# Research İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "Research" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

OpenAI Bets on 'Robot Army': 23-Year-Old Prodigy Wins Favor from Sam Altman

While OpenAI adjusts its video strategy, Sam Altman is setting his sights on the more ambitious field of "multi-agent systems." According to The Wall Street Journal, OpenAI has secretly invested in Isara, an AI startup founded by 23-year-old researchers Eddie Zhang and Henry Gasztowtt. Despite being established only in June last year in San Francisco, Isara has already recruited over a dozen top researchers from Google, Meta, and OpenAI itself, forming a highly skilled technical team. Isara’s core vision is to develop a system that enables thousands of AI agents to collaborate efficiently. While individual AI assistants are powerful, they often struggle with large-scale industrial challenges such as biotech R&D or complex financial modeling. Isara aims to solve this by creating a framework where diverse AI agents can communicate, align goals, share data, and tackle interconnected problems—functioning like a coordinated "robot army." This multi-agent approach is seen as a critical step toward Artificial General Intelligence (AGI). OpenAI’s endorsement signals industry recognition of distributed intelligence. In biopharma, the system could simulate thousands of protein-folding pathways, with specialized agents identifying patterns. In finance, it could perform real-time stress tests using global market data. Led by young innovators, this shift suggests the next breakthrough in AI lies not in building larger models, but in enabling smarter collective intelligence.

marsbit56 dk önce

OpenAI Bets on 'Robot Army': 23-Year-Old Prodigy Wins Favor from Sam Altman

marsbit56 dk önce

What AI Services Are Crypto Companies Offering?

Crypto companies across various sectors—exchanges, security firms, payment infrastructure, and research platforms—are rapidly integrating AI-driven services. Unlike previous hype cycles, this wave is led by established players like Coinbase, Binance, and Bitget, who are motivated by both genuine utility and fear of missing out (FOMO) as AI transitions from concept to operational necessity. Key applications include: - **Research tools** (e.g., Surf AI) that aggregate fragmented on-chain and market data to provide accurate crypto-specific insights. - **Trading automation**, where exchanges enable users to execute strategies via natural language commands, lowering barriers for non-developers but potentially reducing platform loyalty. - **Security and auditing** (e.g., CertiK), using AI to enhance code review efficiency and post-audit monitoring, addressing historical limitations. - **Payment infrastructure** (e.g., Circle’s proposals), exploring AI-agent-compatible stablecoin payments for the emerging autonomous economy, though this remains nascent. While AI adoption is driven by competitive pressure and the need to retain users, gaps remain between feature launches and real-world usage. The focus is on distinguishing value-adding integrations from superficial implementations. The entry of mature firms signals AI’s strategic importance, ensuring crypto remains relevant in the AI evolution.

marsbit2 gün önce 10:41

What AI Services Are Crypto Companies Offering?

marsbit2 gün önce 10:41

From FOMO to Implementation: A Review of the Current State of AI Services in Crypto Companies

From FOMO to Implementation: A Look at Crypto Companies' AI Services Cryptocurrency companies, from exchanges to security firms, are rapidly integrating AI-driven services, driven by FOMO (fear of missing out) rather than just hype. Unlike previous cycles, established players like Coinbase and Binance are leading the charge, treating AI as a business necessity rather than a narrative. Key sectors adopting AI include: - **Research**: Projects like Surf AI address crypto's fragmented data problem by offering specialized tools that aggregate on-chain data, social sentiment, and metrics, providing accurate, crypto-specific insights. - **Trading**: Exchanges are leveraging AI to allow natural language commands for analysis and execution, lowering the barrier for non-developers to create automated strategies via AI agents. - **Security/Audit**: Firms like CertiK use AI to enhance smart contract audits by combining automated code scanning with human review, and adding post-audit monitoring to cover previous blind spots. - **Payment Infrastructure**: Companies are developing protocols for AI agents to make on-chain payments, using stablecoins for API fees or services, with Circle’s proposal for AI-agent payments gaining attention. The push is fueled by AI advancements like MCP and OpenClaw, which make agent-based automation accessible. However, the adoption gap between "having functionality" and "actual usage" remains, with questions about user trust in AI for real trading or payments. Ultimately, crypto firms are acting to avoid obsolescence in the AI era, though real-world utility is still evolving.

比推03/17 18:08

From FOMO to Implementation: A Review of the Current State of AI Services in Crypto Companies

比推03/17 18:08

From Understanding Skill to Learning How to Build Crypto Research Skill

This article explores the evolution and application of Agent Skill, a modular framework introduced by Anthropic in late 2025, which has become a foundational design pattern in the AI Agent ecosystem. Initially a tool to improve Claude's performance on specific tasks, it evolved into an open standard due to high developer adoption. Agent Skill functions like a "dynamic instruction manual" that AI can reference to perform tasks consistently without repetitive user prompting. It is built using a `skill.md` file containing metadata (name and description) and detailed instructions. The system operates through an on-demand loading workflow: the AI first scans lightweight skill metadata, matches the user's intent, then loads only the relevant skill's full instructions, optimizing token usage. Two advanced mechanisms enhance its functionality: - **Reference**: Conditionally loads external documents (e.g., a finance handbook) only when triggered by specific keywords, avoiding unnecessary context consumption. - **Script**: Executes external code (e.g., a Python script) without reading its content, enabling actions like file uploads with zero token cost. The article contrasts Agent Skill with Model Context Protocol (MCP), noting that MCP connects AI to data sources, while Skill defines how to process that data. For advanced use cases like crypto research, combining both is recommended: MCP fetches real-time data (e.g., blockchain info, news APIs), while Skill structures the analysis and output format. A practical example demonstrates building a crypto research agent using an `opennews-mcp` server. The Skill automates workflows like due diligence on new tokens (pulling Twitter data, news sentiment, KOL tracking) and real-time event monitoring (e.g., ZK-proof breakthroughs) to generate structured reports or trading alerts. This combination creates a powerful, automated research system tailored for Web3 analytics.

marsbit03/10 10:41

From Understanding Skill to Learning How to Build Crypto Research Skill

marsbit03/10 10:41

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