US Treasury May Deploy Nearly $1 Trillion in Fiscal Cash to Stabilize Money Markets

According to official announcements, NEAR has been deployed on Hyperliquid's spot market. Users can trade the NEAR/USDC pair on Hyperliquid. The official added that per Hyperliquid's procedures, NEAR will take several more days to appear on the platform's Strict List.

According to an official announcement, Binance will launch USDT spot trading pairs for Axe Compute (AGPUB), AMC Entertainment (AMCB), and Cypherpunk Technologies (CYPHB) at 20:00 China Standard Time (UTC+8), and simultaneously introduce spot algorithmic trading bots. The relevant trading pairs will enjoy zero maker fees until 07:59 on October 1, while withdrawals will open at 21:00 the same day. The aforementioned tokenized securities are not available to US persons.

Beating AI Insight News Flash. Leiphone reports that over the past six months, multiple domestic large language model (LLM) vendors have begun redoing their pre-training processes, with all issues traced to data quality. One vendor spent nearly a year training a trillion-parameter model, only to have its performance outperformed by a small model with just around 1% of the parameter count. The team ultimately found the root cause lay in the training data: web spam, duplicate corpora, and low-quality annotations had been mixed into the training set, rendering even the largest models ineffective. The case comes from an anonymous source, and the specific company has not been disclosed. Tencent Hunyuan has publicly announced a full redo of its pre-training work. Since February this year, the team has rebuilt its pre-training and reinforcement learning infrastructure. Previous media reports revealed that the old version of Hunyuan had issues including ranking manipulation data being mixed into the training set and inconsistent annotation rules. The new team redefined data standards, cleaned up existing corpora, and Hy3 listed data quality and diversity as key improvement areas. Alibaba and Baidu are also stepping up data governance efforts. Alibaba’s Qwen3 uses the Qwen2.5 series models to clean documents, and has significantly supplemented synthetic math and code data. Baidu’s Ernie 4.5 has added deduplication, low-quality filtering, data mapping, and manual review processes. Neither company has publicly stated that they had to redo work due to dirty data, but both have invested more efforts in data processing for their new-generation models.

According to an official announcement, Binance's cross-margin trading now supports the ARB/USDT and ENA/USD1 trading pairs.

Ryan Myher, co-founder of Genius Terminal, announced in a post today that Genius.fun is set to launch its foundation page soon. He added, “I’ve been exploring various new mechanisms lately to better drive narratives on Genius.fun. There’s a really interesting feature that could help users reach their goal of accumulating stocks faster, which is especially exciting for our larger-cap meme coins.” On September 17, Genius Terminal—an on-chain trading terminal backed by YZi Labs and advised by CZ—officially launched its token issuance platform genius.fun on BNB Chain. The platform allows creators to transfer tokenized stocks such as bSTOCKS into a non-custodial foundation and split them, enabling subsequent attempts to exercise equity-related rights off-chain.

Beating AI Exclusive: Mirendil is in talks for a new funding round with a maximum valuation of around $5 billion, targeting a raise of up to $1 billion. Kleiner Perkins is negotiating to lead the round, while a16z may also participate. The deal has not been finalized. Just three months ago, Mirendil closed a $200 million seed round at a $1 billion valuation, led by a16z and Kleiner Perkins, with Nvidia joining as an investor. If this round closes on current terms, Mirendil’s valuation will have quintupled in a single quarter. Founded by former Anthropic researchers Behnam Neyshabur, Harsh Mehta, and others, the company currently has a team of just over 20 people. It focuses on “self-accelerating AI”: enabling AI to independently design experiments, conduct training, evaluate, and tune models, gradually automating the R&D workflow previously handled by AI researchers. Mirendil remains firmly in a heavy investment phase. In August, it signed a multi-year partnership with Google Cloud; TechCrunch notes the deal is worth over $100 million, and will leverage both TPU and Nvidia GPUs. The new financing will primarily fund the launch of Mirendil’s own models and continued training of subsequent iterations, with its first frontier model for engineering and research applications scheduled to launch early next year.