The Altcoin Vector #47

insights.glassnodePubblicato 2026-03-25Pubblicato ultima volta 2026-03-25

Introduzione

This report is part of The Altcoin Vector series. The full content is restricted to paid subscribers, who can unlock it for a monthly fee of $425. Existing subscribers are prompted to log in to access the material.

Executive Summary

Domande pertinenti

QWhat is the main purpose of the 'Unlock' feature mentioned in The Altcoin Vector #47?

AThe 'Unlock' feature allows access to this specific report and additional content for subscribers paying $425 per month.

QHow much does a subscription cost to access The Altcoin Vector #47 and other reports?

AA subscription costs $425 per month to access this report and additional content.

QWhat should existing subscribers do if they cannot access The Altcoin Vector #47?

AExisting subscribers who cannot access the report should log in to their account to gain access.

QWhat type of content is typically covered in The Altcoin Vector reports based on this excerpt?

AThe excerpt does not provide specific details about the report's content, as it is behind a subscription paywall, but it is part of a series focused on altcoins.

QIs the full content of The Altcoin Vector #47 available for free based on the provided text?

ANo, the full content is not available for free; it requires a paid unlock or subscriber login for access.

Letture associate

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.

marsbit36 min fa

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

marsbit36 min fa

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.

marsbit41 min fa

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.

marsbit50 min fa

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

marsbit50 min fa

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.

marsbit1 h fa

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

marsbit1 h fa

Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

In a span of four days, Amazon announced an additional $25 billion investment, and Google pledged up to $40 billion—both direct competitors pouring over $65 billion into the same AI startup, Anthropic. Rather than a typical venture capital move, this signals the latest escalation in the cloud wars. The core of the deal is not equity but compute pre-orders: Anthropic must spend the majority of these funds on AWS and Google Cloud services and chips, effectively locking in massive future compute consumption. This reflects a shift in cloud market dynamics—enterprises now choose cloud providers based on which hosts the best AI models, not just price or stability. With OpenAI deeply tied to Microsoft, Anthropic’s Claude has become the only viable strategic asset for Google and Amazon to remain competitive. Anthropic’s annualized revenue has surged to $30 billion, and it is expanding into verticals like biotech, positioning itself as a cross-industry AI infrastructure layer. However, this funding comes with constraints: Anthropic’s independence is challenged as it balances two rival investors, its safety-first narrative faces pressure from regulatory scrutiny, and its path to IPO introduces new financial pressures. Globally, this accelerates a "tri-polar" closed-loop structure in AI infrastructure, with Microsoft-OpenAI, Google-Anthropic, and Amazon-Anthropic forming exclusive model-cloud alliances. In contrast, China’s landscape differs—investments like Alibaba and Tencent backing open-source model firm DeepSeek reflect a more decoupled approach, though closed-source models from major cloud providers still dominate. The $65 billion bet is ultimately about securing a seat at the table in an AI-defined future—where missing the model layer means losing the cloud war.

marsbit6 h fa

Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

marsbit6 h fa

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278 Totale visualizzazioniPubblicato il 2024.12.11Aggiornato il 2025.03.21

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