A while ago, I did find a tutorial that would do this in Python but never got around to it.
I'm shocked that Grafana is really the only OSS game in town for user-customizable realtime visualization.
p.s. I would love nothing more than to be proven wrong.
I come previously ran an analytics team and I work on an open source BI tool (https://github.com/evidence-dev/evidence) but I have never actually used grafana or come across it when talking to other "business analytics" folks. Everyone in my world is just using tableau or looker or jupyter notebooks.
Meaning pretty much any team with basic know-how can get a monitoring dashboard going, or several for different resources and cases. It's main focus is monitoring.
Once you have your data sources connected, it's easy to create all kind of graphs and alerts.
+ real-time collaborative version
Genode and Hurd aren't ready yet.
"Hey, don't forget me!" (displays sad_puppy_eyes.jpg)
- my wife.
Why does a child need so much less data for “training” to reach “adult brain”?
Apple can’t be our only hope in this regard, can it?
Do they even employ anyone who could substantially push the envelope on the software side there? I would guess they would need to headhunt people from Google and OpenAI and at completely outlandish terms (less of an issue for them; rather than convincing to leave respective AAA “AI” teams). Academia probably the only hope, I’d straight go there if I’d be Apple. It will probably need a complete overhaul of the concepts / architecture of current ways to go about LLMs. Or maybe not, maybe just needs a “small fix”? I for sure don’t know.
Your reply contains a lot of things, except an answer.
Lots of people are probably already working on that but it doesn’t exist yet.
If we count all sensory input for years I do not think a child needs less data, but more.
If, in addition, we consider that the child gets new data (decides new interactions) based on past data, there is an optimization in the child that the LLM lacks: the LLM cannot just decide to get some new data based on the data it just saw. It is stuck with the data fed to it for training.