# Table of Contents - [Overview of DAPPOS | DAPPOS](#overview-of-dappos-dappos) - [Core Features | DAPPOS](#core-features-dappos) - [Introduction | DAPPOS](#introduction-dappos) - [Intelligence Layer | DAPPOS](#intelligence-layer-dappos) - [Generic AI vs. Web3 AI OS | DAPPOS](#generic-ai-vs-web3-ai-os-dappos) - [Multi-Agent Framework (MAF) | DAPPOS](#multi-agent-framework-maf-dappos) - [Core Components | DAPPOS](#core-components-dappos) - [The Bubble Engine | DAPPOS](#the-bubble-engine-dappos) - [Innovations and Future | DAPPOS](#innovations-and-future-dappos) - [Execution Layer | DAPPOS](#execution-layer-dappos) - [Quick Start | DAPPOS](#quick-start-dappos) - [Use Cases | DAPPOS](#use-cases-dappos) - [Web3 AI OS | DAPPOS](#web3-ai-os-dappos) - [Background and Problem Statement | DAPPOS](#background-and-problem-statement-dappos) - [Intent Task Frameworks | DAPPOS](#intent-task-frameworks-dappos) - [How-to Guides | DAPPOS](#how-to-guides-dappos) - [How Intent Execution Network works | DAPPOS](#how-intent-execution-network-works-dappos) - [Security | DAPPOS](#security-dappos) - [Withdraw Delay | DAPPOS](#withdraw-delay-dappos) - [Support | DAPPOS](#support-dappos) - [BubbleUp Tasks | DAPPOS](#bubbleup-tasks-dappos) - [Bug Bounty | DAPPOS](#bug-bounty-dappos) - [External Audits | DAPPOS](#external-audits-dappos) - [Overview of DAPPOS | DAPPOS](#overview-of-dappos-dappos) --- # Overview of DAPPOS | DAPPOS ![Page cover](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252Frgp11duKDjEO6GkmsLgx%252F%25E6%259C%2580%25E7%25BB%2588%25E7%2589%2588.png%3Falt%3Dmedia%26token%3D85162b26-9b50-4073-9fd4-44025bf8a8b7&width=1248&dpr=4&quality=100&sign=85ef56d9&sv=2) [Introduction](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/introduction) [Core Features](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/core-features) [Generic AI vs. Web3 AI OS](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os) [Intelligence Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer) [Execution Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer) [NextIntroduction](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/introduction) Last updated 2 months ago --- # Core Features | DAPPOS * **Multi-Agent Framework (MAF)**: With 300–400 specialized vertical agents and more than 200 integrated tools, DAPPOS delivers comprehensive Web3 generalization—capable of handling everything from quantitative analysis and trading strategies to market intelligence. * **The Bubble Engine**: This Web3-focused reinforcement learning engine drives perpetual evolution, continuously learning from real-time sources such as X and Binance Square. Through the Bubble task platform, users can contribute new knowledge directly, accelerating integration and adaptation. * **Intent Execution Network**: Leveraging the tested Intent Execution Network with over 5 million users and 12 million transactions, DAPPOS ensures safe, efficient execution of AI-generated plans, transforming complex intents into secure on-chain actions with institutional-level reliability. [PreviousIntroduction](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/introduction) [NextGeneric AI vs. Web3 AI OS](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os) Last updated 2 months ago --- # Introduction | DAPPOS DAPPOS is a Web3 AI Operating System (OS) that intelligently researches, plans, and executes anything crypto-related. The Web3 AI OS is structured into two core layers: **the Intelligence Layer** and **the Execution Layer**. **The Intelligence Layer** establishes the cognitive foundation, powered by a scalable **Multi-Agent Framework (MAF)** with 300–400 specialized vertical agents and more than 200 integrated tools. This architecture enables broad Web3 generalization and adaptive intelligence, excelling across diverse domains—from quantitative analysis for trading strategies to marketing insights—while surpassing generalist AIs such as Perplexity in contextual reasoning and task-specific optimization. At the core of the Intelligence Layer lies **the Bubble Engine**, driven by continuous reinforcement learning (RL) models tailored for Web3. It evolves perpetually by ingesting real-time insights from sources such as X and Binance Square, enabling rapid, iterative adaptation to new knowledge. Through the Bubble task platform, users can accelerate this process by posting tweets that “force-feed” the system novel concepts; once verified, these are seamlessly integrated, resolving challenges that may have seemed unsolvable just days earlier. **The Execution Layer** complements the Intelligence Layer through DAPPOS’s proven Intent Execution Network—a stable, time-tested infrastructure that delivers institutional-grade efficiency and safety. Hardened by over 5 million users and 12 million transactions, it reliably transforms AI-generated plans into secure, on-chain interactions. Built on the Web3 AI Operating System, DAPPOS introduces **Instant dApps**—enabling users to create and deploy Web3 applications on demand through simple natural language interactions. The **Discover Page** acts as a dynamic hub, showcasing AI-generated insights and execution plans shared by the community. Users can browse, explore, and adopt strategies that align with their needs, executing them directly if desired. This experience mirrors vibe coding for websites or dashboards—where ideas transform seamlessly into deployable tools. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FwRAtg2rmR8ESZABypuU3%252F%25E6%2596%2587%25E6%25A1%25A3%25E9%25A6%2596%25E9%25A1%25B5%25E5%259B%25BE.png%3Falt%3Dmedia%26token%3D89cc0837-cf68-49d7-8a09-b95407964824&width=768&dpr=4&quality=100&sign=138f8629&sv=2) The User Jouney of DAPPOS Through the Web3 AI OS, DAPPOS unifies the fragmented Web3 landscape, enabling users to orchestrate complex actions through simple conversations as AI seamlessly coordinates across decentralized protocols. This one-stop system covers the entire Web3 lifecycle—from research and strategy design to autonomous execution—lowering barriers to entry and catalyzing innovation across the ecosystem. Ultimately, DAPPOS marks a paradigm shift for Web3, transforming it into an accessible frontier where anyone can engage, build, and create within the Web3 AI OS—unlocking the full potential of decentralized technologies for the advancement of human society. [PreviousOverview of DAPPOS](https://dappos.gitbook.io/docs) [NextCore Features](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/core-features) Last updated 2 months ago --- # Intelligence Layer | DAPPOS [Core Components](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/core-components) [Multi-Agent Framework (MAF)](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf) [The Bubble Engine](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine) [Innovations and Future](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/innovations-and-future) [PreviousGeneric AI vs. Web3 AI OS](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os) [NextCore Components](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/core-components) Last updated 2 months ago --- # Generic AI vs. Web3 AI OS | DAPPOS As AI permeates every industry, **generic, one-size-fits-all models are no longer sufficient**. Web3 requires a vertical AI—one that deeply understands its domain, reasons within domain-specific constraints, and executes user intents with reliability. General-purpose AI models, such as those behind assistants like ChatGPT or Perplexity, are designed for universal applicability. They excel at tasks like summarizing articles or generating code by leveraging vast amounts of general knowledge through large language models. Yet in the specialized realm of Web3—spanning decentralized finance (DeFi), non-fungible tokens (NFTs), meme coins, and broader blockchain ecosystems—these models fall short in two critical areas: **Intelligence** and **Execution**. [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os#intelligence-generic-ais-limitations-and-web3-ai-oss-superiority) Intelligence: Generic AI's Limitations and Web3 AI OS's Superiority -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Intelligence in AI refers to the ability to gather, contextualize, and reason over information to produce actionable insights. Generic AI models, constrained by pre-trained corpora and general web scraping, lack the depth and adaptability required for Web3’s opaque, fast-moving environment. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os#lack-of-domain-relevant-data-sources) Lack of Domain-Relevant Data Sources Generic AI cannot securely or directly access user-specific, real-time blockchain data. For example, when advising on a DeFi strategy—such as optimizing recursive lending across protocols—Generic AI cannot fetch wallet balances, transaction flows, or on-chain risk metrics without external integrations, leaving users with vague, boilerplate recommendations. By contrast, Web3 AI OS integrates directly with blockchain APIs and user-authorized data. It can analyze wallet behaviors, query protocol risk exposures, and simulate yield loops in real time, producing precise and personalized strategies grounded in on-chain truth. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os#improper-weighting-in-information-processing) Improper Weighting in Information Processing Generic AI applies uniform priors from broad training data, often misjudging Web3’s high-signal events. For instance, a meme coin’s collaboration with a major brand (e.g., Pengu’s activity with a top label) or a token’s subtle ties to Binance might be dismissed as noise, even though such signals frequently drive short-term momentum and liquidity. Similarly, project evaluations from Generic AI default to generic metrics—team backgrounds, whitepapers, technical architecture—while overlooking exchange listings, community traction, or partnership leaks that seasoned users treat as critical alpha. Web3 AI OS, powered by the continuous RL-driven **Bubble Engine**, assigns adaptive weights to Web3-specific signals. In the meme coin example, DAPPOS would flag the collaboration as a bullish trigger, cross-reference it with social sentiment, trading volume spikes, and historical meme coin precedents, then forecast potential upside. In project analysis, it would surface hidden affiliations with major exchanges as key catalysts—delivering insights aligned with how Web3 participants actually assess opportunities. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os#misunderstanding-web3-nuances-and-implicit-rules) Misunderstanding Web3 Nuances and Implicit Rules Web3 is rife with implicit rules, slang, and deceptive practices that Generic AI often misreads. A token boosted by bots or paid promotions may appear “popular” to a generic model, while emerging jargon or coded signals slip past its outdated training set—resulting in misleading outputs and overlooked red flags. Web3 AI OS embeds domain expertise directly into its agents. It filters out manipulation by detecting anomalies such as clustered wallet activity or suspicious liquidity inflows, and it continuously adapts to new slang and norms through user-submitted insights on the Bubble platform. This ensures its analyses remain current, accurate, and resistant to common Web3 pitfalls. [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os#execution-from-insight-to-action) Execution: From Insight to Action -------------------------------------------------------------------------------------------------------------------------------------------------------------- Execution is the critical step of turning reasoned plans into tangible outcomes—something Generic AI cannot achieve due to its advisory-only nature. Even when it produces a sound strategy, users must manually navigate wallets, DEXs, and bridges, contending with gas fees, slippage, and cross-chain complexity. Web3 AI OS closes this gap with its dedicated Execution Layer, exemplified by DAPPOS’s Intent Execution Network. Battle-tested with over 12 million transactions and 5 million users, it autonomously manages on-chain operations with institutional-grade security and reliability. A defining strength of DAPPOS is its ability to make insights instantly actionable and shareable. Through the Discover Page, users can publish AI-generated strategies and execution plans, creating a dynamic hub where the community can explore, adopt, and deploy them directly. Generic AI lacks this capability, leaving a gap between intelligence and real-world utility. Web3 AI OS completes the loop—from intelligent research and planning to seamless execution and collaborative deployment—transforming insights into value and accelerating innovation across the decentralized ecosystem. In summary, while Generic AI provides broad utility, its shortcomings in Web3-specific intelligence and execution make it insufficient for the demands of crypto. Web3 AI OS—exemplified by DAPPOS—marks the next modular evolution: specialized, adaptive, and actionable, empowering users to innovate and build without barriers. [PreviousCore Features](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/core-features) [NextIntelligence Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer) Last updated 2 months ago --- # Multi-Agent Framework (MAF) | DAPPOS The Multi-Agent Framework (MAF) in DAPPOS represents a groundbreaking evolution in AI orchestration for Web3, guaranteeing explainable AI decisioning and production-grade reliability. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf#stateful-graph-backbone-the-foundation-of-composable-autonomy) Stateful Graph Backbone: The Foundation of Composable Autonomy At the core of MAF lies the **StateGraph**, a structured state schema composed of a collection of variables that forms the backbone for all operations. The StateGraph encapsulates key system elements such as messages for inter-agent communication, plans for structured workflows, artifacts (e.g., data outputs or models), UI interrupts for human oversight, final outputs, errors for robust handling, and flags for conditional logic. These variables are passed seamlessly across each state in the system, serving as the decision-making foundation for controller nodes at each stage. In MAF, each **node** represents an AI agent for a designed task. Specifically, **controller nodes** evaluate the current state to determine the next flow direction and update the state variables accordingly. The process of a user query begins with the main controller `task_planner_node`, which analyzes user intents to classify the task type—such as information gathering, strategy execution, or alpha detection—and deterministically routes it to the appropriate subgraph (search, DeFi, or opportunity). ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FsbXfb8GUd1gaPGRKXg4y%252F%25E6%2580%25BB%25E6%25B5%2581%25E7%25A8%258B%25E5%259B%25BE.png%3Falt%3Dmedia%26token%3Dea2a0872-cde6-44e6-a01f-177db1fef5ac&width=768&dpr=4&quality=100&sign=6e59e5c2&sv=2) Within each **subgraph**, specialized controller nodes handle internal flows, ensuring predictable paths that minimize variability and enhance trust in dynamic Web3 environments. This setup powers deterministic controllers and AI interrupt mechanisms, preventing errors while allowing freedom in AI decision-making. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf#subgraphs-specialized-modules-for-web3-intelligence) Subgraphs: Specialized Modules for Web3 Intelligence MAF's subgraphs are modular building blocks, each tailored to specific Web3 domains with deterministic node flows that process data efficiently. These subgraphs leverage the stateful backbone to integrate seamlessly, enabling adaptive intelligence across trading, analysis, and execution. For example: * **Search Subgraph**: This subgraph combines autonomous controllers with enhanced review cycles for continuous deepening and refinement, embodying an agentic search loop. The flow starts with `search_controller_node` that selects and parallelizes agents (e.g., `google_search_agent_node` for web data, `twitter_search_agent_node` for social sentiment, `parallel_search_coordinator_node` for multi-source queries). Outputs are merged in `merge_results_node`, filtered for relevance, and refined through an iterative enhanced review loop until confidence criteria (e.g., source diversity or accuracy scores) are met, which is checked by `enhanced_search_review_node`. The `summarize_node` then produces cited answers with frontend-friendly markers, ensuring transparency and usability. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FFszvsff0KOs1EqbAGrJW%252FSearch%2520subgraph%25E3%2580%2581.png%3Falt%3Dmedia%26token%3Df1c7237f-dc1b-42be-b062-e727c892d355&width=768&dpr=4&quality=100&sign=6aa3bc2c&sv=2) Search Subgraph * **DeFi Subgraph**: Centered on tool-augmented execution, this subgraph generates and validates DeFi strategies using over 200 integrated tools for precise operations like API queries or trade simulations. It begins with `fetch_market_data` to retrieve real-time blockchain metrics, followed by `strategy_generator` to create optimized plans (e.g., yield farming or liquidity provision). The `strategy_validator` closes the loop by assessing risks, simulating outcomes, and incorporating optional user choices. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FWaTFagb96mZfEX6qz1fO%252Fdefi.png%3Falt%3Dmedia%26token%3D3c2d5057-dab1-49ee-9898-7a502b501dc0&width=768&dpr=4&quality=100&sign=dc58fa22&sv=2) DeFi Subgraph * **Opportunity Subgraph**: This subgraph identifies high-potential Web3 opportunities by scanning markets, trends, and signals. It flows through `opportunity_search_node` for initial detection, `fetch_market_data` for validation, and an `opportunity_node` that synthesizes insights into actionable recommendations, such as emerging meme coins or protocol upgrades. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FoKxpU6HgqhU4su9vvHus%252Fopportunity%2520subgraph.png%3Falt%3Dmedia%26token%3D6619004e-a585-42a2-a2bf-fae881e7b524&width=768&dpr=4&quality=100&sign=a7d3e119&sv=2) Opportunity Subgraph ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf#advanced-features-enhancing-collaboration-execution-and-interactivity) Advanced Features: Enhancing Collaboration, Execution, and Interactivity Building on the core subgraphs, MAF incorporates advanced capabilities to foster human–AI synergy and real-time adaptability. * **Multi-Agent Search**: Central to the search subgraph, this feature enables autonomous and collaborative intelligence, where a controller dynamically selects agents based on query complexity for parallel execution, boosting speed and breadth. For example, evaluating a meme coin's potential might invoke Google agents for news, Twitter agents for sentiment, and parallel custom agents for on-chain data. The enhanced review loop iterates refinements, and the summarizer outputs structured, cited responses optimized for frontend display, with HCI interrupts for real-time oversight in mission-critical actions. * **Tool-Augmented Plan Generation**: Particularly prominent in DeFi and opportunity subgraphs, this integrates structured tools to transform abstract insights into executable strategies. Validator nodes ensure safety by cross-checking against risk parameters, with optional UI interrupts for user approvals, closing the intelligence-action loop and preparing plans for seamless handover to the Execution Layer. * **Streaming and Interrupts**: To support interactive experiences, MAF streams messages during long-running tasks, keeping users informed in real-time. When user queries or provided information are insufficient for accurate analysis, the AI pauses execution to seek clarification by prompting the user with targeted questions, ensuring completeness and reliability. In essence, DAPPOS's Multi-Agent Framework redefines Web3 intelligence by combining composable autonomy with safety rails, enabling users to tackle complex tasks with unprecedented precision and adaptability. This framework not only outperforms generic AIs in domain-specific reasoning but also paves the way for collaborative, evolving ecosystems where AI and humans co-create value. [PreviousCore Components](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/core-components) [NextThe Bubble Engine](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine) Last updated 2 months ago --- # Core Components | DAPPOS The Intelligence Layer of DAPPOS forms the cognitive backbone of the Web3 AI Operating System, orchestrating research, planning, and decision-making for crypto-related tasks. It is structured of two core components: the **Multi-Agent Framework (MAF)** and the **Bubble Engine**, which together enable scalable, adaptive intelligence tailored to Web3's dynamic environment. The Multi-Agent Framework (MAF) serves as the orchestration module, comprising 300–400 specialized vertical agents and over 200 integrated tools. Built on a stateful graph backbone, MAF facilitates composable autonomy through deterministic routing, subgraphs for tasks, and advanced features such as multi-agent search and tool-augmented generation. This ensures explainable, production-grade AI that excels in domain-specific reasoning and generalization across quantitative analysis, trading strategies, and market insights. Complementing MAF, the Bubble Engine acts as the adaptive learning core, powered by continuous reinforcement learning (RL) models optimized for Web3. It perpetually evolves by ingesting real-time data from sources like X and Binance Square, incorporating user-contributed insights via the Bubble task platform. Key mechanisms include Contextual Retrieval-Augmented Generation (RAG) for provenance-first data aggregation and Compound Memory for blending durable knowledge with episodic learnings, while handling misinformation and leveraging Web3 incentives for collaborative growth. Together, MAF and the Bubble Engine help DAPPOS create a synergistic system that outperforms generic AI in Web3, delivering excellent and evolving intelligence ready for the Execution Layer. [PreviousIntelligence Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer) [NextMulti-Agent Framework (MAF)](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf) Last updated 2 months ago --- # The Bubble Engine | DAPPOS At the core of DAPPOS's Intelligence Layer is the Bubble Engine, a Web3-focused reinforcement learning (RL) engine that drives perpetual evolution and adaptive intelligence. Powered by advanced mechanisms like **Contextual Retrieval-Augmented Generation (RAG)** and **Compound Memory**, the Bubble Engine transforms fragmented Web3 data into coherent, actionable intelligence. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine#contextual-rag-provenance-first-intelligence-aggregation) Contextual RAG: Provenance-First Intelligence Aggregation The Bubble Engine's Contextual RAG forms the backbone of its information processing, unifying diverse sources—web content, internal documents, and on/off-chain signals—into a provenance-first context layer that prioritizes traceability and reliability. This hybrid retriever combines dense (semantic) and sparse (keyword-based) techniques with domain-specific routing to orchestrate sub-queries efficiently. It deduplicates redundant results, reranks them using cross-encoders for relevance, and assembles a minimal sufficient evidence pack tailored to the query's needs. The generation process is citation-locked, producing 1\]-style inline references with graceful fallbacks when evidence is limited. Dynamic budgets intelligently adjust retrieval depth and parallel agent execution based on detected uncertainty levels, optimizing for efficiency in volatile Web3 scenarios. Episodic memory captures short-term learnings from recent interactions, while long-term memory preserves confirmed facts for future recall. Looking ahead, DAPPOS sets the stage for multimodal RAG (incorporating images, videos, and audio) and graph-based extensions, enabling richer contextual intelligence that connects entities, relationships, and temporal dynamics across the blockchain landscape. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine#compound-memory-durable-and-episodic-knowledge-evolution) Compound Memory: Durable and Episodic Knowledge Evolution Complementing Contextual RAG, the Bubble Engine's Compound Memory integrates durable knowledge—core, verified Web3 facts and models—with episodic learnings accumulated over time. This approach ensures the engine retains timeless domain expertise (e.g., DeFi protocols and blockchain fundamentals) while dynamically incorporating transient insights, such as real-time market shifts or user-submitted alphas. As new knowledge is verified and integrated, the memory system evolves, creating a compounding effect that enhances reasoning accuracy and personalization. ### [](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine#handling-misinformation-and-user-incentives) Handling Misinformation and User Incentives To maintain integrity in Web3's deception-prone environment, the Bubble Engine employs advanced fake information handling, cross-verifying sources against on-chain data and community flags to detect and mitigate bots, pumps, or manipulated signals. Additionally, Web3 incentives motivate users to share high-fidelity insights, accelerating collective intelligence growth. In summary, the Bubble Engine redefines adaptive AI for Web3 by blending continuous RL with sophisticated retrieval and memory systems, delivering hyper-relevant, trustworthy insights that evolve in lockstep with the ecosystem. [PreviousMulti-Agent Framework (MAF)](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/multi-agent-framework-maf) [NextInnovations and Future](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/innovations-and-future) Last updated 2 months ago --- # Innovations and Future | DAPPOS The Intelligence Layer of DAPPOS introduces transformative innovations that redefine AI's role in Web3, blending scalable orchestration with perpetual learning to deliver unparalleled intelligence. By synthesizing the **Multi-Agent Framework (MAF)** and the **Bubble Engine**, the Intelligence layer achieves composable autonomy, explainable decisioning, and adaptive reasoning—far surpassing generic AIs in handling the ecosystem's volatility and complexity. Key innovations in MAF center on its stateful graph backbone, the StateGraph, which maintains a typed state as the single source of truth for elements like messages, plans, artifacts, and UI interrupts. This foundation enables hierarchical orchestration through deterministic controllers, routing intents seamlessly to specialized subgraphs for search, DeFi, and opportunity tasks. The agentic search loop in the search subgraph, with parallel agents and enhanced review cycles, powers self-directed exploration, while tool-augmented execution in DeFi and opportunity subgraphs generates validated strategies with human-in-the-loop safeguards via HCI interrupts. Advanced features like multi-agent collaboration, streaming progress, and safety rails ensure trustworthy autonomy, fostering human–AI synergy and production-grade reliability. Complementing MAF, the Bubble Engine drives continuous reinforcement learning (RL) for rapid adaptation, ingesting real-time insights from sources like X and Binance Square. Its Contextual RAG unifies web, internal, and on/off-chain data through a hybrid retriever with domain routing, deduplication, reranking, and citation-locked generation—optimized by dynamic budgets and episodic/long-term memory. Compound Memory layers durable Web3 knowledge with bubble-driven episodic learnings, while robust misinformation handling via cross-verification and Web3 incentives for user contributions accelerate collective evolution. Looking ahead, DAPPOS envisions expanding these innovations into a fully decentralized intelligence ecosystem. Future iterations will integrate multimodal RAG for processing images, videos, and audio alongside graph-based extensions to map intricate blockchain relationships and temporal dynamics. Enhanced RL models will enable predictive simulations of market scenarios, while deeper MAF composability could support more user-defined subgraphs for custom Web3 workflows. Ultimately, this trajectory positions DAPPOS as the cornerstone of Web3's AI-native future, empowering seamless innovation, democratizing access to decentralized technologies, and catalyzing a new era of collaborative value creation across global ecosystems. [PreviousThe Bubble Engine](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/the-bubble-engine) [NextExecution Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer) Last updated 2 months ago --- # Execution Layer | DAPPOS [Background and Problem Statement](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement) [How Intent Execution Network works](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/how-intent-execution-network-works) [Intent Task Frameworks](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/intent-task-frameworks) [PreviousInnovations and Future](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer/innovations-and-future) [NextBackground and Problem Statement](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement) --- # Quick Start | DAPPOS Welcome to the Quick Start guide for DAPPOS's Web3 AI Operating System (OS). This guide walks you through getting started quickly. Whether you're researching DeFi opportunities, executing trades, or building Instant dApps, follow these steps to dive in. ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#step-1-log-in) Step 1: Log In To begin, navigate to the DAPPOS product page([https://dappos.com/app/en](https://dappos.com/app/en) ) and locate the login button in the top-right corner of the interface. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FcOKcWdPYHLAMi5XphETL%252F1.png%3Falt%3Dmedia%26token%3D6cf3b6af-29bd-4e19-9a43-1e658d31e3db&width=768&dpr=4&quality=100&sign=b1685039&sv=2) * Recommended: Use a Web3 Wallet – Connect your preferred Web3 wallet (e.g., Binance Wallet, OKX Wallet, MetaMask) for full access to blockchain interactions * Alternative: Email Login – Sign in with email for quick access. However, this limits functionality—blockchain-related features, such as asset transfers or on-chain simulations, will not be supported. For the complete experience, we strongly recommend using a Web3 wallet to unlock the full potential of our Web3 AI OS. ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#step-2-enter-invitation-code-test-phase-exclusive) Step 2: Enter Invitation Code (Test Phase Exclusive) If you're accessing Web3 AI OS during the internal test phase, you'll be prompted to enter an invitation code. Simply input your provided code in the designated field and proceed. If you don't have a code, reach out to our team or check community channels for opportunities to contribute and gain early access. ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#step-3-input-your-prompt) Step 3: Input Your Prompt At the heart of the Web3 AI OS is the conversation interface. In the central dialog box, type your natural language prompt to engage the system. For example: "Analyze the latest meme coin trends on Solana and suggest a trading strategy." ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FlB42n1j8xPyHWrUmSVY0%252F2.png%3Falt%3Dmedia%26token%3D45e1e966-c4ee-488c-9572-c19889eef5c4&width=768&dpr=4&quality=100&sign=b09ab9f1&sv=2) **You can customize your interaction using the following product features of Web3 AI OS**: #### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#mode-selection) Mode Selection Modes guide the AI's processing tendency for your prompt. You can select only one mode per interaction, influencing how the Multi-Agent Framework handles your request. Choose from the dropdown near the prompt input. * **Auto (Default)**: The AI intelligently analyzes your prompt and responds dynamically, balancing research, planning, and execution based on context. Ideal for general queries where you want adaptive intelligence without specific constraints. * **Analyze**: Directs the AI toward deeper, more time-intensive searches and reasoning. Use this for research-heavy tasks, such as evaluating a project's fundamentals, cross-referencing on-chain data with social sentiment, or simulating DeFi yield strategies. * **Plan**: Guides the AI to analyze and generate executable plans, transforming intents into actionable on-chain steps. Perfect for complex tasks requiring implementation, such as deploying an Instant dApp or automating a trading loop. * **Light**: Prompts the AI for quick, concise results with minimal response time. Great for simple tasks like rapid fact-checks or basic queries, ensuring fast responses without extensive computation. #### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#tag-addition) TAG Addition Enhance your prompt by adding one or more tags from the available options. Current tags include: * **Bubble**: Unlock enhanced intelligence with the premium Bubble Engine for smarter, more adaptive results * **Connected Opportunity**: Instructs the AI to proactively identify executable money-making opportunities related to your prompt, such as buying tokens, opening contracts, or launching memes. * **Marketing**: Ideal for project teams and KOLs when crafting marketing materials. #### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start#quick-buttons) Quick Buttons For convenience, especially for new users, utilize the shortcut buttons below the prompt area to access pre-built functionalities: * **Inquiry**: Triggers common quick-query prompts, such as "My Trading Position" or "My Balances" This helps with fast information retrieval using our integrated tools. * **Transfer**: Quickly deposit or withdraw assets to/from your DAPPOS Wallet. * **Play**: Offers built-in example prompts tailored for beginners. * **Fast Trading**: For straightforward buy/sell needs, jump directly to the DAPPOS IntentEX. [PreviousWeb3 AI OS](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os) [NextUse Cases](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases) Last updated 2 months ago --- # Use Cases | DAPPOS This section explores key use cases, demonstrating how to harness DAPPOS for research, strategy, and deployment. Each example includes recommended modes, tags, and prompt suggestions to optimize results. ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases#generating-analysis-reports) Generating Analysis Reports DAPPOS excels at producing in-depth reports on Web3 topics, drawing on real-time blockchain data, social sentiment, and domain expertise to provide insights that generic AIs often miss—such as adaptive weighting of high-signal events or detection of manipulative practices. Users can request analyses on projects, sectors (e.g., DeFi, NFT, RWA), KOLs, or broader market trends, resulting in structured reports with quantitative metrics, risk assessments, and actionable recommendations. How to Use: * Select the **Analyze** mode to enable deeper searches, longer reasoning chains, and comprehensive data integration via the MAF. * Input a prompt in the dialog box, such as: "Compare $LINK with $XRP, analyze which one has more potential?" ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FN2lOAVqNpQ7gpFJ3OwbD%252F1.png%3Falt%3Dmedia%26token%3D761e1af1-48cb-4b14-a181-51669e21a14c&width=768&dpr=4&quality=100&sign=511e57e5&sv=2) ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases#complex-defi-interactions) Complex DeFi Interactions Navigating DeFi can be intricate, but DAPPOS simplifies it by generating personalized strategies based on your prompt, wallet balances, and on-chain situations. Unlike generic AIs that offer vague advice without real-time data access, DAPPOS simulates yield loops, assesses protocol risks, and crafts executable plans through the Intent Execution Network—enabling one-click deployment with institutional-grade safety. How to Use: * Choose the **Plan** mode to focus on generating and outlining execution schemes, transforming your intent into secure on-chain steps. * Describe your strategy in the prompt, e.g.: ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FTXF9bNi3zlFsfkCkwOH4%252F2.png%3Falt%3Dmedia%26token%3D2aa10a90-7a79-44b0-af26-408008e77603&width=768&dpr=4&quality=100&sign=6da38a67&sv=2) * Review the generated plan, which includes step-by-step actions, simulations, and safeguards. Click **Execute** for autonomous completion via the Execution Layer—no manual approvals needed for routine interactions. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FE2bNmwRBQTwBhdqbCezL%252F3.png%3Falt%3Dmedia%26token%3D08c9a8cf-4e11-4e83-8073-c917efd077fa&width=768&dpr=4&quality=100&sign=21caaade&sv=2) ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases#launching-meme-tokens) Launching Meme Tokens Meme coins thrive on virality and timing, and DAPPOS streamlines their creation by designing tokens based on user-specified themes or AI-identified hotspots. The DAPPOS AI detects emerging trends, slang, and momentum signals that generic models overlook, then generates essentials like tickers, tokenomics, and deployment plans. How to Use: * **Plan** mode is recommended for dynamic generation and execution focus. * Prompt examples: "Create a meme based on the news that Justin Sun sold WLFI." * Click the execution Button for implementation. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FXdsvIfiqqn7BIjz5kIxU%252F4.png%3Falt%3Dmedia%26token%3D73a57ef0-5495-4eed-91be-66bdb30d303b&width=768&dpr=4&quality=100&sign=5d157022&sv=2) ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases#marketing-materials-writing) Marketing Materials Writing DAPPOS empowers project teams, KOLs, and marketers to craft promotional content with ease. How to Use: * Add the #**Marketing** tag. **Analyze** Mode is recommended to use. * Input your prompt with the core content idea, e.g.: "Compared to BTC, why ETH is more worth buying" . The AI will automatically expand this into complete marketing materials. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252FndMeklozRFrFhXgqhqPq%252F5.png%3Falt%3Dmedia%26token%3Dae421eaf-fb89-4303-98c1-2a55b233ab30&width=768&dpr=4&quality=100&sign=ecdb98d7&sv=2) ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases#instant-dapp-generation) Instant dApp Generation Instant dApps represent a core innovation in DAPPOS, allowing the Web3 AI OS to rapidly generate deployable Web3 applications based on AI-designed execution plans. These on-demand dApps, created through simple natural language interactions, mirror vibe coding—transforming ideas into functional tools without coding expertise. Unlike generic AIs limited to suggestions, DAPPOS integrates the Intelligence Layer for plan optimization and the Execution Layer for secure deployment, enabling seamless coordination across decentralized protocols. Instant dApps serve two primary roles: 1. Facilitating User Execution: Through an intuitive dApp UI, they simplify operations for the current user, such as inputting necessary parameters for DeFi actions (e.g., loan amounts, collateral ratios, or yield thresholds). This streamlines complex tasks like recursive lending or token swaps, with built-in simulations and safeguards. 2. Enabling Community Reuse: Shared via the Discover Page, these dApps allow other users to browse, adopt, and reproduce the same execution schemes. This dynamic hub fosters collaboration, where community members can explore AI-generated strategies, customize them, and execute directly—lowering barriers and accelerating innovation. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252F3c1DTGlIBKlTiY0mlzUr%252F6.png%3Falt%3Dmedia%26token%3D4d0713fa-9255-4651-af58-fcf59d748f93&width=768&dpr=4&quality=100&sign=66579959&sv=2) How to Use: * Use **Plan** mode to emphasize generating executable plans that culminate in Instant dApp creation, or Auto mode for flexible ideation. **#Opportunity** tag is also recommended. * Interact with the Instant dApps with UI just like normal dApps. * You can publish the dialogue to the Discover Page to share the instant dApps with other users. [PreviousQuick Start](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start) [NextBubbleUp Tasks](https://dappos.gitbook.io/docs/dappos/how-to-guides/bubbleup-tasks) Last updated 2 months ago --- # Web3 AI OS | DAPPOS [Quick Start](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start) [Use Cases](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases) [PreviousHow-to Guides](https://dappos.gitbook.io/docs/dappos/how-to-guides) [NextQuick Start](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/quick-start) Last updated 2 months ago --- # Background and Problem Statement | DAPPOS [Problem](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement/problem) [Introduction to Intent](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement/introduction-to-intent) [Current Intent-based Systems and Their Limitations](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement/current-intent-based-systems-and-their-limitations) [PreviousExecution Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer) [NextProblem](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement/problem) Last updated 1 year ago --- # Intent Task Frameworks | DAPPOS For convenience, DAPPOS has developed a series of **intent task frameworks**. When users interact with partner infrastructure or dApps of DAPPOS, their intents are automatically generated by the intent task frameworks. Users can then view quotes from various service providers within the dApp and select the most appealing option. [PreviousWorkflow of Intent Execution Network](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/how-intent-execution-network-works/workflow-of-intent-execution-network) [NextUnified Account](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/intent-task-frameworks/unified-account) Last updated 2 months ago --- # How-to Guides | DAPPOS [Web3 AI OS](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os) [BubbleUp Tasks](https://dappos.gitbook.io/docs/dappos/how-to-guides/bubbleup-tasks) [PreviousIntent EX](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/intent-task-frameworks/intent-ex) [NextWeb3 AI OS](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os) Last updated 2 months ago --- # How Intent Execution Network works | DAPPOS DAPPOS has developed an intent execution network designed to simplify and optimize the process of executing user intents on the blockchain. By leveraging the innovative Optimistic Minimum Staking (OMS) mechanism and integrating various roles within the network, DAPPOS ensures efficient and secure execution of intents. [PreviousCurrent Intent-based Systems and Their Limitations](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/background-and-problem-statement/current-intent-based-systems-and-their-limitations) [NextOptimistic Minimum Staking](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer/how-intent-execution-network-works/optimistic-minimum-staking) Last updated 2 months ago --- # Security | DAPPOS ### [](https://dappos.gitbook.io/docs/dappos/security#user-autonomy) User autonomy In the DAPPOS execution network, each user has their own unified account. No role other than the user themselves can control or authorize any assets within the unified account. Advanced users can programmatically separate control permissions and enjoy services such as delegated execution. The team retains the authority to add or remove interoperable public chains, but the chains where abstract accounts are deployed will always remain supported. This means that even if all roles in the DAPPOS intent execution network cease to operate, users can still directly interact with public chains to control their accounts and assets. ### [](https://dappos.gitbook.io/docs/dappos/security#decentralized-validation) Decentralized Validation The execution of Custom Value-Specific tasks operates through a market mechanism, and the supervision of the execution results is a decentralized process involving execution validators in the DAPPOS Intent Execution Network. We employ a Game-theoretic equilibrium model and utilize slashing as a method to governance this POS system. Anyone can raise a challenge against the execution results of a service node. If the challenge is voted through by the execution validators, anyone can participate in the liquidation and earn the service node's collateral. [PreviousBubbleUp Tasks](https://dappos.gitbook.io/docs/dappos/how-to-guides/bubbleup-tasks) [NextExternal Audits](https://dappos.gitbook.io/docs/dappos/security/external-audits) Last updated 2 months ago --- # Withdraw Delay | DAPPOS ### [](https://dappos.gitbook.io/docs/dappos/security/withdraw-delay#withdraw-delay) Withdraw Delay All service providers participating in the DAPPOS Intent Execution Network are required to undergo a 14-day delay when exiting the system. This is to ensure that all value-specific tasks participated in by the service providers have been successfully completed and have passed the period of righteous challenge. Similarly, all validators participating in staking and voting have a 14-day withdrawal delay to ensure that validators have faithfully fulfilled their responsibilities during the staking period. Validators can withdraw their staked governance tokens only after all challenges they have participated in have concluded and passed the cooldown period. [PreviousBug Bounty](https://dappos.gitbook.io/docs/dappos/security/bug-bounty) [NextSupport](https://dappos.gitbook.io/docs/dappos/support) Last updated 2 months ago --- # Support | DAPPOS For any issues, questions, comments, feedback or concerns, please join the [_**discord**_](https://discord.gg/Gu2jD9tREf) [PreviousWithdraw Delay](https://dappos.gitbook.io/docs/dappos/security/withdraw-delay) Last updated 1 year ago --- # BubbleUp Tasks | DAPPOS ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/bubbleup-tasks#publishing-posts-with-bubbleup-for-the-bubble-engine) Publishing Posts with #BubbleUp for the Bubble Engine To contribute directly to the Bubble Engine's knowledge base, users can publish posts on platforms like X (formerly Twitter) or Binance Square. By including the **#BubbleUp** tag, these posts become eligible for ingestion within the system. This process "force-feeds" the engine with fresh, Web3-specific data, helping it adapt to opaque, fast-moving environments where generic AIs falter—such as detecting deceptive practices or weighting high-signal events like partnership leaks. ![](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252F6OTtUhXWzSge9KqhAUNM%252F7.png%3Falt%3Dmedia%26token%3D13856905-7422-4a5a-9a86-cf60e169d35a&width=768&dpr=4&quality=100&sign=2524dbd9&sv=2) By publishing Bubble posts, you actively shape the Intelligence Layer, making DAPPOS more resilient to Web3's implicit rules and new concepts. ### [](https://dappos.gitbook.io/docs/dappos/how-to-guides/bubbleup-tasks#bubbleup-task-platform) BubbleUp Task Platform The Bubble Task Platform is currently in internal beta testing, which offers a structured ecosystem for collaborative contributions to DAPPOS AI’s intelligence. It also introduces incentive mechanisms to reward users for completing bubble tasks. Regarding the quality of information, the platform implements a reward and punishment mechanism: false statements will be penalized, while high-quality information will be rewarded. If you're interested in participating in BubbleUp activities, please join the DAPPOS community channels to get the latest news. [PreviousUse Cases](https://dappos.gitbook.io/docs/dappos/how-to-guides/web3-ai-os/use-cases) [NextSecurity](https://dappos.gitbook.io/docs/dappos/security) Last updated 2 months ago --- # Bug Bounty | DAPPOS Check out the official bug bounty program for DAPPOS (coming soon) [PreviousExternal Audits](https://dappos.gitbook.io/docs/dappos/security/external-audits) [NextWithdraw Delay](https://dappos.gitbook.io/docs/dappos/security/withdraw-delay) Last updated 2 months ago --- # External Audits | DAPPOS ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#dappos-v2-audit-reports) DAPPOS V2 Audit Reports Audit reports are all open for review, showcasing our commitment to transparency. We're actively collaborating with leading security audit firms to thoroughly assess our smart contracts, reinforcing our dedication to a secure ecosystem with no vulnerability. Auditor Date Status Audit Link CertiK August 2023 Completed [CertiK](https://skynet.certik.com/projects/dappos) Secure 3 August 2023 Completed [Github](https://github.com/Secure3Audit/Secure3Academy/blob/main/audit_reports/dappOS/DapposP4_final_Secure3_Audit_Report.pdf) Secure 3 August 2023 Completed [Github](https://github.com/Secure3Audit/Secure3Academy/blob/main/audit_reports/dappOS/DapposP5_final_Secure3_Audit_Report.pdf) Trail of Bits August 2023 Completed [Github](https://github.com/trailofbits/publications/blob/master/reviews/2023-07-dappos-securityreview.pdf) SlowMist October 2023 Completed [Github](https://github.com/slowmist/Knowledge-Base/blob/master/open-report-V2/smart-contract/SlowMist%20Audit%20Report%20-%20DappOS%20Contracts%20Core.pdf) BlockSec June 2024 Completed [Github](https://github.com/blocksecteam/audit-reports/blob/main/solidity/blocksec_dappos_ia_v1.0-signed.pdf) Halborn September 2024 Completed [Halborn Report](https://www.halborn.com/audits/dappos/intent-assets) Halborn November 2024 Completed [Halborn Report](https://www.halborn.com/audits/dappos/internal-exchange-re-assessment) [](https://dappos.gitbook.io/docs/dappos/security/external-audits#audit-report-pdfs) Audit Report PDFs ----------------------------------------------------------------------------------------------------------- ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#certik) CertiK 2MB [REP-final-20230828T150915Z.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FBunyudw6AoyXqWr1f07A%2FREP-final-20230828T150915Z.pdf?alt=media&token=3cc5d045-97bb-4c5c-9b1f-734936d092f5) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FBunyudw6AoyXqWr1f07A%2FREP-final-20230828T150915Z.pdf?alt=media&token=3cc5d045-97bb-4c5c-9b1f-734936d092f5) ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#secure3) Secure3 837KB [DapposP4\_final\_Secure3\_Audit\_Report.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FNOoMtHs7TYNO7jC3fXXp%2FDapposP4_final_Secure3_Audit_Report.pdf?alt=media&token=88f0d89f-9d04-4ee7-af01-60d670b00f1a) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FNOoMtHs7TYNO7jC3fXXp%2FDapposP4_final_Secure3_Audit_Report.pdf?alt=media&token=88f0d89f-9d04-4ee7-af01-60d670b00f1a) 834KB [DapposP5\_final\_Secure3\_Audit\_Report.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2F7Zm8ydbWJ8VsvnYUu8LM%2FDapposP5_final_Secure3_Audit_Report.pdf?alt=media&token=dc929bef-af5f-447c-aa56-559291746a1f) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2F7Zm8ydbWJ8VsvnYUu8LM%2FDapposP5_final_Secure3_Audit_Report.pdf?alt=media&token=dc929bef-af5f-447c-aa56-559291746a1f) ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#trail-of-bits) Trail of Bits 885KB [2023-07-dappos-securityreview.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FIe8Ag7ij5azNAIul0u6n%2F2023-07-dappos-securityreview.pdf?alt=media&token=2ae1b23d-c0cb-4cd8-a0f7-ac73edc138b6) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FIe8Ag7ij5azNAIul0u6n%2F2023-07-dappos-securityreview.pdf?alt=media&token=2ae1b23d-c0cb-4cd8-a0f7-ac73edc138b6) ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#slowmist) SlowMist 2MB [SlowMist\_Audit\_Report\_DappOS\_Contracts\_Core.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FNaa8kReKlvxQsbVOrtDT%2FSlowMist_Audit_Report_DappOS_Contracts_Core.pdf?alt=media&token=c7f0c32a-51af-4eb7-aa4c-d4016b50b963) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FNaa8kReKlvxQsbVOrtDT%2FSlowMist_Audit_Report_DappOS_Contracts_Core.pdf?alt=media&token=c7f0c32a-51af-4eb7-aa4c-d4016b50b963) ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#blocksec) BlockSec 1MB [blocksec\_dappos\_ia\_v1.0-signed.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2F932lLXoj3wMsfqkPzZUF%2Fblocksec_dappos_ia_v1.0-signed.pdf?alt=media&token=b19235fb-5941-4562-afaf-3159e72a579a) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2F932lLXoj3wMsfqkPzZUF%2Fblocksec_dappos_ia_v1.0-signed.pdf?alt=media&token=b19235fb-5941-4562-afaf-3159e72a579a) ### [](https://dappos.gitbook.io/docs/dappos/security/external-audits#halborn) Halborn 12MB [Intent Assets Audit - Halborn.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FCMX3DSUBfxim5VpNJPYA%2FIntent%20Assets%20Audit%20-%20Halborn.pdf?alt=media&token=150df7e0-669a-4e51-bcb0-d1d579e9b90d) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2FCMX3DSUBfxim5VpNJPYA%2FIntent%20Assets%20Audit%20-%20Halborn.pdf?alt=media&token=150df7e0-669a-4e51-bcb0-d1d579e9b90d) 9MB [Internal Exchange Re-Assessment Audit.pdf](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2Fp4q8qil87rCmR78PUV3K%2FInternal%20Exchange%20Re-Assessment%20Audit.pdf?alt=media&token=e68ba4f7-f9f3-4bae-8e03-50d035d66ad1) PDF Download[Open](https://2617172943-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FOiUs3dwRaLnnk3PNO6EB%2Fuploads%2Fp4q8qil87rCmR78PUV3K%2FInternal%20Exchange%20Re-Assessment%20Audit.pdf?alt=media&token=e68ba4f7-f9f3-4bae-8e03-50d035d66ad1) [PreviousSecurity](https://dappos.gitbook.io/docs/dappos/security) [NextBug Bounty](https://dappos.gitbook.io/docs/dappos/security/bug-bounty) Last updated 2 months ago --- # Overview of DAPPOS | DAPPOS ![Page cover](https://dappos.gitbook.io/docs/~gitbook/image?url=https%3A%2F%2F2617172943-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FOiUs3dwRaLnnk3PNO6EB%252Fuploads%252Frgp11duKDjEO6GkmsLgx%252F%25E6%259C%2580%25E7%25BB%2588%25E7%2589%2588.png%3Falt%3Dmedia%26token%3D85162b26-9b50-4073-9fd4-44025bf8a8b7&width=1248&dpr=4&quality=100&sign=85ef56d9&sv=2) [Introduction](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/introduction) [Core Features](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/core-features) [Generic AI vs. Web3 AI OS](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/generic-ai-vs.-web3-ai-os) [Intelligence Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/intelligence-layer) [Execution Layer](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/execution-layer) [NextIntroduction](https://dappos.gitbook.io/docs/dappos/overview-of-dappos/introduction) Last updated 2 months ago ---