# System Architecture {% @mermaid/diagram content=" flowchart LR A(\[User
Web / Mobile]) --> B(RavenMarket Frontend DApp) B --> |Create/participate prediction events, chat interaction, data submission| C(Raven AI Engine) B --> |Create/participate prediction events, submit transactions| F(Smart Contracts
RM Token) C --> |Invoke ChatGPT / GPT-Like Models| D(ChatGPT / LLM) C --> |Real-time data queries| E(Pyth Oracle / Other Data Sources) F --> |Execute trading logic / settlement
Record on-chain| G(Blockchain Network) C --> |Analysis results / predictions| B G --> |Transaction info, prediction outcomes| B B --> |Text / Voice / IM / RTC| H(Real-time Interaction
Chat / Voice / Video) ``` classDef default fill:#f9f,stroke:#333,stroke-width:1px,color:#000 classDef data fill:#BBF3FF,stroke:#333,stroke-width:1px,color:#000 classDef compute fill:#BFFFC8,stroke:#333,stroke-width:1px,color:#000 classDef contract fill:#FFF8B2,stroke:#333,stroke-width:1px,color:#000 classDef user fill:#FFDCFF,stroke:#333,stroke-width:1px,color:#000 classDef external fill:#FFEEB2,stroke:#333,stroke-width:1px,color:#000 class A user class B default class C compute class D external class E data class F contract class G contract class H default" fullWidth="false" %} ``` ## The Three-Eyed Raven: The Symbol of the Decentralized Era The three-eyed raven transcends the ordinary, representing mastery over the past, present, and future. As a bridge between knowledge and the unknown, it merges ancient wisdom with modern technology, offering unmatched precision and foresight. {% @mermaid/diagram content="flowchart LR %% --- Three-Eyed Raven as the central entity --- subgraph Raven\[Three-Eyed Raven
Bridging Knowledge & the Unknown] direction TB ``` %% First Eye: Witnessing the Past subgraph Past[Witnessing the Past] P1[Crypto cycles & market history] P2[Lessons from bull & bear experiences] end %% Second Eye: Understanding the Present subgraph Present[Understanding the Present] S1[Real-time prediction markets] S2[Sentiment analysis
Trend identification] end %% Third Eye: Envisioning the Future subgraph Future[Envisioning the Future] F1[Powered by Raven AI
Big data analytics] F2[Expert-level predictions] F3[Decision-making support] end end %% --- External or overarching elements --- KnowledgeBase[Ancient Wisdom +
Modern Technology] Ecosystem[Decentralized Global Ecosystem] %% --- Connections --- KnowledgeBase --> Raven Raven --> Ecosystem ``` " fullWidth="false" %} ### **The First Eye – Witnessing the Past** Drawing lessons from the crypto world’s cycles of bull and bear markets and the countless experiences that shaped the industry. ### **The Second Eye – Understanding the Present** Deeply integrated with real-time prediction markets, it uncovers market sentiments and identifies developing trends. ### **The Third Eye – Envisioning the Future** Powered by Raven AI, the platform analyzes vast datasets and user behavior to provide expert-level predictions and decision-making support. ## **Mission: Predictions Beyond Simple Bets** In *From Prediction Markets to Information Finance*, Vitalik envisions a future where "prediction" is no longer just a simple vote or bet, but a fundamental element in how the world makes decisions, drives innovation, governs, and evolves. {% @mermaid/diagram content="flowchart LR A(\[Users & External Data
Inputs from Communities, Oracles, etc.]) --> B(Prediction / Info-Finance Hub) B --> C(Decision-Making) B --> D(Innovation) B --> E(Governance) B --> F(Evolution) ``` classDef nodeStyle fill:#FFF8B2,stroke:#333,stroke-width:1px,color:#000 classDef dataStyle fill:#FFEEB2,stroke:#333,stroke-width:1px,color:#000 class A dataStyle class B nodeStyle class C,D,E,F nodeStyle ``` " %} RavenMarket is the key to unlocking this future: * **Predict to Earn:** With AI-driven models, users can easily create prediction events without technical expertise. Early participants and liquidity providers are rewarded, sharing in the platform’s growth. * **Pyth Oracle:** Ensuring real-time, authoritative data accuracy, RavenMarket is the only prediction platform fully powered by Pyth Oracle, offering unparalleled precision and reliability. * **Raven AI:** Advanced AI algorithms mine data, track trends, and assist in decision-making. Whether you're a tech expert or a novice, you’ll have your own AI empowered Three-Eyed Raven, offering personalized insights. * **Real-Time Social Interaction:** Through text, voice, IM, and RTC, users engage in dynamic discussions. Raven AI records and learns from trading habits and effective strategies, creating personalized data samples for better decision support. * **Smart Contracts:** Transparent, on-chain transactions ensure every trade is publicly verifiable — no hidden agendas, just complete trust. * **Value Sharing:** 50% of platform profits are allocated to buy back RM tokens, allowing every participant to become a co-creator of the ecosystem. Unlike traditional "guess the trend" platforms, RavenMarket transforms prediction into a global "consensus experiment," blending collective intuition, experience, and AI algorithms to produce highly accurate outcomes. It allows information to evolve within a decentralized framework, fostering continuous innovation. ## **Scenarios: From Native to Expansive** RavenMarket isn’t just a tool—it’s a revolutionary philosophy, bringing multidimensional value through market practice. {% @mermaid/diagram content="flowchart LR %% Central Node RM(\[RavenMarket
A Revolutionary Philosophy]) ``` %% Scenario: Hot Assets Trading subgraph S1[Hot Assets Trading] S1A[Trending tokens, global hotspots] S1B[Efficient market makers
optimal entries/exits] end %% Scenario: Classic Continuity subgraph S2[Classic Continuity] S2A[Recognized IPs: Pepe, Cheems] S2B[Community-driven resonance
transforms classics] end %% Scenario: Original Narrative subgraph S3[Original Narrative] S3A[New ideas: NFTs, GameFi, RWA...] S3B[Flexible creation of
entirely new projects] end %% Scenario: Evolving Expansion subgraph S4[Evolving Expansion] S4A[AI + DeSci, AI bots/agents] S4B[Community-built
decentralized markets] end %% Connections RM --> S1 RM --> S2 RM --> S3 RM --> S4 %% (Optional) Styling classDef mainNode fill:#FFF8B2,stroke:#333,stroke-width:1px,color:#000 classDef scenario fill:#FFEEB2,stroke:#333,stroke-width:1px,color:#000 class RM mainNode class S1,S2,S3,S4 scenario ``` " %} **Hot Assets Trading** 1. RavenMarket thrives on trending events, such as predictions on hot tokens' prices or global hotspots' outcomes. 2. Market makers efficiently capitalize on trends, entering and exiting at optimal moments. **Classic Continuity** 1. Globally recognized IPs, such as Pepe or Cheems, find new life as prediction assets on RavenMarket. 2. Community-driven resonance ensures liquidity while transforming classics into innovative market experiences. **Original Narrative** 1. Great autonomy and flexibility allow users to integrate new ideas and innovative concepts, such as NFTs, GameFi, RWA, and more, to create entirely new prediction projects. 2. With its endless supply of original content and creative possibilities, RavenMarket holds infinite potential. **Evolving Expansion** 1. As AI continues to advance, RavenMarket is primed to incubate and develop more AI-driven projects, such as AI + DeSci, AI bots, AI agents and beyond. 2. Community-inspired projects can leverage RavenMarket’s infrastructure to build their own decentralized prediction markets. \ RavenMarket’s strength lies in its continuous evolution. Unlike platforms confined to a single trend, RavenMarket constantly adapts and grows, incorporating new prediction scenarios and enhancing user interactions. This dynamic expansion ensures that RM tokens retain long-term value, while empowering users to explore an ever-expanding digital universe. With RavenMarket, the future isn’t just imagined—it’s predicted, shared, and shaped by everyone. ## Raven AI Overview {% @mermaid/diagram content="flowchart LR %% Subgraph: User Interaction Layer subgraph A\[User Interaction Layer] A1(\[DApp UI
Web/Mobile]) A2(\[IM/Chat/Voice/RTC]) end ``` %% Subgraph: Data Sources subgraph B[Data Sources] B1[On-chain Data - transactions] B2[Pyth Oracle - Market Prices] B3[External APIs & Feeds - News, Social Media] end %% Subgraph: Data Ingestion & Processing subgraph C[Data Ingestion & Processing] C1[Data Aggregator] C2[Data Transformation
Normalization, Cleaning] C3[Data Storage
Database / Data Lake] end %% Subgraph: Raven AI - Core subgraph D[Raven AI - Core] D1[Raven AI Orchestrator] D2[Prediction Models
ML & DL] D3[Behavioral Analysis
User Patterns, Sentiment] D4[Knowledge Base
Aggregated Data] end %% Subgraph: LLM & Inference Layer subgraph E[LLM & Inference Layer] E1[ChatGPT / GPT-like Model] E2[Prompt Engineering
Context Builder] E3[Inference Engine
API Gateway] end %% Subgraph: Feedback & Continuous Learning subgraph F[Feedback & Continuous Learning] F1[User Feedback
Ratings, Votes, Comments] F2[Model Fine-tuning
Reinforcement Learning] F3[Behavior Logging
Clickstream, Results] end %% Flows between components A1 --> |User requests / queries / new prediction events| D1 A2 --> |User chats / voice / social interaction| D1 B1 --> |On-chain data| C1 B2 --> |Real-time price and event data| C1 B3 --> |News / social media / external indicators| C1 C1 --> |Data ingestion| C2 C2 --> |Cleaned / normalized data| C3 C3 --> |Enriched dataset| D4 D1 --> |Sub-tasks, data context| D2 D1 --> |Analyze user behavior| D3 D2 --> |Market predictions
Probability outcomes| E3 D3 --> |Behavior insights
Decision patterns| E2 D4 --> |Historical and contextual data| E2 E2 --> |Construct prompts and context| E1 E1 --> |Generated predictions, insights, strategies| E3 E3 --> |LLM-based recommendations and output| D1 D1 --> |AI-driven insights
Strategies, analysis| A1 D1 --> |Social / voice analysis| A2 A1 --> |User results, performance data| F1 A2 --> |User feedback, engagement| F1 F1 --> |Improve user behavior models| D3 F1 --> |Retrain or fine-tune models| F2 F2 --> |Update ML / AI models| D2 F2 --> |Refine AI orchestrator logic| D1 F2 --> |Enrich knowledge base| D4 F1 --> |Logs & usage metrics| C3 %% Optional styling style A1 fill:#FFDCFF,stroke:#333,color:#000 style A2 fill:#FFDCFF,stroke:#333,color:#000 style B1 fill:#BBF3FF,stroke:#333,color:#000 style B2 fill:#BBF3FF,stroke:#333,color:#000 style B3 fill:#BBF3FF,stroke:#333,color:#000 style C1 fill:#BFFFC8,stroke:#333,color:#000 style C2 fill:#BFFFC8,stroke:#333,color:#000 style C3 fill:#BFFFC8,stroke:#333,color:#000 style D1 fill:#FFF8B2,stroke:#333,color:#000 style D2 fill:#FFF8B2,stroke:#333,color:#000 style D3 fill:#FFF8B2,stroke:#333,color:#000 style D4 fill:#FFF8B2,stroke:#333,color:#000 style E1 fill:#FFEEB2,stroke:#333,color:#000 style E2 fill:#FFEEB2,stroke:#333,color:#000 style E3 fill:#FFEEB2,stroke:#333,color:#000 style F1 fill:#f9f,stroke:#333,color:#000 style F2 fill:#f9f,stroke:#333,color:#000 style F3 fill:#f9f,stroke:#333,color:#000" %} ``` ## Raven Prediction System {% @mermaid/diagram content="flowchart LR ``` subgraph U[User Side] U1[User Wallet - Anchor Client] U2[AI-Assisted Frontend] end subgraph SC[anchor_prediction_market Program - on chain] direction TB subgraph Instr[Instruction Handlers] I1[init_state] I2[add_price_feed, remove_price_feed] I3[create_market, pause_market, resume_market] I4[user_bet] I5[auto_settle_all] I6[update_settle_incentive] I7[query_*] end subgraph DataAcc[Contract Accounts - PDAs] DA1[State Account] DA2[Escrow Vault PDA] end subgraph MarketStructs[Markets and Rounds] MS1[Market - market_id, config, rounds] MS2[Round - bets, settled...] MS3[Bet - user, amount, direction] end end subgraph AI[AI Subsystem - Off chain or Hybrid] AI1[AI Engine - ChatGPT or LLM] AI2[Data Analyzer - On chain data, user behavior] end subgraph SP[System & External] SP1[System Program - SOL transfers] SP2[Pyth Oracle - Price feeds] end U1 -->|User consults AI services| U2 U2 -->|Requests strategy or analysis| AI1 AI1 -->|Fetch on chain data if needed| AI2 AI2 -->|Aggregated info, predictions| AI1 AI1 -->|Returns suggestions or insights| U2 U1 -->|Calls instructions| I1 U1 -->|Calls instructions| I2 U1 -->|Calls instructions| I3 U1 -->|Calls instructions| I4 U1 -->|Calls instructions| I5 U1 -->|Calls instructions| I6 U1 -->|Calls instructions| I7 %% --- Instruction to PDAs I1 --> DA1 I1 --> DA2 I2 --> DA1 I3 --> DA1 I3 --> MS1 I4 --> DA1 I4 --> DA2 I4 --> MS1 I4 --> MS2 I4 --> MS3 I5 --> DA1 I5 --> DA2 I5 --> MS1 I5 --> MS2 I5 --> MS3 I6 --> DA1 I7 --> DA1 I7 --> MS1 I7 --> MS2 I7 --> MS3 I3 -->|Creation fee| SP1 I4 -->|User bet transfer| SP1 I5 -->|Fetch price data| SP2 I5 -->|Escrow payouts| SP1 classDef highlight fill:#FFF8B2,stroke:#333,stroke-width:1px,color:#000 classDef account fill:#BBF3FF,stroke:#333,stroke-width:1px,color:#000 classDef external fill:#FFEEB2,stroke:#333,stroke-width:1px,color:#000 classDef user fill:#FFDCFF,stroke:#333,stroke-width:1px,color:#000 classDef ai fill:#FFCFCF,stroke:#333,stroke-width:1px,color:#000 class U1,U2 user class AI1,AI2 ai class I1,I2,I3,I4,I5,I6,I7 highlight class DA1,DA2,MS1,MS2,MS3 account class SP1,SP2 external ``` " %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.ravenmarket.ai/system-architecture.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.