半沐

Binance once again demonstrated its strong fault tolerance and extremely fast response. Unfortunately, Fish God had warned CZ and Sister Yi in advance about the unusual ETH withdrawals, but the response was a little slow. Otherwise, it might have been possible to stop the 140,000 ETH from being transferred. Still, this proves that Binance isn't completely airtight: a breach of an operations engineer's laptop was enough to create such a massive security gap. Binance needs to reflect on this. Of course, from now on, every move the hacker makes will be closely monitored. The hacker can only slowly move the funds out bit by bit. Sister Yi has said she'll keep an eye on the hacker's every move. I doubt they'll get away anytime in the next 10 years.

Fam, should you buy some BNB for your kids? BNB just completed its quarterly burn, bringing the total supply down to 130 million. Treasure it—soon even CZ will have to pay to buy some. $BNB

$ASTER 14 destroying 4.1 million units, converting to a value of about $3 million. That comes out to destroying $200,000 per day. However, the market cap of the Aster token is worth $8 billion.


$UNI destroying a bit over $1 million per day, yet the market cap was only $8 billion.
$HYPE destroying $2.8 million per day, while the market cap is already $95 billion.

So you should understand me—you know who you should buy, right? Accumulate slowly. The next wave of major trend waves will be even more terrifying.

On how Qwen3.8 achieves evolution “on a day-by-day basis”: based on currently available public information, its core mechanisms mainly manifest in three areas: high-frequency automated updates, dynamic activation of the underlying architecture, and overnight validation of Agent (intelligent agent) skills. The details are as follows:

1. High-frequency automation for weight and link updates
Qwen3.8 breaks the traditional single-shot large-scale release model and establishes a high-frequency automated iteration mechanism. The team has disclosed that the model automatically updates weights every day at midnight, refreshes inference chain-link optimizations every 48 hours, and pushes brand-new multimodal alignment capabilities on a weekly basis. This turns model upgrades into a continuously ongoing “live broadcast.”

2. Dynamic expert routing and a hierarchical memory cache architecture
At the underlying architecture level, Qwen3.8 discards the traditional sparse-activation playbook and instead adopts a hybrid architecture combining “dynamic expert routing + hierarchical memory caching.” This design allows the 2.4T parameters to operate dynamically. In runtime, the model is “not a pile of brute-force monsters,” but rather an intelligent agent that can breathe, reflect, and know how to make trade-offs. This architecture-level optimization provides the technical foundation for high-frequency iteration.

3. The “overnight evolution and validation” mechanism for Agent skills
At the level of concrete Agent task execution, the Qwen series (such as Qwen3-Max) applies a “collective skill evolution workflow.” This mechanism converts real user interaction conversations into structured evidence. Evolver analyzes patterns and generates candidate updates. These candidate skills are not deployed directly; instead, they enter an “overnight validation stage”: in a real environment, both the old skills and the new candidate skills are executed simultaneously. Only when the new skills truly outperform the old ones in overall task success rate and execution stability will they be accepted and deployed. This ensures that the deployed skill pool does not degrade over time, enabling continuous and stable performance improvements.

4. Ongoing testing of preview builds and post-training
Currently, the released Qwen3.8-Max preview model is evolving continuously on a day-by-day basis. Analysts point out that continuous updates to small versions of the base parameters suggest the model may be undergoing intensive post-training in preparation for the eventual release of the final 4.0 official version.