2026-07-26 Domingo

Notícias de cripto - Página 6

Mantenha-se a par do mercado de cripto. Notícias em tempo real, análises, preços, histórias em alta e análise de especialistas — tudo num só lugar.

S&P Dow Jones and Pantera Launch Crypto Index, Excluding Bitcoin for "Not Making Money"

S&P Dow Jones Indices and Pantera Capital have launched the S&P Pantera Digital Asset Index, a benchmark that excludes Bitcoin and meme coins like XRP. The index adopts a fundamental screening framework inspired by the S&P 500, requiring constituent tokens to demonstrate positive protocol revenue over multiple quarters, verified by on-chain data, and a clear mechanism for distributing value to token holders. This "financial viability" test is cited as the reason for Bitcoin's exclusion, as it does not generate protocol revenue. The index currently includes 18 assets, with Ethereum (ETH), BNB, Solana (SOL), Tron (TRX), and Hyperliquid (HYPE) as its top five holdings. While initially serving as a benchmark, discussions are underway with asset managers to develop associated ETFs and investment products. Pantera argues the index addresses an institutional product gap, offering a way to evaluate crypto assets based on utility and earnings rather than just market capitalization.

marsbit07/22 08:01

S&P Dow Jones and Pantera Launch Crypto Index, Excluding Bitcoin for "Not Making Money"

marsbit07/22 08:01

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit07/22 07:57

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit07/22 07:57

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit07/22 07:56

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit07/22 07:56

Bitcoin, XRP left out – The S&P Digital Asset Index shows crypto’s biggest shift yet

Pantera Capital and S&P Dow Jones Indices have launched the S&P Digital Asset Index, a basket of 18 cryptocurrencies focused on fundamental strength. Notably, Bitcoin and XRP are excluded from the initial confirmed assets, which include Ethereum, BNB, Solana, Hyperliquid, and Tron. This selection highlights a potential market shift, prioritizing Layer-1 networks with proven scalability, utility, and sustainable on-chain revenue—reportedly $3 billion annualized in the past six months—over mere market capitalization. Analysts suggest this "fundamental index for crypto" could guide institutional capital toward assets with real economic activity, possibly leading to a significant divergence in the next market cycle where fundamentals, not just price momentum, drive investment.

ambcrypto07/22 07:47

Bitcoin, XRP left out – The S&P Digital Asset Index shows crypto’s biggest shift yet

ambcrypto07/22 07:47

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

"White House Speech Prompt Operator Earns Over $100,000 Using Insider Information on Prediction Markets" U.S. White House staffer Gabriel Perez, a long-time teleprompter operator for former President Donald Trump, has been suspended without pay for using non-public information to profit on prediction markets. As one of the few individuals with advance access to Trump's prepared speech texts, Perez placed bets on specific words or phrases Trump would mention in speeches over a three-month period, earning over $100,000. His activities were flagged by the prediction platform Kalshi, which froze over $90,000 in his account and reported him to the Commodity Futures Trading Commission (CFTC). While Perez avoided criminal charges, he is required to return his profits and cease such trading. This case marks the third major instance of insider trading on prediction markets involving government or corporate insiders, following earlier cases involving a special forces soldier and a Google engineer. The incident highlights the vulnerability of "mention" markets on prediction platforms, where individuals with advance knowledge or even the speakers themselves can easily manipulate outcomes. In response, platforms like Kalshi are tightening rules, now requiring users to disclose their employers to help prevent similar abuses.

marsbit07/22 06:50

The White House's "Exclusive" Teleprompter Operator Makes Over $100,000 by Profiting from Insider Information Predictions

marsbit07/22 06:50

White House 'Exclusive' Teleprompter Operator Cashes in Over $100,000 Using Insider Information for Predictions

The White House is embroiled in another insider trading scandal, this time involving a teleprompter operator for former President Trump. Gabriel Perez, a longtime aide who controlled Trump's speech teleprompters, used his advance knowledge of speech content to profit over $100,000 on prediction markets like Kalshi by betting on which specific words Trump would mention. After Kalshi's monitoring systems flagged suspicious trading activity and froze his funds, the case was referred to the CFTC. Perez was suspended without pay by Trump, who called his actions "disgusting." Unlike previous insiders prosecuted in similar cases, Perez faced no criminal charges, only a CFTC settlement requiring him to return profits and cease such trading. The article highlights how "mention" markets are uniquely vulnerable to manipulation by insiders or even the speakers themselves, citing examples like a Grammy host jokingly saying a random word and a Coinbase CEO reading all prediction market options aloud. As prediction markets like Kalshi tighten compliance—such as requiring users to disclose employers—the piece argues this crackdown on insider trading, while promoting regulatory compliance, may move these markets further from "collective wisdom" and closer to being pure gambling venues.

Odaily星球日报07/22 06:46

White House 'Exclusive' Teleprompter Operator Cashes in Over $100,000 Using Insider Information for Predictions

Odaily星球日报07/22 06:46

Bitcoin Mining Farms Are Becoming AI Factories

Bitcoin mines are transforming into AI factories. This shift is driven by the convergence of three key assets from the previous crypto cycle: infrastructure, talent, and capital. Crypto mining companies like Crusoe, CoreWeave, and Bitdeer are repurposing their core competency—securing power, land, and grid connections in remote locations—to build data centers for AI clients. These firms are signing multi-billion dollar, long-term contracts with companies like Anthropic, AWS, and Microsoft, as AI's demand for reliable, high-capacity compute surpasses the profitability of Bitcoin mining. Simultaneously, crypto entrepreneurs and engineers are applying their skills to new AI ventures. Examples include OpenSea's co-founder launching OpenRouter (an AI model aggregator), and former Coinbase engineers building Fal.ai (a generative media infrastructure platform). Their experience in building scalable, global software networks translates effectively to the AI space. Furthermore, capital accumulated during the crypto boom is now fueling AI. Figures like Jed McCaleb (co-founder of Ripple) funded Voltage Park, a large-scale GPU cloud provider. Notably, some crypto investments, like FTX's early bets on Anthropic and Cursor, have generated astronomical paper returns, demonstrating how high-risk crypto capital flowed into AI before it became mainstream. The transition is not just about repurposing hardware, but about redirecting critical resources—power infrastructure, distributed systems expertise, and venture funding—to the next technological frontier: artificial intelligence.

链捕手07/22 06:33

Bitcoin Mining Farms Are Becoming AI Factories

链捕手07/22 06:33

Morpho Launches Fixed-Rate Product Midnight: Lenders and Borrowers Set Their Own Rates, Ending the Era of Interest Rate Models

Morpho Launches Fixed-Rate Product Midnight: Lenders and Borrowers Set Their Own Rates, Ending the Era of Algorithmic Interest Models On-chain lending has grown to $60 billion but remains minuscule compared to traditional finance's $200 trillion annual credit volume. Morpho identifies the lack of fixed rates and maturity dates as key bottlenecks. Institutions need predictability, not the passive floating rates set by algorithmic models. Midnight allows lenders and borrowers to directly quote rates, set terms, and become price makers, not takers. Fixed-rate lending is now viable due to cheaper, faster blockchains and the entry of institutions demanding control and certainty over returns, costs, and duration. Morpho Blue previously gave users control over risk; Midnight adds control over interest rates. Past attempts at on-chain fixed-rate lending failed primarily because they were built on top of floating-rate pools (creating unpredictability) or lacked sufficient active participants. Midnight avoids these pitfalls as a standalone primitive with fixed rates at its core, built upon Morpho Blue's existing large and active user base. Midnight offers distinct value: institutions gain predictable term structures and full control; fintech companies can offer tailored fixed-rate products; lenders/borrowers achieve predictability and efficiency; and curators can now differentiate by configuring both risk and interest rates. Morpho Midnight is not a replacement for Morpho Blue. The Morpho network will now feature two complementary market structures: floating-rate/open-term (Blue) for flexibility and fixed-rate/fixed-term (Midnight) for predictability. Liquidity can flow between them. The launch will be gradual, prioritizing security. Initially, it will support direct lending on Base network with one trading pair (cbBTC/USDC) and limited maturity dates. Advanced features like auto-rollovers will be introduced later.

marsbit07/22 06:32

Morpho Launches Fixed-Rate Product Midnight: Lenders and Borrowers Set Their Own Rates, Ending the Era of Interest Rate Models

marsbit07/22 06:32

Crypto payments for gray-market peptides up by 700% in a year – Details

The use of cryptocurrency payments, primarily Bitcoin and stablecoins, for gray-market peptides in the United States surged by 700% in a year, according to a recent report. These peptides, often sold as wellness or anti-aging aids, exist in a regulatory gray area. Facing tighter restrictions from traditional banks, vendors increasingly accept crypto, with some Chinese suppliers accepting only Bitcoin. This has attracted mainstream consumers seeking cheaper access, even those new to cryptocurrency. Data from Chainalysis shows gray-market peptide vendors received $32 million in crypto in Q1 2026, projecting an annual run rate over $100 million. TRM Labs noted a similar trend, estimating $41.4 million in crypto inflows to Chinese peptide sellers in 2025. Analysts highlight this involves many first-time crypto users, distinct from historical darknet market activity, and underscores crypto's role in facilitating cross-border value transfer. This growth occurred alongside a broader downturn in general retail crypto transaction volumes in early 2026.

ambcrypto07/22 06:02

Crypto payments for gray-market peptides up by 700% in a year – Details

ambcrypto07/22 06:02

human.tech Launches Clean SDK for Privacy-First Web3 Apps

human.tech has launched the Clean SDK, a toolkit enabling developers to build privacy-first Web3 applications with transparent accountability. Released alongside Aztec's version 5, the SDK provides components for integrating zero-knowledge identity verification, sanctions screening, and private transactions, without developers handling sensitive user data or building compliance infrastructure from scratch. It uses zero-knowledge proofs and programmable verification to allow apps to confirm user legitimacy and sanctions compliance while keeping identities confidential. The first application built on the SDK, Shield, a privacy bridge to Aztec, also launched. It allows users to transfer assets privately while proving a unique human is behind each transfer and that funds have passed sanctions checks, as verified by a May 2026 audit. The SDK offers three core verification techniques: Proof of Innocence (sanctions screening against 23 sources), Proof of Personhood (simpler verification via Human Passport), and Proof of Clean Hands (higher-assurance zero-knowledge government ID checks). This allows apps to authenticate users and transactions without exposing personal data. Designed for Aztec builders, the SDK lets developers add programmable privacy to decentralized apps, eliminating the need to create their own verification and ZK infrastructure. Shield demonstrates its practical use for private bridges, but the SDK aims to enable a wider ecosystem of private, accountable financial apps and services. The launch addresses growing demand for infrastructure that balances privacy and accountability. The SDK avoids traditional identity databases, storing encrypted data off-chain, screening at both entry and exit points, and including a gated disclosure mechanism for legal requests. human.tech's products, including the Clean SDK, focus on using zero-knowledge technology to enable verifiable personhood and privacy in digital systems.

TheNewsCrypto07/22 05:56

human.tech Launches Clean SDK for Privacy-First Web3 Apps

TheNewsCrypto07/22 05:56

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