Chainlink Expands On-Chain Access to U.S. Stocks With 24/5 Market Data

TheNewsCryptoPubblicato 2026-01-21Pubblicato ultima volta 2026-01-21

Introduzione

Chainlink has launched "24/5 U.S. Equities Data Streams," a new service providing blockchain applications with continuous access to U.S. stock and ETF market data, including real-time prices, bid/ask quotes, and trading volume, even outside traditional market hours. This addresses a critical gap in DeFi, where outdated after-hours data previously increased risks for derivatives, lending, and synthetic assets. The service, which went live on January 20, 2026, and is operational in over 40 countries, is already being used by major platforms like Lighter and BitMEX Apex. It supports the growth of on-chain financial products and aligns with broader industry and regulatory interest in 24/7 markets.

Chainlink has launched a new tool that lets blockchain apps access U.S. stock and ETF prices around 24/5. This service is named 24/5 U.S. Equities Data Streams and went live on January 20, 2026. This provides the current stock price, buy and sell price, and trading volume even after the market hours.

Chainlink Solves the After-Hours Pricing Gap for On-Chain U.S. Stocks

Right now U.S.stock market closes on weekends and at night. After the market hours, prices on the crypto apps become old and inaccurate, which increases risk for the DeFi products like derivatives, lending, and synthetic stocks. Now Chainlink delivers the tool to solve the problem by keeping stock prices updated before the market opens, closes, and overnight.

Chainlink fixes this problem by giving the accurate stock prices even when the markets are closed, which helps the Defi apps to track stock-based tokens anytime, can run prediction markets using the real prices, and offers loans and borrowing based on stock value without using old or inaccurate prices.

The service is already processed in over 40 countries and supports exchange-traded funds. Several major on-chain trading platforms like Lighter, Bitmex Apex, and multiple decentralized derivative exchanges started using data to support the synthetic stocks, prediction markets, and Equity perpetual futures.

U.S. stocks are worth around $80 trillion, but right now, most of the stocks don’t exist on blockchain. Chainlink is helping to bring the stock market data into the blockchain, and even big traditional players like the New York Stock Exchange agrees and are exploring blockchain trading. U.S. regulators are also thinking about 24/7 markets.

Highlighted Crypto News:

Messari: Only KYC Can Curb Insider Trading on Prediction Markets

Crypto di tendenza

CitreaCTR

wrapped stUSDTWSTUSDT

Velodrome FinanceVELODROME

Domande pertinenti

QWhat is the name of the new service launched by Chainlink to provide U.S. stock and ETF prices?

AThe service is named 24/5 U.S. Equities Data Streams.

QOn what date did the 24/5 U.S. Equities Data Streams service go live?

AThe service went live on January 20, 2026.

QWhat specific problem does Chainlink's new service aim to solve for on-chain DeFi products?

AIt solves the after-hours pricing gap by providing accurate, updated stock prices even when the traditional U.S. markets are closed, reducing risk for derivatives, lending, and synthetic stocks.

QName two types of on-chain trading platforms mentioned that have started using Chainlink's new data service.

ALighter and Bitmex Apex, along with multiple decentralized derivative exchanges, have started using the data.

QAccording to the article, what is the approximate total value of U.S. stocks that Chainlink is helping to bring on-chain?

AU.S. stocks are worth approximately $80 trillion.

Letture associate

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.

marsbit6 min fa

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

marsbit6 min fa

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.

marsbit7 min fa

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

marsbit7 min fa

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.

ambcrypto16 min fa

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

ambcrypto16 min fa

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.

marsbit1 h fa

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

marsbit1 h fa

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1 h fa

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

Odaily星球日报1 h fa

Trading

Spot

Articoli Popolari

Come comprare LINK

Benvenuto in HTX.com! Abbiamo reso l'acquisto di ChainLink (LINK) semplice e conveniente. Segui la nostra guida passo passo per intraprendere il tuo viaggio nel mondo delle criptovalute.Step 1: Crea il tuo Account HTXUsa la tua email o numero di telefono per registrarti il tuo account gratuito su HTX. Vivi un'esperienza facile e sblocca tutte le funzionalità,Crea il mio accountStep 2: Vai in Acquista crypto e seleziona il tuo metodo di pagamentoCarta di credito/debito: utilizza la tua Visa o Mastercard per acquistare immediatamente ChainLinkLINK.Bilancio: Usa i fondi dal bilancio del tuo account HTX per fare trading senza problemi.Terze parti: abbiamo aggiunto metodi di pagamento molto utilizzati come Google Pay e Apple Pay per maggiore comodità.P2P: Fai trading direttamente con altri utenti HTX.Over-the-Counter (OTC): Offriamo servizi su misura e tassi di cambio competitivi per i trader.Step 3: Conserva ChainLink (LINK)Dopo aver acquistato ChainLink (LINK), conserva nel tuo account HTX. In alternativa, puoi inviare tramite trasferimento blockchain o scambiare per altre criptovalute.Step 4: Scambia ChainLink (LINK)Scambia facilmente ChainLink (LINK) nel mercato spot di HTX. Accedi al tuo account, seleziona la tua coppia di trading, esegui le tue operazioni e monitora in tempo reale. Offriamo un'esperienza user-friendly sia per chi ha appena iniziato che per i trader più esperti.

877 Totale visualizzazioniPubblicato il 2024.12.13Aggiornato il 2026.06.02

Discussioni

Benvenuto nella Community HTX. Qui puoi rimanere informato sugli ultimi sviluppi della piattaforma e accedere ad approfondimenti esperti sul mercato. Le opinioni degli utenti sul prezzo di LINK LINK sono presentate come di seguito.