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Google AI Introduces ‘Groundsource’: A New Methodology that Uses Gemini Model to Transform Unstructured Global News into Actionable, Historical Data
Google AI Research team recently released Groundsource, a new methodology that uses Gemini model to extract structured historical data from unstructured public news reports. The project addresses the lack of historical data for rapid-onset natural disasters. Its first output is an open-source dataset containing 2.6 million historical urban flash…

From Text to Tables: Feature Engineering with LLMs for Tabular Data
In this article, you will learn how to use a pre-trained large language model to extract structured features from text and combine them with numeric columns to train a supervised classifier. Topics we will cover include: Creating a toy dataset with mixed text and numeric fields for classification Using a Groq-hosted LLaMA model to…
Model Context Protocol (MCP) vs. AI Agent Skills: A Deep Dive into Structured Tools and Behavioral Guidance for LLMs
In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively. Two common approaches that have emerged are skills and MCPs. While they may appear similar at first, they differ in how they are set up, how they execute…

Setting Up a Google Colab AI-Assisted Coding Environment That Actually Works
In this article, you will learn how to use Google Colab’s AI-assisted coding features — especially AI prompt cells — to generate, explain, and refine Python code directly in the notebook environment. Topics we will cover include: How AI prompt cells work in Colab and where to find them A practical workflow for generating…

Building Smart Machine Learning in Low-Resource Settings
In this article, you will learn practical strategies for building useful machine learning solutions when you have limited compute, imperfect data, and little to no engineering support. Topics we will cover include: What “low-resource” really looks like in practice. Why lightweight models and simple workflows often outperform complexity in constrained settings. How to handle…

Can AI help predict which heart-failure patients will worsen within a year? | MIT News
Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient’s lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously…
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