Most search agents try to handle too many jobs at once. They generate new queries, remember what they have already explored, collect evidence, and decide what is relevant as the search keeps expanding. That can make the whole process messy, expensive, and hard to control. Harness-1 takes a simpler approach. Built with researchers from UIUC, The post Harness-1: The 20B Retrieval Subagent That Beats GPT-5.4 at Search appeared first on Analytics Vidhya.

Explore the best local coding models for private AI coding, fast GGUF inference, agentic workflows, multimodal development, and running powerful open models on your own GPU.

This article breaks down each essential math discipline, explains its role in data science, and maps out an efficient learning path you can start today.

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.