Trump strongly pushes for data center construction: Don’t kill the goose that lays the golden eggs

Trader 0x362a opened a 10x long on 236 $BTC ($19.98M) on @Aster_DEX and is now sitting on a $726K unrealized profit!

Trader 0x362a opened a 10x long on 236 $BTC ($19.98M) on @Aster_DEX and is now sitting on a $726K unrealized profit!

Beating AI Express (Insight): Tencent’s PCG Team Open-Sources Long-Term Memory System T-Mem, Paper Accepted by EMNLP 2026 Main Conference. T-Mem not only stores information but also records potential future use scenarios when memorizing content. For example, if a user previously mentioned “a colleague is allergic to seafood” and later asks “where to eat for a team building trip”, ordinary memory systems would fail to connect these two sentences with almost no overlapping keywords. T-Mem pre-associates “seafood allergy” with “restaurant selection for gatherings”, enabling it to retrieve relevant old information in similar scenarios later. Evaluations show that when queries lack keyword overlap with old memories, ordinary memory systems’ performance drops significantly, while T-Mem is less affected. Removing Horizon Trigger—the component responsible for predicting future scenarios—results in a notable performance decline for T-Mem, confirming this part is the core of its “associative recall” function.

According to market data, privacy AI protocol Phala briefly crossed $0.06, currently trading at $0.05956, with a 24-hour gain of 66.8%. Official information shows that usage of its flagship privacy AI product Confidential AI has surged recently: the product processed around 5.5 billion tokens on September 15, rising to 16.8 billion and 17.8 billion tokens in the following days, and hitting 25.451 billion tokens on September 19. Supported mainstream models include GLM-5.3 (which often accounts for over 40–70% of usage), GLM-5.3 Flash, DeepSeek series, Kimi K3, and others.

Beating AI News Brief: Xiaomi’s MiMo has halted two publicly live-streamed reinforcement learning training runs. Both the Pro and Flash runs completed a full 30 training steps each, with total combined costs of $3.4747 million. The Pro run cost $2.6207 million, while the Flash run cost $854,000. The investment delivered clear performance gains: On DeepSWE v1.1, Pro’s score rose from 58.41 at step 1 to 72.57, a 14.16-point increase; Flash’s score jumped from 48.67 to 65.68, a 17.01-point gain. However, both curves showed noticeable dips in the middle, meaning scores do not rise steadily with longer training. Two additional benchmark tests also improved: Pro’s internal code evaluation score rose from 57.54 to 65.43, and its AutomationBench score increased from 45.2 to 53.1. The training process was not smooth: Pro encountered GPU out-of-memory (OOM) errors due to uneven expert load, plus disconnections and restarts between its training cluster and evaluator. Flash had to restart from step 15 due to infrastructure errors. Later, Xiaomi filtered out tasks that were too simple for Pro and removed the cyber dataset with abnormal rollout patterns from Pro’s subsequent training.

According to TradingBeats' monitoring, the crypto entity known as "Ma Ji" posted a $7.15 million profit over the past 24 hours, pushing its total account value back above $10 million. Its current long positions consist of 30,950 ETH (valued at $84.79 million), 279 BTC (valued at $23.72 million), and 89,000 HYPE (valued at $8.48 million).