NEAR has been deployed to the Hyperliquid spot market.

Whale 0xA799 bought 788,000 $UNI ($4.93M) at $6.26 6 days ago and sold at $8.85, making $2.04M in less than a week!

Solana has begun testing the Alpenglow upgrade on its public testnet, with the goal of cutting transaction finality from roughly 12.8 seconds to 150 milliseconds. The upgrade will replace the TowerBFT consensus protocol with the Votor voting protocol, allowing validators to confirm blocks via one to two rounds of direct voting. Testing is currently only supported by the Agave 4.3 client developed by Anza; Jump Crypto’s Firedancer and Frankendancer clients have not yet been integrated. The mainnet launch date remains undetermined. (CoinDesk)

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.

The U.S. Treasury is developing a plan that could reshape how short-term funding markets function: deploying a portion of cash held in the Treasury General Account (TGA) into the private repo market on a regular basis, rather than leaving all of it parked at the Federal Reserve. The proposal earned backing from multiple market participants at the New York Fed’s annual U.S. Treasury market conference this week. The TGA, which currently holds just under $1 trillion, serves as the Treasury’s day-to-day operating cash account. The Treasury first put forward this idea back in May, and this week’s discussions have advanced to focus on specific implementation methods. Market observers see this move as a potential new stabilizing tool for short-term funding markets. If the Treasury actively deploys part of its TGA cash into the repo market, it would add a direct channel for traditional fiscal cash flows to enter the money market. Market participants are thus particularly concerned about whether the operations will be predictable.

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