火币HTX晒七月成绩单:24小时交易量排名第三次升至全球Top 2,稳步向前

HTX成长学院Pubblicato 2024-08-06Pubblicato ultima volta 2026-06-22

Introduzione

近日,火币HTX团队交出七月成绩单,与用户分享火币HTX在七月的诸多成就和进展。火币HTX七月成绩单围绕外部排名、上新资产、HTX Ventures投资项目与参与活动、11周年平台活动、HTX DAO流动性捐助、安全和客服数据等领域向新老用户做汇报,用行动回馈用户的信任与支持。

近日,火币HTX团队交出七月成绩单,与用户分享火币HTX在七月的诸多成就和进展。火币HTX七月成绩单围绕外部排名、上新资产、HTX Ventures投资项目与参与活动、11周年平台活动、HTX DAO流动性捐助、安全和客服数据等领域向新老用户做汇报,用行动回馈用户的信任与支持。

火币HTX 24小时交易量升至全球第二位,多个优质资产首发上线

面对加密市场大波动,火币HTX七月交易额环比上涨11.3%,7月24日,据 Trust Score 数据,火币 HTX 的 24 小时交易量升至全球第二位,达 28亿美元,这是火币 HTX 排名今年第三次升至 Top2,稳步向前。

七月,火币HTX上线了15个新资产,包括PIXFI、A8、LRDS、AVAIL,这些资产与Coinbase等交易所共同首发。其中,AVAIL上线后的涨幅达90%,FIGHT上线后涨幅达60%,月底社区调侃为DOGE2.0的NEIRO热点也做了首发上线。

HTX Ventures 七月投资两个项目并参加EthCC活动,火币HTX 11周年平台活动开启

HTX Ventures在七月宣布投资Lombard和Redstone。投资团队参加了在布鲁塞尔举办的EthCC活动,在其周边活动中分享了关于“重质押开发”和“如何成功启动Web3生态系统”的见解,并在媒体上发布了参会行业观察。

HTX Ventures始终在探索前沿,努力为用户带来更多的机遇和价值。HTX Ventures致力于支持以太坊生态系统的长期发展,并持续寻求能够提升加密用户体验的技术和项目。

七月,火币HTX 11 周年KOL荣耀之战正式启动,报名参赛即可角逐20万USDT的奖池。同时,火币HTX开启 11 周年老用户回馈计划,千万美金账户管理费返还。恰逢火币HTX 11 周年,一系列感恩用户长期支持的平台活动已陆续进入活动周期,欢迎登录火币HTX平台查看活动详情。

七月火币HTX持续加强安全措施,时刻守护用户资产安全,贴心服务用户

七月,火币HTX继续加强安全措施,发送用户安全提醒292,937次,打击钓鱼网站和假冒App下载网站5个;拦截向诈骗地址提币4笔,挽回损失10万USDT;处理外部被盗资产流入事件12个,帮助用户冻结被盗资金超13万USDT;新增黑地址7,118条,拦截黑地址充值35笔,金额约65万U。火币HTX安全团队正在用切实可靠的行动时刻守护用户资产安全。

与此同时,HTX DAO 于七月公布2024年第二季度的流动性捐助已完成,Q2累计流动性捐助+销毁达3,050万美元,HTX DAO本次流动性捐助2,150万美元,累计捐助4,250万美元。

同时完成对pGALA增发攻击事件受损用户千万美元的赔付,包括赔款、站内权益、Gala节点等。火币HTX始终坚持为用户的利益保驾护航。

火币HTX客服团队在七月共服务38,800位用户,有效处理61,646个用户进线及工单问题,其中,主要涉及用户账户安全项、P2P交易问题的解决,获得用户高于83%的满意评价。

近期市场行情震荡,但火币HTX会坚定的陪伴每一位用户穿越市场牛熊,共同前行。火币HTX也将迎来11周年庆祝活动,感谢用户多年来的信任与支持。火币HTX始终坚持以用户为中心,不断努力提升服务质量,希望给每一位用户提供最佳交易体验。未来火币HTX团队将继续围绕运营数据、资产表现、投资成绩、安全和客服数据等方面向用户公示成绩单,以诚挚之心,与用户携手共进,共创美好未来。

(英文专用图片。面对加密市场大波动,火币HTX七月交易额环比上涨11.3%,7月24日,据 CoinGecko 数据,火币HTX 的 24 小时交易量升至全球第二位,达 28亿美元,这是火币HTX 排名今年第三次升至 Top2,稳步向前。 )

Crypto di tendenza

Letture associate

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.

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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.

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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.

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

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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.

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2.6k Totale visualizzazioniPubblicato il 2024.12.12Aggiornato il 2026.06.02

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