rugs.fun-fork: Solana-based gamified trading and rugpull-detection platform — a professional fork of rugs.fun featuring enhanced risk analytics, NFT leveling, and real-time DeFi insights.

A deep technical article exploring how AI, feature engineering, and static smart-contract analysis uncover rugpull risks before humans detect them. Covers Solidity pattern mining, mint abuse detection, blacklist/fee manipulation signals, ML-inspired scoring models, and how to quantify ERC-20 token scam probability.

A hybrid Solidity + Python security toolkit that analyzes ERC-20 token contracts using static pattern extraction and ML-inspired scoring. Detects mint backdoors, blacklist controls, fee manipulation, trading locks, and rugpull mechanics. Outputs interpretable risk scores, labels, and structured features for deeper analysis.

AI-powered real-time smart contract scanner that connects Machine Learning with Etherscan V2 to analyze newly deployed contracts instantly. Fetches verified Solidity code, performs static risk analysis, computes ML-driven deployer trust scores, and generates full security intelligence pipelines for Web3 threat detection.

A complete Web3 security toolkit combining AI-powered token auditing, ML-based deployer reputation scoring, and live Etherscan V2 data. Includes static analysis for rugpull detection, RandomForest reputation modeling, contract-fetching automation, and Solidity on-chain registries for transparent, reproducible security insights.