Today we're mining @OpenGradient a foundational design
HACA’s core architecture can be summed up in one line:
Separate the doers from the validators—no overlap allowed.
First, let’s talk about the pitfalls to avoid.
Traditional blockchain makes every validating node re-run the same computations to reach consensus. For small transactions, it’s manageable, but AI inference requires GPUs and takes several seconds of heavy lifting. If you ask hundreds of nodes across the network to compute the same large model one by one, the costs skyrocket, making it impractical.
This is why most public chains struggle with AI.
HACA’s solution is all about specialization. The inference nodes are a bunch of state-less GPU workers, dedicated to running models and generating results, and they casually produce a proof once they're done; full nodes never touch the models.
They do one thing—validate the proof and keep the ledger. One focuses on computation, the other on verification; roles are strictly defined, with no crossover. The cost of validating a proof is way cheaper than re-running the AI, preserving decentralization while preventing network overload.
I appreciate this design's restraint.
It doesn’t greedily aim to create an all-encompassing public chain but honestly recognizes that AI computation and blockchain consensus are two separate tasks requiring two sets of nodes. The base layer uses CometBFT consensus, requiring over 2/3 of validators to nod before anything goes on-chain, with a tech stack leveraging the mature Cosmos SDK plus EVM—no need to reinvent the wheel, and boundaries must be clearly defined.
No matter how smart this setup is, it ensures that the validation step is efficient and decentralized, but it can’t guarantee that there will always be enough GPUs and cost-effective pricing for inference. The model is still reliant on those GPU nodes, and whether the network can continuously attract enough affordable computing power is another challenge—architecture alone can’t solve supply issues; real incentives are needed to draw miners in.
So, how do we view HACA? It’s a solid engineering foundation for the entire OpenGradient narrative to stand on.
But a solid foundation is just a necessary condition. To build upwards, we need to see both computing power supply and genuine demand for use cases grow simultaneously.
No matter how pretty the architecture diagram is, it only counts if real work is being done on top of it.
#OPG #OpenGradient $OPG
HACA’s core architecture can be summed up in one line:
Separate the doers from the validators—no overlap allowed.
First, let’s talk about the pitfalls to avoid.
Traditional blockchain makes every validating node re-run the same computations to reach consensus. For small transactions, it’s manageable, but AI inference requires GPUs and takes several seconds of heavy lifting. If you ask hundreds of nodes across the network to compute the same large model one by one, the costs skyrocket, making it impractical.
This is why most public chains struggle with AI.
HACA’s solution is all about specialization. The inference nodes are a bunch of state-less GPU workers, dedicated to running models and generating results, and they casually produce a proof once they're done; full nodes never touch the models.
They do one thing—validate the proof and keep the ledger. One focuses on computation, the other on verification; roles are strictly defined, with no crossover. The cost of validating a proof is way cheaper than re-running the AI, preserving decentralization while preventing network overload.
I appreciate this design's restraint.
It doesn’t greedily aim to create an all-encompassing public chain but honestly recognizes that AI computation and blockchain consensus are two separate tasks requiring two sets of nodes. The base layer uses CometBFT consensus, requiring over 2/3 of validators to nod before anything goes on-chain, with a tech stack leveraging the mature Cosmos SDK plus EVM—no need to reinvent the wheel, and boundaries must be clearly defined.
No matter how smart this setup is, it ensures that the validation step is efficient and decentralized, but it can’t guarantee that there will always be enough GPUs and cost-effective pricing for inference. The model is still reliant on those GPU nodes, and whether the network can continuously attract enough affordable computing power is another challenge—architecture alone can’t solve supply issues; real incentives are needed to draw miners in.
So, how do we view HACA? It’s a solid engineering foundation for the entire OpenGradient narrative to stand on.
But a solid foundation is just a necessary condition. To build upwards, we need to see both computing power supply and genuine demand for use cases grow simultaneously.
No matter how pretty the architecture diagram is, it only counts if real work is being done on top of it.
#OPG #OpenGradient $OPG