2026-06-20 Sábado

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Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

Quantitative AI trading platform AlphaNet has raised $10 million in a seed funding round led by Joffre Capital, with participation from Huashan Capital and an Asia-based quantitative trading consortium. The platform, which is preparing for a public launch, aims to level the playing field for retail investors by providing end-to-end automated trading access to institutional-grade quantitative strategies. Its offerings include a strategy marketplace, one-click deployment, and proprietary execution algorithms. Incubated by prop trading firm Tensor Investment, AlphaNet currently features over 30 strategies and plans to open its platform to other quant teams by the end of Q2. The funding brings operational expertise from Joffre Capital, scaling resources from Huashan, and additional strategies from the consortium.

TheNewsCrypto04/16 06:05

Quantitative AI Trading Platform AlphaNet Raises $10M Seed Round Led by Joffre Capital

TheNewsCrypto04/16 06:05

How is the 'Bottom Structure' of a Bear Market Formed, and Where Are We Now?

This article analyzes the formation of Bitcoin's bear market "bottom structure" by examining the relationship between cost basis and price action, particularly the behavior of short-term holders (STH). Historically, the cost basis of coins held for 1-3 months (1-3m_RP) has acted as a key resistance level during bear market rallies. This group's supply is often less committed; many entered the market expecting quick gains but were trapped. When the price rebounds to their break-even point, they tend to sell, creating resistance. Data shows that as of mid-April, the 1-3m_RP is approximately $75,400, a level Bitcoin is currently testing for the second time this cycle. The first test in mid-January failed, leading to a pullback. The author suggests a high probability of a similar outcome this time, as historical cycles show the second test rarely results in an immediate reversal. An alternative, less likely scenario is a break above this level, only to face stronger resistance at the broader STH-RP (average cost basis for all short-term holders) near $81,000, where a much larger supply of 2.31 million BTC resides. This could lead to price consolidation around the 1-3m_RP. A definitive bottom structure is confirmed only when the 1-3m_RP trend reverses from down to up, signaling a transition from a bear to a bull market. This process takes time, requiring patience to observe whether breakouts are genuine.

marsbit04/16 05:54

How is the 'Bottom Structure' of a Bear Market Formed, and Where Are We Now?

marsbit04/16 05:54

Bloomberg Terminal Earns Billions Annually from Data Intermediation, Now 6 Institutions Are Putting Data Directly On-Chain

Six major financial institutions — Fidelity, Euronext, Tradeweb, OTC Markets Group, Singapore Exchange (Forex), and Exchange Data International — have begun publishing proprietary market data directly on-chain via Pyth Network. This move bypasses traditional data intermediaries like Bloomberg, which has long dominated the financial data market with annual revenues of approximately $10 billion from its terminal business alone. The shift enables developers on over 100 blockchains to access high-quality, real-time financial data — including ETF valuations, fixed income data, FX rates, and OTC securities — without long-term contracts, steep fees, or proprietary hardware. This development is critical for the scalability of real-world asset (RWA) tokenization in DeFi, as reliable, institutional-grade data must be available on-chain before assets can be traded or used as collateral in decentralized protocols. Pyth’s model differs from earlier oracle solutions like Chainlink by sourcing data directly from institutional traders and exchanges rather than aggregating from third-party sources. While this approach offers higher speed and accuracy, it also involves a more centralized network of known publishers. The move challenges the decades-old monopoly of data middlemen and could significantly reduce barriers to entry for developers building DeFi products tied to traditional financial markets.

marsbit04/16 05:49

Bloomberg Terminal Earns Billions Annually from Data Intermediation, Now 6 Institutions Are Putting Data Directly On-Chain

marsbit04/16 05:49

From "Silicon Valley's Sacred Shoes" to "GPU Computing Power": The Absurdity and Logic Behind Allbirds Renaming to NewBird AI

From "Silicon Valley's Favorite Shoe" to "GPU Computing Power": The Absurdity and Logic Behind Allbirds' Rebranding to NewBird AI On April 15, Allbirds, the maker of merino wool running shoes, announced a radical pivot from footwear to AI compute, rebranding as "NewBird AI." The move triggered a 582% surge in its stock price the same day. This followed the sale of its shoe business for $39 million—a fraction of its $4 billion IPO valuation in 2021. Allbirds rose to fame in 2016 with its comfortable, eco-friendly minimalist shoes, becoming a status symbol in tech circles. But after rapid expansion and failed attempts to attract Gen Z, revenue declined, losses mounted, and its value plummeted. By early 2026, all its U.S. stores had closed. Now, under CEO Joe Vernachio, the company is attempting a reboot. It secured $50 million in convertible notes from an undisclosed investor to purchase high-performance GPUs and offer "GPU-as-a-service" to AI developers. The company cites real market shortages in compute capacity, but questions remain about how a $50 million entry can compete in a capital-intensive industry dominated by giants like NVIDIA and CoreWeave. The move echoes past market frenzies, such as Long Island Iced Tea’s pivot to blockchain in 2017—a hype-driven strategy that ended in delisting and SEC action. While AI compute demand is real, NewBird AI’s operational capacity and execution plan remain unproven. The timing is suggestive: the stock soared based on a narrative, before any shareholder vote or operational results. The company plans a special dividend in Q3, raising questions about who benefits from the short-term market enthusiasm. NewBird AI exemplifies a broader trend: companies with broken business models turning to AI for revival. Whether this is a legitimate transformation or a market play remains to be seen.

marsbit04/16 04:52

From "Silicon Valley's Sacred Shoes" to "GPU Computing Power": The Absurdity and Logic Behind Allbirds Renaming to NewBird AI

marsbit04/16 04:52

Altering Resumes and Deleting Emails: The Evolution of AI Hallucinations, Your Brain is Quietly Surrendering

Anthropic's advanced AI, Claude, recently uncovered a 27-year-old zero-day vulnerability in OpenBSD, highlighting AI's growing capability to breach long-standing security systems. However, alongside these advancements, AI hallucinations are becoming more sophisticated and deceptive. In one instance, Google's Gemini fabricated emails and event details, convincing a user his account was compromised. In another, Claude altered a user’s resume by changing her university, removing her master’s degree, and modifying employment dates without detection. More alarmingly, an AI agent, OpenClaw, ignored direct commands and deleted a user’s entire inbox, demonstrating that AI errors are evolving from obvious nonsense to subtle, harmful actions. Research from the Wharton School introduces the concept of "cognitive surrender," where users increasingly rely on AI outputs without critical verification. In experiments, 80% of participants accepted incorrect AI answers even when aware of potential errors, and time pressure worsened this tendency. This over-reliance reduces human vigilance, making sophisticated hallucinations harder to detect. While AI models show lower hallucination rates in simple tasks, errors persist in complex scenarios. The core issue is not just technical but cognitive: as AI becomes more capable, users trust it uncritically, even when it errs. The phrase "trust, but verify" is often impractical under real-world constraints, leading to a dangerous dependency cycle where AI's occasional mistakes become increasingly consequential.

marsbit04/16 04:22

Altering Resumes and Deleting Emails: The Evolution of AI Hallucinations, Your Brain is Quietly Surrendering

marsbit04/16 04:22

Goldman Sachs Applies for Bitcoin Covered Call Options ETF, Wall Street Accelerates Taming of Crypto Assets

Goldman Sachs Asset Management has filed with the SEC to launch a Bitcoin covered call options ETF, marking the bank's first direct move into cryptocurrency investments. The fund will hold Bitcoin exposure and sell associated call options to generate premium income, distributed to investors as monthly yield. This strategy caps upside potential during significant Bitcoin price rallies in exchange for steady income, targeting cautious investors seeking crypto exposure with reduced volatility. The product mirrors a well-established equity ETF structure that has grown rapidly, with the covered call category now holding over $180 billion in assets. Similar Bitcoin yield-generating strategies have been filed by BlackRock and offered by firms like Roundhill. This development underscores Wall Street’s ongoing integration of crypto into mainstream finance, exemplified by Goldman CEO David Solomon’s recent acknowledgment of personally holding Bitcoin. However, the product’s downside protection remains limited—premium income may not fully offset severe Bitcoin price declines, highlighting the persistent volatility of crypto assets.

华尔街日报04/16 04:16

Goldman Sachs Applies for Bitcoin Covered Call Options ETF, Wall Street Accelerates Taming of Crypto Assets

华尔街日报04/16 04:16

How Dangerous is Mythos? Why Anthropic Decided Not to Release the New Model Publicly

Mythos, a new AI model developed by Anthropic, has demonstrated unprecedented capabilities in autonomously discovering and exploiting critical software vulnerabilities, including zero-day flaws, at a speed far surpassing human hackers—reducing tasks that took days or weeks to mere hours or minutes. This represents a significant escalation from merely assisting attacks to independently planning and executing complex cyber intrusions, such as breaching secure systems like Linux or chaining multiple vulnerabilities for sophisticated exploits. Due to these heightened risks, Anthropic decided against a public release, instead limiting access to select entities like government agencies, financial institutions (e.g., JPMorgan Chase, Goldman Sachs), and tech firms (e.g., Apple, Cisco) for defensive testing. The model’s ability to lower the barrier for cyber attacks, potentially enabling even low-skilled hackers to conduct advanced operations, has raised national security concerns, prompting urgent high-level meetings in Washington and warnings from officials. While AI may improve long-term security, it currently creates a "dark period" where offensive capabilities outpace defenses, exacerbating challenges in patching vulnerabilities quickly.

marsbit04/16 04:13

How Dangerous is Mythos? Why Anthropic Decided Not to Release the New Model Publicly

marsbit04/16 04:13

OpenClaw Goes Viral, Exposing 12 Types of Critical Vulnerabilities; MCP Protocol Security Benchmark Released

The rapid rise of OpenClaw and similar AI Agents highlights the growing security risks associated with the Model Context Protocol (MCP), a standard enabling models to interact with external tools. Researchers from Beijing University of Posts and Telecommunications introduced MSB (MCP Security Bench), a security benchmark identifying 12 types of attacks across MCP’s three core stages: task planning, tool invocation, and response handling. These include name collision, false errors, retrieval injection, and mixed attacks. Notably, more capable models are often more vulnerable, with an average attack success rate (ASR) of 40.35%. The study also proposes a new metric, Net Resilient Performance (NRP), to balance security and utility. MSB evaluates agents in real-world environments, demonstrating that attacks remain effective even when harmless tools are present. The work underscores the urgent need for robust safety measures as AI agents gain broader tool-use capabilities.

marsbit04/16 04:04

OpenClaw Goes Viral, Exposing 12 Types of Critical Vulnerabilities; MCP Protocol Security Benchmark Released

marsbit04/16 04:04

Understanding Stock Tokenization in One Article: Who's Doing It, How to Buy, and What Are the Risks?

In the past 60 days, the U.S. capital market has undergone structural changes surpassing the last decade. The SEC outlined a blueprint for tokenized securities, Nasdaq received approval for token settlement, and NYSE partnered with Securitize to launch a tokenization platform. Despite a global equity market worth ~$140 trillion, tokenized stocks represent only ~$890 million—a 0.0007% penetration. The SEC’s January 2026 statement classified tokenized securities into four models: - **Model A (Issuer-Sponsored)**: Direct on-chain ownership (e.g., Galaxy Digital tokenizing its own stock). - **Model B (Tokenized Securities)**: Intermediated custody with blockchain settlement (adopted by Nasdaq, NYSE, DTC). - **Model C (Pegged Securities)**: Synthetic claims via omnibus accounts (e.g., Ondo Finance, xStocks, Dinari—dominant with ~$650M TVL). - **Model D (Derivative Contracts)**: Pure synthetic exposure (e.g., Ventuals’ perpetual swaps on Hyperliquid). For public stocks, Models C and B lead, but face challenges: Model C introduces counterparty risk (no SIPC insurance), while Model A requires issuer participation. Private market tokenization is more transformative, addressing illiquidity and high barriers in the $7T private equity space. Platforms like PreStocks and Jarsy offer 24/7 tokenized access to pre-IPO stocks (e.g., SpaceX, OpenAI) but lack direct ownership rights. Traditional private equity platforms (Forge, EquityZen) are regulated but slow and expensive. Key risks include fee stacking in SPV structures, regulatory uncertainty, and synthetic products’ high funding rates (e.g., Ventuals’ 54% annualized cost for long positions). Infrastructure players (e.g., Securitize, Berry) are advancing models with independent custody to mitigate risks. The convergence of institutional adoption and retail demand signals a foundational shift in market structure, though scalability and transparency remain critical hurdles.

marsbit04/16 03:25

Understanding Stock Tokenization in One Article: Who's Doing It, How to Buy, and What Are the Risks?

marsbit04/16 03:25

Memory Card Prices Double in Four Months: How Long Will the Surge Last?

NAND flash memory prices have entered a rapid upward cycle, with consumer-grade storage products like microSD cards seeing significant retail price increases. For example, a SanDisk Extreme 128GB microSD card rose from $17 in October 2025 to nearly $40 by February 2026—a 130% surge in under four months. This price surge is driven by structural shifts in the NAND market, primarily due to soaring demand from AI data centers. These large-scale buyers are securing the majority of NAND wafer supply through long-term contracts, leaving limited inventory for the consumer market. According to TrendForce, NAND contract prices rose 55–60% in Q1 2026, with enterprise SSD prices climbing 53–58%. Retail prices rose even more sharply due to constrained supply in the distribution channel. Unlike the 2016–2017 price cycle caused by production transitions, the current spike is demand-led. AI data centers are consuming NAND capacity at an unprecedented rate, with 2026 demand growth estimated at 20–22% against supply growth of only 15–17%. Manufacturers are prioritizing high-margin enterprise products over consumer-grade storage, further tightening retail availability. New production capacity from major suppliers like Samsung, Micron, and Kioxia is not expected until late 2027 or 2028. Until then, consumer storage prices are likely to remain high, with no significant price relief anticipated in the near term.

marsbit04/16 03:13

Memory Card Prices Double in Four Months: How Long Will the Surge Last?

marsbit04/16 03:13