Visa Direct processed more than 12.5 billion transactions in fiscal 2025.
Stable announced on September 30 that it is integrating with that network for real-time payouts to bank accounts and mobile wallets, connecting its settlement rail to bank money.
@Stable @mehler

I'm still of a belief, no matter what everyone says, that this is a complex Bitcoin correction. We can even go and make a new low and that would be totally fine from Wyckoff and EW perspective.
Here is a complex count straight from the book (posting as a comment to this).

$JCT That was a brutal wick—down by half in just 24 hours: -23%, from 0.0025 to 0.0018. Turnover was 95.8M, and trading volume hasn’t dried up, which suggests it’s not that nobody is buying—it’s the market maker rotating positions. Let me break it down from the market maker’s perspective: early in the session, they placed sell orders above 0.0024 to create the illusion of a weak rebound, spooking retail traders when the price couldn’t break higher; around midday, they used a series of small sell orders to break through the psychological 0.0020 level, triggering stop-loss orders. That sharp dip

The top-up service that was stuck for half the day is going to get chewed out by agents today
Fully back to normal. Recruiting sub-site owners and offering free second-level domains
Low barrier to entry; top-up profits have always been transparent

A N I M A L S
Absolutely wild ✍️

Data centers are a broken model.
A few companies own the compute. Everyone else rents it.
@LightchainAI does it differently. Independent operators serve open models like gpt oss, Qwen and Llama from their own GPUs, in an office, a bedroom or a garage. The chain picks who serves each job, and every answer leaves a public record.
The news: the Developer API is now live on mainnet. If your app uses OpenAI's chat API, you can switch to open models by changing three things: the base URL, the key and the model name.
Chat: 
Workers: 
Jobs:
Lightchain's release on Decrypt

$ETH #比特币跌破7万美元 2026.11.15
Daily entry point: 1840
Resistance above: 1853–1863-1874-1884-1897
1900-1910-1920-1928-1936-1956
Support below: 1830-1824-1820-1810-1800
At all support and resistance levels, stop out on the first break; if the level holds, take a rebound, and once price stabilizes, follow the trend. If price stabilizes above 1853-1881, or breaks below 1824-1800, follow the move short; otherwise, buy low. Personal opinion for reference only.

The short-selling whale's second position is an $ETH long position, holding approximately 3,986 ETH worth about $7.29 million with 25x leverage and an average entry price of about $1,875. Its current unrealized profit is about $180k, accounting for over 60% of the whale's total unrealized profit. The ETH long position uses cross margin. #Dr.Han牛津辩论社演讲

$ZEC is under heavy distribution on the 4H chart, trading at 868.00 (-8.24%) below all key moving averages. Following a tight consolidation phase beneath the 965.32 resistance ceiling, downward momentum accelerated and sliced cleanly through range support, testing the 861.70 demand floor.

The 860.00–862.00 zone is the final line of defense. Losing this level opens up serious breakdown risk toward the 809.19 liquidity pocket. Any dead-cat bounce faces stacked dynamic resistance at EMA5 (893.73) and EMA10 (914.72).

Smart money fades weak bounces here rather than catching falling knives. Lookin

$DOGE I'd rather believe pigs can climb trees than believe dog coins will rise again!

For anyone who needs hopium.
$DOT and $ADA can close their eyes. 🫣
Crypto Market Cap Evolution (2022–2026)
1. Bitcoin (BTC)
2022: $318.5B | 2023: $827.8B | 2024: $1.83T
2025: $1.77T | 2026: $1.36T
Change: +327%
2. Ethereum (ETH)
2022: $146.5B | 2023: $274.2B | 2024: $404.7B
2025: $360.1B | 2026: $233.1B
Change: +59%
3. Tether (USDT)
2022: $66.2B | 2023: $91.7B | 2024: $138.8B
2025: $187.2B | 2026: $175.3B
Change: +165%
4. BNB
2022: $39.4B | 2023: $47.4B | 2024: $101.4B
2025: $118.5B | 2026: $82.2B
Change: +109%
5. USD Coin (USDC)
2022: $44.5B | 2023: $24.5B | 2024: $43.8B
2025: $76.0B | 2026:

The people who win long term in bitcoin:native aren’t the ones trying to catch the exact bottom.
They’re the ones accumulating while everyone else is panicking.
Nobody knows where the bottom is. And honestly, you don’t need to.
My approach is simple:
Once BTC is 50% down from its ATH, I start DCAing.
If it drops another 10%, I keep buying.
Another 30%? I keep buying.
I don’t care about catching the exact bottom. I just want to accumulate as many discounted satoshis as possible.
Eventually, a new ATH comes.
And all those satoshis accumulated during the drawdown will pay off.
Stop trying to time

**[Blind Scan of the Top 100: Only These 3 Targets Make the Watchlist]**
>
> 90% of retail traders’ losses are caused by chasing highs when major players hunt stop-losses with “fake breakouts,” or by taking oversized positions and grinding away their capital in choppy markets.
>
> To eliminate emotion entirely, I used **4,000+ lines of low-level code** to bundle the six core institutional strategies and several classic traditional methods into this **[Dual-Indicator + Probability-Based Position Sizing Black Box]**:
> ▪️ **Spot the big players**: SMC smart-money order blocks (OB/FVG) + Wycko

I used up the quota just before my monthly plan ended
Domestic models are indeed quite generous with usage; GLM basically resets during off-peak hours
The quota in the subscription can also be connected to an API
For 500 RMB, including API usage, you can get nearly 10 billion tokens out of it
At official API prices, these tokens are worth 5,000–25,000 RMB

enough already, bring back the bull market.

Starting to join Gate—how are the returns?

Everyone knows that image generation requires a person, but many people don't know or can't tell what age the photo should depict. I made a set of age comparison images for everyone to use as a reference.
Of course, your GPT, Grok, or Doubao may tailor the results to your preferences, so the faces may not look exactly the same for everyone.
I’ve put the prompt below for everyone to try generating an age comparison of the character their own AI tool gives them.
Generate a horizontal 16:9, five-panel comparison image showing how a woman’s appearance changes with age.
The same fictional East Asia