GenAI has permeated everyday life. The go-to answer for millions of people faced with any number of different tasks. So why does it have such a basic monochrome interface? Open a browser or app for any of the mainstream GenAIs, and you’ll be faced with some variation of a black page with a box for entering white text into. You can turn the mic on, upload a file or image, but those are ultimately context-driven sideshows. The text box is the start-point for almost all GenAI w

Non-terrestrial networks are a recurring subject I've been asked about over the last year or two. Interest amongst Automotive and IoT teams has been piqued by Starlink, and high-return space stocks like RKLB and ASTS have pulled NTNs into the public consciousness. So to answer these questions I'll skip the full telco perspective here and focus on practical deployment.

The gap between a written product concept and working, tested, version-controlled code can now be closed without a developer writing a line of application code. From PRD to PR without code, automation is happening today.

Once you've played around with AI for any length of time and got under the skin of LLMs, you inevitably start to uncover the quirks, weaknesses and vulnerabilities inherent to the fundamental design concepts, and the execution itself. As seen in my other AI projects on the site, it's not hard to trigger those weaknesses once you get beyond basic GenAI and into genuine design and build scenarios.

Call this the rising floor. Every useful machine absorbs the layer of work it can do better than people. The human contribution does not vanish. It moves upward, into judgement, selection, taste, and ethics, questions a machine cannot answer for you because they are not technical questions at all. That is where AI is heading now, into code, legal drafting, HR policy, marketing copy, the layer of execution that used to define a competent professional.