2026-07-28 Martes

Noticias Cripto

Mantente a la vanguardia del mercado de cripto. Noticias en tiempo real, análisis, precios, historias en tendencia y opiniones de expertos - todo en un solo lugar.

Take a Calm Look at Domestic Lithography Machines: 5 Units Delivered, How Far Are We from Challenging ASML?

This article analyzes recent news about China's domestic DUV lithography machines, advising a measured perspective. A state-backed enterprise plans to deliver about 5 immersion DUV systems this year, with a 2027 target of 20 units, aimed at 28nm processes. While this marks a step from R&D into initial customer validation, the author cautions against over-optimism. The piece clarifies this is DUV, not EUV technology. DUV is crucial but cannot economically solve challenges for advanced nodes like 5nm or below, where EUV is key. The 5-unit volume is minimal compared to ASML's annual output of over 130 such machines. The author highlights unverified claims regarding specs like 90% yield and warns against conflating this news with unconfirmed reports about Shanghai Micro Electronics' production. China's EUV efforts, while progressing with a prototype, remain in early testing, facing significant hurdles in core components like high-precision optics. The analogy of a "DeepSeek moment" is deemed misleading, as lithography involves slow, iterative precision engineering, not rapid software-like breakthroughs. Current progress represents establishing a potential second supply source and gaining vital engineering experience rather than an imminent industry disruption. For investors, the key indicators to watch are formal customer acceptance, stable operational performance, repeat orders, and growth in production volume and component localization. The conclusion is that this is a positive but early-stage development, far from altering the global competitive landscape.

marsbitHace 40 min(s)

Take a Calm Look at Domestic Lithography Machines: 5 Units Delivered, How Far Are We from Challenging ASML?

marsbitHace 40 min(s)

The 800x Golden Dog, "Gacha" Saves NFT Trading

Title: 800x Golden Dog: How 'Gacha' Mechanics Are Rescuing NFT Trading In the past month, the on-chain TCG (Trading Card Game) narrative, centered around "gacha" or loot box mechanics, has emerged as a major crypto-native revenue generator, second only to platforms like Hyperliquid and pump.fun. Recently, this trend hit Ethereum with Fake World Assets (FWA). Within just over a week, FWA generated approximately $1.3 million in revenue, ranking 15th in the past week's crypto app earnings. Its token, $FWA, surged from an initial market cap of ~$47,550 to a peak of ~$38.8 million—an 800x gain. Meanwhile, Collector Cards' token $CARDS declined significantly from its previous highs. FWA, developed by the team behind "PunkStrategy," operates as an NFT gacha system with a built-in token flywheel. Users deposit NFTs paired with ETH as liquidity into pools. Each deposit creates a personal pool; more ETH deposited lowers the chance of the NFT being "won" in a draw. Players spend ETH to "draw" (gacha). If they get an undesirable NFT, they can instantly sell it back to the original depositor at an 85% discount, generating income for the depositor. A 1% fee is taken on each draw and on depositor earnings when an NFT is kept. The key to FWA's momentum is its token $FWA. It cannot be bought directly externally. The primary way to acquire it is by playing the gacha and choosing to receive $FWA (instead of ETH) when selling back an unwanted NFT. This mechanism creates constant buy pressure for $FWA as players engage, especially during its price ascent. Early participants who held $FWA benefited massively from subsequent inflows. This contrasts with projects like Collector Cards, which, despite strong revenues, suffer from perceived low token utility beyond buybacks. In conclusion, while FWA's flywheel design—linking speculative token gains directly to NFT trading activity—has driven rapid growth, its sustainability is questionable. The model relies heavily on continuous $FWA price appreciation to offset the inherent negative expectancy of each draw. When price momentum stalls, activity will likely decline. The case highlights that in crypto markets, pure profitability narratives can be fleeting; understanding the relationship between attention, token buy pressure, and sustainable mechanics is crucial to avoid speculative pitfalls.

marsbitHace 1 hora(s)

The 800x Golden Dog, "Gacha" Saves NFT Trading

marsbitHace 1 hora(s)

The Quantum Computing Threat Approaches, Cryptocurrency May Be Exposed to Risks Before Banks

Quantum computing poses a significant threat to all cryptographic systems, including banks and governments, but decentralized cryptocurrencies with public ledgers like Bitcoin are likely the first practical target. Experts warn that a cryptographically relevant quantum computer (CRQC), capable of running Shor's algorithm to break the elliptic curve cryptography securing most crypto wallets, could emerge around 2029. Recent research shows the required quantum resources for such attacks are shrinking dramatically, potentially enabling key extraction in minutes. The core vulnerability for cryptocurrencies is not the cryptography itself—post-quantum standards are being developed—but the slow, decentralized governance required to implement upgrades. Unlike centralized banks that can swiftly transition, Bitcoin needs near-unanimous consensus among its global network, a historically difficult process as seen in past upgrades. Estimates suggest migrating all vulnerable Bitcoin funds could take at least 76 days of dedicated network time, and it must be completed before a CRQC exists to prevent "now-or-never" attacks on exposed keys. The threat is not binary; it begins when a quantum computer can decrypt data before it loses value, not necessarily in real-time. A significant portion of Bitcoin (estimated at millions of coins) already has public keys permanently exposed on-chain, making them vulnerable to eventual "static attacks." While technical solutions exist, the race is against time for decentralized networks to coordinate a defensive transition, serving as an early warning for the broader financial system.

marsbitHace 1 hora(s)

The Quantum Computing Threat Approaches, Cryptocurrency May Be Exposed to Risks Before Banks

marsbitHace 1 hora(s)

On the First Day of Listing, Changxin Technology's Market Value Exceeds 3 Trillion Yuan, Which Securities Firm Has the Largest Floating Profit?

On July 27th, Changxin Technology, the largest-ever IPO on China's STAR Market, debuted with its share price soaring 465.82% to close at 49 yuan. Its market capitalization reached 3.28 trillion yuan, instantly making it the most valuable A-share company. The stellar performance delivered substantial gains for involved securities firms, primarily through equity investments rather than underwriting fees. China Merchants Securities emerged as the biggest winner. Its direct investment subsidiary, Zhaozheng Investment, alone holds a 0.54% pre-issue stake, translating to a paper profit exceeding 155 billion yuan based on the first-day closing price—surpassing the firm's entire 2025 net profit of 123.5 billion yuan. Other major beneficiaries include Huaan Securities, with an estimated profit of around 123 billion yuan from its 0.44% stake, and the lead underwriters, CICC and CITIC Securities, which each gained approximately 46 billion yuan from mandatory follow-on investments. Firms like Founder Securities, Haitong Securities, and GF Securities also reported significant holdings valued in the billions. Despite these paper gains, shares of some brokerages like Huaan and China Merchants fell on the listing day, reflecting broader market pressures. Analysts remain bullish on Changxin's long-term prospects, citing the AI-driven demand surge for DRAM (Dynamic Random-Access Memory) and a supportive supply-demand dynamic with projected shortages through 2028. As China's largest and most advanced integrated DRAM designer and manufacturer, Changxin is poised to capture growth from domestic substitution and global market shifts, potentially challenging the current "big three" oligopoly (Samsung, SK Hynix, Micron). The IPO proceeds, focused on capacity upgrades and R&D, are expected to accelerate China's semiconductor self-sufficiency.

marsbitHace 1 hora(s)

On the First Day of Listing, Changxin Technology's Market Value Exceeds 3 Trillion Yuan, Which Securities Firm Has the Largest Floating Profit?

marsbitHace 1 hora(s)

Will Changxin Technology Continue to Rise Today?

Changxin Technology made a historic debut on the stock market, with its share price soaring 465.82% to close at 49 yuan. Its market capitalization reached 3.28 trillion yuan, surpassing Industrial and Commercial Bank of China to become the largest company by market cap on the A-share market. Daily trading volume exceeded 140 billion yuan, a first in A-share history. This created a moment of realization for 7.7 million investors who won the lottery for its shares. On the first day, investor strategies varied: some sold immediately and later regretted missing intraday highs, others secured profits to avoid future volatility, while a third group held or even bought more shares, betting on long-term growth. The staggering IPO, massive public enthusiasm, and debut during a peak industry cycle led some to compare Changxin to PetroChina's 2007 listing, which was followed by a long decline. Key similarities noted include comparable fundraising scales (approx. 666 billion yuan for Changxin vs. 668 billion for PetroChina) and both companies listing at a perceived high point in their respective commodity cycles (oil then, memory chips now). However, analysts caution against over-simplifying the comparison. They highlight core differences: Changxin operates in the high-growth semiconductor sector with strong "domestic substitution" tailwinds. Brokerages like Huaxi Securities project significant revenue and profit growth from 2026 to 2028, driven by DDR5 adoption, product mix optimization, and economies of scale. Nomura Securities issued a "buy" rating with a 116 yuan target price, citing AI-driven demand for DRAM, tight supply as major players shift to HBM production, and Changxin's vast room for market share growth. Some analysts position the current memory cycle, fueled by AI, as just beginning, contrasting with the mature energy cycle PetroChina entered. The article concludes that for investors, monitoring the memory cycle's progression and Changxin's breakthroughs in high-end technologies like HBM will be crucial, rather than relying on superficial historical parallels.

marsbitHace 2 hora(s)

Will Changxin Technology Continue to Rise Today?

marsbitHace 2 hora(s)

Claude Designer Lags Behind Engineers, Fires Back by Creating a Million-User Tool

The article tells the story of Nate Parrott, a designer at Anthropic who created Claude Design as a side project to keep pace with his engineering teammates. When Anthropic released Opus 4.5 in November 2025, the two engineers on Parrott's Claude Code team significantly increased their output using the new AI capabilities. Parrott, the sole designer, found himself struggling to match their speed, becoming a bottleneck in the workflow. To catch up, he began experimenting in his spare time. He initially tried prompting Claude to generate designs from text descriptions and screenshots, with limited success. His breakthrough came when he shifted focus from asking Claude to "design" to asking it to generate HTML. He realized HTML could be a rich visual canvas for creating everything from slides and interactive prototypes to full web pages. He built a simple interface with a chat panel on the left and a live HTML preview on the right. The key to making the output useful was incorporating Anthropic's brand system—fonts, colors, assets, and design principles—into the prompts. This ensured generated designs were immediately on-brand. He shared an internal prototype with his team, and other product designers quickly adopted it for creating clickable prototypes, a task traditionally requiring manually drawing every state. The project's "official" turning point came during an Anthropic Labs offsite, where Parrott noticed many attendees were using his tool to build presentation slides on the fly, sometimes right before their turn to speak. This organic adoption convinced the Labs team to formally staff the project, turning the side project into a real product. Claude Design is positioned as a "pre-production" tool for visual communication and exploration—handling slides, landing pages, PDFs, emails, and social media graphics. It integrates with tools like Canva, Adobe, and Vercel. Its core value is accelerating the early stages of design: exploring directions, building consensus, and establishing systems. For actual production code, Anthropic still recommends Claude Code. The story highlights how AI disrupts workflows unevenly and how individuals can respond by building new tools to create their own advantages. Parrott's tool, born from necessity, eventually gained over a million users in its first week.

marsbitHace 2 hora(s)

Claude Designer Lags Behind Engineers, Fires Back by Creating a Million-User Tool

marsbitHace 2 hora(s)

Fields Medalist Warns: AI Could Kill Mathematics

The 2026 Fields Medal award was followed by a startling announcement: laureate Jacob Tsimerman joined OpenAI to pursue AI safety research, predicting AI would surpass humans in all mathematical proof areas within two years. Soon after, Fields Medalists Terence Tao and Timothy Gowers expressed deep concern at ICM 2026. Gorges warned that AI might "kill" mathematics not through stagnation, but through an overwhelming surplus of proofs, likening it to a lake dying from eutrophication. This concern is echoed in the "Leiden Declaration," signed by over 3,000 mathematicians including Tao, Peter Scholze, and Kevin Buzzard, advocating for mathematics as a profoundly human endeavor. However, Gowers, who did not sign, fears a future where AI-generated mathematics proliferates while human expertise and the shared intuition vital to the field vanish, turning math into an unvisited "cemetery of thought." Gowers' perspective shifted dramatically after testing ChatGPT 5.5 Pro. The AI solved a doctoral-level number theory problem and later produced a counterexample for the "unit distance problem," achievements Gorges considered publishable in top journals. He now concedes that large language models can handle advanced research, a realization that left him feeling the "rug pulled out from under" him when AI solved problems he personally contemplated. The debate extends to the nature of mathematical discovery. As noted by Peter Woit, AI agents have no interest in the "credit game" of academia. If theorems cease to be attributed to individual mathematicians, truth may simply return to its impersonal state in the universe. The central question remains: in an age of potentially limitless AI-generated discovery, what is the role and purpose of the human mind in mathematics?

marsbitHace 2 hora(s)

Fields Medalist Warns: AI Could Kill Mathematics

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NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

In a single weekend, Claude, an AI agent from Anthropic, successfully ported and optimized its cutting-edge model to run on a brand-new AMD MI355X server rack without any manual code intervention. This feat demonstrates a potential breakthrough in overcoming NVIDIA's long-established CUDA software ecosystem dominance, built over two decades. Anthropic's team simply instructed Claude to get the AMD machine running. By Monday, it not only worked but was showing a continuously improving performance curve. The achievement impressed AMD CEO Lisa Su and accelerated a major deployment partnership: Anthropic plans to deploy up to 2GW of AMD Instinct GPUs starting in 2027. The key enabler is AMD's new ROCm.AI platform, a toolbox designed specifically for AI agents like Claude. It provides AI-readable documentation, including chip instruction sets (ISA), and tools like the Hyperloom service that allows agents to autonomously profile performance, identify bottlenecks, test configurations, and generate optimized kernels. In a demo, Hyperloom boosted the output speed of a model by 38%. This represents a fundamental shift. While CUDA's strength lies in its vast, human-expert-driven ecosystem of tools and tacit knowledge, AMD's strategy is to make its hardware and software stack directly accessible and optimizable by AI agents. An agent can parallelize tasks—debugging, profiling, coding—that would take human engineers years to master, compressing the traditional software adaptation timeline from years to tasks. The competition is no longer just about peak hardware specs but also about how well AI can read, utilize, and tune a platform.

marsbitHace 2 hora(s)

NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

marsbitHace 2 hora(s)

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