Score (Subnet 44) has announced a new partnership with DSperse (Subnet 2) on the Bittensor network, aiming to bring verifiable inference into real-world Vision AI deployments. The update was shared via Score’s official X account, hinting at a collaboration between SN44 and SN2. Score is building an open computer vision layer for intelligent cameras, powering…
JSTprove has officially launched on DSperse (Bittensor Subnet 2), marking a major step forward for decentralized AI infrastructure. While the release has flown under the radar, it addresses one of the most critical weaknesses in today’s AI agent economy: the lack of provable guarantees that a model actually performed the computation it claims. The core…
Cover image credit: Thesubnetdegen As artificial intelligence moves deeper into high-stakes environments, trust is becoming the defining constraint. In areas like financial trading, autonomous robotics, and real world automation, an AI system does not just need to be powerful. It must be probably correct. DSperse, Subnet 2 on Bittensor, is designed to solve that problem….
Contributor: Kakashi Zero-knowledge proofs answer a simple but critical question: how to verify AI systems independently, without trusting the system that produced the result? For AI infrastructure, it’s verifiable inference: cryptographic proof that a specific model produced a specific output, no matter where the inference was run. This is the problem Inference Labs has been…
When you think about trust in artificial intelligence, it’s easy to forget how much of it relies on faith. We type prompts into a model, get an answer, and simply assume it did the work correctly. But in a world where AI decisions can shape markets, policy, and identity, that assumption is starting to feel…
By: CryptoZPunisher Omron (Subnet 2) is Inference Labs’ attempt to build the world’s largest peer-to-peer network for verifiable artificial intelligence within Bittensor. The core of the protocol relies on Proof of Inference and, more recently, on Proof of Weight. These two zero-knowledge (zk-ML) building blocks ensure that results generated by AI models are cryptographically verifiable,…
