Enterprises using multiple AI models are underestimating failure rates by 2.25x

A study of 67 AI models finds enterprises underestimate multi-model failure rates by 2.25x, and offers a free test to check ...
CRN

The 10 Coolest New AWS Tools, Products And AI Innovation Of 2026

Here are AWS’ hottest new products for AI agents, agentic, Amazon Bedrock AgentCore, AWS Outposts, MCP Server, Quick and ...

Why marketing mix modeling is still hard to get right

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...

Meet Mana Jampala: 12-year-old who built an AI-powered receptionist to help businesses avoid missing calls and clients, learnt Python at 9 and won competitions …

Nvidia CEO Jensen Huang famously predicted that artificial intelligence would generate historic levels of wealth, creating ...

Don’t want Meta using your IG photos for AI? Here’s what you can do

Meta's newly launched Muse Image is the tech giant's first AI image generator.

Micron: Its HBM4 Crucial Catalyst Is Still Being Ignored

HBM4 and strategic customer agreements lock in ~40% revenue, boost margins, and reduce cyclicality amid AI demand—read the ...

AI image generators have escaped nightmare fingers and entered the fake premium era

I tested Meta Muse, Gemini Nano Banana 2, and ChatGPT Images 2.0. The obvious AI failures are fading, but the new flaws are harder to laugh off.
GitHub

MDalamin5/End-to-End-Agentic-Ai-Automation-Lab

This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable ...