This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
The Gemini API documentation provides comprehensive information about Google's Gemini models and their capabilities. It includes guides on generating content with Gemini models, native image generation, long context exploration, and generating structured outputs. The documentation offers examples in Python, Node.js, and REST for using the Gemini API, covering various applications like text and image generation, and integrating Gemini in Google AI Studio.
Model Context Protocol (MCP) is a bridging technology for AI agents and APIs. It standardizes API access for AI agents, making it a universal method for AI agents to trigger external actions.
Anthropic's new feature allows specifying a public URL for images/documents in their API, improving performance and usability. The article details implementation and successful testing with Claude 3.7 Sonnet.
Harbor is a containerized LLM toolkit that allows you to run LLMs and additional services with ease, featuring a CLI and a companion App for managing AI services.
The Cerebras API offers low-latency AI model inference using Cerebras Wafer-Scale Engines and CS-3 systems, providing access to Meta's Llama models for conversational applications.
Llama Stack v0.1.0 introduces a stable API release enabling developers to build RAG applications and agents, integrate with various tools, and use telemetry for monitoring and evaluation. This release provides a comprehensive interface, rich provider ecosystem, and multiple developer interfaces, along with sample applications for Python, iOS, and Android.
Meta has launched Llama-Stack 0.1.0, a development platform designed to simplify the process of building AI applications using Llama models. The platform offers standardized building blocks and flexible deployment options, including remote and local hosting. It features a plugin system for various API providers and supports multiple programming environments with its CLI tools and SDKs. Meta aims to address common challenges faced by AI developers, such as integrating tools and managing data sources.
Sparse autoencoders (SAEs) have been trained on Llama 3.3 70B, releasing an interpreted model accessible via API, enabling research and product development through feature space exploration and steering.
