The Altcoin Vector #42

insights.glassnodePubblicato 2026-02-18Pubblicato ultima volta 2026-02-18

Introduzione

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Executive Summary

Letture associate

Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

"In a volatile crypto market downturn of Q1 2026, institutional players demonstrated divergent strategies. While Bitcoin fell over 25% and Ethereum dropped 35%, key institutions accumulated: corporate treasuries like Strategy (MicroStrategy) added over $10 billion in BTC, sovereign wealth funds like Mubadala increased IBIT holdings by 46%, and major banks launched crypto services and ETFs. Notably, 26 new crypto ETFs were filed or launched under streamlined SEC rules, including bank-led products from Morgan Stanley and BlackRock’s staking ETH ETF. VC funding saw concentration: total investment reached ~$5 billion, but deal count plummeted 49%. Three mega-deals—BVNK ($1.8B), Kalshi ($1B), and Polymarket ($600M)—accounted for half the quarter’s funding. Investment shifted from DeFi/NFTs to payments, prediction markets, and regulated CeFi infrastructure. Selling pressure came from hedge funds (e.g., Brevan Howard cut IBIT by 85%) and Bitcoin miners liquidating holdings. The U.S. strategic Bitcoin reserve saw zero additions, highlighting that real buying came from corporations and sovereign funds, not governments. The institutional crypto landscape is bifurcating: long-term holders accumulate while short-term traders exit, signaling a foundation for the next cycle."

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Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

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Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

Tencent's Hunyuan AI team has released its latest language model, Hy3 preview, marking a significant step forward for the company's AI capabilities. With 295B total parameters and support for 256K context length, the model employs a mixture-of-experts architecture. It shows improvements in complex logic, instruction following, contextual learning, code generation, and agent task execution. In testing, Hy3 preview demonstrated strong performance in multi-step logical reasoning but showed occasional instability in identifying traps in trick questions. It performed well in extracting key information from disordered meeting transcripts and accurately followed new linguistic rules. As an AI agent, it successfully built functional applications like a Snake game and generated data analysis dashboards, though it sometimes fell short in fully completing complex open-ended tasks. In natural language use, it produced coherent and stylistically appropriate narratives with reduced “AI-like” tone. Priced competitively, Hy3 preview is already integrated into Tencent’s key products, including Tencent Cloud and WorkBuddy. While not leading in every benchmark, it represents a solid, practical model that signals Tencent’s renewed momentum in AI development.

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Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

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From Theft to Re-entry: How Was $292 Million "Laundered"?

A sophisticated crypto laundering operation was executed following the $292 million hack of Kelp DAO on April 18. The attack, attributed to the North Korean Lazarus group, began with anonymous infrastructure preparation using Tornado Cash to fund wallets untraceably. The hacker exploited a vulnerability in Kelp’s cross-chain bridge, stealing 116,500 rsETH. To avoid crashing the market, the attacker used Aave and Compound as laundering tools—depositing the stolen rsETH as collateral to borrow $190 million in clean, liquid ETH. This move triggered a bank run on Aave, causing an $8 billion drop in TVL. After consolidating funds, the attacker fragmented them across hundreds of wallets to evade detection. A major breakpoint was THORChain, where over $460 million in volume—30 times its usual activity—was processed in 24 hours, converting ETH into Bitcoin. This shift to Bitcoin’s UTXO model exponentially increased tracing complexity by shattering funds into countless untraceable fragments. The final destination was Tron-based USDT, the primary channel for illicit crypto flows. From there, funds were cashed out via OTC brokers in China and Southeast Asia, using unlicensed underground banks and UnionPay networks outside Western sanctions scope. Ultimately, the laundered money supports North Korea’s weapons programs, which rely heavily on crypto hacking for foreign currency. The incident underscores structural challenges in DeFi: its openness, composability, and lack of central control make such laundering not just possible, but inherently difficult to prevent.

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From Theft to Re-entry: How Was $292 Million "Laundered"?

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Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

China's AI landscape is witnessing a strategic divergence from Silicon Valley’s closed-source competition to a collaborative open-source ecosystem. On April 24, DeepSeek released V4, a top-ranked open-source model on Hugging Face, featuring breakthroughs like million-token context length with minimal KV cache and native support for domestic chips like Huawei’s Ascend. Similarly, Kimi’s K2.6, released days earlier, also adopted open-source principles. Unlike U.S. giants such as OpenAI and Anthropic—locked in revenue disputes and tactical product clashes—Chinese firms embrace shared innovation. DeepSeek and Kimi openly build on each other’s advances, like the MLA architecture and Muon optimizer, avoiding redundant R&D and driving down costs. DeepSeek V4 focused on pushing base model capabilities, while Kimi specialized in Agent-based applications. Although U.S. firms lead in revenue and valuation, China’s open-source models achieve comparable performance at a fraction of the cost (e.g., DeepSeek V3 trained for $5.58M vs. GPT-5’s $500M+). With token usage growing exponentially, China’s collaborative model promises scalable, affordable AI built on domestic hardware, shaping a more accessible path to AGI.

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Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

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OpenClaw vs. Hermes: Which One Is Right for You?

Two leading AI agent projects, OpenClaw and Hermes, represent a fundamental divergence in design philosophy rather than just a feature competition. OpenClaw, a GitHub phenomenon, prioritizes user control and customization, offering a powerful, flexible platform for building and orchestrating agents. It boasts a massive ecosystem with over 44,000 skills, extensive model flexibility (supporting Anthropic, OpenAI, and others), and deep integrations across multiple messaging platforms. In contrast, Hermes Agent, developed by Nous Research, champions automation and efficiency. It is designed for lower cost and a lower barrier to entry, learning and improving automatically from user workflows. It runs tasks in isolated environments for security and offers significantly lower token costs out-of-the-box. The choice between them mirrors historical tech divides like Windows vs. Mac. OpenClaw is for users who want maximum control, customization, and don't mind a complex setup. Hermes is the smarter default for those seeking an affordable, easy-to-use agent that learns autonomously. The real competition is not about which is better, but which philosophy—a programmable personal OS or a self-evolving work proxy—better suits the user's needs for control, cost, and ease of use.

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OpenClaw vs. Hermes: Which One Is Right for You?

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278 Totale visualizzazioniPubblicato il 2024.12.11Aggiornato il 2025.03.21

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