She’s old. She doesn’t want to read a book. As best as I can tell she wants to have something slightly brain engaging during commercials when watching shows, but is far too stubborn to learn a new game she doesn’t already know.
Who are we to tell someone what they should want to do with their twilight years?
The unfortunate reality is they cannot be avoided in some cases. There is not a paid alternative to Facebook, nor are there to a lot of f2p mobile games.
My grandma had a tablet about a decade ago, and I loaded it up with tons of paid $1-$3 casino games for her (it’s what she wanted) but a decade later, when going to reinstall them to a new tablet, all of them no longer exist on the play store and seemingly 100% of current games are either ad supported or require iap to refil your virtual currency.
She literally did what you asked and today she still has no options. What should my 86 year old grandma do in this case?
Chinese, specifically so I can exclusively curse in it like they did on firefly.
There are server chips like the E7-8891 v3 which lived in a weird middle ground of supporting both ddr3 and ddr4. On paper, it’s about on par with a ryzen 5 5500 and they’re about $20 on US eBay. I’ve been toying with the idea of buying an aftermarket/used server board to see if it holds up the way it appears to on paper. $20 for a CPU (could even slot 2), $80 for a board, $40 for 32gb of ddr3 in quad chanel. ~$160 for a set of core components doesn’t seem that bad in modern times, especially if you can use quad/oct channel to offset the bandwidth difference between ddr3 and ddr4.
I think finding a cooler and a case would be the hardest part
+1 for Niagra. It takes a few days to get used to but it’s the launcher every power user didn’t know they wanted. Lifetime purchase options and a very responsive/passionate dev
https://en.wikipedia.org/wiki/Markov_chain
Before the advent of AI, I wrote a slack bot called slackbutt that made Markov chains of random lengths between 2 and 4 out of the chat history of the channel. It was surprisingly coherent. Making an “llm” like that would be trivial.
Inference is dirt cheap in comparison. Hundreds to thousands of concurrent users can be served by hardware costing in the high-thousands to low-ten-thousands.
Training those same foundational models is weeks to months of time on tens to hundreds of millions worth of hardware.