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Velodrome Finance: The Next Generation DeFi Platform
<a href=https://velofili.com/>velodrome exchange</a>
Velodrome Finance is a decentralized finance (DeFi) ecosystem designed to provide efficient, scalable, and user-friendly liquidity and trading solutions. With features like Velodrome Crypto, Velodrome Exchange, and Velodrome Finance Swap, the platform aims to enhance liquidity provision and asset management within the DeFi space.
Velodrome Finance Overview
Velodrome Finance focuses on creating a robust DeFi environment that supports decentralized trading, liquidity pools, and yield farming. Its ecosystem is built to facilitate seamless asset swaps, liquidity provision, and staking, making it a comprehensive platform for DeFi enthusiasts.
Velodrome Crypto
Velodrome Crypto refers to the native tokens and digital assets within the Velodrome ecosystem. These tokens are used for governance, staking, liquidity incentives, and participation in various DeFi activities on the platform.
Velodrome Exchange
Velodrome Exchange is a decentralized trading platform that allows users to swap tokens directly from their wallets. It emphasizes low slippage, high liquidity, and fast transaction speeds, providing a smooth trading experience for DeFi users.
Velodrome Finance Swap
Velodrome Finance Swap is the core swapping protocol within the ecosystem, enabling users to exchange tokens across different pools efficiently. It leverages Velodrome’s liquidity pools to facilitate secure and cost-effective token swaps.
Velodrome Fi
Velodrome Fi encompasses the yield farming, staking, and liquidity mining features of the platform. Users can stake their tokens, earn rewards, and participate in governance, contributing to the growth and security of the Velodrome ecosystem.
Velodrome Finance Exchange
Velodrome Finance Exchange refers to the entire decentralized trading and liquidity platform, integrating swap, staking, and liquidity provision functionalities. It aims to be a comprehensive DeFi hub for traders and liquidity providers.
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https://ufa.smidas.ru/
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These preppers have ‘go bags,’ guns and a fear of global disaster. They’re also left-wing
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This fear is where Marlon Smith’s interest in preparedness began. Growing up in Trinidad, he lived through an attempted coup in 1990 that sparked his concern the government would not be there in times of disaster. This only deepened after he moved to New York City and watched the aftermath of 9/11 and then Hurricane Katrina. “You see the inability of the government to truly help their citizens,” he said.
Smith, who now lives in New Jersey, runs a fashion company by day and spends his weekends teaching survival skills — including how to survive nuclear fallout. “People find it funny that I work in women’s evening wear and yet I do this hardcore prepping and survivalism in the woods,” he said.
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It’s hard to pin down the exact number of preppers in the US. Mills says 5 million is a reasonable estimate; others would say much higher. Chris Ellis, a military officer and academic who researches disaster preparedness, puts the figure at around 20 to 23 million using data from FEMA household surveys.
Figuring out the proportion of preppers on the left is perhaps even trickier. Mills, who has surveyed 2,500 preppers over the past decade, has consistently found about 80% identify as conservatives, libertarians or another right-wing ideology. He doesn’t see any dramatic upswing in left-wing preppers.
necdotal evidence, however, points to increased interest from this side of the political spectrum.
Several left-wing preppers told CNN about the burgeoning popularity of their newsletters, social media channels and prepping courses. Shonkwiler says subscriber numbers to his newsletter When/If increase exponentially whenever right-wing views make headlines, especially elections. He saw a huge uptick when Trump was reelected.
Smith has noticed more liberals among his growing client roster for prepping courses. He has an upcoming session teaching a group in the Hamptons — “all Democrats,” he said.
Smith is at pains to keep politics out of prepping, however, and makes his clients sign a waiver agreeing not to talk about it. “You leave your politics and your religion at the door. … You come here to learn; I’ll teach you,” he said.
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“AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’”
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Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.”
For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.
Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.
“Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,” Luccioni explained.
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If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator.
Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.
When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.
Why it’s hard to measure AI’s environmental impact
Putting a number on the environmental impact of AI has proved challenging.
The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models.
That’s partly why the researchers chose to represent carbon emissions within a range, Dauner said.
Furthermore, many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI’s water consumption.
“You can’t really say AI consumes this much energy or water on average — that’s just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,” Ren said.
One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested.
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