Bags 豪掷 80 万美元收购 WIF 原型帽：meme 发射台的“天价营销”赌局

比推Published on 2025-08-08Last updated on 2025-08-08

作者：Cookie

原标题：这个meme发射台花费近80万美元买了一顶30美元的帽子？

注意看，仅需 30 美元，您就可以获得这样一顶帽子：

但在昨天，meme 发射台 Bags 的创始人及 CEO @finnbags 却花费 6.8 个比特币（约 80 万美元）的价格买下了与上图这顶一模一样的粉色毛帽。

80 万美元的溢价，是 meme 的「原味」价值。@finnbags 拍下的这顶，是 Solana 上著名的 meme 币 $WIF(dogwifhat) 在 2018 年的原型 meme 中所出现的那顶。现在，这顶帽子将永远从上图这只可爱小狗的头上离开，到 Bags 的手中。

在这场竞拍中，另一个有力的竞争者是 @gr3gor14n。他的最高出价一度来到 6.6 比特币。他在 Twitter 上表示，他的计划是买下这顶帽子，然后把帽子戴在自己的狗的头上，并发射一个无捆绑的 $WIF2 币

@finnbags 表示，他将 Bags 平台发射出的 meme 币 $BTH 的全部收入用于购买了这顶帽子。目前，$BTH 的市值接近 500 万美元。

在上图的推文内容也可以看到，@finnbags 表示，他们可以在之后更新 $BTH 的 metadata。加之，目前 @finnbags 与 Bags 官推的头像都已经戴上了这顶帽子，部分玩家期望这个币会成为 Bags 平台在 meme 发射台大战中打响的第一炮。毕竟，80 万美元的真金白银已经花出去了。

在今天上午，@finnbags 又发表推文，宣布将为 Bags 平台上第一枚突破 1000 万美元市值且保持 24 小时的 meme 币提供 25 万美元的奖励，获胜代币的 dev 和持有者都可以分享到这笔奖励。

在 $BTH 之外，另一个有力的竞争者是 $NYAN。这是把经典的彩虹猫 meme 搬到了 Bags 发射台上，并且使用 Bags 的手续费收入指派功能，将收入给到了彩虹猫 meme 的原创者 @PRguitarman。这个举动成功吸引了彩虹猫 meme 原创者的互动。

比起之前 boop 发射台需要 KOL 亲自发币来解锁平台代币空投，Bags 这个收入指派的功能给了创作者/KOL 更大的自由度，既不需要亲自下场发币，也可以选择完全沉默不领取这部分手续费，目前看来是一个更为温和而有效的方式。

这场 80 万美元的市场营销最后会让 $BTH 走到多高？Bags 能够在 meme 发射台大战中杀出重围吗？我们拭目以待。

原文链接:https://www.theblockbeats.info/news/59266

说明: 比推所有文章只代表作者观点，不构成投资建议

PA Graphic | One Picture to Understand Major Web3 Events Worth Watching in June

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marsbit45m ago

PA Graphic | One Picture to Understand Major Web3 Events Worth Watching in June

marsbit45m ago

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marsbit54m ago

marsbit54m ago

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Circle Blacklists Zama Protocol Address, Freezing $12.6M In User Funds – Details

bitcoinist1h ago

Why More AI Agents Does Not Equal Higher Productivity?

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marsbit2h ago

Why More AI Agents Does Not Equal Higher Productivity?

marsbit2h ago

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit9h ago

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit9h ago

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Welcome to HTX.com! We've made purchasing dogwifhat (WIF) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy dogwifhat (WIF) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your dogwifhat (WIF)After purchasing your dogwifhat (WIF), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade dogwifhat (WIF)Easily trade dogwifhat (WIF) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

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1.6k Total ViewsPublished 2024.04.01Updated 2024.12.03

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As WifStash Coin continues to develop its narrative and engage its audience through innovative content like the “Stashville” cartoon series, it is likely to attract attention and participation from both meme enthusiasts and crypto investors alike. In the world of digital finance, WifStash Coin exemplifies how creativity fused with community can create a memorable experience, showcasing the bright, entertaining side of what cryptocurrency can achieve.

966 Total ViewsPublished 2024.04.01Updated 2024.12.03

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