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Pharos Ecosystem Security Guide: Full-Link Risk Control for RWA Asset Integration

"Pharos Ecosystem Security Guide: Comprehensive Risk Control for RWA Asset Integration" This guide provides developers in the Pharos ecosystem with a practical framework for integrating Real-World Assets (RWAs), addressing the unique challenges of combining off-chain legal claims with on-chain functionality. Pharos’s Layer 1 infrastructure, featuring Block-STM for parallel execution and dual EVM/WASM support, offers the high-speed settlement and complex computational power required for RWA operations. The analysis identifies two primary RWA models: 1) the on-chain to off-chain model (e.g., fundraising in stablecoins for off-chain investments like U.S. Treasuries) and 2) the asset tokenization model (e.g., fractionalizing real estate for on-chain ownership). The core focus is mitigating critical risks beyond smart contracts. Key strategies include: enforcing identity compliance via smart contract-level whitelisting and DID integration; implementing oracle-based circuit breakers to halt operations during stablecoin depegging events; ensuring asset authenticity with multi-source oracles for real-time NAV updates; mandating transparency for off-chain Special Purpose Vehicles (SPVs); designing built-in redemption queues and liquidity buffers to prevent secondary market collapses; and rigorously defending against inherited EVM vulnerabilities using audited libraries and reentrancy guards. The conclusion emphasizes that RWA security is a full-stack challenge, requiring robust integration of legal, financial, and technical safeguards to ensure asset authenticity and systemic resilience on Pharos.

marsbit01/20 14:10

Pharos Ecosystem Security Guide: Full-Link Risk Control for RWA Asset Integration

marsbit01/20 14:10

From "Manual Rules" to "AI Mind Reading": X's New Algorithm Reshapes the Information Flow, More Accurate and More Dangerous

Elon Musk's X (formerly Twitter) has transitioned from a recommendation system based on "manually stacked rules and heuristic algorithms" to one that relies entirely on a large AI model to predict user preferences. The new algorithm, For You," mixes content from accounts a user follows with posts from across the platform that the AI believes the user will like. The process begins by building a user profile based on historical interactions (likes, retweets, dwell time) and user features (following list, preferences). The system then gathers candidate posts from two sources: the user's direct network ("Thunder") and a broader network of potentially interesting content from strangers ("Phoenix"). After data hydration and an initial filtering step to remove duplicates, old posts, or content from blacklisted authors, the core scoring process begins. A Transformer model (Phoenix Grok) predicts the probability of a user taking various positive actions (like, retweet, reply, click) or negative ones (block, mute, report) on each post. A final score is calculated by weighting these probabilities. An Author Diversity Scorer is then applied to reduce the visibility of multiple posts from the same author in a single batch. The highest-scoring posts undergo a final filter to remove policy-violating content and remove duplicates from the same thread before being sorted into the user's feed. The shift represents a move from "telling the machine what to do" to "letting the machine learn what to do." While this can lead to more accurate recommendations and a fairer system that breaks the monopoly of large accounts, it also risks deepening users' "information cocoons" and making them more susceptible to targeted emotional content.

比推01/20 13:38

From "Manual Rules" to "AI Mind Reading": X's New Algorithm Reshapes the Information Flow, More Accurate and More Dangerous

比推01/20 13:38

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