Agree. We ought to be measuring the minimum viability of lesser parameter, local models for specific tasks. You don't need opus 4.7 or sonnet 4.6 to accomplish some of these basic, yet tedious tasks, i.e. the news aggregator you demonstrated. Thinking about things like, how many parameters does it take to manipulate a pdf in every way possible with accurate results? Likely, a reason there isn't a coordinated push toward people running local models is the fact that your data couldn't be mined, manipulated, and abused; obviously outside pure capability of some of the frontier models (which truthfully some of which aren't even very good). While I think we may see more things like Apple's models, like you mentioned being run locally, I think we all know at the end of the day they're phoning home in some way (which if that is fine for you, fine). Again though, and you touch on this in the article, highly specified tasks that have a certain amount of redundancy built in are very suitable for these local models right now, without relying on enormous weights and token usage.
I have been working on a VERY SMALL local-first ai lab myself. nothing crazy, a text editor, a claw, and some lightweight models I started playing with. Absolutely looking for contributions as well.