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Delivers insights into price action, technical indicators, market forecasts, and future trends. Data-driven analysis helps investors understand market dynamics and identify potential opportunities for informed decision-making.

Overnight Global Shock: Why Did AI Stocks Plunge Across the Board?

Overnight, U.S. AI-related stocks fell sharply, with the Nasdaq down 1.33% and the Philadelphia Semiconductor Index plunging nearly 5%. Panic spread globally, dragging down Asia-Pacific markets. Key negative drivers included: 1. **Geopolitical Tension & Macro Pressure:** Escalating U.S.-Iran tensions pushed oil prices higher, fueling inflation fears and expectations of prolonged high interest rates. Rising bond yields pressured high-valuation, capital-intensive AI stocks, especially as soaring AI infrastructure financing costs became evident. 2. **AI Commercialization Concerns:** OpenAI's Q2 results showed slowing revenue growth and widening losses, dampening market optimism about near-term AI application profitability. This shifted investor focus from pure capital expenditure narratives to actual commercial returns. 3. **Supply Chain Uncertainty:** U.S.-South Korea semiconductor investment disputes intensified. Market fears that Korean memory giants (Samsung, SK Hynix) face a dilemma—either divert capital to costly U.S. production or risk trade barriers—disrupted the critical HBM memory sector, amplifying sell-offs via leveraged ETFs. While long-term AI demand remains, the market is now scrutinizing real profitability, financing costs, and supply chain stability rather than paying premiums for unchecked growth stories. For markets like China's A-shares, the impact is primarily sentiment-driven, requiring distinction between short-term panic and fundamental deterioration.

marsbitHace 5 hora(s)

Overnight Global Shock: Why Did AI Stocks Plunge Across the Board?

marsbitHace 5 hora(s)

Chen Danqing: I am in no position to have an opinion on this AI thing

This article is an interview with Chinese artist Chen Danqing, who currently serves as the director of the Mu Xin Art Museum in Wuzhen. He discusses his role in managing the museum, which focuses on literary-themed exhibitions featuring figures like Dostoevsky, Shakespeare, and Chopin, rather than traditional visual art. Chen reflects on the museum's unique position within a tourist town, its challenges in engaging visitors who often seek personal advice rather than discuss exhibitions, and his general detachment from the contemporary art scene. He expresses skepticism about the overemphasis on art market prices, referencing the high auction sale of his early work, and shares his views on the abundance of art information available today. Regarding AI, Chen states he has no strong opinions, doesn't use it for his creative work, and humorously deflects questions expecting him to be an authority on the technology. The interview portrays him as a figure navigating museum management, public perception, and cultural commentary while maintaining a distance from mainstream art world trends.

marsbitHace 7 hora(s)

Chen Danqing: I am in no position to have an opinion on this AI thing

marsbitHace 7 hora(s)

BitBox Patches 'Serious' Vulnerabilities in Wallets That Could Have Put Funds at Risk

Hardware wallet manufacturer BitBox has released a firmware update to fix two "serious" vulnerabilities. The first flaw, present in uninitialized BitBox02 Multi and BitBox02 Nova devices, was a memory corruption issue that could allow an attacker to execute arbitrary code and install malicious firmware, potentially leading to fund loss. The second vulnerability involved the implementation of Silent Payments, which could let an attacker redirect a user's bitcoin to an unintended address, though direct theft was impossible; an attacker could then demand a ransom to assist in recovering the coins. BitBox stated it has received no reports of these vulnerabilities being exploited or of user funds being lost. This disclosure comes during a sensitive period for the self-custody sector, following a major incident involving Coldcard wallets. A previously undetected firmware vulnerability in Coldcard, related to weak random number generation for seed phrases, has reportedly led to the theft of over $112 million in bitcoin from more than 8,600 addresses. Recent data leaks from Trezor and SafePal have also exposed information for over 53,000 customers combined, though these incidents did not compromise private keys or recovery phrases. The leaks could, however, facilitate targeted phishing attacks. BitBox did not respond to requests for additional comment by the time of publication.

cryptonews.ruHace 23 hora(s)

BitBox Patches 'Serious' Vulnerabilities in Wallets That Could Have Put Funds at Risk

cryptonews.ruHace 23 hora(s)

Bitcoin Trading Sideways Around $63,500 Points to Upside and Downside Movement

Bitcoin has been consolidating around $63,500 for several days, unable to break above the $64,000 resistance while holding above the $60,000 support. According to Yusuf Fahro from ARP Digital, capital flows have shifted. U.S. spot ETFs saw their strongest inflows since May, attracting over 14,000 BTC in early May, with a net inflow of approximately 11,000 BTC in Q3. This contrasts with the institutional selling that dominated Q2. Market conditions show spot volumes at two-year lows, perpetual volumes at three-year lows, and volatility near multi-year lows. Fahro interprets Bitcoin's six-month stagnation between $60,000 and $80,000 as summer apathy. However, blockchain data is beginning to show signs of a potential bottom forming as sentiment shifts from panic to caution. Significant risk remains in both directions. Bitcoin is trapped in a tight range below $64,000 and above $62,000, with high leverage exacerbating the situation. Open interest for perpetual positions has stayed above 300,000 BTC, above average levels, while trading volumes have plummeted, making the market vulnerable to sharp liquidation-driven moves. The current HCN AI Analyst forecast for BTC at $63,338 is neutral. The base scenario (47% probability) targets $62,249 (-1.70%). The bearish scenario (32%) targets $60,982 (-3.70%), while the bullish scenario (21%) targets $64,098 (+1.20%). The weighted expectation aligns with the base scenario at approximately -1.7%, with downside risk being three times larger in amplitude and 1.5 times more likely than upside potential. Weak technical analysis and momentum readings are being offset only by liquidity inflows. The key practical levels for the week are a break above $64,098 to flip momentum or a break below $62,249 opening the path to $60,982.

cryptonews.ruAyer 13:02

Bitcoin Trading Sideways Around $63,500 Points to Upside and Downside Movement

cryptonews.ruAyer 13:02

Harmony plans rollback, wiping 109,000 transactions after ONE exploit

Harmony plans to roll back its blockchain to August 11th after an exploit created forged ONE tokens, discarding over 109,000 confirmed transactions. The network will revert to a specific block from that date and build a new chain, as selectively restoring transactions was deemed unsafe. The move follows reports of unauthorized ONE minting and distribution to exchanges, with investigators tracing most of the forged tokens. This rollback strategy places Harmony alongside networks like Ravencoin, which also sought to reverse confirmed blockchain activity after an exploit. Ravencoin recently faced a potential chain reorganization due to a consensus flaw.

cointelegraphAyer 12:40

Harmony plans rollback, wiping 109,000 transactions after ONE exploit

cointelegraphAyer 12:40

Shanghai Sees a Semiconductor Equipment IPO Emerge, Led by Former Grace Semiconductor Employee

A Shanghai-based semiconductor equipment company, Mifee Technology, has filed for an IPO on the Shanghai Stock Exchange's STAR Market. The company specializes in developing and manufacturing Automatic Material Handling Systems (AMHS), a core automation system in semiconductor wafer fabrication that directly impacts production efficiency and yield. Mifee is one of the few domestic Chinese companies with proprietary AMHS technology, offering both hardware and software systems. While the global AMHS market is dominated by Japanese giants like Daifuku and Murata Machinery, which hold approximately 90% market share, Mifee has captured about 1.6% globally. The company's revenue has grown significantly, reaching 393 million yuan in 2025, and it achieved profitability that year with a net income of 60.45 million yuan, following losses in 2023 and 2024. Its revenue streams include sales of individual AMHS equipment and complete factory AMHS projects, with the latter starting to contribute revenue from 2024. The company faces risks including high customer concentration, with its top five clients accounting for over 90% of revenue in 2025, and significant fluctuations in gross margin, which was 48.97% in 2025 after dropping to 24.67% in 2024. Mifee is controlled by an 80s-born couple, Chairman/CEO Feng Miao and Deputy General Manager Na Ke, who previously worked at Shanghai Grace Semiconductor Manufacturing Co. The company plans to raise approximately 1.191 billion yuan from its IPO to fund production, R&D, overseas expansion, and working capital.

marsbitAyer 12:21

Shanghai Sees a Semiconductor Equipment IPO Emerge, Led by Former Grace Semiconductor Employee

marsbitAyer 12:21

A PPT Dismissed as 'Nonsense' by MIT Professors 5 Years Ago Predicted the Core Ideas of OpenAI o1 and o3

In 2020, AI researcher Giambattista Parascandolo presented his vision for neural network reasoning at an MIT faculty interview, only to have the committee dismiss the direction as "nonsense." He later posted the details online. His talk centered on enabling artificial neural networks to generalize and plan beyond their training data, closer to human capabilities. Parascandolo proposed three key research directions. First was "open-ended reasoning," where models could dedicate more computation time to harder problems, continuously refining answers—a precursor to today's compute-adaptive reasoning models. He noted that simply adding steps (e.g., in RNNs) wasn't enough without learning to use them effectively. Second, he advocated using language as a medium for reasoning within reinforcement learning. By leveraging the world knowledge in models like GPT, agents could better describe environments, decompose tasks, and plan—foreshadowing concepts like chain-of-thought and agent workflows. His third direction involved giving AI systems the ability to manipulate their own learning process: resetting to past states, creating counterfactual scenarios, and even editing their own activations or weights to facilitate deliberate practice. Parascandolo, who earned his PhD focusing on out-of-distribution generalization and had internships at Google X and DeepMind, joined OpenAI in 2021. He contributed to GPT-4 and later became integral to the foundational research behind the reasoning models o1 and o3. His early, criticized ideas remarkably charted a course for advanced AI reasoning systems developed years later.

marsbitHace 2 días 03:14

A PPT Dismissed as 'Nonsense' by MIT Professors 5 Years Ago Predicted the Core Ideas of OpenAI o1 and o3

marsbitHace 2 días 03:14

AI Can 'Have Moods Too'! New Research from USTC: Confusion and Anxiety Make AI Work Better

The article discusses research from the University of Science and Technology of China and Oxford, revealing that allowing AI to recognize and act upon simulated "internal emotions" can significantly improve its performance. The study demonstrates a coherent pairing between specific emotional states in AI agents and their subsequent skill choices. For instance, an agent feeling curious and desirous will search for products, while one feeling confused and tense will rephrase queries. This mirrors human decision-making influenced by emotions. Statistical validation showed a 76.5% semantic consistency in these pairings. Crucially, the research challenges the traditional view of AI errors as flaws to be eliminated. It found that "bad" emotions like confusion, tension, or frustration serve as useful metacognitive signals, indicating a mismatch between the current strategy and the environment. By responding to these signals, AI can proactively adjust before a failure occurs. This is particularly effective in complex tasks prone to failure. For example, in tasks like "heating an item" and "picking up two items," success rates surged from 9.6% to 56.9% and 4.4% to 31.3%, respectively, when using the emotion-driven skill selection method (EMOTION2SKILL). The AI's "nervous" state about a closed microwave, for instance, prompted it to check and open it first, preventing failure. The article also mentions related work from Tianjin University, which embeds emotional prediction into world models (Large Emotional World Model, LEWM), significantly improving prediction accuracy in human-centric environments. Removing emotional data was found to degrade performance even in unrelated logical reasoning tasks. These studies build on earlier findings, like those from Anthropic, that identifiable emotional representations exist within large language models (LLMs). The focus is shifting from philosophical debate about AI emotion to practically harnessing these internal states as functional signals to enhance AI robustness and capability.

marsbit08/16 07:31

AI Can 'Have Moods Too'! New Research from USTC: Confusion and Anxiety Make AI Work Better

marsbit08/16 07:31

Ashenbrenner Fund Bought Shares of Memory Manufacturing Companies Before Losing 67% of Its Funds

Hedge fund Situational Awareness AI, managed by Leopold Aschenbrenner, lost 67% of its value in July, shortly after having nearly half of its U.S. stock portfolio invested in memory producers SanDisk and Micron, according to SEC filings. A 13F-HR filing from August 14th showed these two positions made up roughly 50% of the fund’s reported equity assets as of June 30th. An earlier March 31st 13F filing indicated a large public portfolio and put options linked to chipmakers. The fund's concentration in memory chips aligned with the AI boom's demand shift from training to inference, which is driving growth in corporate SSD and NAND markets, with prices expected to rise significantly. However, this correct thematic bet did not prevent massive losses. In a letter to investors, Aschenbrenner acknowledged the fund took steps to reduce leverage after facing liquidity issues, confirming the July loss and sale of most public assets. Despite this, Aschenbrenner remains committed to AI, with his firm investing $400 million in semiconductor equipment startup Source Foundry. The incident highlights the risks of concentrated, leveraged positions even within a high-growth sector projected to attract trillions in infrastructure investment.

cryptonews.ru08/15 12:43

Ashenbrenner Fund Bought Shares of Memory Manufacturing Companies Before Losing 67% of Its Funds

cryptonews.ru08/15 12:43

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