无门槛赌场 Pump.fun 最懂利用人性

链捕手Pubblicato 2024-08-15Pubblicato ultima volta 2024-08-15

作者:Jessy,金色财经

 

8月13日,Solana上Memecoin发射平台Pump.fun日收入再创新高——533万美元。7月份,其累计协议收入达到 2873 万美元,创下月度新高。截至8月13日,Pump.fun累计收入超过了9400万美元。

Pump.fun 于 2024 年 2 月推出,到了3 月中旬用户数和使用量开始飙升,而仅在半年的时间内,就达到了多次日收入超过以太坊的成就。

这不禁让人感叹,在币圈,最挣钱的生意还得是“赌场”。

最近Pump.fun又推出了在其上创建代币免手续费的活动,叠加大多数人在其上都是亏钱,目前仿盘又众多这一现象。社区内唱衰Pump.fun的声音也变得多了起来。

一个存粹的赌场,会消失么?而这样的项目对于行业来说,意义在哪?

无门槛的代币发射平台

在Pump.fun上创建Memecoin十分简单,只需想出一个吸引眼球的名字和一张吸引人的图片,然后点击主页顶部的“开始创建货币”按钮即可。用户可以写一段货币的简短描述,以及相关社交媒体渠道的链接,然后支付 0.02 Sol来部署他的memecoin。 

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在行业内,代币发行方式的创新,一直都能带动一波新的叙事。如去年爆火的Ordinary协议,便是创新了在比特币上发行新资产的方式,引发了大家对于比特币生态的关注,追溯更早,则是ICO的掀起的2017年的区块链牛市。

更根本的,区块链本身的一个重要创新,便是在代币的发行,项目经济模型的建立。

Pump.fun其实也融合了ICO的发行方式。其采取了盘前募资的方式,对于用户而言,选择想要参与的代币,然后输入想要购买的代币数量即相当于参与了代币发行募资,并能够在发行前售出对应的份额,Pump.fun 将收取 1% 费用。

Pump.fun为何能成功?核心的原因或在于简化了代币发行方式,以及简化了用户参与项目、买入卖出流程的。本质上是搭建了一个项目丰富,并且参与门槛较低的赌场。

运行机制上,最初在Pump.fun上被创建的代币,只能在平台内进行交易,而当代币达到一定的市值(约 6 万美元)时,pump.fun 网站将自动关闭交易并将代币迁移到 Solana上DEX Raydium。

到了这一步,也就到了大多数人抛售其手上代币的时机。一但买家从Raydium上涌入购买代币,那么在代币Pump.fun低价值阶段购入低价格代币的用户,此时卖出就能获得丰厚的利润。

在行业内,会用“毕业率”来形容在Pump.fun上所发行的Memecoin上线Dex的成功率。

Dune Analytics数据显示,Pump.fun 上创建的代币成功登陆Raydium 的成功率为 1.41%。自 5 月以来,该比率一直在 1.21% 至 1.41% 之间波动。以上可见,这些Memecoin绝大多数都未能上线交易所,赌博成功概率极低。

从Pump.fun上项目“毕业率”如此之低可见,其上项目虽然繁多,可供用户选择参与“赌博”的产品丰富,但是“赔率”也十分大,选中一个好项目,完全靠运气。而Memecoin本身就没有基本面,存粹靠的是注意力经济,追逐热点。

不过和BRC-20一样,其也做到了公平法射,这也是用户们愿意在其上玩的原因之一。

骗子横生,但赌徒不断

虽然Pump.fun打着公平法射的噱头。但你从来都没有绝对的公平。对于在Pump.fun项目上发币的项目方来说,仍然有办法“收割”用户。在一个完全自由,没有任何监管、制约的市场上,丛林法则的贯彻反而更为彻底。

和任何币圈的项目一样,项目方或者庄还是可以用极低的价格持有大量的代币。方法很简单,多创建几个新钱包,早期买入,通过不断地小额买入抬高代币的价格,又通过营销如推特买粉等方式提高项目知名度,引来大量用户接盘,项目方再在合适时机抛售即可。这样的割韭菜方式也是圈内惯用手法。

而Pump.fun的问题在于,任何人都能在Pump.fun上发币,而且发币成本极低。据统计,目前,其上总共发行了超120万个项目。平台上项目太多了,又因为平台有着大量用户,骗子项目的可触达率大大提高。

并且在8月9日,pump.fun 团队在 X 平台上宣布,将代币发行费用从2美元降至0美元,并且还会给完成了“绑定曲线”(bonding curve)并在 Raydium 上市的Memecoin项目0.5Solana的奖励。

团队希望这样的激励能在一定程度上鼓励创作者不要完全卖出他们的代币。这一“免费”政策的推出,从项目经营的角度来说,或是为了应对目前各个链上其仿盘繁盛的举措。

然而这一举措的实际影响,则是再次降低了发币门槛,让骗子项目更多。而Pump.fun本身并无对项目的筛选机制。

不过对于用户来说,来Pump.fun本就是来赌博的,炒做Meme是希望能够“单车变摩托”以小博大的行为。

而Pump.fun平台对比于在链上单打独斗玩Meme ,用户体验其实更好,尤其是其把Meme的交易内置了社交属性,用户们在操作Meme时,不只是观察K线买入卖出享受那种肾上腺素飙升的快感,而是可以在平台上享受各种搞笑的梗图,并且在项目后面评论、回复。这是增加了赌博产品、平台趣味性的创新。

最近,关于Pump.fun相关的负面言论确实很多,其视角大致称其为屠宰场式的收割,并认为在失去赚钱效应之后,用户撤离,项目就会衰亡。

但是去赌场的大部分人都是亏钱的,而去赌场的人依旧络绎不绝。就这一点,Pump.fun不仅不会消失,反而会更加繁盛,尤其是在Memecoin成为极为重要叙事的这一周期内。

而Pump.fun对于行业的意义是什么?直接能看到——它极大地提高了Solana链上活跃度。更多的?哪家赌场创办是为了给社会做贡献的啊!

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Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

Jensen Huang, alongside AI leaders like Peter Norvig, Boris Cherny, and Andrew Ng, is advocating for a shift from "prompt engineering" to "loop engineering" as the new paradigm for AI development. Instead of manually crafting individual prompts, the focus is now on designing autonomous loops—systems where AI agents execute tasks, self-validate results, and iterate until completion without constant human oversight. A loop is a management framework that enables agents to operate independently. Key implementations are seen in Claude Code (with features like /loop, /goal, and /schedule) and OpenAI Codex, which employ multiple agents working in parallel within isolated environments. A core principle is the separation of roles: one agent (or model) performs the task, while an independent agent (or a smaller, separate model) validates the output to ensure objectivity. The article outlines a practical roadmap for implementing loops, starting with a "four-condition test" to assess suitability, building a minimal viable loop, and emphasizing critical pitfalls to avoid, such as lacking hard stop conditions or allowing loops to handle tasks requiring human judgment. This evolution is framed as the fourth major shift in AI interaction: from Prompt Engineering (crafting instructions) to Context Engineering (providing background information), then to Harness Engineering (building tool-enabled environments), and finally to Loop Engineering (creating self-sustaining systems). This progression reflects a consistent trend of increasing abstraction, moving human involvement from direct instruction to system design and rule-setting. The concept has academic roots in frameworks like ReAct, which formalized the "reason-act-observe" cycle. While loop engineering promises greater automation, experts caution about managing token costs and warn against outsourcing understanding—AI can assist, but deep problem comprehension remains essential.

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Jensen Huang: Prompts are Becoming Obsolete, Loops are the New Paradigm

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