Reading Time: 9 minutes In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential. That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success. In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers. You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI. Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.   Mirela Cialai Q&A Interview 1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience? Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives. This could be revenue growth, customer retention, market expansion, or operational efficiency. We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition. We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals. In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance. This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth. Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings. Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences. To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale. By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals. 2. What steps did you take to ensure data accuracy? The data team was very diligent in ensuring that our data warehouse had accurate data. So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc. That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data. 3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy? Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability. I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%. This data helps make a compelling case to stakeholders about the importance of prioritizing retention. Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth. This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives. By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy. 4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement? Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach. The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives. I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse. Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows. Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities. Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape. 5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for? I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels. Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns. Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns. Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability. If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs. 6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap? Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes. Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact. Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert. By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success. 7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives? To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success. Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value. Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities. Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth. By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs. In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability. In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first. 8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you? Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability. We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success. To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams. To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together. 9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like? A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine. In one word: PAPER. Here’s how it breaks down. Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals. Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps. Prioritize: initiatives based on impact, feasibility, and ROI potential. Execute: by implementing the roadmap in manageable phases. Refine: by continuously improving CRM performance and refining the roadmap. So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy. 10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively? The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences. The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth. Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies. The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes. Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution. A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions. Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others. While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends. By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success. 11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind? I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives. Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives. Another important lesson: The roadmap is only as effective as the data and systems it’s built upon. I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on. A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers. So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.     This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage. #mirela #cialai #qampampa #customer #engagement

Many don’t like it, buteverybody agrees it’s the future.“Vibe Coding” is everywhere. Tools and game engines are implementing AI-assisted coding, vibe coding interest skyrocketed on Google search, on social media, everybody claims to build apps and games in minutes, while the comment section gets flooded with angry developers calling out the pile of garbage code that will never be shipped.A screenshot from Andrej Karpathy with the original “definition” of Vibe CodingBUT, how do professionals feel about it?This is what I will cover in this article. We will look at:How people react to the term vibe coding,How their attitude differs based on who they are and their professional experienceThe reason for their stance towards “vibe coding”How they feel about the impact “vibe coding” will have in the next 5 yearsIt all started with this survey on LinkedIn. I have always been curious about how technology can support creatives and I believe that the only way to get a deeper understanding is to go beyond buzzwords and ask the hard questions. That’s why for over a year, I’ve been conducting weekly interviews with both the founders developing these tools and the creatives utilising them. If you want to learn their journeys, I’ve gathered their insights and experiences on my blog called XR AI Spotlight.Driven by the same motives and curious about people’s feelings about “vibe coding”, I asked a simple question: How does the term “Vibe Coding” make you feel?Original LinkedIn poll by Gabriele RomagnoliIn just three days, the poll collected 139 votes and it was clear that most responders didn’t have a good “vibe” about it. The remaining half was equally split between excitement and no specific feeling.But who are these people? What is their professional background? Why did they respond the way they did?Curious, I created a more comprehensive survey and sent it to everyone who voted on the LinkedIn poll.The survey had four questions:Select what describes you best: developers, creative, non-creative professionalHow many years of experience do you have? 1–5, 6–10, 11–15 or 16+Explain why the term “vibe coding” makes you feel excited/neutral/dismissive?Do you think “vibe coding” will become more relevant in the next 5 years?: It’s the future, only in niche use cases, unlikely, no idea)In a few days, I collected 62 replies and started digging into the findings, and that’s when I finally started understanding who took part in the initial poll.The audienceWhen characterising the audience, I refrained from adding too many options because I just wanted to understand:If the people responding were the ones making stuffWhat percentage of makers were creatives and what developersI was happy to see that only 8% of respondents were non-creative professionals and the remaining 92% were actual makers who have more “skin in the game“ with almost a 50/50 split between creatives and developers. There was also a good spread in the degree of professional experience of the respondents, but that’s where things started to get surprising.Respondents are mostly “makers” and show a good variety in professional experienceWhen creating 2 groups with people who have more or less than 10 years of experience, it is clear that less experienced professionals skew more towards a neutral or negative stance than the more experienced group.Experienced professionals are more positive and open to vibe codingThis might be because senior professionals see AI as a tool to accelerate their workflows, while more juniors perceive it as a competitor or threat.I then took out the non-professional creatives and looked at the attitude of these 2 groups. Not surprisingly, fewer creatives than developers have a negative attitude towards “vibe coding”, but the percentage of creatives and developers who have a positive attitude stays almost constant. This means that creatives have a more indecisive or neutral stance than developers.Creatives have a more positive attitude to vibe coding than developersWhat are people saying about “vibe coding”?As part of the survey, everybody had the chance to add a few sentences explaining their stance. This was not a compulsory field, but to my surprise, only 3 of the 62 left it empty. Before getting into the sentiment analysis, I noticed something quite interesting while filtering the data. People with a negative attitude had much more to say, and their responses were significantly longer than the other group. They wrote an average of 59 words while the others barely 37 and I think is a good indication of the emotional investment of people who want to articulate and explain their point. Let’s now look at what the different groups of people replied.😍 Patterns in Positive Responses to “Vibe Coding”Positive responders often embraced vibe coding as a way to break free from rigid programming structures and instead explore, improvise, and experiment creatively.“It puts no pressure on it being perfect or thorough.”“Pursuing the vibe, trying what works and then adapt.”“Coding can be geeky and laborious… ‘vibing’ is quite nice.”This perspective repositions code not as rigid infrastructure, but something that favors creativity and playfulness over precision.Several answers point to vibe coding as a democratizing force opening up coding to a broader audience, who want to build without going through the traditional gatekeeping of engineering culture.“For every person complaining… there are ten who are dabbling in code and programming, building stuff without permission.”“Bridges creative with technical perfectly, thus creating potential for independence.”This group often used words like “freedom,” “reframing,” and “revolution.”.😑 Patterns in Neutral Responses to “Vibe Coding”As shown in the initial LinkedIn poll, 27% of respondents expressed mixed feelings. When going through their responses, they recognised potential and were open to experimentation but they also had lingering doubts about the name, seriousness, and future usefulness.“It’s still a hype or buzzword.”“I have mixed feelings of fascination and scepticism.”“Unsure about further developments.”They were on the fence and were often enthusiastic about the capability, but wary of the framing.Neutral responders also acknowledged that complex, polished, or production-level work still requires traditional approaches and framed vibe coding as an early-stage assistant, not a full solution.“Nice tool, but not more than autocomplete on steroids.”“Helps get setup quickly… but critical thinking is still a human job.”“Great for prototyping, not enough to finalize product.”Some respondents were indifferent to the term itself, viewing it more as a label or meme than a paradigm shift. For them, it doesn’t change the substance of what’s happening.“At the end of the day they are just words. Are you able to accomplish what’s needed?”“I think it’s been around forever, just now with a new name.”These voices grounded the discussion in the terminology and I think they bring up a very important point that leads to the polarisation of a lot of the conversations around “vibe coding”.🤮 Patterns in Negative Responses to “Vibe Coding”Many respondents expressed concern that vibe coding implies a casual, unstructured approach to coding. This was often linked to fears about poor code quality, bugs, and security issues.“Feels like building a house without knowing how electricity and water systems work.”“Without fundamental knowledge… you quickly lose control over the output.”The term was also seen as dismissive or diminishing the value of skilled developers. It really rubbed people the wrong way, especially those with professional experience.“It downplays the skill and intention behind writing a functional, efficient program.”“Vibe coding implies not understanding what the AI does but still micromanaging it.”Like for “neutral” respondents, there’s a strong mistrust around how the term is usedwhere it’s seen as fueling unrealistic expectations or being pushed by non-experts.“Used to promote coding without knowledge.”“Just another overhyped term like NFTs or memecoins.”“It feels like a joke that went too far.”Ultimately, I decided to compare attitudes that are excitedand acceptingof vibe coding vs. those that reject or criticise it. After all, even among people who were neutral, there was a general acceptance that vibe coding has its place. Many saw it as a useful tool for things like prototyping, creative exploration, or simply making it easier to get started. What really stood out, though, was the absence of fear that was very prominent in the “negative” group and saw vibe coding as a threat to software quality or professional identity.People in the neutral and positive groups generally see potential. They view it as useful for prototyping, creative exploration, or making coding more accessible, but they still recognise the need for structure in complex systems. In contrast, the negative group rejects the concept outright, and not just the name, but what it stands for: a more casual, less rigorous approach to coding. Their opinion is often rooted in defending software engineering as a disciplined craft… and probably their job.😍 “As long as you understand the result and the process, AI can write and fix scripts much faster than humans can.”🤮 “It’s a joke. It started as a joke… but to me doesn’t encapsulate actual AI co-engineering.”On the topic of skill and control, the neutral and positive group sees AI as a helpful assistant, assuming that a human is still guiding the process. They mention refining and reviewing as normal parts of the workflow. The negative group sees more danger, fearing that vibe coding gives a false sense of competence. They describe it as producing buggy or shallow results, often in the hands of inexperienced users.😑 “Critical thinking is still a human job… but vibe coding helps with fast results.”🤮“Vibe-Coding takes away the very features of a good developer… logical thinking and orchestration are crucial.”Culturally, the divide is clear. The positive and neutral voices often embrace vibe coding as part of a broader shift, welcoming new types of creators and perspectives. They tend to come from design or interdisciplinary backgrounds and are more comfortable with playful language. On the other hand, the negative group associates the term with hype and cringe, criticising it as disrespectful to those who’ve spent years honing their technical skills.😍“It’s about playful, relaxed creation — for the love of making something.”🤮Creating a lot of unsafe bloatware with no proper planning.”What’s the future of “Vibe Coding”?The responses to the last question were probably the most surprising to me. I was expecting that the big scepticism towards vibe coding would align with the scepticism on its future, but that was not the case. 90% of people still see “vibe coding” becoming more relevant overall or in niche use cases.Vibe coding is here to stayOut of curiosity, I also went back to see if there was any difference based on professional experience, and that’s where we see the more experienced audience being more conservative. Only 30% of more senior Vs 50% of less experienced professionals see vibe coding playing a role in niche use cases and 13 % Vs only 3% of more experienced users don’t see vibe coding becoming more relevant at all.More experienced professionals are less likely to think Vibe Coding is the futureThere are still many open questions. What is “vibe coding” really? For whom is it? What can you do with it?To answer these questions, I decided to start a new survey you can find here. If you would like to further contribute to this research, I encourage you to participate and in case you are interested, I will share the results with you as well.The more I read or learn about this, I feel “Vibe Coding” is like the “Metaverse”:Some people hate it, some people love it.Everybody means something differentIn one form or another, it is here to stay.What professionals really think about “Vibe Coding” was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story. #what #professionals #really #think #about

Microsoft is giving its Windows Update software stack more power, and the tool will soon be able to update other software and drivers within Windows systems. The company is establishing the capability for system administrators to wrangle all software updates into a one-click experience, Microsoft said in a blog post on Wednesday. Sysadmins today have to run Windows Update to keep the OS updated, and separately patch individual pieces of software, which can be a lot of work. “To solve this, we’re building a vision for a unified, intelligent update orchestration platform capable of supporting any updateto be orchestrated alongside Windows updates,” Microsoft said. Typically, system administrators deploy patch management tools to update Windows and related enterprise software, but Microsoft wants to bring it all to a Windows Update-style deployment. Potential benefits include more streamlined and lower-cost deployment of updates, the company said. A unified patch management system also reduces computing requirements. The current process for doing updates to Windows systems is a hodgepodge of different tools and techniques, said Jack Gold, principal analyst at J. Gold Associates. “I applaud Microsoft for finally trying to bring all of this under one umbrella but wonder why it took them so long to do this,” Gold said. In addition to Windows, Windows Update today updates Microsoft’s development tools such as .NET and Defender, and also updates system drivers. With ARM-based PCs, it also delivers system BIOS and firmware so users don’t have to download it from the PC maker’s website. But how quickly companies adopt this new way of doing things will depend on how easy Microsoft makes it to adopt the new service, Gold said. Microsoft is providing a tool for software providers to put their software updates into its orchestration platform. The company has only provided information on how developers can test it out with their applications, and Microsoft will then provide further information. Developers who have access to the Windows Runtime environment can test it out and implement it. APIs are also available to test out the system. Microsoft separately announced that Windows Backup for Organizations, a data backup feature announced last year, is now in public preview. The product will allow for a smooth transition to Windows 11 from Windows 10 for enterprises, the company said. Windows 10 support ends in October 2025. “This capability helps reduce migration overhead, minimize user disruption, and strengthen device resilience against incidents,” Microsoft wrote in a blog entry. Microsoft’s Entra identity authentication is a key component of such transitions via Windows Backup for Organizations, Microsoft said. Further reading: How to handle Windows 10 and 11 updates Windows 10: A guide to the updates Windows 11: A guide to the updates How to preview and deploy Windows 10 and 11 updates How to troubleshoot and reset Windows Update How to keep your apps up to date in Windows 10 and 11 #coming #soon #enterprises #one #windows

Engineering Lead, Data PlatformEpic GamesCary, North Carolina, United States11 hours agoApplyWHAT MAKES US EPIC?At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.DATA ENGINEERINGWhat We DoOur mission is to provide a world-class platform that empowers the business to leverage data that will enhance, monitor, and support our products. We are responsible for data ingestion systems, processing pipelines, and various data stores all operating in the cloud. We operate at a petabyte scale, and support near real-time use cases as well as more traditional batch approaches.What You'll DoEpic Games is seeking a Senior Engineering Lead to guide the Data Services team, which builds and maintains the core services behind our data platform. This team handles telemetry collection, data schematization, stream routing, data lake integration, and real-time analytics, bridging platform, data, and backend engineering. In this role, you’ll lead team growth and mentorship, drive alignment on technical strategy, and collaborate cross-functionally to scale our data infrastructure.In this role, you willLead, mentor, and grow a team of senior and principal engineersFoster an inclusive, collaborative, and feedback-driven engineering cultureDrive continuous improvement in the team’s processes, delivery, and impactCollaborate with stakeholders in engineering, data science, and analytics to shape and communicate the team’s vision, strategy, and roadmapBridge strategic vision and tactical execution by breaking down long-term goals into achievable, well-scoped iterations that deliver continuous valueEnsure high standards in system architecture, code quality, and operational excellenceWhat we're looking for3+ years of engineering management experience leading high-performing teams in data platform or infrastructure environmentsProven track record navigating complex systems, ambiguous requirements, and high-pressure situations with confidence and clarityDeep experience in architecting, building, and operating scalable, distributed data platformsStrong technical leadership skills, including the ability to review architecture/design documents and provide actionable feedback on code and systemsAbility to engage deeply in technical discussions, review architecture and design documents, evaluate pull requests, and step in during high-priority incidents when needed — even if hands-on coding isn’t a part of the day-to-dayHands-on experience with distributed event streaming systems like Apache KafkaFamiliarity with OLAP databases such as Apache Pinot or ClickHouseProficient in modern data lake and warehouse tools such as S3, Databricks, or SnowflakeExperience with distributed data processing engines like Apache Flink or Apache SparkStrong foundation in the JVM ecosystem, container orchestration with Kubernetes, and cloud platforms, especially AWSEPIC JOB + EPIC BENEFITS = EPIC LIFEOur intent is to cover all things that are medically necessary and improve the quality of life. We pay 100% of the premiums for both you and your dependents. Our coverage includes Medical, Dental, a Vision HRA, Long Term Disability, Life Insurance & a 401k with competitive match. We also offer a robust mental well-being program through Modern Health, which provides free therapy and coaching for employees & dependents. Throughout the year we celebrate our employees with events and company-wide paid breaks. We offer unlimited PTO and sick time and recognize individuals for 7 years of employment with a paid sabbatical.ABOUT USEpic Games spans across 25 countries with 46 studios and 4,500+ employees globally. For over 25 years, we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.Like what you hear? Come be a part of something Epic!Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment OpportunityPolicy here.Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party. We will not pay any fees to any unauthorized third party. Further details on these matters can be found here. Create Your Profile — Game companies can contact you with their relevant job openings. Apply #engineering #lead #data #platform #epic

John Edwards, Technology Journalist & AuthorMay 30, 20254 Min ReadTanapong Sungkaew via Alamy Stock PhotoAI adoption can help enterprises function more efficiently and productively in many internal and external areas. Yet to get the most value out of AI, CIOs and IT leaders need to find a way to measure their current and future gains.Measuring AI efficiency and productivity gains isn't always a straightforward process, however, observes Matt Sanchez, vice president of product for IBM's watsonx Orchestrate, a tool designed to automate tasks, focusing on the orchestration of AI assistants and AI agents."There are many factors to consider in order to gain an accurate picture of AI’s impact on your organization," Sanchez says,  in an email interview. He believes the key to measuring AI effectiveness starts with setting clear, data-driven goals. "What outcomes are you trying to achieve?" he asks. "Identifying the right key performance indicators -- KPIs -- that align with your overall strategy is a great place to start."Measuring AI efficiency is a little like a "chicken or the egg" discussion, says Tim Gaus, smart manufacturing business leader at Deloitte Consulting. "A prerequisite for AI adoption is access to quality data, but data is also needed to show the adoption’s success," he advises in an online interview.Still, with the number of organizations adopting AI rapidly increasing, C-suites and boards are now prioritizing measurable ROI.Related:"We're seeing this firsthand while working with clients in the manufacturing space specifically who are aiming to make manufacturing processes smarter and increasingly software-defined," Gaus says.Measuring AI Efficiency: The ChallengeThe challenge in measuring AI efficiency depends on the type of AI and how it's ultimately used, Gaus says. Manufacturers, for example, have long used AI for predictive maintenance and quality control. "This can be easier to measure, since you can simply look at changes in breakdown or product defect frequencies," he notes. "However, for more complex AI use cases -- including using GenAI to train workers or serve as a form of knowledge retention -- it can be harder to nail down impact metrics and how they can be obtained."AI Project Measurement MethodsOnce AI projects are underway, Gaus says measuring real-world results is key. "This includes studying factors such as actual cost reductions, revenue boosts tied directly to AI, and progress in KPIs such as customer satisfaction or operational output. "This method allows organizations to track both the anticipated and actual benefits of their AI investments over time."Related:To effectively assess AI's impact on efficiency and productivity, it's important to connect AI initiatives with broader business goals and evaluate their progress at different stages, Gaus says."In the early stages, companies should focus on estimating the potential benefits, such as enhanced efficiency, revenue growth, or strategic advantages like stronger customer loyalty or reduced operational downtime." These projections can provide a clear understanding of how AI aligns with long-term objectives, Gaus adds.Measuring any emerging technology's impact on efficiency and productivity often takes time, but impacts are always among the top priorities for business leaders when evaluating any new technology, says Dan Spurling, senior vice president of product management at multi-cloud data platform provider Teradata. "Businesses should continue to use proven frameworks for measurement rather than create net-new frameworks," he advises in an online interview. "Metrics should be set prior to any investment to maximize benefits and mitigate biases, such as sunk cost fallacies, confirmation bias, anchoring bias, and the like."Key AI Value MetricsMetrics can vary depending on the industry and technology being used, Gaus says. "In sectors like manufacturing, AI value metrics include improvements in efficiency, productivity, and cost reduction." Yet specific metrics depend on the type of AI technology implemented, such as machine learning.Related:Beyond tracking metrics, it's important to ensure high-quality data is used to minimize biases in AI decision-making, Sanchez says. The end goal is for AI to support the human workforce, freeing users to focus on strategic and creative work and removing potential bottlenecks. "It's also important to remember that AI isn't a one-and-done deal. It's an ongoing process that needs regular evaluation and process adjustment as the organization transforms.”Spurling recommends beginning by studying three key metrics:Worker productivity: Understanding the value of increased task completion or reduced effort by measuring the effect on day-to-day activities like faster issue resolution, more efficient collaboration, reduced process waste, or increased output quality.Ability to scale: Operationalizing AI-based self-service tools, typically with natural language capabilities, across the entire organization beyond IT to enable task or job completion in real-time, with no need for external support or augmentation.User friendliness: Expanding organization effectiveness with data-driven insights as measured by the ability of non-technical business users to leverage AI via no-code, low-code platforms.Final Note: Aligning Business and TechnologyDeloitte's digital transformation research reveals that misalignment between business and technology leaders often leads to inaccurate ROI assessments, Gaus says. "To address this, it's crucial for both sides to agree on key value priorities and success metrics."He adds it's also important to look beyond immediate financial returns and to incorporate innovation-driven KPIs, such as experimentation toleration and agile team adoption. "Without this broader perspective, up to 20% of digital investment returns may not yield their full potential," Gaus warns. "By addressing these alignment issues and tracking a comprehensive set of metrics, organizations can maximize the value from AI initiatives while fostering long-term innovation."About the AuthorJohn EdwardsTechnology Journalist & AuthorJohn Edwards is a veteran business technology journalist. His work has appeared in The New York Times, The Washington Post, and numerous business and technology publications, including Computerworld, CFO Magazine, IBM Data Management Magazine, RFID Journal, and Electronic Design. He has also written columns for The Economist's Business Intelligence Unit and PricewaterhouseCoopers' Communications Direct. John has authored several books on business technology topics. His work began appearing online as early as 1983. Throughout the 1980s and 90s, he wrote daily news and feature articles for both the CompuServe and Prodigy online services. His "Behind the Screens" commentaries made him the world's first known professional blogger.See more from John EdwardsWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like #how #measure #efficiency #productivity #gains

From large language modelsto reasoning agents, today’s AI tools bring unprecedented computational demands. Trillion-parameter models, workloads running on-device, and swarms of agents collaborating to complete tasks all require a new paradigm of computing to become truly seamless and ubiquitous. First, technical progress in hardware and silicon design is critical to pushing the boundaries of compute. Second, advances in machine learningallow AI systems to achieve increased efficiency with smaller computational demands. Finally, the integration, orchestration, and adoption of AI into applications, devices, and systems is crucial to delivering tangible impact and value. Silicon’s mid-life crisis AI has evolved from classical ML to deep learning to generative AI. The most recent chapter, which took AI mainstream, hinges on two phases—training and inference—that are data and energy-intensive in terms of computation, data movement, and cooling. At the same time, Moore’s Law, which determines that the number of transistors on a chip doubles every two years, is reaching a physical and economic plateau. For the last 40 years, silicon chips and digital technology have nudged each other forward—every step ahead in processing capability frees the imagination of innovators to envision new products, which require yet more power to run. That is happening at light speed in the AI age. As models become more readily available, deployment at scale puts the spotlight on inference and the application of trained models for everyday use cases. This transition requires the appropriate hardware to handle inference tasks efficiently. Central processing unitshave managed general computing tasks for decades, but the broad adoption of ML introduced computational demands that stretched the capabilities of traditional CPUs. This has led to the adoption of graphics processing unitsand other accelerator chips for training complex neural networks, due to their parallel execution capabilities and high memory bandwidth that allow large-scale mathematical operations to be processed efficiently. But CPUs are already the most widely deployed and can be companions to processors like GPUs and tensor processing units. AI developers are also hesitant to adapt software to fit specialized or bespoke hardware, and they favor the consistency and ubiquity of CPUs. Chip designers are unlocking performance gains through optimized software tooling, adding novel processing features and data types specifically to serve ML workloads, integrating specialized units and accelerators, and advancing silicon chip innovations, including custom silicon. AI itself is a helpful aid for chip design, creating a positive feedback loop in which AI helps optimize the chips that it needs to run. These enhancements and strong software support mean modern CPUs are a good choice to handle a range of inference tasks. Beyond silicon-based processors, disruptive technologies are emerging to address growing AI compute and data demands. The unicorn start-up Lightmatter, for instance, introduced photonic computing solutions that use light for data transmission to generate significant improvements in speed and energy efficiency. Quantum computing represents another promising area in AI hardware. While still years or even decades away, the integration of quantum computing with AI could further transform fields like drug discovery and genomics. Understanding models and paradigms The developments in ML theories and network architectures have significantly enhanced the efficiency and capabilities of AI models. Today, the industry is moving from monolithic models to agent-based systems characterized by smaller, specialized models that work together to complete tasks more efficiently at the edge—on devices like smartphones or modern vehicles. This allows them to extract increased performance gains, like faster model response times, from the same or even less compute. Researchers have developed techniques, including few-shot learning, to train AI models using smaller datasets and fewer training iterations. AI systems can learn new tasks from a limited number of examples to reduce dependency on large datasets and lower energy demands. Optimization techniques like quantization, which lower the memory requirements by selectively reducing precision, are helping reduce model sizes without sacrificing performance.  New system architectures, like retrieval-augmented generation, have streamlined data access during both training and inference to reduce computational costs and overhead. The DeepSeek R1, an open source LLM, is a compelling example of how more output can be extracted using the same hardware. By applying reinforcement learning techniques in novel ways, R1 has achieved advanced reasoning capabilities while using far fewer computational resources in some contexts. The integration of heterogeneous computing architectures, which combine various processing units like CPUs, GPUs, and specialized accelerators, has further optimized AI model performance. This approach allows for the efficient distribution of workloads across different hardware components to optimize computational throughput and energy efficiency based on the use case. Orchestrating AI As AI becomes an ambient capability humming in the background of many tasks and workflows, agents are taking charge and making decisions in real-world scenarios. These range from customer support to edge use cases, where multiple agents coordinate and handle localized tasks across devices. With AI increasingly used in daily life, the role of user experiences becomes critical for mass adoption. Features like predictive text in touch keyboards, and adaptive gearboxes in vehicles, offer glimpses of AI as a vital enabler to improve technology interactions for users. Edge processing is also accelerating the diffusion of AI into everyday applications, bringing computational capabilities closer to the source of data generation. Smart cameras, autonomous vehicles, and wearable technology now process information locally to reduce latency and improve efficiency. Advances in CPU design and energy-efficient chips have made it feasible to perform complex AI tasks on devices with limited power resources. This shift toward heterogeneous compute enhances the development of ambient intelligence, where interconnected devices create responsive environments that adapt to user needs. Seamless AI naturally requires common standards, frameworks, and platforms to bring the industry together. Contemporary AI brings new risks. For instance, by adding more complex software and personalized experiences to consumer devices, it expands the attack surface for hackers, requiring stronger security at both the software and silicon levels, including cryptographic safeguards and transforming the trust model of compute environments. More than 70% of respondents to a 2024 DarkTrace survey reported that AI-powered cyber threats significantly impact their organizations, while 60% say their organizations are not adequately prepared to defend against AI-powered attacks. Collaboration is essential to forging common frameworks. Universities contribute foundational research, companies apply findings to develop practical solutions, and governments establish policies for ethical and responsible deployment. Organizations like Anthropic are setting industry standards by introducing frameworks, such as the Model Context Protocol, to unify the way developers connect AI systems with data. Arm is another leader in driving standards-based and open source initiatives, including ecosystem development to accelerate and harmonize the chiplet market, where chips are stacked together through common frameworks and standards. Arm also helps optimize open source AI frameworks and models for inference on the Arm compute platform, without needing customized tuning.  How far AI goes to becoming a general-purpose technology, like electricity or semiconductors, is being shaped by technical decisions taken today. Hardware-agnostic platforms, standards-based approaches, and continued incremental improvements to critical workhorses like CPUs, all help deliver the promise of AI as a seamless and silent capability for individuals and businesses alike. Open source contributions are also helpful in allowing a broader range of stakeholders to participate in AI advances. By sharing tools and knowledge, the community can cultivate innovation and help ensure that the benefits of AI are accessible to everyone, everywhere. Learn more about Arm’s approach to enabling AI everywhere. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review. #fueling #seamless #scale

Menu Home News Hardware Gaming Mobile Finance Deals Reviews How To Wccftech Microsoft Debuts Windows Update Orchestration Platform For Updating All Apps From A Single Place Sarfraz Khan • May 30, 2025 at 10:42am EDT To simplify the process, Microsoft has rolled out a new orchestration platform that can help developers update their apps through Windows Update. Microsoft Says That Through Windows Update Orchestration Platform, Users Can Have Access to a Simplified Update Process and Will Also Help Developers in Managing Their Apps Conveniently Windows' built-in update feature usually updates the OS components, but Microsoft's line-of-business as well as third-party apps are still managed independently. Apart from aiming to manage its LOB apps in a single place for easier updates, Microsoft is also interested in managing third-party apps from its latest platform. Microsoft calls it the Windows Update Orchestration Platform, a new feature that will allow Windows users to download the latest app updates from a single place instead of having to download them independently. Microsoft has released its private preview for developers, who can now sign up to explore this unified approach to reduce the hassles in managing their apps independently. Microsoft released this orchestration platform citing various reasons, such as CPU and bandwidth spikes, confusing and conflicting notifications, added support costs etc. With the new Windows Update stack, not only will users be able to download all the updates from one place, but app developers will benefit highly from them as well. IT admins will particularly benefit from the orchesration as they have to rely on independent update mechanisms. Each tool brings its own logic for scanning, downloading, installing and notifying users, which results in a fragmented experience. Through the new platform, there are multiple benefits developers can have, including eco-efficient scheduling, which helps reduce the impact on productivity and energy consumption, consistent notifications via native Windows Update notfiications, centralized update history, unified troubleshooting tools and more. The Windows Update orchestration platform allows developers to integrate their apps through the preview via a set of Windows Runtime APIs and PowerShell commands. Once they are in, developers can handle the behavior of their apps, like registration, defining updates, custom update logic, managed scheduling and status reporting. News Source: Microsoft Subscribe to get an everyday digest of the latest technology news in your inbox Follow us on Topics Sections Company Some posts on wccftech.com may contain affiliate links. We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com © 2025 WCCF TECH INC. 700 - 401 West Georgia Street, Vancouver, BC, Canada #microsoft #debuts #windows #update #orchestration

With millions of businesses now using Amazon Web Servicesfor their cloud computing needs, it’s become a vital consideration for IT security teams and professionals. As such, AWS offers a broad range of cyber security tools to secure AWS-based tech stacks. They cover areas such as data privacy, access management, configuration management, threat detection, network security, vulnerability management, regulatory compliance and so much more.  Along with being broad in scope, AWS security tools are also highly scalable and flexible. Therefore, they’re ideal for high-growth organisations facing a fast-expanding and increasingly sophisticated cyber threat landscape. On the downside, they can be complex to use, don’t always integrate well with multi-cloud environments, and become outdated and expensive quickly. These challenges underscore the importance of continual learning and effective cost management in the cyber security suite. One of the best things AWS offers cyber security professionals is a centralised view of all their different virtual environments, including patch management, vulnerability scanning and incident response, to achieve “smoother operations”, according to Richard LaTulip, field chief information security officer at cyber threat intelligence platform Recorded Future. Specifically, he says tools like AWS CloudTrail and AWS Config allow cyber security teams to accelerate access management, anomaly detection and real-time policy compliance, and that risk orchestration is also possible thanks to AWS’s support for specialist platforms such as Recorded Future.  This sentiment is echoed by Crystal Morin, cyber security strategist at container security firm Sysdig, who describes AWS CloudTrail and AWS GuardDuty as “the bedrock” for organisations with a multi- or hybrid cloud environment.  She says these tools offer “great insight” into cloud environment activity that can be used to identify issues affecting corporate systems, better understand them and ultimately determine their location for prompt removal.  Having made tons of cloud security deployments for Fortune 200 companies in his previous role as global AWS security lead at consulting giant Accenture, Shaan Mulchandani, founder and CEO of cloud security firm HTCD, knows a thing or two about AWS’s cyber security advantages.  Mulchandani says AWS implementations helped these companies secure their baseline configurations, streamline C-suite IT approvals to speed up AWS migration, eliminate manual post-migration security steps and seamlessly scale environments containing thousands of workloads. “I continue to help executives at organisations architect, deploy and maximise outcomes using AWS-native tools,” he adds. As a senior threat researcher at cyber intelligence platform EclecticIQ, Arda Büyükkaya uses AWS tools to scale threat behaviour analysis, develop secure malware analysis environments, and automate threat intelligence data collection and processing.  Calling AWS an “invaluable” threat analysis resource, he says the platform has made it a lot easier to roll out isolated research environments. “AWS’s scalability enables us to process large volumes of threat data efficiently, whilst their security services help maintain the integrity of our research infrastructure,” Büyükkaya tells Computer Weekly. At log management and security analytics software company Graylog, AWS usage happens across myriad teams. One of these is led by EMEA and UK lead Ross Brewer. His department is securing and protecting customer instances using tools like AWS GuardDuty, AWS Security Hub, AWS Config, AWS CloudTrail, AWS Web Application Firewall, AWS Inspector and AWS Identity and Access Management.  Its IT and application security department also relies on security logs provided by AWS GuardDuty and AWS CloudTrail to spot anomalies affecting customer instances. Brewer says the log tracking and monitoring abilities of these tools have been invaluable for security, compliance and risk management. “We haven’t had any issues with our desired implementations,” he adds. Cyber law attorney and entrepreneur Andrew Rossow is another firm believer in AWS as a cyber security tool. He thinks its strongest aspect is the centralised security management it offers for monitoring threats, responding to incidents and ensuring regulatory compliance, and describes the usage of this unified, data-rich dashboard as the “difference between proactive defence and costly damage control” for small businesses with limited resources.  But Rossow believes this platform’s secret sauce is its underlying artificial intelligenceand machine learning models, which power background threat tracking, and automatically alert users to security issues, data leaks and suspicious activity. These abilities, he says, allow cyber security professionals to “stay ahead of potential crises”. Another area where Rossow thinks AWS excels is its integration with regulatory frameworks such as the California Consumer Privacy Act, the General Data Protection Regulation and the Payment Card Industry Data Security Standard. He explains that AWS Config and AWS Security Hub offer configuration and resource auditing to ensure business activities and best practices meet such industry standards. “This not only protects our clients, but also shields us from the legal and reputational fallout of non-compliance,” adds Rossow. AWS tools provide cyber security teams with “measurable value”, argues Shivraj Borade, senior analyst at management consulting firm Everest Group. He says GuardDuty is powerful for real-time monitoring, AWS Config for security posture management and IAM Access Analyzer for privilege sprawl prevention. “What makes these tools powerful is their interoperability, enabling a scalable and cohesive security architecture,” says Borade. Although AWS is a valuable tool for cyber security professionals, Borade emphasises that it’s “not without limitations”. He says the platform’s lack of depth and flexibility means it isn’t always suitable for modelling complex cyber security threats or handling specific compliance issues. Rather, cyber security professionals should use AWS as a foundational element of their wider tech stack.  Using the AWS Security Hub as an example, Borade says it can effectively serve the purpose of an “aggregation layer”. But he warns that incorrect configurations often result in alert fatigue, meaning people can become oblivious to notifications when repeatedly spammed with them.  Borade also warns of misconfigurations arising from teams’ lack of understanding of how cloud technology works. Consequently, he urges cyber security teams to “embed cloud-native security into the DevSecOps lifecycle” and “invest in continuous cross-functional training”. For Morin, the biggest challenge of using AWS as a security tool is that it’s constrained by best practice gaps around areas like workload protection, vulnerability management, identity management and threat detection. She says one classic example is the difficulty cyber security teams face when monitoring access permissions granted over time, leaving organisations with large IT environments dangerously exposed.  Using multiple AWS security tools also increases the attack surface for cyber criminals to exploit. Morin warns that hackers may look for “visibility gaps” by sifting through different AWS planes, helping them “mask their activities” and “effectively bypass detection”. To stay one step ahead of cyber crooks, she advises organisations to invest in runtime solutions alongside AWS-native tools. These will provide real-time security insights. Technical and cost issues may also impact AWS implementations in cyber security departments, warns Mulchandani. For instance, Amazon Macie may be able to create inventories for all object versions across different buckets, but Mulchandani says this creates a “mountain of medium-severity findings” to decipher. “Without strict scoping, licence costs and analyst time balloon,” he adds. “Costs can also increase when an organisation requires a new AWS launch that isn’t available in their region and they subsequently invest in a temporary solution from a different vendor. For those new to using AWS security tools, Morin says an important first step is to understand the cloud security shared responsibility model. She explains that the user is responsible for securing their deployments, correctly configuring them and closing any security visibility gaps. AWS, on the other hand, must ensure the underlying infrastructure provided is safe to use.  As part of the users’ role in this model, she says they should enable logging and alerts for AWS tools and services used in their organisation. What’s also key is detailing standard organisational operating behaviour in a security baseline. This, she claims, will let organisations tell suspicious user actions apart from normal ones. Many tried-and-tested best practices can be found in professional benchmarks such as the AWS Well-Architected framework and the Center of Internet Security’s Benchmark for AWS. “Make use of the work of those who have been fighting the good fight,” says Morin. Finally, she urges anyone working in cloud security to remember that real-time operations are essential. Runtime security can help by protecting all running applications and data from the latest cyber security threats, many of which are preventable through automated processes.  Starting small is a good idea, too. Mulchandani recommends that AWS newbies begin with AWS tooling, and if any gaps persist, they can then look for third-party offerings. “Do not try to procure and integrate 20-plus external tools upfront as this will cause numerous architectural, security and cost challenges,” he says. With the rapid pace of innovation across the AWS ecosystem, Borade urges anyone using this platform to stay up-to-date with the latest releases by participating in certification programmes, attending re:Inforce sessions and tracking the latest release notes from AWS. In the future, he expects automation, AI-fuelled insights, “tighter” third-party integrations, and identity orchestration and policy-as-code frameworks to dominate the AWS cyber security ecosystem.  On the whole, understanding the AWS platform and its role in cloud security is a vital skill for cyber security professionals. And AWS certainly offers some great tools for managing the biggest risks impacting its popular cloud platform. But cyber security professionals looking to leverage AWS in their day-to-day roles must be willing to get to grips with some complex tools, keep up-to-date with the latest releases in the vast AWS ecosystem and ensure their department budget can accommodate spiralling AWS costs. about AWS An AWS tech stack can aid business growth and facilitate efficient operations, but misconfigurations have become all too common and stall this progress. The AWS Summit in London saw the public cloud giant appoint itself to take on the task of skilling up hundreds of thousands of UK people in using AI technologies. Amazon Web Services debuts new Outposts racks and servers that extend its infrastructure to the edge to support network intensive workloads and cloud radio access applications. #how #cyber #security #professionals #are

Pope-Leighey House | © Peter Thomas via Unsplash Constructed in 1940, the Pope-Leighey House represents Frank Lloyd Wright’s Usonian vision, his architectural response to the social, economic, and aesthetic conditions of mid-20th-century America. Designed for middle-class clients, the Usonian houses were intended to democratize quality design, providing spatial dignity at an affordable cost. In stark contrast to the mass-produced suburban housing of the post-Depression era, Wright sought to design individualized homes rooted in site, economy, and human scale. Pope-Leighey House Technical Information Architects1-6: Frank Lloyd Wright Original Location: Falls Church, Virginia, USA Current Location: Woodlawn Plantation, Alexandria, Virginia, USA Gross Area: 111.5 m2 | 1,200 Sq. Ft. Project Years: 1939 – 1940 Relocation: 1964Photographs: © Photographer The house of moderate cost is not only America’s major architectural problem but the problem most difficult for her major architects. I would rather solve it with satisfaction to myself and Usonia than anything I can think of. – Frank Lloyd Wright 7 Pope-Leighey House Photographs © Lincoln Barbour © Peter Thomas via Unsplash © Peter Thomas via Unsplash © Lincoln Barbour © Lincoln Barbour © Peter Thomas via Unsplash © Peter Thomas via Unsplash © Peter Thomas via Unsplash Contextual Framework and Commissioning The house, commissioned by journalist Loren Pope, was initially situated in Falls Church, Virginia, on a wooded lot chosen to amplify Wright’s principles of organic architecture. Working within a modest budget, Pope approached Wright after reading his critique of conventional American housing. Wright accepted the commission and delivered a design reflecting his social idealism and formal ingenuity. In 1964, the house was relocated to the grounds of the Woodlawn Plantation in Alexandria, Virginia, due to the construction of Interstate 66. While disrupting the original site specificity, this preservation affirms the cultural value placed on the work and raises enduring questions about the transposability of architecture designed for a particular place. Design Principles and Architectural Language The Pope-Leighey House distills the essential characteristics of Wright’s Usonian ideology. Modest in scale, the 1,200-square-foot house is arranged in an L-shaped plan, responding to programmatic needs and solar orientation. The linearity of the bedroom wing intersects perpendicularly with the open-plan living space, forming a sheltered outdoor terrace that extends the perceived interior volume into the landscape. Wright’s orchestration of spatial experience is central to the house’s architectural impact. The low-ceilinged entrance compresses space, setting up a dynamic release into the double-height living area, an architectural maneuver reminiscent of his earlier Prairie houses. Here, horizontality is emphasized in elevation and experience, reinforced by continuous bands of clerestory windows and built-in furnishings that draw the eye laterally across space. Materially, the house embodies a deliberate economy. Red tidewater cypress, brick, and concrete are left exposed, articulating their structural and tectonic roles without ornament. The poured concrete floor contains radiant heating, a functional and experiential feature that foregrounds the integration of structure, comfort, and environmental control. Window mullions extend into perforated wooden panels, demonstrating Wright’s inclination to merge architecture and craft, blurring the line between enclosure and furnishing. Structural Rationality and Construction Methodology A defining feature of the Usonian series, particularly the Pope-Leighey House, is the modular planning system. Based on a two-foot grid, the plan promotes construction efficiency while enabling spatial flexibility. This systemic logic underpins the entire design, from wall placements to window dimensions, allowing the house to feel simultaneously rigorous and organic. Construction strategies were purposefully stripped of excess. The flat roof, cantilevered overhangs, and minimal interior partitions reflect an architecture of subtraction. Without a basement or attic, the house resists hierarchy in its vertical organization. Walls are built with simple sandwich panel techniques, and furniture is integrated into the architecture, reducing material use and creating visual unity. Despite the constraints, the house achieves a high level of tectonic expression. The integration of structure and detail is particularly evident in the living room’s perforated wood screens, which serve as decorative elements, light diffusers, and spatial dividers. These craft elements reinforce the Gesamtkunstwerk ambition in Wright’s residential works: a house as a total, synthesized environment. Legacy and Architectural Significance Today, the Pope-Leighey House is a critical touchstone in Wright’s late-career trajectory. It encapsulates a radical yet modest vision, architecture not as monumentality but as a refined environment for everyday life. Preserved by the National Trust for Historic Preservation, the house continues to serve as a pedagogical model, offering insights into material stewardship, compact living, and formal economy. In architectural discourse, Wright’s larger commissions often overshadow the Usonian homes. Yet the Pope-Leighey House demands recognition for what it accomplishes within limitations. It is a project that questions conventional paradigms of domestic space and asserts that thoughtful design is not a luxury reserved for the elite but a right that can and should be extended to all. The house’s quiet radicalism remains relevant in today’s discussions of affordable housing, sustainable design, and spatial minimalism. Its influence is evident in contemporary explorations of prefab architecture, passive environmental systems, and spatial efficiency, fields that continue to grapple with the same questions Wright addressed eight decades ago. Pope-Leighey House Plans Floor Plan | © Frank Lloyd Wright Section | © Frank Lloyd Wright East Elevation | © Frank Lloyd Wright North Elevation | © Frank Lloyd Wright West Elevation | © Frank Lloyd Wright Pope-Leighey House Image Gallery About Frank Lloyd Wright Frank Lloyd Wrightwas an American architect widely regarded as one of the most influential figures in modern architecture. Known for developing the philosophy of organic architecture, he sought harmony between human habitation and the natural world through forms, materials, and spatial compositions that responded to context. His prolific career includes iconic works such as Fallingwater, the Guggenheim Museum, and the Usonian houses, redefined residential architecture in the 20th century. Credits and Additional Notes Original Client: Loren Pope Architectural Style: Usonian Structure: Wood frame on a concrete slab with radiant heating Materials: Tidewater cypress, brick, concrete, glass Design Team: Frank Lloyd Wright and Taliesin Fellowship apprentices Preservation: Owned and maintained by the National Trust for Historic Preservation #popeleighey #house #frank #lloyd #wrights