frnkng
Joined 7 years ago
Joined 7 years ago
It rated the company where I work as completely replaceable. Which I rate as insane because if I think it could be replaced, I’d quit tomorrow and start a competitor that is cheaper/faster/better.
Also I wonder why it is not common to run interception drones that automatically fly towards incoming drones and captures them mid air. Like a wasp is capturing other insects.
So pretty much like the iron dome but not with single use rockets but reusable drones instead.
However, there are two things that worry me:
a) what about datagrams bigger then MSS? Here the risk of losing the whole datagram doubles, triples, … Locally, it may not be so bad when one can use jumbo frames, but one the internet, it will be 1452 bytes per Datagram, before fragmentation happens
b) how to handle encryption and authentication?
So, whoever has significant wealth will do exactly this and take out loans to purchase productive assets and then, later on, pay back much less because the dollar lost value in the mean time.
This works out as long as the interest rate is lower then the actual annual loss in purchasing power.
https://polymarket.com/event/xi-jinping-out-in-2025?tid=1751...
How much concurrent tasks can you run on a mid sized server in 2024?
Definition of mid size: machine that will have 1TB Ram and maybe 64 Cores.
A small machine would be my workstation. 192GB Ram and 32 Cores.
What programming env do I have to use to eat up all those Ressources without petering out?
At some point in time, some dev splits the file to get faster responses again.
Despite this, we have no issues, git works fine and search and navigation in the code is also just fine.
“ Last year, the Census Bureau projected that the U.S. population would peak around 2080 and head into a continuous decline thereafter.”
The census bureau has to make those projections because they are supposed to do that. But that projection is most likely so inaccurate that it is not worth the paper it is printed on.
Example: A.I. provider delivers bad models -> ai provider eventually gets less resources
Example 2: A.I. Provider delivers good models -> provider gets more usage and eventually more ressources
So, the system does not self improve but it gets the feedback from the rest of the world on how useful it is and also on how it can become more useful.