SOL暴涨风暴来袭 “赌博链”为何能领先山寨之王ETH ?SOL 在本轮牛市将达到什么高度?

币界网Published on 2024-08-12Last updated on 2024-08-12

币界网报道:

到目前为止,可以说,SOL 几乎是唯一在整个牛市周期中保持相对实力和持续热度的山寨币,而且Solana 在MemeCoin 领域的主导地位也是推动这一趋势的主要影响因素之一。不夸张地说,Solana 目前似乎已经成为了一个最大的加密赌场,MemeCoin 成了一种赌博游戏,而SOL 就成了一种需求筹码,只要赌的人越多,那么对SOL 的价格就会越有利。

ymOww0ZR4Vm4yffgrJ7USwFN8a6AAwr7pq2MQzcH.png

有个叫彼得·勃兰特的交易高手,他直接放话说SOL在接下来的几个月里可能会比ETH涨得还猛,多赚一倍都不止!

他是怎么看的呢?他盯着SOL和ETH之间的那个比值,就是SOL值多少ETH的那个数,发现这个数最近又冲到了历史高点附近,这是第三次了。他还说,这个比值要是再往上冲一冲,SOL就能赚得盆满钵满,比ETH多赚一倍。咱们再瞅瞅这个比值,现在SOL大概值0.059个ETH。如果这个比值涨了,那就说明SOL比ETH涨得快;要是跌了,那就是SOL没ETH涨得快。

从图表上看,SOL和ETH的这个比值好像在走一个“杯子加手柄”的图形,这种图形在股市里可是个涨势的好兆头。勃兰特说,按照这个图形,SOL要是真冲破了那个点,就能涨到0.11个ETH。

实际上,最近SOL的表现也确实很给力,短短几天就涨了12%,现在都超过150美元了。反观ETH,虽然价格还在2500美元以上,但这几天还跌了快3%。

虽然SOL有时候被说成是“赌博链”,但有人觉得这只是加密货币圈里的通病,不是SOL独有的问题。而且,SOL在很多方面都比ETH强,比如每天用的人多、交易量大、赚的钱也多。不过,要说投资者信心,还是ETH更厉害,锁定的钱是SOL的十倍多!

总的来说,SOL这段时间的表现真的很抢眼,未来能不能真的超过ETH,时间会给我们答案。

2025年牛市SOL会超过1000美元吗?

至于SOL 本轮牛市会不会像一些人预测的那样将超过1000 美元,这个我不知道。小孩子才做选择,成年人只看利益,如你纠结买ETH 还是SOL,那么两个都去买点就好了。

现在还可以买SOL 吗?如果你是从去年一直看到现在,目前手里还没有SOL,那么我建议你什么都不要买了,即便SOL 后面还可能会有一定的涨幅,你可能也会亏钱。而如果你是刚刚进入这个领域,对该领域还不甚了解,那么我不建议你现在就着急想着去通过做交易赚钱,先学习和观察一下再考虑交易的事情可能会更稳妥些。而如果你非要买点什么,那么我依然只建议买BTC 然后拿着就好了,但赚了钱是你的,亏了钱也别找我。

Trending Cryptos

Related Reads

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 News8m ago

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

Foresight News8m 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.

marsbit15m ago

The Computing Power Dilemma in the Sino-US AI Rivalry

marsbit15m 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.

marsbit19m ago

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

marsbit19m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

The insurance industry, long a stable "ballast" in the economy, may face a significant challenge from the rise of prediction markets, which are beginning to function as a new form of risk hedging and insurance. Platforms like Kalshi and Polymarket are demonstrating their utility in areas traditionally dominated by insurers. Examples include Kalshi's partnership with sports insurance broker Game Point Capital to offer more cost-effective hedging for NBA team performance bonuses, and Polymarket's collaboration with real estate platform Parcl, allowing users to hedge against housing price fluctuations in major US cities. A New York bar also used Kalshi to hedge a marketing promotion tied to an NBA game outcome, highlighting prediction markets' potential for small business risk management. These markets offer advantages over traditional insurance and sports betting in transparency, liquidity, and flexibility. They allow information monetization across a wider range of events, act as neutral platforms rather than direct counterparties, and provide clearer pricing. A historical precedent is the "Mattress Mack" marketing campaigns, which used sports betting for large-scale customer refunds, but prediction markets offer a more systematic and accessible model. Experts like SIG CEO Jeff Yass see their potential for efficient, parameter-based risk sharing, such as for weather-related property damage. However, challenges remain, including liquidity issues, unclear regulatory boundaries, and potential manipulation of event outcomes. Despite these hurdles, prediction markets represent a growing competitive force for both traditional gambling platforms and segments of the insurance industry.

marsbit20m ago

The Insurance Industry Faces Its Biggest Competitor: Are Prediction Markets the "Barbarians at the Gate"?

marsbit20m 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 ETH (ETH) are presented below.

活动图片