WebMCP is an open web standard that lets websites expose structured, callable tools directly to browser-based agents. Find out what makes it exciting.

For years, AI progress has centered on scaling individual foundation models: larger parameters, longer context windows, stronger reasoning, and better tool use. Sakana AI’s Fugu points elsewhere, behaving like one model from the outside while coordinating multiple expert agents internally. A single API call can trigger direct answering, specialist delegation, intermediate verification, and final synthesis, The post Sakana Fugu: Multi-Agent System as a Model  appeared first on Analytics Vidhya.

Outliers can easily ruin the performance of any predictive analysis models you build: robustly detecting and handling them is crucial in any data project. This article lists and compares five essential approaches for detecting them.

An in-depth review of ChatLLM by Abacus AI, covering supported AI models, AI agents, coding tools, integrations, pricing, usage limits, and how it compares to ChatGPT.

Agentic AI is not failing because the technology is bad. It is failing because of five specific misconceptions that teams carry into their first deployments and each one is correctable.

In this article, we will walk through three essential NLTK tricks to elevate your text preprocessing: preserving phrase integrity with the MWETokenizer, context-aware lemmatization with POS mapping, and statistical collocation extraction using association measures.

Everyone says Claude can’t make pictures. That’s partly true. Here is the kind of art it makes on its own, with no plugins and no connectors: Drawn by Claude in SVG, no image model anywhere near it. Not pixels but code: shapes and coordinates that stay sharp at any size and redraw themselves when you The post Claude’s Hidden Art Skill: Making Illustrations With Code appeared first on Analytics Vidhya.

ML system design interviews test how well you can think beyond models. In these interviews, choosing an algorithm is only one part of the answer. You also need to explain how data is collected, how features are created, how predictions are served, and how the system improves over time.  Most real ML systems are built The post System Design for ML Interviews: 10 Real Problems Walked Through appeared first on Analytics Vidhya.