │ │ │ | ||
| │ ┌─────────────┐ │ │ ┌─────────────┐ │ | ||
| │ │ Market │───────────▶│ │─────────▶│ Yearly │ │ | ||
| │ │ Data │ │ │ │ Reviews │ │ | ||
| │ └─────────────┘ └─────────────┘ └─────────────┘ │ | ||
| │ │ | ||
| │ ┌─────────────┐ │ | ||
| │ │ AGENT │ │ | ||
| │ │ MEMORY │◀─────── Persistent Context │ | ||
| │ │ │ │ | ||
| │ └─────────────┘ │ | ||
| │ │ | ||
| └─────────────────────────────────────────────────────────────────────────────┘ | ||
| ``` | ||
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| --- | ||
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| ## Temporal Hierarchy | ||
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| > "Daily to weekly to monthly to yearly—is not just an organizational convenience. It is a deliberate architecture for making sense of complexity at different scales." | ||
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| ### Scale Characteristics | ||
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| | Scale | Focus | Content Type | Audience | | ||
| |-------|-------|--------------|----------| | ||
| | **Daily** | Immediate activity | Events, transactions, posts | Active contributors | | ||
| | **Weekly** | Short-term patterns | Trends, aggregations, highlights | Regular participants | | ||
| | **Monthly** | Medium-term themes | Analysis, comparisons, milestones | Stakeholders | | ||
| | **Yearly** | Long-term evolution | Strategic review, major shifts | Everyone | | ||
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| --- | ||
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| ## Knowledge Sources | ||
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| ### Primary Sources (KOI-Indexed) | ||
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| | Source | Type | Update Frequency | | ||
| |--------|------|------------------| | ||
| | **Forum** | Discussion | Real-time | | ||
| | **GitHub** | Development | Real-time | | ||
| | **Ledger** | On-chain | Per block | | ||
| | **Notion** | Internal docs | As updated | | ||
| | **Discord/Telegram** | Chat | Continuous | | ||
| | **YouTube** | Media | Weekly | | ||
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| --- | ||
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| ## Agent Learning Integration | ||
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| ### How Agents Learn | ||
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| 1. **KOI MCP Queries**: Access tens of thousands of indexed documents | ||
| 2. **Ledger MCP Queries**: Real-time on-chain state | ||
| 3. **Digest Consumption**: Structured summaries for context | ||
| 4. **Memory Persistence**: Cross-session knowledge retention | ||
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| ### Agent Memory Architecture | ||
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| ```yaml | ||
| agent_memory: | ||
| short_term: | ||
| type: "conversation_context" | ||
| duration: "single_session" | ||
| storage: "in_memory" | ||
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| working_memory: | ||
| type: "task_context" | ||
| duration: "task_completion" | ||
| storage: "redis" | ||
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| long_term: | ||
| type: "learned_patterns" | ||
| duration: "persistent" | ||
| storage: "postgresql_pgvector" | ||
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| semantic_memory: | ||
| type: "knowledge_graph" | ||
| duration: "permanent" | ||
| storage: "apache_jena" | ||
| access: "sparql_queries" | ||
| ``` | ||
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| --- | ||
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| ## Access Points | ||
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| ### Regen Heartbeat | ||
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| **URL**: https://gaiaaiagent.github.io/regen-heartbeat/digests/ | ||
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| **Contents**: | ||
| - Daily digests (rolling 7 days) | ||
| - Weekly summaries (rolling 4 weeks) | ||
| - Monthly reports (rolling 12 months) | ||
| - Yearly reviews (permanent archive) | ||
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| ### KOI Search | ||
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| **Access**: Via MCP tools or authenticated API | ||
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| **Capabilities**: | ||
| - Semantic search across all sources | ||
| - Entity resolution and linking | ||
| - Historical pattern retrieval | ||
| - Graph-based exploration | ||
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| --- | ||
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| ## References | ||
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| - [Regen Heartbeat README](https://gaiaaiagent.github.io/regen-heartbeat/digests/README) | ||
| - [KOI Master Implementation Guide](https://github.com/gaiaaiagent/koi-research/blob/main/docs/KOI_MASTER_IMPLEMENTATION_GUIDE.md) | ||
| <!-- TODO: Migrate http-config-architecture-v2.md to regen-network org and update link (see PR #5 discussion) --> | ||
| - [HTTP Config Architecture v2](https://github.com/DarrenZal/koi-research/blob/regen-prod/docs/http-config-architecture-v2.md) | ||
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Contributor
There was a problem hiding this comment. The link for 'HTTP Config Architecture v2' points to a personal fork ( |
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| --- | ||
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| *This document is part of the Regen Network Agentic Tokenomics framework.* | ||
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| # Contributor Learning Paths | ||
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| ## Overview | ||
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| Structured learning paths help contributors build competence progressively, from ecosystem newcomer to expert contributor. | ||
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| ## Learning Path: Ecosystem Newcomer | ||
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| **Goal**: Understand what Regen Network is and how it works | ||
| **Duration**: 1-2 weeks | ||
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| ### Week 1: Foundations | ||
| - Read ecosystem overview at handbook.regen.network | ||
| - Explore credit marketplace at app.regen.network | ||
| - Join community channels (Discord, Forum) | ||
| - Read weekly digest from Regen Heartbeat | ||
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| ### Completion Criteria | ||
| - [ ] Can explain Regen Network to a friend | ||
| - [ ] Joined Discord and introduced self | ||
| - [ ] Read at least 2 weekly digests | ||
| - [ ] Identified area of interest | ||
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| --- | ||
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| ## Learning Path: Technical Contributor | ||
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| **Goal**: Contribute code or technical documentation | ||
| **Duration**: 4-6 weeks | ||
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| ### Phase 1: Technical Foundations (Weeks 1-2) | ||
| - Study system architecture | ||
| - Understand Cosmos SDK module structure | ||
| - Explore regen-ledger repository | ||
| - Set up development environment | ||
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| ### Phase 2: Hands-On Learning (Weeks 3-4) | ||
| - Review open PRs on GitHub | ||
| - Help categorize issues | ||
| - Improve documentation | ||
| - Write/run tests locally | ||
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| ### Phase 3: First Contribution (Weeks 5-6) | ||
| - Pick a "good first issue" | ||
| - Submit and iterate on PR | ||
| - Engage with reviewers | ||
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| ### Completion Criteria | ||
| - [ ] Development environment set up | ||
| - [ ] At least 3 PRs reviewed (as observer) | ||
| - [ ] At least 1 documentation improvement merged | ||
| - [ ] At least 1 code contribution merged | ||
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| --- | ||
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| ## Learning Path: Governance Participant | ||
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| **Goal**: Actively participate in network governance | ||
| **Duration**: 4 weeks | ||
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| ### Week 1-2: Governance Basics | ||
| - Read governance documentation | ||
| - Understand proposal types | ||
| - Learn voting mechanics | ||
| - Review past proposals | ||
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| ### Week 3-4: Active Participation | ||
| - Follow active proposals | ||
| - Attend community calls | ||
| - Vote on a proposal | ||
| - Comment on forum discussion | ||
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| ### Completion Criteria | ||
| - [ ] Understands proposal types and voting | ||
| - [ ] Voted on at least 1 proposal | ||
| - [ ] Contributed to at least 1 forum discussion | ||
| - [ ] Attended at least 1 community call | ||
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| --- | ||
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| ## Learning Path: Agentic Contributor | ||
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| **Goal**: Contribute to or with AI agents | ||
| **Duration**: 6-8 weeks | ||
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| ### Phase 1: Agent Ecosystem (Weeks 1-2) | ||
| - Study ElizaOS and MCP architecture | ||
| - Understand agent personas | ||
| - Learn KOI knowledge infrastructure | ||
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| ### Phase 2: MCP Tool Proficiency (Weeks 3-4) | ||
| - Practice KOI MCP search and entity resolution | ||
| - Query Ledger MCP for governance and credits | ||
| - Combine tool outputs in workflows | ||
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| ### Phase 3: Agent Contribution (Weeks 5-8) | ||
| - Design multi-step agent workflow | ||
| - Suggest workflow enhancements | ||
| - Improve KOI content | ||
| - Propose agent features | ||
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| ### Completion Criteria | ||
| - [ ] Can query KOI and Ledger MCP effectively | ||
| - [ ] Designed at least 1 multi-step agent workflow | ||
| - [ ] Contributed at least 1 agent-related improvement | ||
| - [ ] Understands agent governance boundaries | ||
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| --- | ||
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| ## Resources | ||
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| - [Regen Registry Handbook](https://handbook.regen.network) | ||
| - [KOI Knowledge Base](https://regen.gaiaai.xyz) | ||
| - [Regen Heartbeat Digests](https://gaiaaiagent.github.io/regen-heartbeat/digests/) | ||
| <!-- TODO: Migrate http-config-architecture-v2.md to regen-network org and update link (see PR #5 discussion) --> | ||
| - [HTTP Config Architecture v2](https://github.com/DarrenZal/koi-research/blob/regen-prod/docs/http-config-architecture-v2.md) | ||
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Contributor
There was a problem hiding this comment. The link for 'HTTP Config Architecture v2' points to a personal fork (
Contributor
Author
There was a problem hiding this comment. @DarrenZal please generate a plan to migrate these important repos over into the regen-network org github, and/or to the gaiaaiagent org. I'd prefer we centralize everything onto regen-network so that we can more easily manage permissions and coordinate multi-agent development. but it can be phased, and start with a migration over to gaia ai agent repo then from there to regen-network repo. |
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| --- | ||
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| *This document is part of the Regen Network Agentic Tokenomics framework.* | ||
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