Truthful-AI
Verifiable AI-Human responses stored on blockchain, attested, time-stamped and community governed with Truth Token
About
The problem it solves truthful.ai addresses a critical challenge in the AI era: determining the reliability of information. By leveraging multiple LLM responses to the same query, analyzing their consensus patterns, and permanently recording verified answers, we create a trustworthy knowledge repository that grows more valuable over time. 73% of professionals distrust AI outputs due to hallucinations Current AI models produce conflicting/unverified information with no accountability Centralized knowledge bases create single points of failure and lack transparency No incentive structures exist for quality contributions or verification Critical decisions increasingly rely on unverifiable AI-generated content The Problem Information Accuracy: Misinformation spreads 70% faster than factual content AI Hallucinations: Up to 27% of AI-generated content contains factual errors No Verification Standard: Lack of consensus mechanism across AI models Knowledge Obsolescence: Information without temporal context becomes outdated Trust Deficit: 76% of professionals question information sources How It Works User submits a question Multiple AI models process the same query independently Consensus engine analyzes similarities and differences across responses System generates a verified answer. truthful.ai builds a verifiable knowledge base by: Multi-Model Analysis: Querying diverse AI systems simultaneously Consensus Mechanism: Analyzing response similarity and divergence Statistical Validation: Quantifying confidence through metrics Blockchain Permanence: Storing verified knowledge immutably Temporal Context: Timestamping all entries for relevance tracking Blockchain-verified AI consensus at 10,000 TPS Multi-LLM Verification: Every query validated across 3+ independent models Cryptographic Proof of Validity: ZK-SNARKs ensure computational integrity Tokenized Reputation System: $TRUTH rewards accurate knowledge contribution No more "trust one model" - only consensus truth is immutable User Interaction and Data Flow How It Works User submits a question Multiple AI models process the same query independently Consensus engine analyzes similarities and differences across responses System generates a verified answer with statistical confidence metrics Knowledge is permanently stored on Flare blockchain with timestamp Repository grows into valuable intellectual capital and public good More technical documentation here: https://drive.google.com/file/d/1YZXVUClQfPuWT5Xr1zLXgZ-UM7_aLp_K/view?usp=drive_link Pitch deck here: https://drive.google.com/file/d/1TnzZcfE41i05kl46DZh-gH_wacbTCSXV/view?usp=drive_link The project architecture and development process Technical Implementation A Community-built, Verifiable Knowledge Base truthful.ai is a platform driven by AI for consensus learning that generates and validates knowledge through multi-model verification, statistical analysis, and blockchain-based immutability. Trusted Knowledge Hub App front end deployed at [https://trusted-monad-hub-2.vercel.app/]. TruthToken deployed at: 0xFb75AfE2F631fC2935D8e4D293D26aeC5fd718A1 TrustedVerification deployed at: 0x9dEECfCF14c9F2a321aE65e4cd18169A47de6945 🚀 Key Features Oracle APEX Integration / authentication Live Consensus & Statistics Visualisation Leaderboard Decentralised Monad-Powered Consensus of AI-generated content : High throughput 10,000 TPS validation on Monad Staking Mechanism: TRUTH token staking contract (ERC-20 compatible) ZK-Proof Verification: Private voting using zk-SNARKs Real-Time Dashboard: Monad EVM block explorer integration ** Dedicated ZK utilities for Monad blockchain ** Enhanced verification badge to display ZK verification status ** Smart contract integration for voting on content validity ** Transaction recording on Monad blockchain with content hash verification ** Created verification metadata generation with validator signatures Truth Token & Tokenomics ** Token Incentives System ** Implemented $TRUTH token dashboard for staking and rewards ** Added token reward claiming for accurate submissions and validations ** Created UI for displaying token incentives and earning opportunities On-Chain Governance ** Added DAO governance panel for voting on proposals ** Implemented proposal creation and voting mechanics ** Integrated staking for voting power in governance decisions 🔗 Monad Integrations Component Monad Advantage Consensus Protocol 1-second finality via pipelining Data Storage Quick Start Install dependencies npm install @monadchain/sdk @wagmi/core Configure environment cp .env.example .env Add your MONAD_RPC_URL and wallet config Start local node npx hardhat node --network monad Product Integrations Getting Started Prerequisites Node.js v16+ Python 3.9+ API keys for supported LLMs Monad network access credentials Installation bash Copy git clone https://github.com/agentzeta/trusted-monad-hub-2.git cd trusted-monad-hub-2 npm install Configuration Create a .env file in the root directory with the following variables: Copy OPENAI_API_KEY=your_openai_api_key ANTHROPIC_API_KEY=your_anthropic_api_key FLARE_NODE_URL=your_flare_node_url FLARE_PRIVATE_KEY=your_flare_private_key NEAR_NODE_URL=your_near_node_url NEAR_PRIVATE_KEY=your_near_private_key Running the Application bash Copy npm start The application will be available at http://localhost:3000. APIs of multiple LLMs used to generate individual LLM responses and a multi-model consensus response based on bespoke consenus derivation logic: Anthropic Llama Eleven Labs Grok Open AI Gemini Google Cloud Openrouter Private Keys for wallet to connect with blockchains and record transactions. .env Monad Network MONAD_RPC_URL=your_monad_rpc_url_here PRIVATE_KEY=0xyour_private_key_here Contract Addresses (to be filled after deployment) VERIFICATION_CONTRACT_ADDRESS=0x9dEECfCF14c9F2a321aE65e4cd18169A47de6945 TRUTH_TOKEN_ADDRESS=0xFb75AfE2F631fC2935D8e4D293D26aeC5fd718A1 For development MONAD_CHAIN_ID=10143 foundry.toml [profile.default] src = "contracts" out = "out" libs = ["lib"] solc_version = "0.8.13" # Adjust to match your contract's pragma [rpc_endpoints] monad = "${MONAD_RPC_URL}" Pragma Solidity Key differentiators and uniqueness of the project Community built public good with Human-Ai collaboration: Multi-AI Verification statistical Validation Consensus Visualisation Model Performance Leaderboard and Verified Models Blockchain Storing Temporal Context Global Accessibility Automated Scaling Decentralsied governance Human verification using voting/staking features of truth token Independent Why is this tool important? In a world of abundant generative AI, we need tools to parse truth from fiction, and be able to consider information with some confidence, so this tool tries to compare multiple (LLM) model outputs to arrive at a single consensus response (with text that is extracted from multiple models with statistical rigor ), which can be stored in decentralised public annals, with timestamps for relevance. Analysis Function: A function that provides detailed analysis of the consensus visually and textually. This eliminates outlier responses through similarity analysis, Includes only high-consensus information by clustering similar responses. Provides confidence intervals through the consensus confidence calculation Suitable for high fidelity scenarios such as medical diagnosis by emphasizing agreement among models and including appropriate disclaimers, which is crucial for critical applications requiring precision and accuracy like medical diagnosis, space, scientific missions etc. While this is not meant to be a substitute for human precision and judgement, tools such as this are a step towards Artificial General Intelligence. Serving the $17B Verification Economy Solves pain points of multiple industries: Medical / Healthcare : Clinical decision support lacking verification - Size $4.2 billion - Impact - 62% diagnostic errors reducible Legal : Document authenticity w/o centralized authority - $5.8 billion - Contracts with audit trail Journalism: Fake news proliferation overwhelming fact-checkers - $3.2 billion - Source verification at scale Web3 DAOs: information assymetry in voting cycles Trade-offs and shortcuts while building Limited features displayed on front end, though the github code has more comprehensive features. Demo: https://www.loom.com/share/927d090c40734b578274664470ff3e20 ** Used Flare's Trusted Execution Environment for Privacy ** ** Implemented Zk proofs for private verification but could not display these blockchain features in demo** ** Had to use a round robin approach for API keys so as not to hit limits** **Had to debug front end multiple times so as to maximise number of LLMs called and responses fetched** **Context window should ideally be high for accurate consensus, but had to be limited for practical considerations** **API keys hit limits and demo app gets affected in executed voice Agent , using LLMs etc - so provided a dashboard for user to input their own API keys, private keys in browser so info is private and secure, while not affecting the app's performance. ** **Deployed smart contracts to Monad testnet successfully, however user interactions with monad not shown in demo** **Token economy and voting/staking features for community governance of knowledge repository building to be completed in future.** - This would solve the problem of models with temporal context only, and outdated knowledge, which can be overridden with human insight. This project needs your valued validation to build a much needed public good - web3's first truth layer - a verifiable community-built knowledge hub of consensus AI output made possible with human-AI collaboration. Additional Features Adding support for additional LLM providers Improving consensus algorithms Enhancing the user interface Building tools to explore the knowledge base Developing governance mechanisms Please see CONTRIBUTING.md for details. Roadmap Phase 1: Initial platform with basic consensus engine and blockchain storage Phase 2: Enhanced statistical analysis and confidence metrics Phase 3: Community governance mechanisms Phase 4: API access to the verified knowledge base Phase 5: Integration with other knowledge tools and platforms