Google Cloud получила статус основного валидатора в блокчейне Cronos

investing.ruPublished on 2024-11-08Last updated on 2024-11-08

Happycoin.club - Компания Google (NASDAQ:GOOGL) Cloud ещё больше расширила партнёрские отношения с Cronos Labs и стала основным валидатором блокчейна Cronos. По словам Риши Рамчандани, главы web3-подразделения Google Cloud в Азиатско-Тихоокеанском регионе, новая инициатива свидетельствует о том, что компания укрепляет свои позиции в сфере блокчейна.

Google Cloud будет сотрудничать с Cronos, чтобы предоставить разработчикам ресурсы, необходимые для создания децентрализованных приложений нового поколения, с использованием нашей безопасной инфраструктуры, передовых возможностей искусственного интеллекта и мощных инструментов анализа данных, — написал Риши Рамчандани.

Google Cloud работает в блокчейне Cronos

В качестве основного валидатора Google Cloud присоединится к пулу из 32 валидаторов протокола Cronos виртуальной машины Ethereum (EVM). Другие крупные валидаторы блокчейна — Crypto.com, Blockdaemon, Ubisoft и Exaion.

По словам управляющего директора Cronos Labs Кена Тимсита, всё больше мировых гигантов, таких как Google Cloud, присоединяются к web3-сфере.

Пространство web3 трудно игнорировать любой компании, ориентированной на разработчиков, учитывая огромное количество разработок с открытым исходным кодом и инноваций, которые здесь происходят. Насколько мы можем судить, эта тенденция характерна не только для 2024 года, она наблюдается уже несколько лет, — написал Тимсит.

Среди других крупных компаний, которые заинтересованы в web-3, известны производители автомобилей Lamborghini, Ferrari и компании из фэшн-индустрии Dolce & Gabanna, Nike (NYSE:NKE) и Adidas (ETR:ADSGN).

Читайте оригинальную статью на сайте Happycoin.club

Anthropic Creates an AI Jailbreak 'Penal Code': Your Requests, Four Ways to Die

Anthropic has publicly detailed its security measures and a new "Cyber Jailbreak Severity" (CJS) framework following the controversial takedown of its Fable 5 model. The incident, triggered by simple user requests like counting letters or stating a profession, highlighted overzealous safety filters. Anthropic classifies cybersecurity-related prompts into four tiers: malicious activities (blocked), high-risk dual-use (like pentesting, with strict limits), low-risk dual-use (often blocked by "safety margin" errors), and harmless tasks (theoretically allowed but still frequently flagged). The company admits its classifiers are tuned for high sensitivity, leading to many false positives. The newly proposed CJS framework aims to objectively score the severity of AI "jailbreaks" (prompts that bypass safety rules) on a 0-10 scale across four dimensions: Capability Gain (does it grant new attack abilities?), Breadth (does it work across multiple attack types?), Weaponization Ease (how hard is it to turn into a real attack?), and Discoverability (how easy is it to find?). The score determines the response, from no action (CJS-0) to a potential model takedown (CJS-4). The score is context-dependent; for example, discovering a major unknown vulnerability today scores high, while asking about a well-known one scores low. The article raises concerns about Anthropic's dual role: it is both creating powerful models (like the restricted Mythos 5) and defining the rules (CJS) for judging their misuse, potentially giving it disproportionate influence. This is set against the backdrop of U.S. export controls, which for the first time directly restricted API access to a model (Fable 5), creating a "tiered" system where public models are heavily filtered and advanced ones are limited to vetted partners. The CJS framework is portrayed as potentially providing regulators with a metric to justify future API shutdowns. For users, the advice is to carefully phrase prompts, watch for signs of being downgraded to a weaker model, and wait indefinitely for promised filter improvements.

marsbit28m ago

Anthropic Creates an AI Jailbreak 'Penal Code': Your Requests, Four Ways to Die

marsbit28m ago

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

Arena, the AI model ranking platform, has become a $100 million annual revenue business just eight months after launching its commercial service. Originally a UC Berkeley open-source research project called Chatbot Arena, it created a "battle arena" where users blind-test and vote on anonymous AI model responses. This has generated a highly trusted, community-driven leaderboard based on over 10 million user evaluations and 82 million votes. Major AI companies like OpenAI, Google, and Anthropic submit their flagship models to be ranked. The core monetization strategy is its AI Evaluations service, where model developers and large enterprises pay for in-depth performance analysis from Arena's massive user community. This provides real-world feedback on model strengths, weaknesses, and hallucinations—a critical service as models become more complex. The company, spun out from Berkeley in early 2025, quickly raised $100 million in seed funding at a $600 million valuation and later secured a $150 million Series A at a $1.7 billion valuation. The founding team includes CEO Anastasios Angelopoulos, a mathematician focused on rigorous model evaluation; CTO Wei-Lin Chiang, creator of the popular Vicuna chatbot; and co-founder Ion Stoica, a renowned Berkeley professor. Arena is now expanding beyond chat benchmarks into "Agent Mode," evaluating AI agents on complex, multi-step tasks like coding and research. The company's success illustrates the growing value and cost of independent, real-world AI model evaluation as the industry intensifies.

marsbit32m ago

$100M Annual Revenue, Two Berkeley Roommates in Their 20s Build the Most Profitable AI Business

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Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

Vercel, known for its developer tools like Next.js, has launched 'skills', a package manager for AI coding agents, garnering 24,000 GitHub stars. It allows developers to add specialized capabilities, such as React best practices, to AI assistants like Claude Code or Cursor with a single command: `npx skills add <package>`. Skills are shareable, reusable modules that define an AI agent's behavior for specific tasks, moving beyond one-off prompt engineering towards standardized 'capability engineering'. A key innovation is the 'find-skills' skill, which acts as an internal search engine, allowing an agent to autonomously find and install the right skill for a user's request. This lowers the barrier for non-developers to leverage advanced AI coding assistance. However, this 'npm moment' for AI brings significant security risks. Security audits of thousands of skills on platforms like skills.sh and ClawHub found over 30% contained security flaws, with about 13% classified as severe. Threats include malicious scripts that can access local files and credentials, and prompt injection hidden within skill documentation. Unlike traditional code packages, skills blend instructions, code, and system access, posing a direct risk to user machines and data. Experts advise treating skills like code—reviewing them carefully before installation, especially their scripts, and being wary of excessive permissions. Ultimately, Vercel's initiative represents a major shift towards modular, reusable AI capabilities, but its rapid adoption requires developers to bring the same caution used in managing traditional software dependencies.

marsbit33m ago

Racking Up 24,000 Stars: With One Command, AI Can Now Find Its Own Skills

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Token Devours One-Third of Payroll, Silicon Valley's AI Bill is Spinning Out of Control

The article discusses the dual reality of AI token costs in Silicon Valley. While research firm SemiAnalysis reports spending 30% of its employee salary budget on internal LLM tokens—translating to massive productivity gains like converting complex Excel models in minutes—other giants are struggling with ballooning, uncontrolled AI bills. Uber exhausted its annual AI budget in months after rapid engineer adoption, and Microsoft is cutting third-party AI tools due to high costs. NVIDIA's CEO argues tokens are becoming "means of production" and plans substantial AI budgets per engineer. Despite current cost concerns, the analysis emphasizes that cost collapse is just beginning. Through software optimizations (like 14x throughput boosts) and next-gen hardware (e.g., GB300 NVL72 with 17-32x H100 performance), real token costs can fall far below list prices. Anthropic's gross margins reportedly soared as token prices dropped. Gartner predicts a >90% inference cost drop by 2030. The piece highlights a split: massive AI capex ($740B announced) contrasts with tech layoffs and minimal measured economic impact so far. The transition mirrors past infrastructure shifts—investment precedes widespread productivity. For early adopters like SemiAnalysis, tokens already deliver high leverage; for others, the choice is to adopt now or risk falling behind.

marsbit37m ago

Token Devours One-Third of Payroll, Silicon Valley's AI Bill is Spinning Out of Control

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Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models

This article, titled "Claude Engineer Finally Releases Fable 5 'Skill-Burning' Guide, Teaching How to Bridge the Information Gap with Models," details a blog post by Claude Code engineer Thariq Shihipar. The core concept is the "information gap" or "unknowns"—the disconnect between a user's instructions (the "map") and the actual task requirements (the "territory"). The article argues that with powerful models like Claude Fable 5, work quality depends on the user's ability to identify and clarify these unknowns. Shihipar categorizes unknowns into four types: Known Knowns (explicit instructions), Known Unknowns (awareness of gaps), Unknown Knowns (implicit, unstated knowledge), and Unknown Unknowns (unforeseen issues). The blog provides a framework for addressing these gaps throughout the workflow: * **Before Implementation:** Techniques include "Blindspot Scanning" to uncover Unknown Unknowns, brainstorming/prototyping for visual or complex tasks, having Claude ask clarifying questions, using reference code/examples, and creating implementation plans. * **During Implementation:** Maintaining an "implementation notes" file for Claude to document deviations and decisions made due to encountered edge cases. * **After Implementation:** Creating summary documents for review and having Claude generate quizzes to ensure the user fully understands the completed changes. The article concludes that as models become more capable, the key to success is systematically discovering and defining these unknowns through low-cost methods like prototyping and planning, allowing for more effective collaboration.

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Claude Engineer Finally Unveils Fable 5's Ultimate Strategy, Teaching You How to Bridge the Information Gap with AI Models