AI-Tools

Introduction The Model Context Protocol (MCP) is rapidly becoming a foundational layer for connecting AI models to external tools, data sources, and services in a standardized way. As more powerful capabilities are exposed to models—querying databases, sending emails, acting in SaaS systems—authorization becomes a central concern. This article walks through: What MCP is and how resources fit into its design What link resources are and why they matter How link resources are typically used to drive authorization flows Example patterns for building MCP servers that handle auth securely Best practices and common pitfalls The goal is to give you a solid mental model for how MCP authorization with link resources works in practice, so you can design safer, more capable integrations. ...

FAISS (Facebook AI Similarity Search) is an open-source library developed by Meta’s AI Research team for efficient similarity search and clustering of dense vectors, supporting datasets from small sets to billions of vectors that may not fit in RAM.[1][4][5] This comprehensive guide dives deep into FAISS’s architecture, indexing methods, practical implementations, optimizations, and real-world applications, equipping you with everything needed to leverage it in your projects. What is FAISS? FAISS stands for Facebook AI Similarity Search, a powerful C++ library with Python wrappers designed for high-performance similarity search in high-dimensional vector spaces.[4] It excels at tasks like finding nearest neighbors, clustering, and quantization, making it ideal for recommendation systems, image retrieval, natural language processing, and more.[5][8] ...