@cnakazawa So I guess the way to adapt existing backend would be similar to going from rest -> graphql style server with resolvers? https://t.co/WQwMsagFVx
I guess you could easily do this if you work in an office? When you leave work connect the Macbook to the cluster and let it grind while you sleep. If company is globally distributed you could I guess use the clusters of people across the ocean, or maybe even rent out the compute.
I found this website and apparently I'm the #1 open source contributor in Turkey π
https://t.co/zIu4VnCA3Q
https://t.co/yBukp03ZBv
#opensource#github
In 2005 I built Fon on a simple idea: millions of WiFi routers sit idle most of the day. Share that spare capacity and you build a global network without laying a single cable. Millions of homes joined. Telcos partnered. No infrastructure needed.
Now Jack Dorseyβs Block just launched mesh-llm. Same idea, applied to AI. Pool your idle GPU and suddenly a group of people with modest machines can run large open models that none of them could run alone. Models split automatically across nodes. No cloud provider, no API fees, no one controls your data.
The timing matters. Google DeepMind released Gemma 4 today under Apache 2.0. A 31B model that competes with much larger closed models. A 26B MoE variant that only activates 3.8B parameters at inference. Edge models that run on a phone. All free to download, free to use commercially, no restrictions.
Combine mesh-llm with Gemma 4 and you get the Fon of AI. Distributed compute running frontier open models. No central server. No per-query cost. Total privacy. The intelligence stays with the people who pool the hardware.
Twenty years ago the scarce resource was connectivity. Today it is compute. The solution is the same: share what you have, access what you need.
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