4. Finally, there is BabelFast, which has a very high video frequency:
When crypto users read the white papers of many projects, the most frustrating part is the content, which often runs to dozens of pages. Using a browser’s automatic translation requires waiting for the entire page to load, which greatly reduces efficiency. Immersive Translate’s newly launched BabelFast allows the translation to quickly follow wherever you scroll on the page:

#余币宝USDT享11%年化 Don’t leave idle funds idle—come to Gate’s Idle Money Treasure & Spare Coins Treasure to manage your assets, activate with one click, and enjoy passive earnings 🔥🔥🔥
The core of this campaign is “meet the net deposit requirement to earn bonus interest”: Idle Money Treasure offers a maximum combined annualized rate of 5%, while Spare Coins Treasure’s 7-day USDT fixed-term product offers a maximum combined annualized rate of 11%.
1. Idle Money Treasure: Maximum annualized rate of 5%
Campaign period: September 28, 2026, 07:00—October 28, 2026, 07:00 (UTC+8). After registering an

A gambler (or possibly a hacker) sold 262.78 $ETH for 643K $USDC and deposited it into Hyperliquid, then opened a 40x short on 305.51 $BTC ($25.4M) 40 minutes ago.
Liquidation price: $84,086.03

bitcoin:native spot demand has fully retraced the Q3 pump.
That’s not a great look.
If buyers don’t step in soon, BTC could be setting up for another leg lower.

Filecoin charges no egress fees for stored data, with no per-GB fee for taking data out
Moving 1 PB from one cloud provider to another can result in a $134,690 bill: about $90,000 in egress fees, plus another $44,690 in compute costs for the 13.2 days the machines sat idle during the transfer. Before the data even starts working, you first pay a moving tax.
At $0.09/GB, 1 PB costs approximately $90,000 to transfer out; no matter how fast the pipeline is, the machines still have to stay on until the byte stream finishes. Cloud providers mark “going out” as a premium: keeping data is cheap, but

This post is not criticizing beautiful women:
The more beautiful women there are in an industry/cycle,
the more signs there are of a top signal.
The same applies as the number of graduates from prestigious schools and young people increases.

bitcoin:native $MSTR
BTC is below the correction band, but it hasn’t lost its initial averages. I’m expecting a right shoulder in this area

🦅HAWK Soars Toward 100X
The starry sky is vast, as the eagle spreads its wings
#HawkArmy United in purpose and strength, we advance toward new heights
Upholding ecological balance and the principles of freedom, we witness the miracle that belongs to us✨

#BTCPullsBackTo81000 Bitcoin’s $80,391 Flush: The Technical Levels That Decide Whether This Is a Dip or a Deeper Breakdown
The $81,000 Zone Is Now a Market Test
Bitcoin’s latest pullback pushed price as low as approximately $80,391 on October 8 before a rebound carried it toward $82,468 on October 9. That recovery matters, but it does not yet erase the damage from the sharp decline out of the mid-$85,000 region. The market is testing whether buyers can turn the $80,000–$81,000 area into a dependable base or whether every rebound will attract sellers.
The Technical Structure Has Changed
The Oc

What does the number sequence “no 4, no 8” mean?
Is it a baby boy or a baby girl after all?

$BTC Refusing to crash again, it is now surging‼️‼️
Everyone is waiting for Bitcoin to continue falling, but look at what is happening now$BTC It has strongly rebounded from $80,400 and is currently back above $83,300. Bitcoin has not fallen; instead, it is showing strong buying pressure. I am now watching $83,500 to see whether the next breakout can occur. If the bulls continue to push, it could surge toward $84,000–$85,000 next. It looks like the bears may be in trouble!

The short-sellers’ rebound lacks strength, and the bulls are in control. $AAOI Opened a long at 111.50 with 50x leverage; it rose to 113.93, generating an unrealized profit of 105.02%. The key level has been tested repeatedly and could break upward with acceleration at any time.
Withdraw profits first and set a breakeven stop-loss for the core position. Don’t chase the final leg; secure your profits. Earn money you understand, don’t blindly expand your risk, and don’t hold losing positions.
Those who haven’t entered should not chase longs at high levels. Wait for a pullback near 111.50 and lo

At 25, am I already part of the target audience for ads about preparing for pregnancy?🫪
It’s practically a horror movie.
In 2026, although I didn’t make much money, I don’t have a goblin husband in my bed or goblin children at home that I can’t stuff back in. I spent a lot of money, but all of it was on myself. Thinking about it that way, it was really a great year.

#AIStocksFallALABDropsOver9%
AI Stocks Under Pressure: Separating a Valuation Reset From a Demand Reset
The supplied market brief reports a decline of more than 9% in Astera Labs, alongside weakness in Nvidia, AMD and Arm. Those figures describe the reported session, not fresh live quotes. The key question is whether investors are reassessing the price of AI growth or questioning the growth itself.
These are different problems. A valuation reset happens when investors become less willing to pay a premium for future earnings, even if a company continues growing. A demand reset happens when exp

$PONS This round of PONS falling from 0.96 to 0.33 is essentially a fundamental collapse—the buyback engine can no longer keep running. Previously, it could buy back about $1 million worth of tokens per day; now it is less than $200k, which cannot withstand the selling pressure at all. From late September to early October, the PONS platform’s average daily token issuance fell by about 72% from the first half of September, while average daily fee revenue plunged from $6.87 million to $1.48 million, a decline of about 78%. Even if the project team is genuinely willing to buy back tokens, it nee

Recently, looking at the cooperation between @axisrobotics and Lotus, what impressed me most was their approach to data processing. Handling sorting at an automotive factory means dealing with 500 to 800 types of parts. It sounds tedious, but it is actually all technical debt.
Traditional automation relies on “rote memorization”: change a part, and you have to reprogram everything.
Axis’s approach uses a simulation strategy to cover this huge assortment of parts. This generalization capability is highly convincing in industrial scenarios. But I have to pour some cold water on it: covering more