
Built Farcaster integration tools and metadata APIs to support AI-powered social data analysis
GetEmbed.ai is an algorithm engine for Web3 that provides real-time, personalized feeds for crypto-social experiences. The ❜embed SDK enables developers to integrate TikTok-grade recommendation algorithms into their applications, delivering highly relevant onchain content to users based on their social graph and activity patterns. The platform powers feeds for millions of users, including integration with Base wallet's Discover tab.
The ❜embed SDK enables developers to integrate personalized Web3 feeds without worrying about rate limits or boilerplate code, powering discovery and ranking in advanced crypto-social applications with features like "For You" feeds, trending content, and Zora integration.
Built a Farcaster Miniapp demonstrating the ❜embed SDK's capabilities for real-time, personalized Web3 feeds with built-in error handling, retries, and rate limit management.
Built comprehensive metadata extraction API supporting Farcaster casts, Zora content, NFT data, and Web3 platforms enabling AI-powered feed recommendations and social proof scoring.
Created data processing pipelines with intelligent caching layers to support AI tooling for analyzing social interactions, NFT trends, and Web3 engagement patterns.
Implemented robust Web3 integrations for accessing blockchain data, NFT metadata, and decentralized social platform information.
Built scalable TypeScript APIs with comprehensive metadata extraction capabilities, supporting multiple data sources and formats for AI processing.
Developed native Farcaster Miniapp with frame detection, user enrichment, and social graph analysis capabilities.
Implemented support for OpenSea, Zora, and other NFT platforms with unified metadata extraction and normalization.
Zenkai provided development support to dtech.vision, delivering specialized Web3 and AI integration expertise.
Key components of this work have been open-sourced to benefit the broader developer community:
Implementation demonstrating ❜embed SDK integration for personalized Web3 feeds with TikTok-grade recommendation algorithms for onchain content discovery