Coinlocally Lists Tesla, Amazon, Apple, More Tokenized Stock Pairs, Launches Zero-Fee Trading Campaign

TheNewsCryptoPubblicato 2026-04-22Pubblicato ultima volta 2026-04-22

Introduzione

Coinlocally has expanded its trading platform by listing 10 new tokenized stock pairs, including major companies like Tesla, Amazon, Apple, NVIDIA, and Alphabet. From April 14 until May 14, 2026, users can trade these tokenized stocks—such as TSLAX, AAPLX, and AMZNX—against USDT with zero fees. The move is part of a growing trend in tokenized real-world assets, which now exceed $26 billion in on-chain value. According to COO Sam Baumann, the zero-fee campaign aims to make these products more accessible. The listings align with Coinlocally’s strategy of blending traditional market exposure with digital asset trading, offering users a seamless way to access tokenized equities within its existing ecosystem.

Coinlocally today launched 10 new tokenized stock pairs on its trading platform and introduced a zero-fee trading campaign for all newly-listed stock pairs. The new listings include widely recognized companies such as Tesla, Amazon, Apple, NVIDIA, and Alphabet.

Starting on April 14, users can trade TSLAX, COINX, AMZNX, AAPLX, NVDAX, GOOGLX, MCDX, HOODX, METAX, and CRCLX against USDT with zero trading fees through May 14, 2026. This new group of listings gives users exposure to some of the most closely Marco watched names across technology, consumer internet, and digital finance, while keeping that access within Coinlocally’s existing trading environment.

Tokenized real-world assets (RWAs) continue to grow across the digital asset market, with more than $26 billion in distributed on-chain value. At the same time, interest in tokenized equities has been building as more companies look at blockchain-based versions of traditional financial products. Coinlocally’s new listings arrive as tokenized stocks begin to attract wider attention from both crypto platforms and traditional market infrastructure players.

“We want users to be able to access newly-listed tokenized stock markets without extra cost during the launch period,” said Sam Baumann, COO at Coinlocally. “Listing these pairs with zero-fee trading is a practical way to make the product easier to try and more accessible to a wider range of traders.”

The rollout reflects Coinlocally’s broader strategy of connecting traditional market exposure with digital asset trading. The platform supports more than 600 digital assets across spot, margin, and futures markets, with tools for both retail and professional users. The new tokenized stock pairs expand that offering by bringing another set of familiar market names onto the platform.

Coinlocally has also been building out a wider product ecosystem beyond its main trading markets. In addition to spot and derivatives trading, the platform offers services such as P2P trading, Earn, Launchpad, and educational resources aimed at users with different levels of experience. Within that broader mix, the new stock pairs give users another way to access tokenized versions of traditional assets without leaving the platform.

Users can visit Coinlocally’s trading platform to explore the newly listed tokenized stock pairs and start trading with zero fees.

About Coinlocally

Founded in 2020, Coinlocally is a global fintech and digital asset exchange offering secure, fast, and transparent access to cryptocurrency and forex markets. With high liquidity and advanced trading tools, including spot, futures, bot trading, grid strategies, and copy trading, the platform serves both beginners and professional traders worldwide. Coinlocally’s mission is to bridge traditional finance with the emerging world of decentralized finance, empowering users with greater control of their assets through a compliance-driven, seamless transition from centralized (CEX) to decentralized (DEX) trading and broader Web3 innovation.

For more information, users can visit coinlocally.com or follow Coinlocally on Telegram or X.

Disclaimer: TheNewsCrypto does not endorse any content on this page. The content depicted in this Press Release does not represent any investment advice. TheNewsCrypto recommends our readers to make decisions based on their own research. TheNewsCrypto is not accountable for any damage or loss related to content, products, or services stated in this Press Release.

Domande pertinenti

QWhat new feature did Coinlocally launch alongside the 10 new tokenized stock pairs?

ACoinlocally launched a zero-fee trading campaign for all the newly-listed stock pairs.

QWhich major companies are included in the new tokenized stock listings on Coinlocally?

AThe new listings include Tesla, Amazon, Apple, NVIDIA, Alphabet, and others.

QUntil what date will the zero-fee trading campaign for the new stock pairs last?

AThe zero-fee trading campaign will last through May 14, 2026.

QAccording to the article, what is the total distributed on-chain value of tokenized real-world assets (RWAs) in the digital asset market?

AThere is more than $26 billion in distributed on-chain value for tokenized real-world assets.

QWho is the COO of Coinlocally that commented on the new listings?

ASam Baumann is the COO of Coinlocally.

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.

marsbit3 min fa

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

marsbit3 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.

marsbit5 min fa

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

marsbit5 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.

ambcrypto13 min fa

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

ambcrypto13 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