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​woj
@woj.eth
what is the highest TAM crypto startup possible to build today? best answer wins a cofounder
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Cassie Heart
@cassie
A decentralized alternative to AWS/Azure/GCP (TAM > $400B)
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​woj
@woj.eth
have you heard of @quilibrium
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Cassie Heart
@cassie
Not sure, it’s got a funny archaic name
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​woj
@woj.eth
seriously tho, great example is there a whitepaper / summary of what you are building? tried to jump the rabbithole the other day but didn't get far
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Samuel ツ
@samuellhuber.eth
have a look at akash.network they are close. asked the team about supporting k8s clusters a lot and main issue is securing api access as you could manipulate a lot with that and still need it though...
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@cassie
I tried to find a recent copy of their whitepaper — there is no info I can find about how they actually secure these nodes, it just looks like a marketplace for kubernetes deploys, which sounds incredibly dangerous as providers have direct access to the running containers.
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@promptrotator.eth
Is this the case with every cloud provider? Like If I use a smaller GPU specific cloud device, do they also have direct access to those containers or is a Akash specific thing?
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Cassie Heart
@cassie
The difference with cloud providers versus Akash is that the cloud providers would destroy their goodwill and reputation if they were to take advantage of their privileged access, whereas Akash is an open marketplace.
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@promptrotator.eth
Got it, that makes sense. So if Akash is successful, then there would theoretically be provider consolidation because smaller providers would be less trustworthy.
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Cassie Heart
@cassie
Ultimately, yes, it comes down to trust. Trustlessness is a crucial component of decentralization.
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@promptrotator.eth
Have you taken a look at https://www.gensyn.ai/?
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Cassie Heart
@cassie
I’m amused because now they’re doing the same scrambled text I did (a previous iteration did not have this). Their litepaper is light on details about how they make this work but notably there is no holistic solution involved for privacy, & zero fee sharing with the sources of the data which trained the model
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