AI Technology

AI racks are getting denser. Here is why liquid cooling is becoming a data-center design requirement, and what operators must still prove.

Water claims around AI data centers are easy to oversimplify. A buyer’s guide to WUE, system boundaries, local context and contract questions.

Factory humanoids are moving beyond demonstrations. Read what BMW’s Leipzig deployment shows, which metrics matter, and what remains unproven.

Optical processors promise efficient AI math. What research demonstrates, where electronics remain essential, and how buyers should evaluate claims.

Logical qubits are the practical test for quantum error correction. What recent research proves, what a logical circuit shows, and what buyers should ask.

DeepSeek’s open-source Harness puts tools, memory, sandboxes and orchestration around AI models. Learn what an agent harness is, why it matters, and how teams should evaluate it.

Google’s Agent2Agent protocol has joined the Agentic AI Foundation. Learn how A2A works, how it differs from MCP, and what it means for enterprise multi-agent systems.

AMD’s agreement to acquire Taalas highlights a growing AI infrastructure shift: lower-latency, lower-cost inference through specialized silicon. Here is what enterprises should understand.

MCP is Anthropic's open-source protocol for connecting AI models to external tools, databases, and systems. It's already seen half a billion downloads. Here's what it is, how it works, and why it matters.

Test-time compute scaling is the technique behind OpenAI o1, o3, and DeepSeek-R1. Instead of training bigger models, it lets AI 'think longer' to get smarter. Here's how it works and why it changes everything.

All three major AI platforms now remember you across conversations. Here's exactly how ChatGPT, Claude, and Gemini memory systems work, what they store, how to control them, and what's coming next.