๐ธ๏ธ3 degen โข pleb
Trenches
Joined January 2024
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ritual will be the most expressive blockchain in existence, with a single focus: enrich what users can do onchain today to attract the users of tomorrow.
and if by now you havenโt gotten on ritual, you just might be missing something as pivotal as what started this space.
itโs worth your time, itโs worth the effort.
itโs not waiting for you, itโs here already. be a part of it.
before we had language, we had ritual.
itโs how humans remembered what mattered.
birth. death. seasons. stars. spirit. not as ideas, but as actions. it was felt, repeated, embodied.
ritual was memory made flesh, repetition with meaning, movement and pattern with soul.
ritual is how we rejoin memory with meaning, computation with care, signal with soul. a new kind of system is possible. one where ai doesn't just serve prompts it observes, aligns, remembers.
TRICK, TREAT ORโฆRITUAL.
๐๐จ๐ฐ ๐๐๐ง๐ฌ๐ฒ๐ง ๐๐ง๐ฅ๐จ๐๐ค๐ฌ ๐๐ ๐๐จ๐ซ ๐๐ฏ๐๐ซ๐ฒ๐จ๐ง๐: ๐๐๐๐ฅ ๐๐จ๐ซ๐ฅ๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ ๐๐จ๐ฅ๐ฏ๐๐
when compute becomes open and trustless, machine learning stops being the domain of a few well funded labs. imagine a world where connected devices everywhere contribute processing power, each one helping train and improve shared models without relying on massive budgets or centralized systems.
the effect compounds quickly. more compute leads to more models, which leads to faster innovation. as access broadens, collaboration replaces control, and progress accelerates for everyone. hereโs why compute scarcity still holds back ai progress and how gensyn smashes the bottleneck and powers real world ai:
โข ๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐จ๐ฌ๐ญ๐ฌ ๐๐ซ๐ ๐๐จ๐จ ๐๐ข๐ ๐ก
using big cloud providers is expensive. with gensyn, prices drop because anyone with spare compute can join the network. this lets teams run more experiments on the same budget.
โข ๐๐ข๐๐๐ข๐๐ฎ๐ฅ๐ญ๐ฒ ๐ข๐ง ๐
๐๐๐๐ซ๐๐ญ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ญ ๐๐๐๐ฅ๐
large scale federated training suffers from bandwidth and coordination bottlenecks. gensynโs protocol reduces friction and verifies contributions efficiently.
โข ๐๐จ๐จ ๐๐ฎ๐๐ก ๐๐จ๐ฐ๐๐ซ ๐ข๐ง ๐๐ง๐ ๐๐ฅ๐๐๐
putting all your work on one platform creates risk, price hikes, outages, or regional restrictions can stop progress overnight. a decentralized setup spreads workloads across many nodes, reducing lock in and points of failure.
โข ๐๐ซ๐ข๐ฏ๐๐๐ฒ ๐๐๐๐ ๐๐จ๐ฅ๐ฅ๐๐๐จ๐ซ๐๐ญ๐ข๐จ๐ง
hospitals, banks, and other sensitive organizations can contribute to joint training projects without exposing raw data. each participant keeps control while still benefiting from shared learning.
โข ๐๐๐ฌ๐๐๐ซ๐๐ก ๐๐ก๐๐ญ ๐๐จ๐ฏ๐๐ฌ ๐๐จ๐จ ๐๐ฅ๐จ๐ฐ๐ฅ๐ฒ
long queues and quota limits delay progress. with more accessible compute, teams can iterate faster, shorten research cycles, and turn ideas into products sooner.
โข ๐
๐๐ข๐ซ ๐๐๐ฐ๐๐ซ๐๐ฌ ๐๐ง๐ ๐๐ก๐๐ซ๐๐ ๐๐ฐ๐ง๐๐ซ๐ฌ๐ก๐ข๐ฉ
those who provide computing power or help verify work earn rewards directly. it creates a fairer system where contributors share in the value of the models they help build.
โข ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ ๐๐ญ ๐ญ๐ก๐ ๐๐๐ ๐
factories, farms, and retail locations often sit far from central data centers. local devices can now join in the training process, improving models directly where data is created.
โข ๐๐๐๐ค ๐จ๐ ๐๐จ๐ฆ๐ฆ๐ฎ๐ง๐ข๐ญ๐ฒ ๐๐จ๐ฏ๐๐ซ๐ง๐๐ ๐๐ ๐๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐
today, infrastructure is owned by private firms. gensyn allows communities to control and participate in ai compute governance.
โข ๐๐๐ซ๐ซ๐ข๐๐ซ๐ฌ ๐ญ๐จ ๐๐ฉ๐๐ง ๐๐จ๐ฎ๐ซ๐๐ ๐๐
open source projects often fail because contributors canโt afford compute. gensyn makes public models feasible to train at scale.
โข ๐๐๐๐จ๐ซ๐๐๐๐ฅ๐, ๐๐ฉ๐๐๐ข๐๐ฅ๐ข๐ณ๐๐ ๐๐จ๐๐๐ฅ๐ฌ
smaller industries, like precision farming, regional language tech, or industrial automation, can finally afford to train and maintain their own domain specific models instead of paying enterprise cloud rates.
