MiddleKid
Bridge for the newbie to the complex world
About
The problem it solves MiddleKid makes DeFi safer and more accessible by protecting users from scams and simplifying complex blockchain analysis. 🔒 Scam Protection DeFi users lose millions to honeypot tokens and rug pulls. MiddleKid's multi-source Researcher combines GoPlusLabs security scans + DEX Screener market data to detect: - Honeypot tokens (can't sell after buying) - High buy/sell taxes (>10%) - Hidden owners & malicious contracts - No liquidity or fake volume Before buying any token on Base, users can instantly check if it's legitimate or a scam - preventing costly mistakes. 💰 Simplified Wallet Analysis Instead of navigating multiple block explorers, users can: View all Base holdings in one clean interface Track token balances with real-time USD values Monitor DeFi positions and health scores 🎯 User-Friendly Experience No technical knowledge required - Just paste a token address Clear risk scores (0-100) with actionable flags Multi-source verification - Not relying on single data provider 🚀 Future Features (Planned) Translator: Decode complex transactions into plain English Time Traveller: Historical price contracts for strategic trading AI Chat: Get instant answers about tokens, risks, and DeFi strategies MiddleKid acts as your "sidekick" in DeFi - making the Base ecosystem safer, easier to navigate, and more accessible to everyone, from beginners to advanced traders.
How it's made
Challenges we ran into Challenge 1: Risk Scoring Accuracy The Problem: Initial implementation used only Alchemy's getAssetTransfers API to count holders and transfers. Both legitimate tokens (SHIBA, Polkadot) and scam tokens showed similar transfer activity - making the system useless for scam detection. Solution: Pivoted to a multi-source approach: Integrated GoPlusLabs API for security scans (honeypot detection, tax analysis) Added DEX Screener API for market legitimacy (liquidity, volume) Implemented weighted scoring (60% security, 30% market, 10% metadata) This reduced false positives dramatically while catching actual scams. Challenge 2: Balancing Scoring Penalties The Problem: Many legitimate tokens are "mintable" or have certain owner privileges, causing high risk scores for safe tokens like Polkadot (scored 12/100 when it should be ~3). Solution: Reduced mintable penalty from 20 → 5 points (common in legit tokens) Increased "no market data" penalty from 30 → 50 points (high risk indicator) Added granular liquidity thresholds (<$1K = 40pts, <$10K = 25pts, etc.) 🎨 Challenge 3: Z-Index Layering Chaos The Problem: Navigation tabs appeared behind the wallet connection modal - but only after the modal opened. Tried increasing navigation z-index to 9999, still didn't work! Root Cause: The wallet button wrapper had z-index: 101, creating a stacking context that isolated its children from the global z-index hierarchy. Solution: Set top bar elements (logo, title, wallet) to z-index: 10000 and navigation to z-index: 100. Added click-outside-to-close for better UX. Challenge 4: State Management The Problem: Researcher page required hard refresh between searches - results from previous search persisted. Solution: Implemented proper state clearing with a unique key prop on ResearchResults component based on search query, forcing React to unmount/remount on new searches. Challenge 5: API Rate Limiting & Reliability The Problem: Free APIs (GoPlusLabs, DEX Screener) don't always have data for every token. Solution: Built fallback scoring system - if an API fails or returns no data, assign conservative penalties instead of crashing. Added detailed console logging for debugging.