SlowMist's Cosine questions THORChain's double standard: the protocol intervened in its own hack incident, yet insists it has "no right to interfere" in external related events.

According to on-chain analyst Ai Yi (@ai_9684xtpa), a whale has accumulated a total of 9,158.25 ETH (valued at approximately $24.34 million) over the past three weeks, with an average entry price of $2,658.12. The whale appears to add to its position every time ETH’s price drops, and has an unrealized profit of $363,000 so far.

According to YuE Monitoring, a whale went long on 550,000 SOL at an average price of $80.8 in the early stage of this round’s market rebound and has held the position ever since, currently sitting on an unrealized profit of $22.43 million.

According to an official announcement, Quant has announced it will provide technical support for The Clearing House’s "On-Chain Currency Initiative" to build an interoperable payment network for financial institutions. Quant will oversee the network’s interoperability, orchestration, and transaction management layers, and integrate existing fiat payment systems including RTP and CHIPS, enabling financial institutions to access clearing and settlement capabilities for tokenized deposit transactions. The initiative aims to meet the growing demand from enterprises and financial institutions for faster, more automated, and flexible fund flow methods, enabling instant payment settlement and supporting auto-triggered transactions when pre-set conditions are met, thereby reducing manual operations and processing delays. Tokenized deposits are the digital form of traditional bank deposits, retaining the regulatory and protection mechanisms of conventional deposits while being recorded and transferred on-chain. The network will be applied to use cases including corporate treasury management, liquidity management, cross-border payments, and digital asset settlement, and is expected to open to participating institutions in the first half of 2027. The Clearing House stated that its U.S. payment network processes over $2 trillion in daily clearing and settlement volume, and this partnership will provide infrastructure support for financial institutions of all sizes to participate in the tokenized deposit network.

According to HTX market data, supported by Bitcoin’s minor strength, altcoins are extending their rally. Key performance metrics for select altcoins are as follows: QNT trades at $176.33, marking a 77.7% 24-hour gain; RUNE is priced at $0.819, with a 23.8% 24-hour increase; KMNO stands at $0.04773, up 14.3% in 24 hours; COW is at $0.1733, rising 14.27% over the past 24 hours; DASH is valued at $71.25, with a 13.62% 24-hour gain; PYTH trades at $0.08347, up 12.26% in the last 24 hours.

According to YuEmber monitoring, a whale or institution that has held ETH for three years realized further profit-taking of 30,825 ETH (worth approximately $83.03 million) 9 hours ago. Over the past week, the entity has transferred a total of 112,053 ETH (around $300 million) to Bitfinex, generating a total profit of $72.83 million.

Beating AI Insight News Brief: AI lab Supersonic Labs has launched Julia-1, a lightweight decision-making model with just 144.3 million parameters. Unlike models that generate long-form text, Julia-1 takes in context, a query, and 2 to 20 candidate answers to directly perform classification, scoring, and yes/no judgments, enabling agents to select tools, handle routing, or determine next steps. Built on the multilingual encoder model mmBERT-small, Julia-1’s model weights are around 550MB, with a focus on low hardware requirements. The developer has completed tests on Apple M4, Intel Core i5-1235U, and Samsung Android tablets. On the M4, the median latency per decision is roughly 33ms; on Samsung tablets running solely on CPU, latency is around 203ms, with a peak process memory usage of about 393MB. In four sets of comparative tests against Jev announced by the developer, Julia-1 took three wins. Its scores hit 73.15% on Typed Decisions (vs Jev’s 72.70%), 94% on AG News (vs 91%), and 86% on sentiment classification (vs 48%). However, on Banking77—a dataset with 72 categories—Julia-1 scored just 64%, significantly lower than Jev’s 87%. The developer noted that when there are too many highly similar candidate categories, the current grouping screening process may incorrectly discard the correct answer early. Julia-1’s total cloud GPU training and experimental costs amounted to just around $104. The model weights, inference code, full evaluation results, and source documentation are all open-source and released under the Apache 2.0 license. The developer is also developing an API, with plans to charge $0.025 per million input tokens; since the model only performs judgments and does not generate long-form text, output tokens are free of charge.