MODEL-CONTEXT-PROTOCOL

Anthropic has updated its Model Context Protocol (MCP) to allow Claude to interact directly with popular productivity apps like Slack, Figma, and Canva, enabling users to perform complex tasks without leaving the chat interface.

Large Language Models often feel disconnected from real-world tasks. This guide explores how the Model Context Protocol (MCP) bridges the gap between AI intelligence and your local data, transforming fragmented tools into a unified system.

A deep dive into the Model Context Protocol (MCP), explaining how to bridge the gap between static LLMs and real-time data using TypeScript, Python, and Rust implementations.

Discover how the Model Context Protocol (MCP) solves the statelessness of AI agents, enabling persistent context, tool integration, and deterministic workflows for modern LLM development.

An in-depth technical analysis of why traditional LLM gateways fail under production loads and how a Go-based architecture like Bifrost achieves 50x lower latency for enterprise AI applications.

Anthropic expands its AI agent capabilities with Cowork, a new tool designed to handle general computing tasks by granting Claude direct access to local folders and files.

An in-depth analysis of LLM predictions 2026 based on Simon Willison's insights, exploring the shift toward agentic workflows, small models, and the evolution of AI infrastructure.

A deep dive into the Model Context Protocol (MCP), explaining how Large Language Models discover and execute external tools through a standardized architecture.

Learn how to optimize the Model Context Protocol (MCP) for complex AI agents. Before upgrading to a larger model, discover how improving tool-use infrastructure and context management via MCP can yield better performance and lower costs.