【一周靓文】Blur空投激发市场热情,比特币风光独好?

火币资讯2023-02-18 tarihinde yayınlandı2023-02-18 tarihinde güncellendi

Özet

一周靓文,介绍过去一周最值得关注的热点文章,帮助投资者深刻理解市场动态。

一周靓文,介绍过去一周最值得关注的热点文章,帮助投资者深刻理解市场动态。

1、《情人节特辑:Blur空投+发币,惊喜在哪里(The Floor is All Yours, Blur)》

如果看⼀下最近3个⽉的NFT交易量市场流量排名,不难发现Blur的总交易量超过了OpenSea,⼏乎所有的NFT项⽬的地板都挂在Blur上。

究竟是什么原因让Blur如此成功?

它的成功可以持续下去吗?

本⽂会带你详细剖析⼀下Blur的前世今⽣。

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2、《Blur发币 它是如何在短期之内超越Opensea的?未来潜力如何》

Blur是一个针对专业交易员的聚合NFT交易平台,其于2022年10月正式上线。

根据 Dune Analytics 数据,截至 2 月为止,Blur 每天都在整个 NFT 领域的日交易量中名列前茅,在 1 月初和 12 月的大部分时间里,Blur 的交易量也超过了 OpenSea。

点击查看

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3、《美国CPI数据高于预期,加息之路持续》

伴随 Token 发行预期,Arbitrum 生态近期成了市场关注的最大热点,生态内的诸多项目 Token 均有较大幅度的上涨。当前社区都在讨论什么项目?它们都布局在哪些赛道?

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4、《美国SEC等监管机构前后夹击Paxos,醉翁之意或不在BUSD》

BUSD是否会出现挤兑风险?稳定币又是否应被视为证券?稳定币的监管管辖权究竟该归属哪个部门?

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5、《【研报精选】比特币的过去,现在和未来(一):步步回首》

The Generalist 最近发了一篇文章:主要讲了关于谷歌、ChatGPT 和搜索的未来。其中探讨了几个问题:自去年年底 ChatGPT 推出以来,OpenAI 产品一直被誉为是:搜索的未来,也是潜在的谷歌杀手。那么,谷歌将跟随大英百科全书和黄页一样,逐渐被颠覆吗,还是,ChatGPT 出现,仅仅会带动谷歌的转型?

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6、《【研报精选】比特币的过去,现在和未来(二):中本聪其人》

中本聪的第一个被记录下来的踪迹可以追溯到 2008 年,虽然在许多公开的帖子中,这位匿名人士(也许是匿名人士团体)漫不经心地表示他在 2007 年就已经开始开发叫做 “比特币(Bitcoin)” 的项目了。想象一下,2007 年,这地球上的某个地方,某人已经开始开发比特币了。

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7、《【研报精选】板块轮动不在?这波“小牛市”的背后推动力是?》

方舟投资在报告中指出,加密货币和智能合约在未来十年内,可以分别达到20万亿美元和5万亿美元的市值,2030年,比特币价格将达到100万美元一枚。

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İlgili Okumalar

What Are Some Good Paths for Chinese Web3 Entrepreneurship? (Part 5)

This article explores pathways for Chinese Web3 teams to pivot toward AI, building on a previous discussion. It focuses on two specific team profiles: **Security & Risk Control Teams:** These teams, skilled in smart contract auditing, wallet security, and on-chain monitoring, can transition to providing **Agent behavior auditing and AI security governance**. As AI Agents automate tasks, access data, and trigger payments, enterprises will need solutions to monitor permissions, audit logs, control data access, and prevent anomalies—creating a strong B2B demand. **Application & Community-Focused Teams:** Instead of completely rebranding as AI companies, these teams should use AI to **enhance their existing products**. For example, research platforms can use AI to summarize information and identify signals; community tools can automate user support and analysis; and educational products can create personalized learning paths. The key is integrating AI to solve existing user pain points, like information overload or high operational costs. The article also advises against certain AI directions for Chinese Web3 teams, such as building general-purpose large language models (too resource-intensive), creating overly broad Agent platforms (hard to monetize), developing AI traders/automated yield products (high regulatory and risk sensitivity), or simply adding superficial AI features without genuine value. The core conclusion: Successful migration depends not on chasing AI hype, but on **identifying how a team's existing Web3 capabilities—be it in data, payments, security, or user operations—can address real needs in new AI application scenarios.**

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What Are Some Good Paths for Chinese Web3 Entrepreneurship? (Part 5)

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