Market Cap: $3.2512T -1.790%
Volume(24h): $132.4389B 6.020%
  • Market Cap: $3.2512T -1.790%
  • Volume(24h): $132.4389B 6.020%
  • Fear & Greed Index:
  • Market Cap: $3.2512T -1.790%
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CNNS
CNNS CNNS
#4384
$
%(1d)

0.00 USDT

100.00%(1d)

Price performance

Low

High

$

$

Buy / Sell

All-time High

$0.02

May 09, 2021

-99.64%(1d)

All-time Low

$0.00

Apr 15, 2025

113.37%(1d)

Volume(24h)

$90.63K

{{decimal(volume_24h_change,false,2)}}%(1d)

Turnover rate

13.97%

Market Cap

$648.9288K

FDV

$648.9K

Circulating supply

$0

Total supply

$10B

Max supply

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Community sentiment

26%

74%

Bullish

Bearish

Henry

Jun 19, 2025 at 01:22 am

READ MORE
$ETH in 2021: $2,600 $ETH in 2022: $2,600 $ETH in 2023: $2,600 $ETH in 2024: $2,600 $ETH in 2025: $2,600 $ETH before elections: $2,600 $ETH after elections: $2,600 $ETH before 100k BTC: $2,600 $ETH after 100k BTC: $2,600 $ETH before $TRUMP: $2,600 $ETH

From Twitter

well $pengu might just be the best asset to load at this point agreed?

From Twitter

Markets & Mayhem

Jun 19, 2025 at 12:43 am

READ MORE
We can see that put flow below targeting $SPX 6000, but call flows are more aggressive here. Suggesting that some traders are starting to see a more bullish outcome for today's trading.

From Twitter

Markets & Mayhem

Jun 19, 2025 at 12:42 am

READ MORE
$SPX 6000 remains the BIG level to watch here. It is currently our volatility trigger level and also where most of the put activity is focused today.

From Twitter

More

CNNS News

May / 11

Nov / 21

  • Attention Transfer: Isolating the Role of Attention Mechanisms in Vision Transformers
    Nov 21, 2024 at 06:00 pm
    Vision Transformers (ViTs) have revolutionized computer vision by offering an innovative architecture that uses self-attention mechanisms to process image data. Unlike Convolutional Neural Networks (CNNs), which rely on convolutional layers for feature extraction, ViTs divide images into smaller patches and treat them as individual tokens. This token-based approach allows for scalable and efficient processing of large datasets, making ViTs particularly effective for high-dimensional tasks such as image classification and object detection. Their ability to decouple how information flows between tokens from how features are extracted within tokens provides a flexible framework for addressing various computer vision challenges.

Jul / 01

May / 27

Apr / 29

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