Ethena: USDe reserves remain overcollateralized, with no supporting funds held directly on Binance.

According to 1inch’s official statement, the team is aware of the attack on the Binance exchange and is reaching out to the platform to offer assistance. The risk management team, in collaboration with ZeroShadow, SEAL 911, TRM Labs and other partners, blocked attack-related addresses across the 1inch dApp, 1inch Wallet and 1inch API within one hour, and is continuously adding new addresses to the blocklist. The same list has been synchronized to resolvers and integrators. Some 1INCH tokens are included in the stolen assets. As 1INCH is a standard ERC-20 token with no built-in freeze or blacklist functions, the stolen tokens cannot be frozen by any party. 1inch said it will fully cooperate with Binance and law enforcement agencies in recovery efforts.

According to Whale Alert monitoring, 172 million USDT (valued at approximately $171,846,059) was transferred from OKX to an unknown wallet.

According to on-chain detective ZachXBT, approximately 45 minutes ago, the $6.3 billion in stolen funds from Binance and $388 million from Bitget were mixed at the same address after being transferred via THORChain, indicating the two hacks may be linked to a single attacker. The Bitget breach was previously tied to North Korea-associated actor TraiderTraitor, who also overlaps with cases including Kelp DAO. The mixing address is 0x151....eb7.

According to on-chain analyst Yu Jin’s monitoring, whale address "wenyu8888888" bought the dip in UNI and PEPE after a hacker sell-off triggered a market panic drop this morning, pocketing around $270,000 in profits in roughly five hours. At around 7 AM today, the address spent approximately 2.61 million USDC to acquire about 297.9 billion PEPE tokens and 208,000 UNI tokens. Roughly 20 minutes ago, all of the dip-buying positions were transferred to Gate, with a combined value of roughly $2.88 million; compared to the purchase cost of ~$2.61 million, the unrealized profit stands at around $270,000.

According to Whale Alert monitoring, 172,175,782 USDT (valued at approximately $172,075,748) was transferred from an unknown wallet to OKX.

Beating AI News (Dongcha) — Brain-computer interface firm Sabi has unveiled a new study titled RLNF (Reinforcement Learning with Neural Feedback). In the demo, an AI-generated face evolves continuously, gradually transforming into a shape more aligned with the viewer’s aesthetic preferences. Researchers aim to enable AI to learn individual preferences from brain signals, eliminating the need to repeatedly ask users which images they prefer. The team first had 5 participants view a large number of faces, while recording their electroencephalogram (EEG) and eye movements, and asking them to rate each face’s attractiveness. Using this data, the AI learns the physiological responses each person exhibits when viewing faces they like or dislike, building a model of individual aesthetic preferences. Subsequently, researchers used the preference data to fine-tune the FLUX.2 Klein 4B image model via reinforcement learning. Each time the AI generates a new batch of faces, the system compares them to previously recorded faces and delivers rewards based on individual preferences inferred from brain signals. The model adjusts continuously based on these rewards, gradually learning to generate faces that better match a specific user’s aesthetic, with no need for participants to rate each image individually throughout the process. Previous studies have used EEG feedback to guide AI in generating faces people like. Sabi, however, applies this feedback to reinforcement learning for image models, allowing individual aesthetics to directly influence the model’s generation tendencies. That said, the experiment currently only has 5 participants, with the preferences of 2 of them being judged primarily via eye movements rather than direct identification from brain activity. More experiments are needed to confirm that EEG can reliably reflect individual aesthetic preferences.