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⏰ Featured AI: NVIDIA Introduces CLIMB and ByteDance Releases UI-TARS-1.5
Hi There,
Dive into the hottest AI breakthroughs of the week—handpicked just for you!
Super Important AI News 🔥 🔥 🔥
🧵 Atla AI Introduces the Atla MCP Server: A Local Interface of Purpose-Built LLM Judges via Model Context Protocol (MCP)
📢 NVIDIA Introduces CLIMB: A Framework for Iterative Data Mixture Optimization in Language Model Pretraining
🚨 Meta AI Introduces Perception Encoder: A Large-Scale Vision Encoder that Excels Across Several Vision Tasks for Images and Video
💡💡 Serverless MCP Brings AI-Assisted Debugging to AWS Workflows Within Modern IDEs
🧲 🧲 Anthropic Releases a Comprehensive Guide to Building Coding Agents with Claude Code
Coding Tutorial </>
🖥️ A Step-by-Step Coding Guide to Defining Custom Model Context Protocol (MCP) Server and Client Tools with FastMCP and Integrating Them into Google Gemini 2.0’s Function‑Calling Workflow
In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and hosting our MCP tools in‑process, httpx for making HTTP requests to the Open‑Meteo weather API, and nest_asyncio to patch Colab’s already‑running asyncio event loop...….
from getpass import getpass
import os
api_key = getpass("Enter your GEMINI_API_KEY: ")
os.environ["GEMINI_API_KEY"] = api_key