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Geeky Gadgets
an hour ago
- Business
- Geeky Gadgets
From iOS 19 to iOS 26: Apple's iOS Leap Explained
Apple is making a significant shift in its operating system strategy by skipping iOS 19 and jumping directly to iOS 26. This bold decision aligns the version numbers with the calendar year 2026, reflecting a broader rebranding initiative across its platforms, including macOS, iPadOS, watchOS, tvOS, and visionOS. By unifying version numbers and introducing a major visual redesign, Apple aims to strengthen its ecosystem, enhance user experience, and address criticisms of its AI and software development efforts. This move is not just about aesthetics or branding. It represents a calculated strategy to solidify Apple's position in the tech industry. The video below from Proper Honest Tech gives us more details on what Apple has planned. Why the Leap to iOS 26? Apple's decision to align its operating system version numbers with the calendar year is a deliberate and strategic move designed to simplify its ecosystem. This change will apply across all platforms, creating a unified and consistent experience for both users and developers. For you, this means a more intuitive update cycle and seamless integration across devices, whether you're using an iPhone, iPad, Mac, or Apple Watch. By standardizing version numbers, Apple not only reinforces its brand identity but also makes it easier to track updates and understand the features tied to each release. This alignment reflects Apple's commitment to clarity and consistency, making sure that its ecosystem remains user-friendly and interconnected. A Fresh Look: VisionOS-Inspired Redesign The transition to iOS 26 will introduce the first major visual overhaul of iOS since the release of iOS 7 in 2013. Drawing inspiration from VisionOS, Apple's augmented reality platform, the redesign will feature a translucent, layered interface with softer, curved aesthetics. This new design language is intended to modernize the user experience while maintaining a sense of familiarity. For you, this redesign means a fresh, visually appealing interface that feels intuitive and comfortable. The updated design will extend across Apple's entire ecosystem, making sure a cohesive visual experience whether you're navigating your iPhone, using your Mac, or exploring Vision Pro. This unified aesthetic underscores Apple's focus on creating a seamless and immersive environment for its users. AI and Siri: Closing the Competitive Gap Apple has faced criticism for lagging behind competitors in the field of artificial intelligence, but the iOS 26 update signals a renewed focus on AI-driven features. These advancements aim to enhance functionality and provide a more personalized user experience. Key updates include: Context-Aware Siri: Siri will become more intelligent, capable of handling complex tasks such as searching emails, editing photos, and offering personalized assistance based on your habits and preferences. Siri will become more intelligent, capable of handling complex tasks such as searching emails, editing photos, and offering personalized assistance based on your habits and preferences. AI-Powered Battery Management: Your device will use machine learning to optimize battery usage based on your daily routines, extending battery life without requiring manual adjustments. Your device will use machine learning to optimize battery usage based on your daily routines, extending battery life without requiring manual adjustments. AI-Driven Health Coach: Using data from your Apple Watch and other devices, this feature will provide personalized health insights and recommendations to help you achieve your wellness goals. While these updates represent significant progress, Apple's long-term goal is to develop a large language model (LLM) Siri. However, this ambitious project is expected to take years to fully materialize. For now, these incremental improvements demonstrate Apple's commitment to closing the gap with competitors and enhancing the practical utility of its AI features. Accessibility Enhancements: Technology for Everyone Apple continues to prioritize inclusivity with new accessibility features designed to make its devices usable for everyone. These updates reflect Apple's ongoing commitment to making sure that its technology serves a diverse range of users. Key enhancements include: App Store Accessibility Labels: These labels will help you identify apps that support specific accessibility features, making it easier to find tools that meet your needs. These labels will help you identify apps that support specific accessibility features, making it easier to find tools that meet your needs. Improved CarPlay Accessibility: Enhancements to CarPlay will provide a safer and more user-friendly experience for drivers with disabilities. Enhancements to CarPlay will provide a safer and more user-friendly experience for drivers with disabilities. Reader Mode: A new Reader mode will improve text readability, making sure content is accessible to all users, including those with visual impairments or reading difficulties. These updates highlight Apple's dedication to creating technology that is inclusive and adaptable, making sure that no user is left behind in its ecosystem. Strategic Implications: Strengthening Apple's Ecosystem This rebranding and redesign effort is more than a cosmetic change—it represents a strategic move to solidify Apple's ecosystem. For Apple, it's an opportunity to rebuild consumer trust after facing criticism regarding its AI strategy and software development. For you, it means a more cohesive and integrated experience across all Apple devices, making it increasingly difficult to switch to competitors. By focusing on ecosystem integration, Apple ensures that its devices and services remain indispensable. This strategy not only enhances user loyalty but also positions Apple as a leader in the tech industry. The leap to iOS 26 is a clear signal of Apple's intent to innovate while addressing past shortcomings, making sure that its ecosystem remains both relevant and competitive. What's Next? Incremental Updates and Long-Term Goals Looking ahead, you can expect incremental updates to core apps like Notes, Reminders, and Safari, alongside deeper AI integration into existing tools. While some of the promised AI and Siri upgrades may face delays, Apple remains committed to delivering polished, reliable features that enhance the overall user experience. This strategic shift positions Apple to innovate while addressing past challenges. By focusing on long-term goals and incremental improvements, Apple aims to stay competitive in an ever-evolving tech landscape. For you, this means continued enhancements that prioritize usability, functionality, and seamless integration across all Apple devices. Check out more relevant guides from our extensive collection on iOS 26 that you might find useful. Source & Image Credit: Proper Honest Tech Latest Geeky Gadgets Deals 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
Independent Singapore
18 hours ago
- Business
- Independent Singapore
Man finds out his colleague earns S$500 more than him, asks if he should speak up or stay silent
SINGAPORE: A Singapore-based tech sales employee recently discovered that he's being paid hundreds of dollars less than a colleague who joined the company at the same time. Posting anonymously on the r/askSingapore forum, the employee shared that the topic of pay arose during a casual dinner with his teammates, where they began comparing their base salaries. 'We were discussing our base pay, and I realized that I've been getting paid significantly less (around S$400 to S$500),' he said. 'At first, I thought it might be because they had been with the company longer, but one colleague who joined at the same time as me (we even went through the interview process together last year) is being paid much more as well.' Uncertain about how to proceed, he turned to the online community for advice, asking, 'Am I supposed to just accept this, or should I bring it up with HR? Even though I know that might go against what the contract says?' 'The salary offered will differ from one person to another…' In the comments, one user pointed out that while the situation may feel unfair, the employee had ultimately agreed to his salary terms when he accepted the offer. 'You agreed to your terms of employment when you signed the contract,' the user wrote. 'You can ask, but there is really no basis. I mean, you agreed on the contract and (signed), right?' Another added, 'In the same job role, there are several factors to be considered that qualify you—market rates (at that moment), experience, qualifications, particular niche skillsets, and others. Hence, the salary offered will differ from one person to another. Every candidate is different, even for the same role. You also agreed to the contract when you signed the offer.' A third, however, urged him to 'leave' the company, adding, 'You're responsible for yourself. Your boss and HR are not responsible for increasing your pay. Their priority is for the company, not you. Now that you know what the basic pay is for your job, it should be easier to go find another job and negotiate. Heck, you can even find higher pay; that's what job hopping is for.' What to do when you find out your colleague earns more Finding out a colleague earns more than you can feel like a punch to the gut, or at least a hard nudge, but before you jump to conclusions or spiral into frustration, take a moment to pause. According to staffing agency Mondo, there are a few helpful steps you can take to make sense of the situation: Assess your colleague's background Your colleague may possess more years of experience, different qualifications, or a longer tenure with the company, which could have contributed to several salary increases over time. Research about your role Use this as a chance to reflect on where you're at. Research what people in similar roles are earning in your field and location. Sites like Glassdoor or Payscale can give you a rough idea of what's fair. Bring it up with your manager If, after your research, you still feel like you're underpaid, you can bring it up with your manager. However, it's important to remember that when you do have that conversation, focus on your own work, progress, and what you bring to the team. Read also: 'The reno worker just let them in': Woman horrified as neighbours invade her new BTO unit
Yahoo
2 days ago
- Business
- Yahoo
DigitalOcean Holdings, Inc. (DOCN): A Bull Case Theory
We came across a bullish thesis on DigitalOcean Holdings, Inc. (DOCN) on Rene Sellmann's Substack. In this article, we will summarize the bulls' thesis on DOCN. DigitalOcean Holdings, Inc. (DOCN)'s share was trading at $28.82 as of 27th May. DOCN's trailing and forward P/E were 25.50 and 15.02 respectively according to Yahoo Finance. Copyright: melpomen / 123RF Stock Photo DigitalOcean stands out as a capital-disciplined, customer-focused cloud provider serving startups, developers, and small to mid-sized businesses underserved by hyperscalers like AWS and Azure. Its core advantage lies in offering a frictionless experience—simple pricing, rapid deployment, and intuitive UX—tailored for teams that value speed, control, and predictability. While traditionally viewed as a 'starter cloud,' DigitalOcean has begun reversing customer churn by expanding platform depth and targeting higher-value accounts. The number of customers spending over $100K annually rose 41% YoY in Q1, showing early success in its move upmarket. Metrics such as Net Dollar Retention (a proxy for monetization and satisfaction), growth in Scalers+ accounts, and free cash flow margin form the essential dashboard for tracking its operational execution, with rising GPU workloads and GenAI adoption offering long-term upside. Despite lacking the scale advantages of hyperscalers, DigitalOcean operates in a stable, cash-generative segment of cloud infrastructure where recurring revenue, modest churn, and growing capital efficiency create increasing predictability. Its niche is defensible because hyperscalers over-serve this segment with complexity, and most smaller competitors lack its support quality, developer brand, or productized experience. The business is not without risks—particularly from low-end competition and evolving CapEx needs—but its focused approach, improving retention, and solid FCF profile position it well. It won't become AWS, but it does not have to. By continuing to execute against its playbook—prioritizing customer value, simplicity, and operational leverage—DigitalOcean can carve out a durable, profitable space in a structurally sound and often misunderstood corner of the cloud market. For a deeper look into another technology stock, be sure to check out our article on Microsoft Corporation (MSFT), wherein we summarized a bullish thesis by Ray Myers on Substack. Since our coverage, the stock is up 0.40%. DigitalOcean Holdings, Inc. (DOCN) is not on our list of the 30 Most Popular Stocks Among Hedge Funds. As per our database, 20 hedge fund portfolios held DOCN at the end of the first quarter which was 18 in the previous quarter. While we acknowledge the risk and potential of DOCN as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns, and doing so within a shorter timeframe. If you are looking for an AI stock that is more promising than DOCN but that trades at less than 5 times its earnings, check out our report about the cheapest AI stock. READ NEXT: 8 Best Wide Moat Stocks to Buy Now and 30 Most Important AI Stocks According to BlackRock. Disclosure: None. This article was originally published at Insider Monkey. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Geeky Gadgets
3 days ago
- Business
- Geeky Gadgets
How to Become a Data Engineer: Essential Skills and Strategies for 2025
What if you could future-proof your career by stepping into one of the most in-demand tech roles of the decade? As companies increasingly rely on data to drive decisions, the role of a data engineer has evolved from a behind-the-scenes function to a cornerstone of organizational success. This field demands not just technical expertise but also the ability to navigate a rapidly changing landscape of tools, methodologies, and real-world challenges. Whether you're drawn to the idea of designing scalable data pipelines or solving complex problems with innovative cloud technologies, the path to becoming a data engineer is as rewarding as it is rigorous. But here's the catch: without a clear roadmap, it's easy to lose focus or fall into common traps that could derail your progress. In this walkthrough, Mo Chen breaks down the essential steps to help you thrive in this competitive field. You'll uncover the key skills that set successful data engineers apart, from mastering foundational tools like SQL to building robust data pipelines that stand up to real-world demands. Along the way, you'll learn how to avoid pitfalls like overengineering solutions or neglecting data quality—mistakes that could cost you both time and credibility. This guide doesn't just skim the surface; it prepares you to tackle the challenges of 2025 and onward with confidence and clarity. So, what does it really take to build a career in data engineering? Let's explore the principles, strategies, and insights that can turn your aspirations into reality. Data Engineering Career Guide Common Mistakes to Avoid Starting your journey as a data engineer requires awareness of common mistakes that can hinder your progress. By identifying these missteps early, you can save time and effort while building a solid foundation for your career. Overemphasizing tools instead of principles: While tools like Apache Spark, Hadoop, or cloud platforms are important, a strong grasp of core concepts—such as data modeling, distributed systems, and pipeline architecture—is crucial for long-term success. While tools like Apache Spark, Hadoop, or cloud platforms are important, a strong grasp of core concepts—such as data modeling, distributed systems, and pipeline architecture—is crucial for long-term success. Neglecting data quality and validation: Reliable data pipelines depend on robust validation processes. Ignoring this can lead to inaccurate analyses and flawed decision-making. Reliable data pipelines depend on robust validation processes. Ignoring this can lead to inaccurate analyses and flawed decision-making. Overengineering solutions: Simplicity is key. Overcomplicating architectures often results in inefficiencies, increased maintenance costs, and reduced scalability. Simplicity is key. Overcomplicating architectures often results in inefficiencies, increased maintenance costs, and reduced scalability. Skipping documentation: Comprehensive documentation ensures that your systems are maintainable and scalable. It also minimizes knowledge gaps within teams, making collaboration smoother. Comprehensive documentation ensures that your systems are maintainable and scalable. It also minimizes knowledge gaps within teams, making collaboration smoother. Ignoring error handling and monitoring: Without proper monitoring and error management, you risk prolonged downtime, operational inefficiencies, and difficulty troubleshooting issues. By avoiding these pitfalls, you can streamline your learning process and focus on building effective, scalable systems. Major Challenges in Data Engineering Data engineering is a dynamic and rapidly evolving field. As you progress in your career, you will face challenges that test both your technical expertise and strategic thinking. Understanding these hurdles can help you prepare effectively. Keeping pace with technological advancements: The data engineering landscape evolves quickly, with new tools, frameworks, and methodologies emerging regularly. Staying updated is essential to remain competitive. The data engineering landscape evolves quickly, with new tools, frameworks, and methodologies emerging regularly. Staying updated is essential to remain competitive. Understanding the full data ecosystem: A holistic view of data flow—from ingestion and storage to processing and analysis—is critical for designing efficient and reliable systems. A holistic view of data flow—from ingestion and storage to processing and analysis—is critical for designing efficient and reliable systems. Bridging theory and practice: Applying theoretical knowledge to real-world scenarios, particularly when working with large-scale datasets, can be challenging but is necessary for success. Applying theoretical knowledge to real-world scenarios, particularly when working with large-scale datasets, can be challenging but is necessary for success. Simulating production environments: Building scalable and reliable solutions requires experience with realistic datasets and production-like environments to test and refine your systems. Anticipating these challenges will help you develop the resilience and adaptability needed to excel in this field. How to Become a Data Engineer in 2025 Watch this video on YouTube. Here are more detailed guides and articles that you may find helpful on Data Engineering. Essential Skills for Success To thrive as a data engineer, you must cultivate a diverse skill set that encompasses technical expertise, problem-solving abilities, and practical experience. Below are the core areas to focus on: SQL Mastery SQL remains a fundamental skill for data engineers. Mastering both basic and advanced SQL concepts will enable you to work effectively with relational databases. Key areas to focus on include: Data modeling and schema design Query optimization for performance Transaction management and concurrency control Building portfolio projects, such as designing a relational database or optimizing queries for large datasets, can showcase your SQL expertise to potential employers. Cloud Computing Expertise Cloud platforms like AWS, Google Cloud, and Azure are integral to modern data engineering. Developing proficiency in cloud computing will allow you to design scalable and cost-efficient solutions. Focus on: Understanding core cloud services, including storage, compute, and data warehousing Learning infrastructure-as-code tools like Terraform or CloudFormation Implementing cost optimization strategies to manage cloud expenses effectively These skills will ensure you can build and maintain robust cloud-based data systems. Data Pipeline Development Building and maintaining data pipelines is a core responsibility for data engineers. To excel in this area, you should: Understand ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes Learn Python, a widely used programming language for data engineering Gain experience with batch and streaming data processing Use tools like Apache Airflow for workflow orchestration Implement data quality validation to ensure reliable outputs These capabilities will enable you to create efficient, scalable, and resilient data pipelines. Practical Advice for Aspiring Data Engineers Breaking into the field of data engineering requires more than technical knowledge. Here are some actionable tips to help you stand out in the competitive job market: Focus on mastering a few key tools: Instead of spreading yourself thin, prioritize widely used tools and platforms. For example, gaining expertise in Apache Kafka or Snowflake can significantly enhance your employability. Instead of spreading yourself thin, prioritize widely used tools and platforms. For example, gaining expertise in Apache Kafka or Snowflake can significantly enhance your employability. Build real-world projects: Create projects that address practical problems, such as designing a data pipeline to process streaming data from a public API. These projects demonstrate your ability to apply technical skills in meaningful ways. Create projects that address practical problems, such as designing a data pipeline to process streaming data from a public API. These projects demonstrate your ability to apply technical skills in meaningful ways. Develop strong professional habits: Emphasize documentation, error handling, and monitoring in your workflows. These practices not only improve the quality of your work but also assist collaboration with team members. By following these steps, you can position yourself as a capable and reliable candidate, ready to tackle the challenges of data engineering. Building a Future in Data Engineering Becoming a data engineer in 2025 requires a principle-driven approach to learning and problem-solving. By mastering foundational concepts, staying adaptable to new technologies, and focusing on practical applications, you can establish yourself as a skilled professional in this dynamic field. Avoid common mistakes, proactively address challenges, and develop the essential skills outlined in this guide to build a successful and fulfilling career in data engineering. Media Credit: Mo Chen Filed Under: 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.
Globe and Mail
3 days ago
- Business
- Globe and Mail
The Nasdaq Bounces Back: These AI Stocks Could Be the Smartest Buys of the Year
The Nasdaq Composite is only at about breakeven for the year, as of this writing, but it's made a powerful comeback in recent weeks. The tech-heavy index is now down less than 1% year to date, a massive improvement from the drop of more than 20% it recorded in early April. That doesn't mean it's too late to own tech names that are thriving from the boom in artificial intelligence (AI) spending. Here are three names that are in a prime position to benefit from the expansion of generative and agentic AI in the enterprise, education, healthcare, and beyond. Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now. Learn More » King of the artificial intelligence (AI) ecosystem The best way to profit from any growing field is to build an ecosystem around it. Think about how successful Apple has been with its app store, iPhone, Mac computers, and iCloud offerings. That's the approach Nvidia (NASDAQ: NVDA) is taking with hardware, software, and architecture for AI. CEO Jensen Huang is a proven visionary, and the company even has growth triggers beyond the world of AI. AI is enabling change and improvement in every industry. Uses include generative AI and speech recognition, medical imaging and more efficient supply chain management. Nvidia is delivering enterprises the computing power, systems, tools, and algorithms needed. It's not just the powerful graphics processing units (GPUs) like Blackwell and the next-generation Rubin AI chips that Nvidia supplies, either. Nvidia's NeMo provides a full platform for developing custom generative AI -- including large language models (LLMs), video models, and conversational AI. Its CUDA (compute unified device architecture) software platform allows developers to leverage Nvidia's leading chips using parallel processing power for more intensive applications. Nvidia's CUDA-Q is a quantum development platform enabling the use of large-scale quantum computing applications. This is just a small sample of examples showing Nvidia's reach throughout the ecosystem. Nvidia is heavily involved even at the earliest stages of building AI factories where enterprises can deploy on-premises scalable, high-performance, AI platforms. Nvidia is also part of larger-scale projects like the recently announced United Arab Emirates Stargate global tech alliance. That will become a massive 1-gigawatt compute capacity data center. Look upstream and downstream of Nvidia, too These offerings and projects all create a flywheel effect that should only increase demand for Nvidia's products in the months and years ahead. It won't be the only beneficiary, though. Other obvious places to look next are Nvidia's direct upstream and downstream partners. Taiwan Semiconductor (TSMC) (NYSE: TSM) fabricates many of the advanced chips that Nvidia and other major semiconductor companies design. So it shouldn't be surprising that the Nvidia supplier and partner saw revenue surge nearly 42% higher in the first quarter. Those are profitable sales, too. Net income soared 60% versus the prior-year period. TSMC is also well on its way to accomplishing the approximately 40% revenue growth guidance it provided for the current quarter. April net sales jumped 48.1% compared to 2024. One Nvidia customer that is also positioned to benefit from growth in data centers and AI compute capacity is Dell Technologies (NYSE: DELL). Dell is also a partner, working closely with Nvidia. It recently announced the "Dell AI Factory with Nvidia," which it says is "designed to help enterprises accelerate AI adoption and achieve faster time to value." Dell is using its latest air- and liquid-cooled server solutions for AI servers and racks containing Nvidia hardware to deploy in data centers. The collaboration will help accelerate enterprises ramping up computing power. Dell is quickly gaining business in this area. Its server and networking revenue surged 37% in the fiscal fourth quarter ended Jan. 31. At that time, the company said its server business backlog more than tripled year over year to $9 billion. All three companies play an important role in the booming artificial intelligence field. Each has its own role and expertise. Investors would do well to own all three to participate in the growth over the course of 2025 and beyond. Should you invest $1,000 in Nvidia right now? Before you buy stock in Nvidia, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and Nvidia wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $651,761!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $826,263!* Now, it's worth noting Stock Advisor 's total average return is978% — a market-crushing outperformance compared to170%for the S&P 500. Don't miss out on the latest top 10 list, available when you join Stock Advisor. See the 10 stocks » *Stock Advisor returns as of May 19, 2025
