盘点哈里斯概念七大Meme币,谁能成为龙头?

Odaily星球日报Published on 2024-08-12Last updated on 2024-08-12

Abstract

KAMA和HARRIS或为哈里斯概念“龙一”、“龙二”。

原创|Odaily星球日报(@OdailyChina

作者|Wenser(@wenser 2010 

盘点哈里斯概念七大Meme币,谁能成为龙头?

在 7 月创纪录地获得 3.1 亿美元竞选资金后,哈里斯 (Kamala Harris)在预测市场 PolyMarket 上赢得 2024 年美国总统选举的概率上升至 52% ,押注资金超 6000 万美元;特朗普胜率下降至 45% 。目前,该活动投注资金已超 5.74 亿美元,堪称“预测市场最受关注活动”。

哈里斯,摇身一变,从“拜登的副手”,成为“民主党总统大选的希望之星”。Odaily星球日报将于本文对哈里斯相关概念 Meme 币进行简要盘点,供读者参考。

Odaily星球日报注:Meme 币价格波动较大,投资风险较高,本文不作为投资建议,请谨慎选择投资标的,注意资产安全。

KAMA:抽象的画风,老牌的 Meme

盘点哈里斯概念七大Meme币,谁能成为龙头?

作为与 TREMP、BODEN 等“抽象漫画风”Meme 币同一时期的“老牌项目”,合约上线于今年 5 月底的 KAMA 已经是难得的“龙头 Meme 币”项目了,而且其提出的颇具 Meme 属性的“Real president of Amurica”口号也对“哈里斯有望成为美国历史上第一位女总统”进行了明示,因而成为在“市值超 1 亿”竞赛中突围的“有力竞争者”。

代币价格: 0.01443 美元;

合约地址:HnKkzR1YtFbUUxM6g3iVRS2RY68KHhGV7bNdfF1GCsJB;

代币市值: 1430 万美元;

持有人数量: 7947 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

HARRIS:同名概念,乾坤未定

作为名人 Meme 币最常见的类型之一,同名 Meme 币项目往往能够在初期获得更多的市场关注度,但如果没有下一个“炒作点”,很容易泯然众人,这里我们会挑出相对而言具有一定代表性、市值较高的项目予以介绍。

HARRIS-ETH

官方 X 平台账号为@KamalaHarrisERC,主打概念为“粉丝项目”,为支持哈里斯竞选而创建的非官方项目。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.0000159 美元;

合约地址:0x155788dd4b3ccd955a5b2d461c7d6504f83f71fa;

代币市值:  670 万美元;

持有人数量: 3889 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

HERRIS-ETH

官方 X 平台账号为 @KumalaHerris,同样是漫画风格 Meme,不同的是,这个项目更为“政治正确”——

  • 项目图片素材是一个黑色皮肤的哈里斯形象;

  • 将美国、美国总统英文单词 POTUS 中的的字母“U”加入了名字当中;

  • 强调了哈里斯的“女性竞选者”身份,用“HER”加以凸显。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.0004532 美元;

合约地址:0x4704cf8d968aa0af61ed7cf8f2de0d0b31cab623;

代币市值:  45.3 万美元;

持有人数量: 1093 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

KAMALA-ETH

官方 X 平台账号为 @CoinKamala。看得出来,这个项目是之前“蹭”拜登热度才应运而生的,官方账号主页背景图为哈里斯推着轮椅上的拜登的形象。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.0000001923 美元;

合约地址:0x8e3f2543f946a955076c137700ead4c9439e7fca;

代币市值:  19.2 万美元;

持有人数量: 1214 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

HARRIS-SOL

官方 X 平台账号为@KamaHarrisSol,该代币为 pump.fun 平台发行项目,合约部署于 7 月 23 日,价格曾一度突破 0.002 美元,但从其 Dexscreener 主页头图和其他项目一样来看,或许是一个“批量化制作”的一波流项目。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.0009865 美元;

合约地址:FTyr4aoR52GY5EWGuxSzEAY6szaYKzi3WAmHmeYppump;

代币市值:  98.3 万美元;

持有人数量: 990 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

副总统概念币:Walz

作为哈里斯“钦点”的竞选搭档,明尼苏达州州长 Tim Walz 因其关注工会关系与创造就业机会、促进创新以及推动能源政策而受到高度关注。哈里斯近期的竞选 Slogan 也着重强调了“Harris & Walz”,因此也具有一定炒作空间。

WALZ-SOL

官方 X 平台账号为 @tem_walz,与前文的“漫画风 Meme 币项目”一脉相承,同样出自 pump.fun 平台,相较高点 0.001 美元的价格已下跌超 5 倍。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.0001911 美元;

合约地址:2H9xCZ6KyV3WeEhDaEr62JZNKBVxoe6wmDQnVtNppump;

代币市值:  19.1 万美元;

持有人数量: 546 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

WALZ-ETH

官方 X 平台账号为@TimWalzERC,主打概念为 Tim Walz 或将支持加密货币,但结合其能源政策倾向,该项目很是有些“南辕北辙”的感觉。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币价格: 0.000000093 美元;

合约地址:0xD812d28cD40848d9c0AE01Ae4e3b42E42A707d6F;

代币市值:  9.3 万美元;

持有人数量: 406 。

盘点哈里斯概念七大Meme币,谁能成为龙头?

代币信息

小结:哈里斯概念 Meme 币远逊于特朗普概念 Meme 币,但仍有机会

从 Dexscreener.com 官网排名前列的 Meme 币项目名称来看,特朗普相关概念的 Meme 币仍占据主流,哈里斯相关概念 Meme 币无论是从市场关注度还是从价格表现、项目市值等方面的表现都远逊色于前者。

但随着 9 月 10 日美国总统大选辩论的日渐临近,哈里斯概念 Meme 币仍有获得更大范围内关注度的可能,由此或将具备一定的价格增长机会。

对于 Meme 币玩家来说,现在的策略有 2 种:一是采取“彩票策略”,少量埋伏,纯粹作为彩票;二是作为“低买高卖的门票”,在大选辩论消息利好放出之后将手中筹码卖出,以获得盈利。

当然,无论是哪种操作,Meme 币都是一场风险颇高的游戏,如果抗风险能力较弱,还是不建议入场,谨慎观望即可。

盘点哈里斯概念七大Meme币,谁能成为龙头?

PolyMarket 预测界面

Trending Cryptos

Related Reads

Fed Turns Hawkish, Wall Street Capitulates, Citi Stands as 'Last Holdout': Insists on Resuming Rate Cuts in October

Amid a surprisingly hawkish shift from the Fed and most of Wall Street capitulating on rate cut expectations, Citigroup stands as a notable outlier, holding firm to its forecast for monetary easing to restart this October. Following the June FOMC meeting, where the "dovish bias" was removed and the dot plot shifted dramatically, markets priced in nearly 37bps of tightening for 2026. Major banks like Deutsche Bank and Goldman Sachs revised their calls, predicting rate hikes as soon as September. Citigroup, however, maintains a baseline scenario for a 25bps rate cut in October, followed by two more cuts in December and January 2027. Its counter-consensus view rests on three key arguments: 1) Plunging oil prices are eliminating a major inflation upside risk. 2) Rising initial jobless claims are mirroring seasonal weakening patterns seen in 2024-2025, signaling a labor market cool-down. 3) The strong core PCE is an "outlier," heavily influenced by AI-related prices and equity market gains rather than broad consumer price pressures, with other inflation metrics showing more moderation. While Wall Street largely "surrenders" to the hawkish Fed narrative, with Deutsche Bank forecasting two hikes and Goldman Sachs warning of potential back-to-back moves, Citigroup remains the "last holdout," betting that disinflationary forces will pave the way for cuts before year-end.

marsbit1m ago

Fed Turns Hawkish, Wall Street Capitulates, Citi Stands as 'Last Holdout': Insists on Resuming Rate Cuts in October

marsbit1m ago

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

The article "Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?" argues that Ethereum, like Linux before it, will triumph over closed, proprietary systems in finance due to its open, permissionless, and credibly neutral nature. It draws a historical parallel: just as the open internet defeated corporate private networks and Linux outcompeted proprietary Unix systems, open financial infrastructure like Ethereum will surpass private blockchains. The core advantage lies in the "bazaar" development model (as described in Eric Raymond's "The Cathedral and the Bazaar"), where decentralized, permissionless innovation by a global community of developers outpaces the controlled "cathedral" approach of centralized entities. This model fosters rapid innovation, as seen with Ethereum standards like ERC-20 and applications like Uniswap, which were built without needing permission. Ethereum's key, irreplicable strength is its credible neutrality: transparent, equally applicable, immutable rules that allow anyone to participate. This ensures sovereign independence, meaning no single entity (company, government) can control or change its core rules—a critical feature for global financial infrastructure. In contrast, private blockchains and consortium chains (like SWIFT or various bank-led projects) suffer from platform risk, central control, and an inability to attract broad developer ecosystems, leading to frequent failures. The article notes that major institutions (e.g., BlackRock, JPMorgan, Coinbase, Robinhood) are already building on Ethereum or its Layer 2 networks, recognizing its security, developer ecosystem, and network effects. While critics argue finance requires accountable, controlled systems, the response is that compliance (KYC, regulations) can be built at the application layer on top of a neutral settlement layer like Ethereum, just as secure commerce was built on the open internet via HTTPS. Ultimately, the thesis is that attempting to build walled-garden, proprietary financial networks is a flawed strategy that stifles innovation. The winning approach is to build applications on top of open, credibly neutral infrastructure like Ethereum, which is poised to become the foundational settlement layer for global finance.

Foresight News12m ago

Open Systems Will Ultimately Prevail: Why Ethereum Is the Next Linux?

Foresight News12m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

The Sino-US AI rivalry faces a fundamental bottleneck: the widening compute power gap. While Chinese AI chip companies have seen investment surges, their current focus remains largely on the less demanding inference market. The real challenge lies in the high-end training chip sector, crucial for developing cutting-edge large language models (LLMs), where Nvidia holds a near-monopoly. The compute disparity is stark. US tech giants like Meta, Google, and xAI command massive GPU clusters, enabling them to train trillion-parameter models rapidly. Estimates suggest US data center count and total compute capacity significantly outstrip China's. This "brute force" advantage allows for faster model iteration and exploration of larger parameter scales, with top US models reportedly leading their Chinese counterparts by 8 to 15 months. Chinese alternatives, such as Huawei's Ascend and others from companies like Moore Thread and Biren, are emerging. They show promise in inference and some training scenarios, closing the performance gap with mid-range Nvidia products. However, the core hurdle extends beyond raw chip performance to the entrenched software ecosystem, exemplified by Nvidia's CUDA platform. The path forward involves "walking on two legs": navigating import restrictions while heavily investing in the domestic chip industry. Though still in a catch-up phase, China's vast market, talent pool, and capital are fostering progress. The ultimate test is whether Chinese firms can build a competitive hardware-software ecosystem to power the next generation of AI.

marsbit19m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit19m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

KaiMing He's team introduces **MiniT2I**, a minimalist text-to-image (T2I) model that challenges the complexity of mainstream approaches. It eliminates components commonly considered essential: the VAE encoder-decoder, AdaLN conditioning mechanisms, auxiliary losses, private training data, and post-training alignment stages like RL/DPO. Instead, it uses a pure flow-matching objective trained directly on RGB pixels. The model employs a simplified **MM-JiT** Transformer architecture. It removes AdaLN blocks for conditioning and instead prepends two lightweight text adapter blocks to a standard pre-norm Transformer, allowing frozen T5 text features to adapt to the denoiser. Training follows a two-stage, LLM-like paradigm using only public datasets: pre-training on LLaVA-recaptioned CC12M for coverage, followed by fine-tuning on ~120k high-quality image-text pairs. With just 258M parameters (B/16), MiniT2I achieves competitive scores (0.87 on GenEval, 84.2 on DPG-Bench), outperforming larger pixel-space models. Scaling to 912M parameters (L/16) yields results comparable to SD3-Medium (~2B parameters) in style, composition, and imagination, though it lags in text rendering and named entities due to public data limitations. Key advantages include lower computational cost (~570 GFLOPs vs. ~1379 for latent models) and architectural simplicity. Acknowledged limitations include patch boundary artifacts in pixel space, side effects of high CFG scales, resolution ceilings for sequences longer than 1024 tokens, and the aforementioned data bottlenecks. The work demonstrates that high-performance T2I generation is possible with a radically simplified, publicly reproducible baseline.

marsbit23m ago

He Kaiming's Team's New Work: After Deleting VAE and Private Data, Text-to-Image Generation Becomes Even Stronger

marsbit23m ago

Trading

Spot
Futures

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