CybTT
@cybtt.eth
Over the past couple of years, we’ve seen a surge in Crypto x AI projects , but let’s be honest: most of them are either riding hype waves or still rely heavily on web2 infrastructure behind the scenes. @ritualnet sits right at that intersection, but it’s clearly trying something more foundational than just “bringing AI to crypto.” It starts by acknowledging a simple but overlooked truth: the way most blockchains work today doesn’t match what AI workloads actually need.
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CybTT
@cybtt.eth
The Core Problem Blockchains replicate everything — every node runs the same code. That works fine for things like swaps or NFT mints, but AI doesn’t work that way. Different models require different hardware. Running a simple classifier on an H100 is overkill; trying to run a massive LLM on a CPU is pointless. That mismatch explains why most chains struggle to meaningfully integrate AI. Ritual’s insight is: you can’t just plug AI into a blockchain , you need to redesign the chain around it.
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CybTT
@cybtt.eth
So What Actually Sets Ritual Apart? There’s quite a lot that Ritual does differently , if you dive into their docs, you’ll find an entire stack rebuilt around expressive compute. And I’ll touch on more of those innovations later. But for now, let’s look at just three key things that already show how fundamentally this chain is thinking differently: ⚪️Node Specialization: Instead of every node doing the same thing, Ritual lets them specialize based on their hardware capabilities. Feels obvious ( we already see this in cloud infra ) but it’s rare in crypto. It opens the door for GPU-heavy tasks to coexist with lighter workloads in the same network. ⚪️Sidecars: This is one of the biggest technical unlocks. Sidecars handle compute-heavy tasks like model inference, ZK proving, or TEE execution off the main execution client, but still tightly integrated. It keeps the chain lean while enabling much more powerful operations.
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