I’ve been following the AI hardware race pretty closely—comes with the territory when you work in IT—but I’ll admit the latest announcement about StarCloud planning to launch GPUs into space had me doing a double-take over my morning latte. The idea of a 4-kilometre-wide, 5-gigawatt datacenter orbiting Earth sounds like something straight out of a sci-fi novel, and honestly, I’m not entirely convinced it’s anything more than that.

Let me be clear from the start: this isn’t actually NVIDIA launching GPUs into space, despite what the initial buzz suggested. StarCloud is a startup that’s part of NVIDIA’s Inception program, which is essentially a support network for companies building on NVIDIA tech. The distinction matters because it shifts this from “tech giant’s ambitious project” to “startup’s moonshot pitch,” and those two things have very different probabilities of success.

I stumbled across an interesting thread yesterday that got me thinking about our relationship with stuff. Someone was asking for help cleaning their discolored flip-flops - they’d tried soap, scrubbing, dish soap, vinegar, the whole nine yards. The responses were fascinating and really divided into two camps: those saying “just bin them and buy new ones” and others offering actual cleaning solutions.

What struck me most wasn’t the cleaning advice itself, but the underlying tension between our throwaway culture and environmental consciousness. The original poster mentioned their flip-flops cost $40 USD (which is about $60 AUD these days), and they’d been wearing them for three years. When they finally found success with a magic eraser, they added an update that really resonated with me: “I refuse to throw away what is still perfectly good but not as pretty anymore. Our landfills are full and we are drowning in fast fashion.”

The tech world is buzzing with another breakthrough in AI optimization - Sparse Transformers. Looking at the numbers being thrown around (2x faster with 30% less memory), my inner DevOps engineer is definitely intrigued. But let’s dive deeper into what this really means for the future of AI development.

The concept is brilliantly simple: why waste computational resources on parts of the model that won’t contribute meaningfully to the output? It’s like having a massive team where some members are essentially twiddling their thumbs during certain tasks. By identifying these “sleeping nodes” and temporarily sidelining them, we can achieve significant performance gains without sacrificing quality.

Reading through an online discussion about toilet cleaning today sparked some thoughts about our throwaway culture. The thread featured someone’s heroic journey of restoring a severely stained toilet using various cleaning products instead of simply replacing it - and the responses were quite telling about our society’s approach to maintenance versus replacement.

The discussion revealed a stark divide between two camps: those applauding the restoration effort and those suggesting replacement as the easier solution. What caught my attention wasn’t just the division itself, but how it perfectly encapsulates a broader societal issue we’re facing.

Reading about someone’s 14x RTX 3090 home server setup this morning made my modest 32GB VRAM setup feel like I brought a butter knife to a nuclear war. This absolute unit of a machine, sporting 336GB of total VRAM, represents perhaps the most extreme example of the local AI computing arms race I’ve seen yet.

The sheer audacity of the build is both impressive and slightly concerning. We’re talking about a setup that required dedicated 30-amp 240-volt circuits installed in their house - the kind of power infrastructure you’d typically associate with industrial equipment, not a home computer. The cooling requirements alone must be enough to heat a small neighbourhood.