SHELL Buyback Address has once again withdrawn 1 million tokens from Binance, and the buyback progress has reached 81.2%.

According to GMGN market data, the Solana-based meme coin CATE has rebounded to a market cap exceeding $50 million, currently trading at $51.54 million, with a 24-hour increase of over 99% and a 24-hour trading volume of $12.8 million. Earlier on July 26, Atsuko Sato, owner of Kabosu—the Shiba Inu that inspired the Doge meme—posted a video of a kitten. The community quickly launched the token on Pump.fun, positioning it as "the successor to Doge / the cat version of Doge" to carry on the meme culture of "dogs came first, now it's the cats' turn". Subsequently, Atsuko Sato clarified in a post that the CATE tokens circulating in the recent market have no connection with her at all. She stated that she only shared a moment on Instagram before, but this content was later used without authorization by others, who took advantage of it to issue fake tokens and create the illusion of a link with her. She expressed regret over this, noting that some accounts involved in spreading related information even include users with considerable influence on the X platform. BlockBeats reminds users that most meme coins have no practical use cases and feature highly volatile prices, so investment requires caution.

Beating AI Express: Thibault Sottiaux, OpenAI’s core product lead, noted that demand for GPT-6 Astra is “unprecedented”. If demand continues to grow, OpenAI may suspend new ChatGPT Pro subscriptions to prioritize service quality for existing users.

As oil prices edge back toward $100 per barrel, James Thorne, chief market strategist at Wellington Altus, warned that if the Federal Reserve raises interest rates amid an energy supply shock, it could repeat the policy mistake made before the 2008 financial crisis—misjudging rising energy prices as overheating economic demand. Thorne noted that while rising energy prices fuel inflation, they also erode household purchasing power and weigh on economic growth. If the Fed tightens financial conditions further to demonstrate its commitment to fighting inflation, it could instead amplify the risk of an economic downturn. He sees parallels between the current policy environment and the period before the 2008 crisis, when the Fed focused too heavily on inflation risks from energy prices while underestimating the economic impact of high energy costs. WTI crude oil has surged more than 20% in the past month, while Brent crude has risen over 18%. Meanwhile, a New York Fed survey shows U.S. consumers’ views on their household finances have worsened further, with the perceived probability of a rise in unemployment over the next year climbing to 44.4%—the highest level since April 2020. The Federal Reserve is set to hold its policy meeting on September 15-16, having kept interest rates unchanged for five consecutive meetings. Markets are now closely watching the U.S. August PPI and CPI data, due this week, to see if the energy shock will prompt the Fed to reconsider raising interest rates.

Official sources report that the total value locked (TVL) of decentralized finance (DeFi) on X Layer has hit an all-time high of $232 million. Star, founder and CEO of OKX, stated in a post on X that TVL itself is not the end goal; more importantly, lending, stablecoins, real-world assets (RWA), yield markets, and on-chain capital markets are interconnecting and mutually reinforcing on X Layer. It is reported that X Layer is continuously enhancing its DeFi and RWA infrastructure to advance the development of more real-world assets, on-chain liquidity, and financial applications within its ecosystem.

At the 2026 JD Global Tech Explorer Conference today, JD Cloud announced plans to partner with Moore Threads to build a 100,000-card full-featured GPU cluster, creating a large-scale domestic intelligent computing infrastructure. The cluster will focus on large language model (LLM) training, inference, and embodied intelligence, and will open its computing resources to the entire industry. The two parties will conduct full-stack collaboration around chips, cloud platforms, and model training, supporting the iteration of JD’s JoyAI large model, accelerating model training, simulation, and high-quality data generation, with a key focus on serving supply chain AI, embodied intelligence, integrated LLM training and inference, and industrial applications. Previously, JD Cloud had built a domestic 10,000-card cluster with partners including Moore Threads; the latest plan for a 100,000-card cluster marks an expansion of their collaboration from model R&D and inference platforms to large-scale intelligent computing infrastructure. Moore Threads stated that the 100,000-card AI factory will cover scenarios such as LLM training, token generation, and agent training, and serve various industries. Moore Threads has made continuous progress in commercial operations recently: the company reported revenue of 1.736 billion yuan in the first half of the year, a year-on-year increase of 147.42%, with cloud product revenue reaching 1.693 billion yuan, accounting for approximately 97.5% of total revenue. The company had previously completed the deployment of a 10,000-card intelligent computing cluster, and is advancing the construction of GPUs, interconnection networks, software stacks, and computing power scheduling platforms required for the 100,000-card cluster.

Beating AI News reports that Google DeepMind has launched the AlphaGenome Atlas, a searchable database precomputing all ~9 billion possible single-letter DNA variations in the human genome. The entire dataset is around 1 petabyte (PB), over 30 times larger than the AlphaFold Database. With the human genome containing roughly 3 billion DNA letters, each position can be replaced by 3 other bases, leading to the ~9 billion single-letter variations. Previously, researchers had to analyze variations individually using AlphaGenome; now they can directly retrieve results from the Atlas, avoiding repeated model runs. The Atlas also provides an AVI score for each variation, integrating predictions from AlphaGenome and AlphaMissense to help researchers prioritize variants most likely to have functional impacts. It covers both the ~2% protein-coding regions and the remaining 98% non-coding regions, which are harder to study. Multiple research teams have already used the tool to identify previously overlooked rare disease-related variations, with experiments confirming one such variant causes abnormal RNA splicing.