No servers. No cloud. No masters. 🍐 Traditional deployment is broken. In this @thejsnation talk, @davidmarkclem breaks down how to build and scale battle-tested P2P production apps using Pear. Check out the full video here:

Our new @digitalocean servers are already getting put to the torture test building our new bespoke kernel! This one built in just 11 minutes maxing out all cores on the AMD EPYC 9555P!! 🔥

Looks like Apple iOS servers are a little overloaded, given it's taking longer than usual to update to the new iOS 27 that came out about 1p ET. This is similar to what happened when Apple Intelligence was released in October of 2024. The new Siri AI: Cons: It takes way to long, 5-15 seconds to get a response. Pros: It's the first compelling case for personalized AI. I can have it search email and text at the same time. $AAPL

NVIDIA B300 servers retail around $350k min, crazy power/cooling requirements/etc. (bad maths) per GPU (8) is $43,750, if you can even find them (narrator: you can't) then pay for collocation etc... 9 PFLOPS dense FP4. DGX Spark: $4700, plug in anywhere, 1 PFLOP (fp4 sparse). B300 9ish PFLOP dense FP4 super expensive impossible to collocate anywhere etc. etc., $4861.11 per PFLOP. DGX Spark $4700 plug in any 15 amp outline in any house, $4700/PFLOP (but sparse). Memory is slower, so I guess use less of it/less manipulation of it... Yep, math checks out - pretraining on decentralized DGX Spark with native sparse FP4 (checks notes: yes we're using ternary weights which slot perfectly into sparse FP4 compute) should be a thing. Time to test it out...