跨链Meme Coin Base Dawgz在预售中筹集了数百万美元——Dawgz会成为下一个BRETT吗?

币界网Published on 2024-08-22Last updated on 2024-08-22

币界网报道:

加密货币投资者无法获得足够的模因币。

最新引起轰动的是Base Dawgz(Dawgz)——一种在预售中筹集了数百万美元的多链硬币。

一些投资者想知道它是否会成为下一个像基础链代币Brett(Brett)这样的大事件。

Base Dawgz——释放多链功能的力量

那么,Dawgz基地到底是什么?

这是一枚模因币,灵感来自跳台精神和标志性的Shiba Inu“Doge”模因。

但它不仅仅是一个无用的代币——它还有一些花招。

最值得注意的是,Base Dawgz可以在多个区块链上无缝运行,包括Base、以太坊、Solana、BNB Chain和Avalanche。

该代币的跨链功能是其关键卖点,因为它允许DAWGZ利用更广泛的流动性池,从而可能增加交易量。

Base Dawgz还为Dawgz提供了一个质押协议。

该协议为那些愿意锁定代币的人提供了806%的年度奖励。

超过2.09亿DAWGZ已被质押,占预售代币总数的37%。

随着进一步扩大这一生态系统的计划,Base Dawgz开始吸引市场各个角落的关注。

其Telegram频道目前拥有8900多名活跃成员。

DAWGZ甚至在CoinMarketCap和99Bitcoins等顶级加密货币出版物的文章中被提及。

DAWGZ准备起飞,预售价格接近300万美元

市场已经注意到了Base Dawgz的炒作。

在撰写本文时,该项目的预售资金已接近300万美元的里程碑。

这是一项令人印象深刻的成就,突显了投资者对Base Dawgz的兴趣程度。

正如白皮书所概述的那样,DAWGZ总供应量的20%已预留给预售买家。

这意味着五分之一的代币将掌握在社区手中。

另外15%的资金将用于营销,这对于确保Base Dawgz能够继续发展其支持者社区至关重要。

剩余的DAWGZ代币已分配给DEX上市、质押支付和神秘的“DAWGZ奖励”

这些DEX上市已经引起了投资者的关注。

由于被压抑的需求,预售硬币在首次在DEX上推出时通常表现良好。

由于已经有一批DAWGZ代币用于流动性,因此交易条件很有可能一开始就很顺利。

Base Dawgz能否跟随BRETT在基础链上的脚步?

除了令人印象深刻的预售表现外,Base Dawgz还设法在网上引起了轰动。

DAWGZ社区在推特上特别活跃,该项目的官方账户现在拥有5800名粉丝。

这种不断增长的社交媒体存在使Base Dawgz能够利用加密影响者的力量。

几个流行的名字已经开始覆盖DAWGZ。

例如,YouTuber ClayBro表示,DAWGZ可以提供“令人难以置信的回报”

加密货币大师Matthew Perry甚至将其与BRETT硬币进行了比较。

与BRETT的这种比较是围绕Base Dawgz大肆宣传的主要原因之一。

对于那些不知道的人来说,BRETT是Base链上最大的模因币,市值超过9亿美元。

它最近在韩国最大的加密货币交易所Upbit上市。

本周,币安甚至为其推出了永久期货合约。

因此,通过与BRETT进行比较,Base Dawgz开始受到投资者的关注,他们将其视为Base链上的下一个潜在登月计划。

如果DAWGZ只取得BRETT成功的一小部分,它可能会改变Base的游戏规则。

这就是为什么随着Base Dawgz的预售继续升温,那么多人都在关注它。

参观Dawgz预售基地

免责声明:以上文章为赞助内容;它是由第三方写的。CryptoPotato不对本页面上的内容、广告、产品、质量、准确性或其他材料进行背书或承担责任。其中的任何内容都不应被解释为财务建议。强烈建议读者在参与任何提及的公司或项目之前,独立、仔细地核实信息,并进行自己的研究。投资加密货币存在资金损失的风险,建议读者在做出任何可能基于或可能不基于上述赞助内容的决定之前咨询专业人士。

建议读者阅读CryptoPotato的完整免责声明。

Trending Cryptos

Related Reads

You Use Claude and Codex Every Day, but Meta Has Restricted Internal Use

In May, Meta imposed internal restrictions on its engineers regarding the use of Claude Code and Codex, two widely used AI programming tools. Despite being a major client, Meta's guidelines, still in effect, prohibit these external models from being used for specific tasks to prevent potential "escalations with partners." The core concern is "distillation"—the risk that outputs from Claude or Codex could inadvertently contaminate the training data and evaluation processes for Meta's in-house AI coding assistant, MetaCode. If MetaCode is trained or evaluated using data generated by these external models, it risks learning their capabilities rather than developing its own, blurring the line of intellectual origin. The restrictions are precise: engineers cannot use the external models to generate test questions, debug source code, or suggest test cases. AI-generated content is also barred from environments accessible to MetaCode. However, AI can still assist with peripheral tasks like workflow setup and code organization, provided all outputs are manually reviewed. This caution reflects a broader industry dilemma. While distillation is a common technique, using a competitor's model output for training raises legal and ethical questions about the ownership of derived capabilities. Contractual terms from companies like OpenAI and Anthropic explicitly forbid using their outputs to build competing products, putting enforcement power in the hands of rivals. The move is also financially motivated, as Meta seeks to reduce its hefty internal AI spending, estimated in the billions this year. Meta's policy illustrates the delicate balance companies must strike: leveraging powerful external AI tools while safeguarding the integrity and independence of their own AI development. As AI systems increasingly help build other AIs, distinguishing the origin of capabilities becomes a fundamental challenge for the entire industry.

marsbit54m ago

You Use Claude and Codex Every Day, but Meta Has Restricted Internal Use

marsbit54m ago

Why Do We Need an AI Content Perspective Today?

The article "Why Do We Need an AI Content Perspective Today?" explores the complex and often contentious integration of AI into the cultural and creative industries, particularly film and television. It begins with the cancellation of Amazon's AI-generated animation "Punky Duck," highlighting the ethical debates surrounding AI content. AI's rapid advancement is transforming video production, enabling cost-effective, full-length AI films (e.g., "RAPHAEL," "Dreams of Violets") while sparking industry resistance over issues like "synthetic actors." The core debate has shifted from whether to use AI to how to use it responsibly. The article analyzes why AI's entry into film is uniquely unsettling. It distinguishes between "cultural fast food" (short-form, fast-paced content like micro-dramas) and "cultural main courses" (traditional, long-form film/TV). AI currently excels at the former, matching its fragmented narratives, shallow emotional needs, and free-to-consumer models. However, venturing into the latter challenges the human-centric essence of storytelling—creativity, emotional depth, and the unique value of human labor and experience. While AI can generate massive volumes of content and lower costs, it risks devaluing human creativity, leading to homogenized output, and creating unfair competition through potential intellectual property infringement. Its efficiency also amplifies content safety risks, making preemptive governance crucial. To counter these risks, the article proposes establishing clear boundaries guided by a human-centered AI content perspective. It outlines four principles: 1) Amplify, rather than displace, human creative space; 2) Respect and protect human creative output; 3) Ensure human creative control and responsibility remain paramount; and 4) Guarantee transparency and traceability in AI creation. The conclusion emphasizes that humans must act as the "helmsmen" of technology, steering AI development to enhance, not replace, the core human values at the heart of cultural expression.

marsbit1h ago

Why Do We Need an AI Content Perspective Today?

marsbit1h ago

Planck Retracted? The Father of Quantum Tripped by an Algorithm

The recent discovery that two articles (published in 1940 and 1942) by Max Planck, the Nobel laureate and founder of quantum theory, are marked as "retracted" on Springer's digital platform highlights a curious clash between historical publishing practices and modern automated systems. An investigation suggests these retractions are algorithmic errors, not due to fraud or misconduct. The papers, philosophical reflections on science published in *Die Naturwissenschaften*, were likely flagged by the platform's systems. One article, a republished lecture, may have been mistaken for duplicate publication. Another, sharing a title with a prior article by a different author (a common practice for continuing debates at the time), may have triggered a similar automated check. The digital versions have even been replaced with blank pages, contrary to normal practice of preserving retracted texts. This incident underscores how contemporary digital infrastructure, built around concepts like "self-plagiarism" and strict copyright, can misclassify and obscure legitimate historical scholarly communication. It serves as a warning that digital archives are not neutral mirrors of the past but are filtered by platform rules, potentially distorting the scientific record. As AI systems increasingly rely on such databases, such erroneous metadata could propagate, affecting how future tools interpret and access historical knowledge.

marsbit1h ago

Planck Retracted? The Father of Quantum Tripped by an Algorithm

marsbit1h ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of MEME (MEME) are presented below.

活动图片