The field of verifiable AI and zero-knowledge proofs is rapidly advancing, with a focus on developing innovative solutions to ensure the transparency, security, and accountability of AI-driven decisions. Recent developments have centered around the creation of specialized toolkits and frameworks that enable the generation and verification of proofs of AI inference, without exposing sensitive data. These solutions have the potential to transform industries such as healthcare, finance, and cybersecurity, where the use of AI is becoming increasingly prevalent. Notable papers in this area include: JSTprove, which introduces a pioneering verifiable AI toolkit, and ZK-SenseLM, which presents a secure and auditable wireless sensing framework with zero-knowledge proofs of inference. Additionally, Optimizing Optimism achieves significant speedups in zkVM validity proofs via sparse derivation, while ZKMLOps introduces a novel MLOps verification framework that operationalizes Zero-Knowledge Proofs within Machine-Learning Operations lifecycles.