logo
Crypto token Solana ETF hopefuls amend filings with SEC

Crypto token Solana ETF hopefuls amend filings with SEC

Reuters15 hours ago

June 13 (Reuters) - A slew of companies seeking to launch exchange-traded funds tracking the price of crypto token Solana amended their filings with the U.S. Securities and Exchange Commission on Friday.
The amendments are intended to address the queries of the U.S. markets regulator, but there is "no sense of urgency" on the SEC's part to move forward with the launches, a person familiar with the matter told Reuters.
The filings were made by the Canary Marinade Solana ETF, 21Shares Core Solana ETF and Bitwise Solana ETF, among others.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

UK broadcasters hail rare win over Netflix in battle for streaming ads
UK broadcasters hail rare win over Netflix in battle for streaming ads

The Guardian

timean hour ago

  • The Guardian

UK broadcasters hail rare win over Netflix in battle for streaming ads

Shows such as Netflix's TV history-making Adolescence and Disney's romp Rivals are among the latest hits to continue the subscriber juggernaut, as the US streamers continue to mount pressure on UK TV broadcasters. However, research reveals that a new breed of viewers being banked on to drive their next era of growth are watching up to 40% less content on some services, giving traditional broadcasters hope that their own streaming services will not ultimately be outmuscled in the battle over the rapidly growing £1bn-plus streaming ad market. It has been two and a half years since Netflix reversed its resistance to advertising, leading the charge to tap a new market as subscriber growth petered out and the cost of living crisis made consumers more open to paying less in return for seeing ads. The strategy has helped breathe life into stalling subscription growth. Netflix added the most customers in a quarter in its history in the final three months of 2024, with 55% choosing its ad-supported package. About a third of its 300 million-strong global subscriber base are now watching with ads. Disney+ followed suit in late 2022 and has since amassed 157 million ad-tier subscribers, including its US-only ESPN and Hulu services. And in February last year, Amazon started automatically introducing ads to the 200 million potential monthly viewers of Prime Video, requiring customers to pay if they wanted an ad-free experience. However, research into streaming households shows that homes that watch with ads are 'lighter' viewers, in the words of one media agency executive, compared with those who pay for higher-priced, ad-free packages. A snapshot of UK streaming in the fourth quarter of 2024 showed that Netflix households with advertising-supported subscriptions watched an average of 22 minutes less content a day than those with an ad-free subscription, a difference of almost 22%. Netflix is estimated to have about 17.6 million subscribers in total in the UK, of whom just over 4 million are on an ad-supported package, according to Ampere Analysis. At Amazon's Prime Video, which is estimated to have about 12 million UK users, the same trend has emerged. Viewers who accepted ads watched an average of 23 minutes less content a day than those who had opted to pay for an ad-free experience – a difference of 44%. While viewing minutes were not available for Disney+ UK subscribers, the research showed it had the narrowest gap, with those on ad-supported accounts watching just five fewer minutes of content a day on average than those paying for an ad-free subscription. Matt Ross, the chief analytics officer at the streaming research firm Digital i, says two distinct types of viewer have emerged, but adds that lower levels of viewing in ad-supported households is partly because those subscriptions also typically offer access on fewer devices. 'We've seen that more engaged viewers typically opt for ad-free tiers, valuing the uninterrupted experience they provide,' Ross says. 'More premium plans offer multiple simultaneous streams, which appeals especially to larger households and families. This combination of premium features and flexibility often results in higher daily activity for ad-free plans.' Nevertheless, the phenomenon of 'light viewers' will be grasped by UK broadcasters trying to stop the deep-pocketed US giants conquering the streaming advertising market in the same way as they have the world of paid subscriptions. 'The appeal of the global streamers' ad tiers to advertisers doesn't stack up against the streaming services offered by British broadcasters,' says one senior TV industry executive. Certainly in the UK, at least, the drive into advertising by the big US streamers has had a mixed reception from the media agencies that buy commercial space for brands. Netflix started with a gung-ho attitude, buoyed up by the success it had had building a huge paid subscriber base and the belief advertisers would leap at the chance to be able to place commercials in its mega-hits for the first time. However, it demanded almost 50% more than ITV or Channel 4's services charge for advertising, alongside a hefty commitment to a minimum spend, despite initially only having a small audience and extremely limited ability to target ads. 'The rollout was a disaster,' says the chief executive of one media agency. 'Take-up was underwhelming, to say the least. They had to try again six months later and lost their lead over rivals and are now behind the curve in terms of pricing, data and reach versus, say, Amazon.' Amazon charges about the same as the public service broadcasters' streaming services, while Disney+ charges more, despite having the smallest base of the big three US streamers, a situation the media executive describes as a 'mad outlier, given their volume'. Sign up to Business Today Get set for the working day – we'll point you to all the business news and analysis you need every morning after newsletter promotion Last month Netflix rung the changes, announcing that Warren Dias, the head of UK's ad sales, was to leave after two years in the post. The world's biggest and most profitable streaming service has acknowledged it is still a newbie when it comes to the ad market. 'I think you can say that 2025 is the year that we transition from crawl to walk,' Greg Peters, the co-chief executive of Netflix, said in a recent call with analysts. Peters said overall viewing hours per subscriber on its ads plans internationally was similar to those on its standard non-ad plans, and that it expected to double advertising income this year as it focuses on improving ad targeting for brands. The company launched its in-house ad-tech platform in the US in April and intends to start rolling it out to other markets in the coming months. While UK broadcasters feel the tentative start by the US giants has given them the upper hand in the British streaming advertising, which is putting further pressure on the shrinking £3.58bn traditional TV ad market, there is a sense of foreboding that history may ultimately repeat itself. 'We were successful and revolutionised TV viewing,' says Damien Bernet, the vice-president of ad sales for the EMEA region at Netflix. 'We believe we are going to be able to do the same for ads.' More people visit and watch Netflix than any other streaming service in the UK, and in March it made TV history with Adolescence becoming the first programme on a streaming platform to top the weekly audience charts of all shows aired in Britain. In February, 65% of 18- to 64-year-old internet users accessed Netflix, compared with 59% for the BBC's iPlayer, 48% for Prime Video, 46% for ITVX and 34% for Channel 4's streaming service, according to survey data from Ampere Analysis. The US streamers' ad tier strategies have reignited overall growth, are rapidly increasing the scale and attractiveness of the offering for advertisers, and the cheaper pricing has made users more 'sticky' and less likely to think about cancelling. 'Fundamentally, advertising is a scale game, and in that regard many of the streamers are only just getting started,' says Richard Broughton, a director at Ampere. 'UK and European broadcasters will be far from complacent, given the competition they have faced for viewers over the past decade, but they have only a narrow window to batten down the hatches before they start to feel more pressure across their advertiser base too.'

When you should (and shouldn't) take out a prenup: Top divorce lawyer VANESSA LLOYD PLATT on how to protect your assets when tying the knot
When you should (and shouldn't) take out a prenup: Top divorce lawyer VANESSA LLOYD PLATT on how to protect your assets when tying the knot

Daily Mail​

timean hour ago

  • Daily Mail​

When you should (and shouldn't) take out a prenup: Top divorce lawyer VANESSA LLOYD PLATT on how to protect your assets when tying the knot

When the children of my friends and clients announce they are getting married, it is not the wedding venue or dress that is the talk of the table – it is whether to prenup or not to prenup. For the uninitiated, a prenup – signed pre-nuptial, i.e. before the wedding – is a contract which defines how assets will be divided should the marriage end. It ultimately helps couples avoid messy and costly disputes in the event of a divorce or separation.

Unlock the Secret to Building Insane AI Video Pipelines with Google Veo3 API
Unlock the Secret to Building Insane AI Video Pipelines with Google Veo3 API

Geeky Gadgets

timean hour ago

  • Geeky Gadgets

Unlock the Secret to Building Insane AI Video Pipelines with Google Veo3 API

Have you ever wondered how some of the most innovative AI systems seamlessly process massive amounts of data, deliver real-time insights, and adapt to ever-changing demands? The secret often lies in the powerful pipelines that fuel their performance. Enter the Google Veo3 API—a versatile tool that's rewriting the rules of AI development. With its ability to integrate natural language processing, image recognition, and advanced analytics into a single framework, this API is a fantastic option for developers and businesses alike. But here's the catch: building a truly insane AI pipeline isn't just about plugging in tools—it's about crafting a system that's efficient, scalable, and tailored to your unique goals. That's where this learning tool from All About AI comes in, guiding you to unlock the full potential of the Google Veo3 API. In this step-by-step guide by All About AI, you'll uncover the secrets to designing a high-performance AI pipeline that doesn't just work—it thrives under pressure. From mastering data collection and API integration to deploying machine learning models and automating workflows, this resource equips you with a clear roadmap to success. But it doesn't stop there. You'll also explore how to optimize for scalability, troubleshoot common challenges, and integrate with platforms like Google Cloud or Tableau to supercharge your system's capabilities. Whether you're an AI enthusiast or a seasoned developer, this guide promises to spark new ideas and empower you to build solutions that redefine what's possible. After all, the real question isn't whether you can build an AI pipeline—it's how far you're willing to take it. What is the Google Veo3 API? The Google Veo3 API is a powerful and versatile tool designed to enable developers to integrate advanced AI capabilities into their systems. It offers a wide range of features, including natural language processing, image recognition, and data analysis, making it an ideal choice for building AI pipelines. By using this API, you can automate complex tasks, enhance system efficiency, and unlock innovative possibilities for AI-driven solutions. Its adaptability allows businesses to streamline operations and improve decision-making processes, making it a valuable asset for organizations aiming to harness the power of artificial intelligence. Building AI with Google Veo3 Designing an AI pipeline with the Google Veo3 API involves a clear and methodical approach. Begin by defining the objectives of your system. Determine the specific tasks it will perform, such as data preprocessing, model training, or real-time inference. Once your goals are established, follow these essential steps: Data Collection: Gather and prepare high-quality data for training and testing your AI models. Ensure the dataset is clean, diverse, and relevant to improve the accuracy and reliability of your models. Gather and prepare high-quality data for training and testing your AI models. Ensure the dataset is clean, diverse, and relevant to improve the accuracy and reliability of your models. API Integration: Connect the Google Veo3 API to your system by following the API documentation. Configure endpoints, set up authentication protocols, and define data input/output formats to ensure seamless integration. Connect the Google Veo3 API to your system by following the API documentation. Configure endpoints, set up authentication protocols, and define data input/output formats to ensure seamless integration. Model Deployment: Incorporate machine learning models into your pipeline. The API supports various frameworks, allowing you to choose the one that aligns best with your project requirements. Incorporate machine learning models into your pipeline. The API supports various frameworks, allowing you to choose the one that aligns best with your project requirements. Workflow Automation: Automate repetitive tasks such as data preprocessing, model retraining, and performance monitoring. This reduces manual effort and ensures consistent operations. By following these steps, you can create a streamlined and efficient AI pipeline tailored to your specific needs. Constructing a Google Veo3 API AI Pipeline Watch this video on YouTube. Here are additional guides from our expansive article library that you may find useful on Google Veo 3. Enhancing Functionality with Platform Integration To maximize the potential of your AI pipeline, consider integrating the Google Veo3 API with other platforms and tools. Strategic integrations can significantly enhance functionality and improve the overall user experience. Here are some key options: Cloud Services: Use platforms like Google Cloud or AWS to provide scalable storage and processing power. These services ensure your pipeline can handle large datasets and high traffic volumes efficiently. Use platforms like Google Cloud or AWS to provide scalable storage and processing power. These services ensure your pipeline can handle large datasets and high traffic volumes efficiently. Data Visualization Tools: Use tools such as Tableau or Google Data Studio to present insights in a clear and actionable format, allowing better decision-making and communication of results. Use tools such as Tableau or Google Data Studio to present insights in a clear and actionable format, allowing better decision-making and communication of results. Third-Party Applications: Integrate with CRM systems, analytics platforms, or IoT devices to expand the pipeline's capabilities and address specific business needs. These integrations not only enhance the pipeline's functionality but also enable seamless collaboration across different systems and teams. Optimizing for Performance and Scalability Making sure your AI pipeline operates efficiently under varying workloads requires a focus on optimization and scalability. Implementing the following strategies can help maintain performance and prepare your system for future growth: Resource Allocation: Monitor and allocate computational resources effectively to prevent bottlenecks and ensure smooth operations. Monitor and allocate computational resources effectively to prevent bottlenecks and ensure smooth operations. Model Fine-Tuning: Regularly update and fine-tune your machine learning models to maintain accuracy and adapt to changing data patterns. Regularly update and fine-tune your machine learning models to maintain accuracy and adapt to changing data patterns. Load Balancing: Distribute tasks across multiple servers to handle high traffic volumes and ensure system reliability. By designing your pipeline with scalability in mind, you can accommodate increases in data volume or user demand without compromising performance or efficiency. Troubleshooting Common Challenges Building and maintaining an AI pipeline can present several challenges. Addressing these issues proactively is essential to ensure a stable and reliable system. Here are some common challenges and their solutions: API Errors: Consult the API documentation to identify error codes and implement recommended solutions. Regularly update your API configurations to avoid compatibility issues. Consult the API documentation to identify error codes and implement recommended solutions. Regularly update your API configurations to avoid compatibility issues. Latency Issues: Optimize network configurations, implement caching mechanisms, and use content delivery networks (CDNs) to reduce delays and improve response times. Optimize network configurations, implement caching mechanisms, and use content delivery networks (CDNs) to reduce delays and improve response times. Data Inconsistencies: Establish robust data validation checks and logging systems to identify and resolve discrepancies in your datasets. By addressing these challenges effectively, you can minimize downtime, enhance system reliability, and maintain a smooth user experience. Real-World Applications of the Google Veo3 API The Google Veo3 API is highly adaptable and can be applied across a wide range of industries and use cases. Here are a few examples of its real-world applications: Customer Support: Develop AI-powered chatbots to handle customer inquiries efficiently, providing quick and accurate responses at scale. Develop AI-powered chatbots to handle customer inquiries efficiently, providing quick and accurate responses at scale. Healthcare: Use the API for medical image analysis, patient data management, and diagnostics, improving the accuracy and speed of healthcare services. Use the API for medical image analysis, patient data management, and diagnostics, improving the accuracy and speed of healthcare services. E-commerce: Implement personalized product recommendations, dynamic pricing strategies, and inventory optimization to enhance the shopping experience and drive sales. These examples highlight the API's ability to drive innovation and improve operational efficiency across diverse sectors, making it a valuable tool for businesses looking to stay competitive in today's AI-driven landscape. Media Credit: All About AI Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store