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a16z: Why does cc0 make NFT creators go further?

a16z: Why does cc0 make NFT creators go further?

Strategies for building brands, communities, and content through IP vary widely among NFT projects.

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2022

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Back to the source: Where did the value of Bitcoin and Ethereum come from?

Back to the source: Where did the value of Bitcoin and Ethereum come from?

Whenever people mention cryptocurrencies such as Bitcoin it is always associated with speculation

03

2022

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How does a bear market find "potential" targets? Inventory of projects co-invested by top crypto VCs

How does a bear market find "potential" targets? Inventory of projects co-invested by top crypto VCs

Fractal: Game NFT Trading Platform Aptos: a new public chain created by the former technical team of Facebook

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2022

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What kind of GPU is the best choice for AI training?

What kind of GPU is the best choice for AI training?

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2022

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Why GPU can be used to accelerate the computing speed of artificial intelligence or machine learning (parallel computing power)

Why GPU can be used to accelerate the computing speed of artificial intelligence or machine learning (parallel computing power)

Calculation is calculation. Mathematically, it is the same. 1+1 is calculated as 2. The CPU can also calculate the neural network. The calculated neural network works well in practical applications, but the speed will be very slow. That's it.

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2022

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AI chip characteristics and comparison

AI chip characteristics and comparison

At present, in the field of intelligent driving, general-purpose chips suitable for parallel computing such as GPUs and FPGAs are mainly used to achieve acceleration in processing deep learning AI algorithms. At the same time, some chip companies have begun to design ASIC-specific chips for AI algorithms, such as Google TPU, Horizon BPU, etc. Before the large-scale rise and mass launch of intelligent driving industry applications, the use of existing general-purpose chips such as GPUs and FPGAs can avoid the high investment and high risk of specializing in the development of custom chips (ASICs). However, because the original intention of such general-purpose chips is not Specifically for deep learning, there are problems such as insufficient performance and high power consumption. These problems will become increasingly prominent as the application scale of the autonomous driving industry expands.

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Shenzhen Lianli Liquid Cooling Technology Co., Ltd

E-mail: sales@lianliai.com

Tel：+86-18002936305

Headquarters Address: 12th Floor, Chaopinsan Technology Building, No.18 Gaoke Avenue, Longgang District, Shenzhen City, Guangdong Province, China

Factory Address: Unit 101, Building 15 (Ersha Shenlian), No. 6 Baolong Avenue, Baolong Community, Baolong Subdistrict, Longgang District

© 2022 ShenZhen LIANLI Liquid Cooling Technology Co., Ltd

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