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Google ADK Multi-Agent Pipeline Tutorial: Data Loading, Statistical Testing, Visualization, and Report Generation in Python

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In this tutorial, we build an advanced data analysis pipeline using Google ADK and organize it as a practical multi-agent system for real analytical work. We set up the environment, configure secure API access, create a centralized data store, and define specialized tools for loading data, exploring datasets, running statistical tests, transforming tables, generating visualizations, and producing reports. As we move through the workflow, we connect these capabilities through a master analyst agent that coordinates specialists, allowing us to see how a production-style analysis system can handle end-to-end tasks in a structured, scalable way. Copy Code Copied Use a different Browser !pip install google-adk -q !pip install litellm -q !pip install pandas numpy scipy matplotlib seaborn -q !pip install openpyxl -q print(" All packages installed!") import os import io import json import getpass import asyncio from datetime import datetime from typing import Optiona...
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Google AI Research Proposes Vantage: An LLM-Based Protocol for Measuring Collaboration, Creativity, and Critical Thinking

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Standardized tests can tell you whether a student knows calculus or can parse a passage of text. What they cannot reliably tell you is whether that student can resolve a disagreement with a teammate, generate genuinely original ideas under pressure, or critically dismantle a flawed argument. These are the so-called durable skills — collaboration, creativity, and critical thinking — and for decades they have resisted rigorous, scalable measurement. A new research from Google Research proposes a technically novel solution called Vantage: orchestrated large language models that can both simulate authentic group interaction and score the results with accuracy rivaling human expert raters. https://ift.tt/bcZtEp9 The Core Problem: Ecological Validity vs. Psychometric Rigor To understand why this is technically interesting, it helps to understand the measurement paradox the research team was trying to crack. Measuring durable skills effectively requires two conflicting properties. On o...
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A Step-by-Step Coding Tutorial on NVIDIA PhysicsNeMo: Darcy Flow, FNOs, PINNs, Surrogate Models, and Inference Benchmarking

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In this tutorial, we implement NVIDIA PhysicsNeMo on Colab and build a practical workflow for physics-informed machine learning. We start by setting up the environment, generating data for the 2D Darcy Flow problem, and visualizing the physical fields to clearly understand the learning task. From there, we implement and train powerful models such as the Fourier Neural Operator and a convolutional surrogate baseline, while also exploring the ideas behind Physics-Informed Neural Networks. Also, we compare architectures, evaluate predictions, benchmark inference, and save trained models, providing a comprehensive hands-on view of how PhysicsNeMo can be used for scientific machine learning problems. Copy Code Copied Use a different Browser print("="*80) print("SECTION 1: INSTALLATION AND SETUP") print("="*80) import subprocess import sys def install_packages(): """Install required packages for the tutorial.""" ...
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