The Altcoin Vector #34

insights.glassnodeОпубліковано о 2025-12-24Востаннє оновлено о 2025-12-24

Анотація

This report, titled "The Altcoin Vector #34", is locked content for subscribers only. Access to the full executive summary and the complete article requires a paid membership starting at $425 per month. The brief preview indicates that existing subscribers can log in to unlock and read the material.

Executive Summary

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

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

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QWhat type of content is The Altcoin Vector #34 based on the executive summary section?

AThe Altcoin Vector #34 is a report that appears to be part of a series on altcoins, though the full content is behind a subscription paywall.

QIs the full content of The Altcoin Vector #34 freely available to read?

ANo, the full content is not freely available; it requires a paid subscription to unlock.

Пов'язані матеріали

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.

marsbit25 хв тому

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

marsbit25 хв тому

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.

marsbit30 хв тому

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

marsbit30 хв тому

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.

marsbit39 хв тому

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

marsbit39 хв тому

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 год тому

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

marsbit1 год тому

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 год тому

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

marsbit6 год тому

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