This is a very important point - the market for training chips might be a bubble, but the market for inference is much, much larger. At some point we might have good enough models and the need for new frontier models will cool down. The big power-hungry datacenters we are seeing are mostly geared towards training, while inference-only systems are much simpler and power efficient.

A real shame, BTW, all that silicon doesn't do FP32 (very well). After training ceases to be that needed, we could use all that number crunching for climate models and weather prediction.

Loneliness and boredom are good ingredients as well. Some physical isolation helps with that. I lived in a neighbourhood where I was the only kid. That made me bored frequently and drove me to habits that still serve me well.

Recently, with my mom’s passing, I realised I’m now an orphan.

It really sucks, at any age.

I love tools like this. I remember using a similar one (made by Red Hat IIRC) that I used to look inside the sources for the Brazilian voting machine (I was asked to map duplicate files and functionality and simplify the codebase) in 2002. It was a desktop app with a very Motif interface.

It’s often better to overlay caching and other tricks on top of naive implementations than making the implementation more complicated.