Category: IT News Blog

Title - AI Learns to Think Fast

AI Learns to Think Fast: DeepSeek Running for the Masses

The currently over-hyped GenAI hysteria backed by bloated AI infrastructure implementations will predictably cause big problems:

  1. Massive 100+ billion node models use a boatload of power for both training and inference. MS and Amazon (et.al.) all publicly want to build
image of anime character in pilot uniform standing in front of large mech

Private AI, We Salute You!

So VMware made the case this morning that data privacy is the big obstacle in the way of enterprises leveraging new generative AI solutions (e.g., ChatGPT). If “private AI” is so important especially with generative AI (genAI) large language models …

Surreal image of VR goggles on an earth-like planet

The Better Apple In My Eye? – VR, AR or Digital Twin

With their new Vision Pro goggles offering, has Apple placed a too large bet in the consumer Augmented Reality (AR) space? Is there a really a consumer market for AR and/or can they make this one happen?

AR vs VR

AI art image implying future IT tech opportunities

Seven Free Technologies To Secure, Scale and Accelerate IT

In this post I’ve listed seven (7) technologies from some recent analyst interviews that I think are especially interesting going into the new year. I’m sure you’ve got fresh new IT goals to make your data safer, increase it’s value, …

AI generated abstract image implying data aggregation and distributed edge computing

The Best Way to Manage Future IT Data Growth is to Follow These Three Steps – (3/3)

This series of posts was originally going to be about the easiest data architecture and management steps to take, but no, none of these are easy. They are hard. Very hard in fact. But quite possible necessary. Let me know what

AI generated abstract image implying IT data stream aggregation to higher levels of value

The Best Way to Manage Future IT Data Growth is to Follow These Three Steps – (2/3)

This series of posts was originally going to be about the easiest data architecture and management steps to take, but no, none of these are easy. They are hard. Very hard in fact. But quite possible necessary. Let me know what