# Data Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Data", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

ChatGPT Can Manage Your Money for You. Would You Trust It with Your Bank Account?

OpenAI has launched a personal finance tool for ChatGPT, currently in preview for US-based ChatGPT Pro users. This feature allows users to connect their bank and investment accounts (via Plaid, supporting over 12,000 institutions) directly to ChatGPT. It analyzes transactions, generates visual dashboards, and offers conversational financial advice—such as budgeting or planning for major purchases—based on the user's actual data. This move follows OpenAI's acquisitions of fintech startups Roi and Hiro Finance, signaling a strategic push into vertical "super assistant" applications, similar to its earlier health-focused feature. However, the launch has sparked significant privacy concerns. Critics question the safety of granting such sensitive financial access to an AI, especially amid ongoing lawsuits alleging OpenAI shared user chat data with third parties like Meta and Google. OpenAI emphasizes that ChatGPT only reads data (no transaction capabilities), deletes it within 30 days if disconnected, and offers opt-out options for model training. Yet, trust remains a major hurdle. The trend reflects a broader industry shift: AI companies like Anthropic and Perplexity are also targeting high-value, data-rich domains like finance and health. While technically promising, the tool operates in a regulatory gray area—it provides personalized guidance but disclaims formal financial advice or liability. Ultimately, OpenAI's challenge is convincing users to trust an AI with their most private financial information.

marsbit05/16 10:58

ChatGPT Can Manage Your Money for You. Would You Trust It with Your Bank Account?

marsbit05/16 10:58

US Stock Storage Fever Spreads to Crypto for the Third Time, VVV Leads 'AI Data Infrastructure' Sector Gains

U.S. stock storage rally spreads to crypto for the third time, with VVV leading the 'AI data infrastructure' sector. On May 11, Venice Token (VVV) surged 17.63% with $65.05M volume, followed by Chainbase, SQD, and Vana, rising 5-8%. This mirrors gains in U.S. storage stocks like SanDisk and Micron. The crypto AI narrative is shifting. The initial wave (May 6-7) saw pure storage tokens like FIL, AR, and STORJ rise, driven by spillover from the U.S. storage chip boom. The second wave saw broader DePIN sector gains. The current third wave has moved beyond pure storage to broader "AI infrastructure." VVV, a decentralized AI inference platform, leads due to its own catalysts like a token burn mechanism and partnership news, alongside the broader market trend. Notably, traditional storage tokens showed minimal gains (<4%) on May 11, indicating the rally is becoming more selective, focusing on projects with specific narratives like AI data or compute. The pattern suggests the sector rally may be in a later stage, with sustainability depending on whether the U.S. storage chip boom continues.

marsbit05/11 10:58

US Stock Storage Fever Spreads to Crypto for the Third Time, VVV Leads 'AI Data Infrastructure' Sector Gains

marsbit05/11 10:58

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?

AI Relay Stations: The Hidden Risks Behind Low Costs and How to Avoid Pitfalls AI relay stations are becoming a popular gateway to various models, offering lower prices, a wider selection, and a unified interface for tools like Claude Code and Cursor. However, their appeal masks significant risks. Users may unknowingly surrender prompts, code, business documents, customer data, and even full project contexts. The demand is driven by genuine needs: cost savings compared to expensive official APIs (e.g., GPT, Claude), easier access amid regional restrictions, and the push from AI-powered development tools. But not everyone needs a relay station. Light users should exhaust free official quotas first. Heavy users, like developers, can adopt a layered approach, using top models for critical tasks and cheaper local models for routine work. If a relay station is necessary, follow a careful selection and usage protocol: 1. **Verify First:** Test model authenticity, latency, and stability before purchasing credits. Check the quality of provided documentation. 2. **Isolate Configuration:** Use unique API keys for each service, manage them via environment variables, and set usage limits to control costs and potential damage from leaks. 3. **Classify Your Data:** Develop a habit of data grading before sending requests. Only send non-sensitive, public information directly. Desensitize semi-sensitive data (e.g., internal documents) by removing names and specifics. Never send highly sensitive data like passwords, private keys, or confidential customer information. 4. **Handle AI Coding Tools Separately:** Tools like Cursor can send extensive project context (file contents, directory structures, error logs). Use relay stations only for independent, non-core code tasks. For sensitive projects, switch back to official APIs or local models. 5. **Monitor and Prepare an Exit:** Regularly check billing statements, follow platform updates and community feedback, and always have a backup provider. Ensure your setup uses standard OpenAI-compatible APIs for easy migration. Ultimately, relay stations are tools, not default solutions. Their value lies in solving access needs at a controlled cost, but maintaining that control requires proactive risk management through verification, isolation, data classification, and continuous monitoring.

marsbit05/09 10:16

AI Relay Stations: The Hidden Pitfalls Behind Low Costs, How to Screen and Avoid Them?

marsbit05/09 10:16

Historical Data Shows Bitcoin Price Has Never Breached This Level – Will It Start Now?

Historical data reveals a consistent pattern in Bitcoin's price action: after recovering 30% from a cycle low, it has never retested that bottom. This has held true across six major cycles since 2011. The current cycle, which saw a low near $61,300 in February, is approaching this critical threshold at approximately $79,694. Bitcoin has already up about 28% and needs just a 2.7% increase to breach this historically significant level. Supporting this bullish signal, exchange reserves have hit new lows, and large investors have accumulated the most BTC in a month since 2013.

bitcoinist04/26 14:23

Historical Data Shows Bitcoin Price Has Never Breached This Level – Will It Start Now?

bitcoinist04/26 14:23

Long-Term Outlooks Suggest Ozak AI Could Sustain Multi-Dollar Price Levels Through Multiple Market Cycles

Long-term analyst outlooks suggest Ozak AI ($OZ) could sustain multi-dollar price levels across multiple market cycles, rather than retracing after an initial spike. Currently priced at $0.014 in presale with over $6.8M raised, the project is viewed as a long-term AI infrastructure asset rather than a short-term trade. Key factors supporting this view include its utility-driven demand, capital-efficient pricing structure, and ecosystem integrations with partners like Pyth Network and SINT. Analysts highlight that Ozak AI’s lower initial valuation requires smaller capital inflows to maintain higher price zones, increasing the likelihood of stability in the $3–$5 range as adoption grows. The platform’s AI Prediction Agents, data vaults, and EigenLayer AVS validation are expected to generate persistent token demand through various market conditions.

TheNewsCrypto04/24 13:32

Long-Term Outlooks Suggest Ozak AI Could Sustain Multi-Dollar Price Levels Through Multiple Market Cycles

TheNewsCrypto04/24 13:32

Ozak AI’s Forecast Range Expands to $8–$11 as AI Adoption Accelerates Across DeFi, Data, and Automation Markets

Ozak AI ($OZ) is emerging as a leading AI-backed crypto investment, positioned at the intersection of AI, DePIN (Decentralized Physical Infrastructure Network), and tokenized economic growth. With AI adoption accelerating in DeFi, data analytics, and automation, the project's forecasted value has expanded to a range of $8–$11. Currently in advanced presale stages, Ozak AI has raised over $6 million, with tokens priced at $0.014, indicating strong pre-listing commitment. Its DePIN architecture supports scalable, decentralized AI processing, distinguishing it from other AI tokens. The $OZ token offers utility through staking, voting, and network expansion, driving demand. The $8–$11 valuation is based on successful presale activity, exchange listings, and growing adoption in decentralized markets, positioning Ozak AI for significant potential growth.

TheNewsCrypto04/24 12:19

Ozak AI’s Forecast Range Expands to $8–$11 as AI Adoption Accelerates Across DeFi, Data, and Automation Markets

TheNewsCrypto04/24 12:19

Bitcoin Transactions Hit Highest Since 2024—But Fees Remain Low

Bitcoin network activity has surged to its highest level since November 2024, with daily transactions reaching 615,000, according to on-chain data from CryptoQuant. The Network Activity Index, which tracks metrics like active addresses and transactions, has sharply rebounded above its 365-day moving average after a prolonged decline. Despite the increase in transactions, network fees remain low, suggesting the activity may not be driven entirely by organic user demand. Instead, it could be due to exchanges, custodians, and large holders conducting operational moves like UTXO management amid low fee conditions. Meanwhile, Bitcoin’s price briefly recovered above $70,000 before retracing to around $69,000.

bitcoinist04/08 00:01

Bitcoin Transactions Hit Highest Since 2024—But Fees Remain Low

bitcoinist04/08 00:01

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

The year 2026 is being positioned as the dawn of the "Physical AI" era, marked by major funding rounds and technological breakthroughs. This shift signifies AI's evolution from understanding the digital world to perceiving and acting within the physical world. Key events include Yann LeCun's AMI Labs raising $1.03 billion to develop "world models," Fei-Fei Li's World Labs securing funding, and companies like Tesla deploying humanoid robots (Optimus) in factories. This transition expands the AI model competition into a broader infrastructure battle encompassing hardware, data, simulation, and real-world integration. The core debate is between two AI paths: the established LLM (Large Language Model) approach focused on text prediction and the emerging "world model" approach, which aims to understand physical states for action-oriented tasks. Hardware, particularly dexterous robotic hands, is a critical and expensive challenge. Companies are racing to build capable robotic bodies, with Tesla, Boston Dynamics, and Figure AI making significant progress. NVIDIA is positioning itself as the essential infrastructure provider for this new era, offering a full suite of development tools and platforms. A major bottleneck is the scarcity of high-quality physical world interaction data, with companies exploring solutions through real-world data collection, synthetic data generation, and human teleoperation. Substantial investments in Q1 2026, exceeding $6.4 billion, signal strong belief in Physical AI's potential, moving beyond concept validation into infrastructure building. While challenges like the sim-to-real gap, unproven business models, and safety regulations remain, the tangible engineering progress suggests this is a genuine technological inflection point, not merely a bubble. For the global Chinese community, this shift represents a significant structural opportunity to leverage their strengths in technology, engineering, hardware manufacturing, and cross-border collaboration to become key players in building the foundational layers of the Physical AI ecosystem.

marsbit04/03 09:39

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

marsbit04/03 09:39

Adding Another Oracle, Polymarket's Ambition Exposed

Polymarket, a leading prediction market platform, has announced the integration of Pyth Network as its oracle provider for settling prediction markets related to traditional assets. This new batch of markets will cover commodities like gold, silver, WTI crude oil, and natural gas, as well as U.S. stocks including NVIDIA, Apple, Tesla, and ETFs. Pyth will supply real-time pricing data via WebSocket, enabling second-by-second sampling and live charts for traders. This move is part of Polymarket’s ongoing expansion of its oracle infrastructure. Initially reliant on UMA’s optimistic oracle for subjective events like elections, the platform later integrated Chainlink for automated crypto price settlements (e.g., BTC and ETH). With Pyth—known for its institutional data sources like Jump Trading and Jane Street—Polymarket now extends into traditional finance, forming a multi-layered oracle system: UMA for social and political events, Chainlink for crypto, and Pyth for traditional assets. By expanding its data sources, Polymarket is broadening the scope of tradable futures—effectively turning real-world uncertainties into betable markets. The end goal appears to be a unified super-platform where any verifiable event—macro data, earnings, sports, weather, or AI releases—can be priced and traded.

Odaily星球日报04/03 04:10

Adding Another Oracle, Polymarket's Ambition Exposed

Odaily星球日报04/03 04:10

Agentic Workflows for Crypto Research

AI coding agents are transforming crypto research by automating data analysis through natural language. This article demonstrates using an agent (like Claude Code) with the Glassnode CLI to test a hypothesis: whether extreme Bitcoin exchange inflows predict 7-day price drawdowns. The agent autonomously discovers metrics, fetches data, performs statistical analysis, and generates insights. Results show a moderate association—spike days saw ~1.9% greater drawdowns—though the sample is small. A follow-up prompt produces visualizations for validation. The workflow allows rapid iteration and is applicable to various research questions using Glassnode data, requiring only an API key and an AI agent.

insights.glassnode04/01 12:41

Agentic Workflows for Crypto Research

insights.glassnode04/01 12:41

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