A whale transferred 1000 ETH to Binance after a year of hibernation

According to on-chain monitoring, the U.S. government has just transferred funds seized from the Bitfinex hack, with approximately 264.863 BTC (worth around $22.87 million) moved to a new address.

Winklevoss has filed a listing application for a ZEC spot ETF with the U.S. SEC, under the ticker symbol WINK.

Oct 6 Update: #Bitcoin ETFs: 1D NetFlow: -1,059 $BTC(-$91.72M)?? 7D NetFlow: +1,074 $BTC(+$93M)?? #Ethereum ETFs: 1D NetFlow: -21,432 $ETH(-$58.29M)?? 7D NetFlow: -79,193 $ETH(-$215.37M)??

Nasdaq-listed DeFi Development Corp. has authorized the launch of a CHAD preferred stock repurchase program, which allows for the repurchase of all outstanding CHAD preferred shares, including any future shares the company may issue. The program enables the company to conduct repurchases flexibly when CHAD’s share price falls below its $10 par value per share. The Solana treasury-focused company stated it has no immediate plans to repurchase CHAD, noting it first wants the security to build market liquidity and stabilize at or near its $10 par value. This indefinite authorization adds a potential capital allocation tool to DeFi Development (DFDV)’s existing $300 million CHAD ATM issuance program. The company previously said it plans to raise funds via this program to purchase additional SOL, and paid CHAD’s first dividend on October 1.

Beating AI Express News: AI lab Banbury Road has released Kardashev-0.7, a system composed of 32 distinct models. The team trained these models together via reinforcement learning, without predefining each model’s specific tasks, enabling them to develop unique specialties and complementary capabilities during training. This methodology is called RL for Population Scaling (RLPS). Prior RLPS experiments only reached up to 16 models. In the 8-model trial, the jointly trained model population scored 81.70%, outperforming 71.04% for 8 independently trained models and 72.65% for the same model sampled 8 times. In the 16-model experiment, 22 questions were answered correctly by only one model each, verifying that different models did learn distinct capabilities. Kardashev-0.7 expands this to 32 models. Banbury Road claims it delivers state-of-the-art performance at 0.007 to 0.02x inference cost and 0.03x memory usage. Its API beta is currently on a waitlist. However, the population scores from the earlier 8 and 16-model trials were derived post-hoc by picking the best result from multiple model outputs using standard answers. Since real-world applications lack standard answers, Banbury Road has not yet disclosed a full solution for the system to automatically select correct responses.

P2P AI inference network Antseed has officially launched, with its non-profit operator Antseed Foundation disclosing that it has completed a $2.4 million token round financing. Spark Capital led the round, with participation from Collider, DCG, North Island Ventures, Reciprocal Ventures, Relay Capital, and Venice.ai. The project aims to adapt BitTorrent’s peer-to-peer model for AI inference, enabling different providers to directly sell models and agent services. After users run Antseed’s local router on their computers, they can connect tools like Claude Code, Codex, and OpenCode to a single address. Each inference call is routed by the router based on price, speed, reputation, and privacy criteria, with fees settled directly via USDC on Base. Providers can run their own local models, fine-tuned models, or other AI services, and set their own pricing. Its key difference from OpenRouter is the absence of a unified forwarding platform in the middle layer: buyers connect directly to providers, while Antseed handles discovery, routing, reputation, and payment protocols. Regular providers can still view the prompts they process; users also have the option to select TEE-verified providers, where requests run in a trusted execution environment (hardware-isolated from request content). Antseed is also refining verification mechanisms including model fingerprinting, signed responses, and auditing to prevent providers from passing off cheaper models as more expensive ones. The code is open-source and the network is operational, though compatibility issues with tools like Codex and Claude Desktop are still being addressed on GitHub. It is best described as a rapidly iterating "decentralized OpenRouter".