regestsud445
196 posts








⚠️ Important reminder The Zama Protocol is not a new L1 or L2, but rather a cross-chain confidentiality layer sitting on top of existing chains. As such, users don’t need to bridge to a new chain and can interact with confidential dapps from wherever they choose. It leverages Zama’s state-of-the-art Fully Homomorphic Encryption (FHE) technology, which enables computing directly on encrypted data. FHE has long been considered the “holy grail” of cryptography, as it allows end-to-end encryption for any application, onchain or offchain. We believe that just like the internet went from zero encryption with HTTP to encrypting data in transit with HTTPS, the next natural step will be to use FHE to enable end-to-end encryption by default in every application, something we call HTTPZ. Until recently however, FHE was too slow, too limited in terms of applications it could support, and too difficult to use for developers. This is what our team at Zama has spent the last 5 years solving. We now have a highly efficient FHE technology that can support any type of application, using common programming languages such as Solidity and Python, while being over 100x faster than 5 years ago. Importantly, Zama’s FHE technology is already post-quantum, meaning there is no known quantum algorithms that can break it. While FHE is the core technology used in the Zama Protocol, we also leverage Multi-Party Computation (MPC) and Zero-Knowledge Proofs (ZK) to address the shortcomings of other confidentiality solutions: ◼️ FHE enables confidentiality while being fully publicly verifiable (anyone can recompute the FHE operations and verify them). Using GPUs will soon allow scaling to 100+ transactions/s while dedicated hardware accelerators (FPGAs and ASICs) will enable scaling to thousands of transactions per second. ◼️ MPC enables decentralizing the global network key, ensuring no single party can access it. Using MPC only to generate keys and decrypt data for users minimizes latency and communication, thereby making it far more scalable and decentralized than using it for private computation. ◼️ ZK ensures the encrypted inputs provided by users were actually encrypted correctly. Using ZK only for this specific purpose makes the ZK proofs lightweight and cheap to generate in a browser or mobile app. Read more: docs.zama.org/protocol/zama-…










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SENTIENT RESEARCHER SPOTLIGHT: Sewoong Oh Director of AI Research at Sentient and Professor at the @UW’s Paul G. Allen School (previously UIUC), @sewoong79 leads our research efforts across the stack. After his PhD in Electrical Engineering at @Stanford, his work focused on differential privacy, federated learning, robustness, and optimization, including a stint at @Google, and he now leads research across Sentient’s main threads. He brings rigorous theory, including generalization under distribution shift, private and robust optimization, and online learning, into production-grade mechanisms, ensuring ownership proofs and behavior constraints that survive fine-tuning, distillation, and tool-augmented execution. His scholarship and leadership translate frontier research into deployed capabilities across Sentient’s stack, advancing verifiable attribution, safe model control, and practical privacy for real-world AI.

We’re building the agentic economy in the open. Partnering up with @UnifaiNetwork connects Sentient’s decentralized model invocation to UnifAI’s execution layer—so intelligence and capital can flow transparently, upgrade continuously, and remain community-owned.






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