Technical insights into homomorphic encryption, privacy-preserving AI, and confidential computation.
As AI systems move from passive tools to autonomous agents that handle sensitive information, questions of trust and security become critical. Palitra AI addresses this problem with a dynamic environment where AI agents learn to protect secrets through an endless cycle of challenges and improvements.
Stablecoins have become a critical building block of modern crypto infrastructure. But despite their widespread use, one key element remains unsolved: privacy. Fair Math Payments introduces a modular and programmable privacy layer, built to solve this gap without disrupting existing systems.
Exploring Kolmogorov–Arnold Networks (KANs), a novel architecture that redefines machine learning, and how they're being combined with FHE to enable privacy-preserving AI.
Despite rapid stablecoin adoption, privacy remains the missing primitive. We explore why existing solutions fail and what real-world privacy for stablecoins actually requires.
A groundbreaking platform designed to create the foundation for trustworthy, autonomous AI models and agents through continuous testing, defense, and improvement cycles.
A deep dive into the fundamental security and scalability challenges of using a single global encryption key in decentralized FHE systems, and why threshold MPC isn't enough.