Binance will add 10 new types of bStocks as collateral.

A crypto whale has recently generated profits from consecutive trades of ASTEROID and the token Niu Lai, according to monitoring by on-chain analytics firm Lookonchain. Four months ago, the whale spent 1.25 ETH (approximately $3,000) to purchase 10.05 billion ASTEROID across five wallets, then sold 6.85 billion ASTEROID for 90.95 ETH (around $220,000). The whale currently holds 3.2 billion ASTEROID valued at roughly $206,000, bringing its total profit to $423,000 for a 140x return. Over the past two days, the whale spent $16,552 to acquire 22.1 million Niu Lai, which is now worth $663,000, translating to an unrealized profit of $646,000 and a 39x return.

Meet a smart trader 0x0121. 4 months ago, he bought $ASTEROID at $126K MC and is now up 140x. Recently, he bought $牛来 below $750K MC and is now up 39x. 4 months ago, he spent 1.25 $ETH ($3K) to buy 10.05B $ASTEROID using 5 wallets, then sold 6.85B $ASTEROID for 90.95 $ETH($220K). He still holds 3.2B $ASTEROID($206K), with a total profit of $423K(140x). Over the past 2 days, he spent $16,552 to buy 22.1M $牛来, now worth $663K, with an unrealized profit of $646K — a 39x return. Wallet: 0x0121525...

Swiss banking giant UBS significantly boosted its bullish option exposure to BlackRock’s Bitcoin spot ETF IBIT in Q2 2026, jumping from 80,000 underlying shares at the end of March to 1.95 million shares as of June 30, a surge of over 24 times. Meanwhile, UBS’ direct holdings of IBIT shares rose 12% to 407,890 units, valued at roughly $13.6 million. Its bearish option exposure dropped around 53% to 143,300 shares.

According to Bitget market data, at the opening of Hong Kong stocks: semiconductor stocks advanced, with Hua Hong Semiconductor surging over 5%, GigaDevice climbing nearly 5%, Innoscience jumping more than 4%, SMIC gaining nearly 4%, and Montage Technology rising over 3%. Optical communication stocks also rallied, with Yangtze Optical Fibre and Cable surging nearly 6%, Cambridge Technology up more than 5%, and InnoLight gaining over 2%. In the AI applications sector, MINIMAX-W rose more than 3%, while Zhipu AI fell over 4%. For the storage sector, the Southern 2x Leveraged SK Hynix ETF gained more than 2%, and the Southern 2x Leveraged Samsung ETF dropped 0.5%. South Korea’s stock market is closed today for a make-up holiday in observance of Liberation Day.

Bitunix has won the "Best Chart Trading Platform" award at the Peru Blockchain Conference 2026, a high recognition of the platform’s continuous efforts to refine chart trading experiences and build more professional, intuitive trading tools for global traders. This award comes on the back of Bitunix’s series of key initiatives in the chart trading space: the platform recently launched its official Ultra Chart Trading landing page, integrating market analysis, order execution, and risk management into a single interface; its exclusive Super Alerts feature supports multi-dimensional alert conditions including price, technical indicators, and candlestick patterns, helping traders capture market signals in real time. Data shows that users who have used Super Alerts have significantly higher average trading volume than non-users. Currently, Bitunix’s Super Alerts Challenge is in full swing, with a 10,000 USDT prize pool up for grabs. Global traders are welcome to join and participate.

According to market reports cited in a SemiAnalysis client note, Google is collaborating with AMD to develop a product for its 10th-generation Tensor Processing Unit (TPU) line. This would mark AMD’s first major involvement in a custom AI Application-Specific Integrated Circuit (ASIC) project. Analysts note that Google has already built nine generations of TPUs, with Broadcom long handling the actual silicon design, meaning traditional TPU development does not require AMD’s involvement. Thus, the core draw of this partnership lies in AMD’s CPU intellectual property (IP), advanced packaging, and interconnect technologies. The report specifically points out that Google and its clients are pushing for integrating CPU cores within TPU packages to meet the demands of CPU-intensive workloads such as reinforcement learning. Traditional large language model (LLM) training still relies primarily on accelerators, but inference and reinforcement learning for agent-based models require more general-purpose compute resources to operate alongside accelerators. This trend is already evident in Google’s recent hardware deployments. The TPU 8i system, designed for inference and reinforcement learning workloads, pairs one Google Axion CPU with every two TPUs; by contrast, the 7th-generation TPU server only includes one Intel Xeon processor for every four TPUs. Sources say a 1:1 CPU-to-accelerator ratio is the optimal setup in certain scenarios, suggesting the future of AI computing may rely far more on CPUs than the market currently assumes. If the collaboration with AMD materializes, Google will combine its in-house TPU accelerators with AMD’s general-purpose compute cores to build a hybrid AI ASIC, which could reshape the competitive landscape of data center chips.