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Curve Finance: Leading DeFi Liquidity and Stablecoin Protocol
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Curve Finance is a decentralized exchange (DEX) optimized for stablecoin and low-volatility asset trading. Known for its efficient liquidity pools and low slippage, Curve has become a cornerstone in the DeFi ecosystem for stable asset swaps and liquidity provision.

Curve Fi
Curve Fi refers to the platform's core protocol that enables users to swap stablecoins and other similar assets with minimal slippage and low fees. It leverages specialized liquidity pools to facilitate efficient trading.

Curve Finance Crypto
Curve finance crypto encompasses the native tokens (such as CRV), liquidity provider tokens, and other assets within the Curve ecosystem. These tokens are used for governance, staking, and earning rewards.

Curve DeFi
Curve DeFi describes the broader decentralized finance ecosystem built around Curve’s liquidity pools and protocols. It includes yield farming, liquidity mining, and integrations with other DeFi platforms like Yearn, Convex, and more.

Curve Finance Liquidity Pool
A Curve liquidity pool is a smart contract that holds assets like stablecoins or similar tokens, allowing users to deposit and earn fees or rewards. These pools are optimized for low slippage and high efficiency, supporting large trading volumes.

Curve Finance Borrow
While primarily known for liquidity provision and swaps, Curve also supports borrowing mechanisms indirectly through integrations with lending protocols like Aave or Compound, where users can collateralize assets and borrow against their liquidity pool tokens.

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The CO2 that is extracted from the water is run through a purification process that uses activated carbon in the form of charred coconut husks, and is then ready to be stored.
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In a scaled up system, it would be fed into geological CO2 storage. Before the water is released, its acidity is restored to normal levels, making it ready to absorb more carbon dioxide from the air.

“This discharged water that now has very low carbon concentrations needs to refill it, so it’s just trying to suck CO2 from anywhere, and it sucks it from the atmosphere,” says Halloran. “A simple analogy is that we’re squeezing out a sponge and putting it back.”

While more tests are needed to understand the full potential of the technology, Halloran admits that it doesn’t “blow direct air capture out the water in terms of the energy costs,” and there are other challenges such as having to remove impurities from the water before releasing it, as well as the potential impact on ecosystems. But, he adds, all carbon capture technologies incur high costs in building plants and infrastructure, and using seawater has one clear advantage: It has a much higher concentration of carbon than air does, “so you should be able to really reduce the capital costs involved in building the plants.”
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Mitigating impacts
One major concern with any system that captures carbon from seawater is the impact of the discharged water on marine ecosystems. Guy Hooper, a PhD researcher at the University of Exeter, who’s working on this issue at the SeaCURE site, says that low-carbon seawater is released in such small quantities that it is unlikely to have any effect on the marine environment, because it dilutes extremely quickly.

However, that doesn’t mean that SeaCURE is automatically safe. “To understand how a scaled-up version of SeaCURE might affect the marine environment, we have been conducting experiments to measure how marine organisms respond to low-carbon seawater,” he adds. “Initial results suggest that some marine organisms, such as plankton and mussels, may be affected when exposed to low-carbon seawater.”

To mitigate potential impacts, the seawater can be “pre-diluted” before releasing it into the marine environment, but Hooper warns that a SeaCURE system should not be deployed near any sensitive marine habitats.

There is rising interest in carbon capture from seawater — also known as Direct Ocean Capture or DOC — and several startups are operating in the field. Among them is Captura, a spin off from the California Institute of Technology that is working on a pilot project in Hawaii, and Amsterdam-based Brineworks, which says that its method is more cost-effective than air carbon capture.
According to Stuart Haszeldine, a professor of Carbon Capture and Storage at the University of Edinburgh, who’s not involved with SeaCURE, although the initiative appears to be more energy efficient than current air capture pilot tests, a full-scale system will require a supply of renewable energy and permanent storage of CO2 by compressing it to become a liquid and then injecting it into porous rocks deep underground.

He says the next challenge is for SeaCURE to scale up and “to operate for longer to prove it can capture millions of tons of CO2 each year.”

But he believes there is huge potential in recapturing carbon from ocean water. “Total carbon in seawater is about 50 times that in the atmosphere, and carbon can be resident in seawater for tens of thousands of years, causing acidification which damages the plankton and coral reef ecosystems. Removing carbon from the ocean is a giant task, but essential if the consequences of climate change are to be controlled,” he says.

Study reveals how much energy AI uses to answer your questions
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Whether it’s answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people’s lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up.

Each word in an AI prompt is broken down into clusters of numbers called “token IDs” and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations.

The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute.
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So, for each prompt you give AI, what’s the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers.

In addition, “smarter” LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported.

“This shows us the tradeoff between energy consumption and the accuracy of model performance,” said Maximilian Dauner, a doctoral student at Hochschule Munchen University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday.

Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models.

“You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,” Dauner said.
What you can do to reduce your carbon footprint
Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said.

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